v1
This commit is contained in:
Cyber Dick Lang
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MIT License
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Copyright (c) 2024 ZhengPeng
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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<h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
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<div align='center'>
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<a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>, 
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<a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>, 
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<a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>, 
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<a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>, 
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<a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>, 
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<a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>, 
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<a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a><sup> 6</sup>
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</div>
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<div align='center'>
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<sup>1 </sup>Nankai University  <sup>2 </sup>Northwestern Polytechnical University  <sup>3 </sup>National University of Defense Technology 
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<br />
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<sup>4 </sup>Aalto University  <sup>5 </sup>Shanghai AI Laboratory  <sup>6 </sup>University of Trento 
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</div>
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<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
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<a href='https://www.sciopen.com/article/pdf/10.26599/AIR.2024.9150038.pdf'><img src='https://img.shields.io/badge/Journal-Paper-red'></a> 
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<a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-Paper-red'></a> 
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<a href='https://drive.google.com/file/d/1FWvKDWTnK9RsiywfCsIxsnQzqv-dlO5u/view'><img src='https://img.shields.io/badge/中文版-Paper-red'></a> 
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<a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-Project-red'></a> 
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<a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/GDrive-Stuff-green'></a> 
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<a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a> 
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<a href='https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-Space-blue'></a> 
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<a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-Model-blue'></a> 
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</div>
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<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
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<a href='https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba'><img src='https://img.shields.io/badge/Multiple_Images_Inference-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a> 
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<a href='https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl'><img src='https://img.shields.io/badge/Inference_&_Evaluation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a> 
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<a href='https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK'><img src='https://img.shields.io/badge/Box_Guided_Segmentation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a> 
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</div>
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| *DIS-Sample_1* | *DIS-Sample_2* |
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| :------------------------------: | :-------------------------------: |
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| <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> | <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> |
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This repo is the official implementation of "[**Bilateral Reference for High-Resolution Dichotomous Image Segmentation**](https://arxiv.org/pdf/2401.03407)" (___CAAI AIR 2024___).
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> [!note]
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> **We need more GPU resources** to push forward the performance of BiRefNet, especially on *matting* tasks, higher-resolution inference (*2K*), and more *efficient* model design. If you are happy to cooperate, please contact me at zhengpeng0108@gmail.com.
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## News :newspaper:
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* **`Aug 30, 2024`:** We uploaded notebooks in `tutorials` to run the inference and ONNX conversion locally.
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* **`Aug 23, 2024`:** Our BiRefNet is now officially released [online](https://www.sciopen.com/article/10.26599/AIR.2024.9150038) on CAAI AIR journal. And thanks to the [press release](https://www.eurekalert.org/news-releases/1055380).
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* **`Aug 19, 2024`:** We uploaded the ONNX model files of all weights in the [GitHub release](https://github.com/ZhengPeng7/BiRefNet/releases/tag/v1) and [GDrive folder](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N). Check out the **ONNX conversion** part in [model zoo](https://github.com/ZhengPeng7/BiRefNet?tab=readme-ov-file#model-zoo) for more details.
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* **`Jul 30, 2024`:** Thanks to @not-lain for his kind efforts in adding BiRefNet to the official huggingface.js [repo](https://github.com/huggingface/huggingface.js/blob/3a8651fbc6508920475564a692bf0e5b601d9343/packages/tasks/src/model-libraries-snippets.ts#L763).
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* **`Jul 28, 2024`:** We released the [Colab demo for box-guided segmentation](https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK).
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* **`Jul 15, 2024`:** We deployed our BiRefNet on [Hugging Face Models](https://huggingface.co/ZhengPeng7/BiRefNet) for users to easily load it in one line code.
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* **`Jun 21, 2024`:** We released and uploaded the Chinese version of our original paper to my [GDrive](https://drive.google.com/file/d/1aBnJ_R9lbnC2dm8dqD0-pzP2Cu-U1Xpt/view).
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* **`May 28, 2024`:** We hold a [model zoo](https://github.com/ZhengPeng7/BiRefNet?tab=readme-ov-file#model-zoo) with well-trained weights of our BiRefNet in different sizes and for different tasks, including general use, matting segmentation, DIS, HRSOD, COD, etc.
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* **`May 7, 2024`:** We also released the [Colab demo for multiple images inference](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba). Many thanks to @rishabh063 for his support on it.
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* **`Apr 9, 2024`:** Thanks to [Features and Labels Inc.](https://fal.ai/) for deploying a cool online BiRefNet [inference API](https://fal.ai/models/fal-ai/birefnet/playground) and providing me with strong GPU resources for 4 months on more extensive experiments!
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* **`Mar 7, 2024`:** We released BiRefNet codes, the well-trained weights for all tasks in the original papers, and all related stuff in my [GDrive folder](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM). Meanwhile, we also deployed our BiRefNet on [Hugging Face Spaces](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo) for easier online use and released the [Colab demo for inference and evaluation](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl).
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* **`Jan 7, 2024`:** We released our paper on [arXiv](https://arxiv.org/pdf/2401.03407).
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## :rocket: Load BiRefNet in _ONE LINE_ by HuggingFace, check more: [](https://huggingface.co/ZhengPeng7/birefnet)
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```python
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from transformers import AutoModelForImageSegmentation
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birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True)
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```
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## :flight_arrival: Inference Partner:
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We are really happy to collaborate with [FAL](https://fal.ai) to deploy the **inference API** of BiRefNet. You can access this service via the link below:
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+ https://fal.ai/models/fal-ai/birefnet
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Our BiRefNet has achieved SOTA on many similar HR tasks:
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**DIS**: [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te1?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te2?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te3?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te4?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-vd?p=bilateral-reference-for-high-resolution)
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<details><summary>Figure of Comparison on DIS Papers with Codes (by the time of this work):</summary>
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<img src="https://drive.google.com/thumbnail?id=1DLt6CFXdT1QSWDj_6jRkyZINXZ4vmyRp&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1gn5GyKFlJbMIkre1JyEdHDSYcrFmcLD0&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=16CVYYOtafEeZhHqv0am2Daku1n_exMP6&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=10K45xwPXmaTG4Ex-29ss9payA9yBnyLn&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=16EuyqKFJOqwMmagvfnbC9hUurL9pYLLB&sz=w1620" />
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</details>
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<br />
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**COD**:[](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-cod?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-nc4k?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-camo?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-chameleon?p=bilateral-reference-for-high-resolution)
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<details><summary>Figure of Comparison on COD Papers with Codes (by the time of this work):</summary>
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<img src="https://drive.google.com/thumbnail?id=1DLt6CFXdT1QSWDj_6jRkyZINXZ4vmyRp&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1gn5GyKFlJbMIkre1JyEdHDSYcrFmcLD0&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=16CVYYOtafEeZhHqv0am2Daku1n_exMP6&sz=w1620" />
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</details>
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<br />
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**HRSOD**: [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-davis-s?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-hrsod?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-uhrsd?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/salient-object-detection-on-duts-te?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/salient-object-detection-on-dut-omron?p=bilateral-reference-for-high-resolution)
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<details><summary>Figure of Comparison on HRSOD Papers with Codes (by the time of this work):</summary>
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<img src="https://drive.google.com/thumbnail?id=1hNfQtlTAHT4-AVbk_47852zyRp1NOFLs&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1bcVldUAxYkMI3OMTyaP_jNuOugDfYj-d&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1p1zgyVz27cGEqQMtOKzm_6zoYK3Sw_Zk&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1TubAvcoEbH_mHu3I-AxflnB71nkf35jJ&sz=w1620" />
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<img src="https://drive.google.com/thumbnail?id=1A3V9HjVtcMQdnGPwuy-DBVhwKuo0q2lT&sz=w1620" />
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</details>
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<br />
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#### Try our online demos for inference:
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+ **Inference and evaluation** of your given weights: [](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl)
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+ **Online Inference with GUI** with adjustable resolutions: [](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo)
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+ Online **Multiple Images Inference** on Colab: [](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba)
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<img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1620" />
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## Model Zoo
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> For more general use of our BiRefNet, I extended the original academic one to more general ones for better real-life application.
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>
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> Datasets and datasets are suggested to be downloaded from official pages. But you can also download the packaged ones: [DIS](https://drive.google.com/drive/folders/1hZW6tAGPJwo9mPS7qGGGdpxuvuXiyoMJ), [HRSOD](https://drive.google.com/drive/folders/18_hAE3QM4cwAzEAKXuSNtKjmgFXTQXZN), [COD](https://drive.google.com/drive/folders/1EyHmKWsXfaCR9O0BiZEc3roZbRcs4ECO), [Backbones](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms).
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>
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> Find performances (almost all metrics) of all models in the `exp-TASK_SETTINGS` folders in [[**stuff**](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM)].
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<details><summary>Models in the original paper, for <b>comparison on benchmarks</b>:</summary>
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| Task | Training Sets | Backbone | Download |
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| :---: | :-------------------------: | :-----------: | :----------------------------------------------------------: |
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| DIS | DIS5K-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1J90LucvDQaS3R_-9E7QUh1mgJ8eQvccb/view) |
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| COD | COD10K-TR, CAMO-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1tM5M72k7a8aKF-dYy-QXaqvfEhbFaWkC/view) |
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| HRSOD | DUTS-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1f7L0Pb1Y3RkOMbqLCW_zO31dik9AiUFa/view) |
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| HRSOD | HRSOD-TR | swin_v1_large | google-drive |
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| HRSOD | UHRSD-TR | swin_v1_large | google-drive |
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| HRSOD | DUTS-TR, HRSOD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1WJooyTkhoDLllaqwbpur_9Hle0XTHEs_/view) |
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| HRSOD | DUTS-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1Pu1mv3ORobJatIuUoEuZaWDl2ylP3Gw7/view) |
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| HRSOD | HRSOD-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1xEh7fsgWGaS5c3IffMswasv0_u-aVM9E/view) |
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| HRSOD | DUTS-TR, HRSOD-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/13FaxyyOwyCddfZn2vZo1xG1KNZ3cZ-6B/view) |
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</details>
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<details><summary>Models trained with customed data (general, matting), for <b>general use in practical application</b>:</summary>
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| Task | Training Sets | Backbone | Test Set | Metric (S, wF[, HCE]) | Download |
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| :-----------------------: | :----------------------------------------------------------: | :-----------: | :-------: | :-------------------: | :----------------------------------------------------------: |
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| **general use** | DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE, TR-P3M-10k, TE-P3M-500-NP, TE-P3M-500-P, TR-humans | swin_v1_large | DIS-VD | 0.911, 0.875, 1069 | [google-drive](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) |
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| **general use** | DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE, TR-P3M-10k, TE-P3M-500-NP, TE-P3M-500-P, TR-humans | swin_v1_tiny | DIS-VD | 0.882, 0.830, 1175 | [google-drive](https://drive.google.com/file/d/1fzInDWiE2n65tmjaHDSZpqhL0VME6-Yl/view) |
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| **general use** | DIS5K-TR, DIS-TEs | swin_v1_large | DIS-VD | 0.907, 0.865, 1059 | [google-drive](https://drive.google.com/file/d/1P6NJzG3Jf1sl7js2q1CPC3yqvBn_O8UJ/view) |
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| **matting segmentation** | [P3M-10k](https://github.com/JizhiziLi/P3M), [humans](https://huggingface.co/datasets/schirrmacher/humans) | swin_v1_large | P3M-500-P | 0.983, 0.989 | [google-drive](https://drive.google.com/file/d/1uUeXjEUoD2XF_6YjD_fsct-TJp7TFiqh) |
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</details>
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<details><summary>Segmentation with box <b>guidance</b>:</summary>
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+ Given box guidance: [](https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK)
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</details>
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<details><summary>Model <b>efficiency</b>:</summary>
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> Screenshot from the original paper. All tests are conducted on a single A100 GPU.
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<img src="https://drive.google.com/thumbnail?id=1mTfSD_qt-rFO1t8DRQcyIa5cgWLf1w2-&sz=h300" /> <img src="https://drive.google.com/thumbnail?id=1F_OURIWILVe4u1rSz-aqt6ur__bAef25&sz=h300" />
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</details>
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<details><summary><b>ONNX</b> conversion:</summary>
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> We converted from `.pth` weights files to `.onnx` files.
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> We referred a lot to the [Kazuhito00/BiRefNet-ONNX-Sample](https://github.com/Kazuhito00/BiRefNet-ONNX-Sample), many thanks to @Kazuhito00.
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+ Check our [Colab demo for ONNX conversion](https://colab.research.google.com/drive/1z6OruR52LOvDDpnp516F-N4EyPGrp5om) or the [notebook file for local running](https://drive.google.com/file/d/1cgL2qyvOO5q3ySfhytypX46swdQwZLrJ), where you can do the conversion/inference by yourself and find all relevant info.
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+ As tested, BiRefNets with SwinL (default backbone) cost `~90%` more time (the inference costs `~165ms` on an A100 GPU) using ONNX files. Meanwhile, BiRefNets with SwinT (lightweight) cost `~75%` more time (the inference costs `~93.8ms` on an A100 GPU) using ONNX files. Input resolution is `1024x1024` as default.
|
||||
+ The results of the original pth files and the converted onnx files are slightly different, which is acceptable.
|
||||
+ Pay attention to the compatibility among `onnxruntime-gpu, CUDA, and CUDNN` (we use `torch==2.0.1, cuda=11.8` here).
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
## Third-Party Creations
|
||||
|
||||
> Concerning edge devices with less computing power, we provide a lightweight version with `swin_v1_tiny` as the backbone, which is x4+ faster and x5+ smaller. The details can be found in [this issue](https://github.com/ZhengPeng7/BiRefNet/issues/11#issuecomment-2041033576) and links there.
|
||||
|
||||
We found there've been some 3rd party applications based on our BiRefNet. Many thanks for their contribution to the community!
|
||||
Choose the one you like to try with clicks instead of codes:
|
||||
1. **Applications**:
|
||||
+ Thanks [**lbq779660843/BiRefNet-Tensorrt**](https://github.com/lbq779660843/BiRefNet-Tensorrt) and [**yuanyang1991/birefnet_tensorrt**](https://github.com/yuanyang1991/birefnet_tensorrt): they both provided the project to convert BiRefNet to **TensorRT**, which is faster and better for deployment. Their repos offer solid local establishment (Win and Linux) and [colab demo](https://colab.research.google.com/drive/1r8GkFPyMMO0OkMX6ih5FjZnUCQrl2SHV?usp=sharing), respectively. And @yuanyang1991 kindly offered the comparison among the inference efficiency of naive PyTorch, ONNX, and TensorRT on an RTX 4080S:
|
||||
|
||||
| Methods | [Pytorch](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) | [ONNX](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N) | TensorRT |
|
||||
|:------------------------------------------------------------------------------------:|:--------------:|:--------------:|:--------------:|
|
||||
| First Inference Time | 0.71s | 5.32s | **0.17s** |
|
||||
|
||||
| Methods | [Pytorch](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) | [ONNX](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N) | TensorRT |
|
||||
|:------------------------------------------------------------------------------------:|:--------------:|:--------------:|:--------------:|
|
||||
| Avg Inf Time (excluding 1st) | 0.15s | 4.43s | **0.11s** |
|
||||
|
||||
+ Thanks [**dimitribarbot/sd-webui-birefnet**](https://github.com/dimitribarbot/sd-webui-birefnet): this project allows to add a BiRefNet section to the original **Stable Diffusion WebUI**'s Extras tab.
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=159bLXI71FWh4ZsHTvc-wApSN9ytVRmua&sz=w1620" /></p>
|
||||
|
||||
+ Thanks [**fal.ai/birefnet**](https://fal.ai/models/birefnet): this project on `fal.ai` encapsulates BiRefNet **online** with more useful options in **UI** and **API** to call the model.
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1rNk81YV_Pzb2GykrzfGvX6T7KBXR0wrA&sz=w1620" /></p>
|
||||
|
||||
+ Thanks [**ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO**](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO): this project further improves the **UI** for BiRefNet in ComfyUI, especially for **video data**.
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1GOqEreyS7ENzTPN0RqxEjaA76RpMlkYM&sz=w1620" /></p>
|
||||
|
||||
<https://github.com/ZhengPeng7/BiRefNet/assets/25921713/3a1c7ab2-9847-4dac-8935-43a2d3cd2671>
|
||||
|
||||
+ Thanks [**viperyl/ComfyUI-BiRefNet**](https://github.com/viperyl/ComfyUI-BiRefNet): this project packs BiRefNet as **ComfyUI nodes**, and makes this SOTA model easier use for everyone.
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1KfxCQUUa2y9T-aysEaeVVjCUt3Z0zSkL&sz=w1620" /></p>
|
||||
|
||||
+ Thanks [**Rishabh**](https://github.com/rishabh063) for offering a demo for the [easier multiple images inference on colab](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba).
|
||||
|
||||
2. **More Visual Comparisons**
|
||||
+ Thanks [**twitter.com/ZHOZHO672070**](https://twitter.com/ZHOZHO672070) for the comparison with more background-removal methods in images:
|
||||
|
||||
<img src="https://drive.google.com/thumbnail?id=1nvVIFt_Ezs-crPSQxUDqkUBz598fTe63&sz=w1620" />
|
||||
|
||||
+ Thanks [**twitter.com/toyxyz3**](https://twitter.com/toyxyz3) for the comparison with more background-removal methods in videos:
|
||||
|
||||
<https://github.com/ZhengPeng7/BiRefNet/assets/25921713/40136198-01cc-4106-81f9-81c985f02e31>
|
||||
|
||||
<https://github.com/ZhengPeng7/BiRefNet/assets/25921713/1a32860c-0893-49dd-b557-c2e35a83c160>
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
#### Environment Setup
|
||||
|
||||
```shell
|
||||
# PyTorch==2.0.1 is used for faster training with compilation.
|
||||
conda create -n birefnet python=3.9 -y && conda activate birefnet
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
#### Dataset Preparation
|
||||
|
||||
Download combined training / test sets I have organized well from: [DIS](https://drive.google.com/drive/folders/1hZW6tAGPJwo9mPS7qGGGdpxuvuXiyoMJ)--[COD](https://drive.google.com/drive/folders/1EyHmKWsXfaCR9O0BiZEc3roZbRcs4ECO)--[HRSOD](https://drive.google.com/drive/folders/18_hAE3QM4cwAzEAKXuSNtKjmgFXTQXZN) or the single official ones in the `single_ones` folder, or their official pages. You can also find the same ones on my **BaiduDisk**: [DIS](https://pan.baidu.com/s/1O_pQIGAE4DKqL93xOxHpxw?pwd=PSWD)--[COD](https://pan.baidu.com/s/1RnxAzaHSTGBC1N6r_RfeqQ?pwd=PSWD)--[HRSOD](https://pan.baidu.com/s/1_Del53_0lBuG0DKJJAk4UA?pwd=PSWD).
|
||||
|
||||
#### Weights Preparation
|
||||
|
||||
Download backbone weights from [my google-drive folder](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM) or their official pages.
|
||||
|
||||
## Run
|
||||
|
||||
```shell
|
||||
# Train & Test & Evaluation
|
||||
./train_test.sh RUN_NAME GPU_NUMBERS_FOR_TRAINING GPU_NUMBERS_FOR_TEST
|
||||
# Example: ./train_test.sh tmp-proj 0,1,2,3,4,5,6,7 0
|
||||
|
||||
# See train.sh / test.sh for only training / test-evaluation.
|
||||
# After the evaluation, run `gen_best_ep.py` to select the best ckpt from a specific metric (you choose it from Sm, wFm, HCE (DIS only)).
|
||||
```
|
||||
|
||||
#### Well-trained weights:
|
||||
|
||||
Download the `BiRefNet-{TASK}-{EPOCH}.pth` from [[**stuff**](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM)]. Info of the corresponding (predicted\_maps/performance/training\_log) weights can be also found in folders like `exp-BiRefNet-{TASK_SETTINGS}` in the same directory.
|
||||
|
||||
You can also download the weights from the release of this repo.
|
||||
|
||||
The results might be a bit different from those in the original paper, you can see them in the `eval_results-BiRefNet-{TASK_SETTINGS}` folder in each `exp-xx`, we will update them in the following days. Due to the very high cost I used (A100-80G x 8) which many people cannot afford to (including myself....), I re-trained BiRefNet on a single A100-40G only and achieve the performance on the same level (even better). It means you can directly train the model on a single GPU with 36.5G+ memory. BTW, 5.5G GPU memory is needed for inference in 1024x1024. (I personally paid a lot for renting an A100-40G to re-train BiRefNet on the three tasks... T_T. Hope it can help you.)
|
||||
|
||||
But if you have more and more powerful GPUs, you can set GPU IDs and increase the batch size in `config.py` to accelerate the training. We have made all this kind of things adaptive in scripts to seamlessly switch between single-card training and multi-card training. Enjoy it :)
|
||||
|
||||
#### Some of my messages:
|
||||
|
||||
This project was originally built for DIS only. But after the updates one by one, I made it larger and larger with many functions embedded together. Finally, you can **use it for any binary image segmentation tasks**, such as DIS/COD/SOD, medical image segmentation, anomaly segmentation, etc. You can eaily open/close below things (usually in `config.py`):
|
||||
+ Multi-GPU training: open/close with one variable.
|
||||
+ Backbone choices: Swin_v1, PVT_v2, ConvNets, ...
|
||||
+ Weighted losses: BCE, IoU, SSIM, MAE, Reg, ...
|
||||
+ Adversarial loss for binary segmentation (proposed in my previous work [MCCL](https://arxiv.org/pdf/2302.14485)).
|
||||
+ Training tricks: multi-scale supervision, freezing backbone, multi-scale input...
|
||||
+ Data collator: loading all in memory, smooth combination of different datasets for combined training and test.
|
||||
+ ...
|
||||
I really hope you enjoy this project and use it in more works to achieve new SOTAs.
|
||||
|
||||
|
||||
### Quantitative Results
|
||||
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1Ymkh8WN16XMTBOS8dmPTg5eAf-NIl2m5&sz=w1620" /></p>
|
||||
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1W0mi0ZiYbqsaGuohNXU8Gh7Zj4M3neFg&sz=w1620" /></p>
|
||||
|
||||
|
||||
|
||||
### Qualitative Results
|
||||
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1TYZF8pVZc2V0V6g3ik4iAr9iKvJ8BNrf&sz=w1620" /></p>
|
||||
|
||||
<p align="center"><img src="https://drive.google.com/thumbnail?id=1ZGHC32CAdT9cwRloPzOCKWCrVQZvUAlJ&sz=w1620" /></p>
|
||||
|
||||
|
||||
|
||||
### Citation
|
||||
|
||||
```
|
||||
@article{zheng2024birefnet,
|
||||
title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
|
||||
author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
|
||||
journal={CAAI Artificial Intelligence Research},
|
||||
volume = {3},
|
||||
pages = {9150038},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Contact
|
||||
|
||||
Any questions, discussions, or even complaints, feel free to leave issues here or send me e-mails (zhengpeng0108@gmail.com). You can also join the Discord Group (https://discord.gg/d9NN5sgFrq) or QQ Group (https://qm.qq.com/q/y6WPy7WOIK) if you want to talk a lot publicly.
|
||||
|
||||
@@ -0,0 +1,174 @@
|
||||
import os
|
||||
import math
|
||||
|
||||
|
||||
class Config():
|
||||
def __init__(self) -> None:
|
||||
# PATH settings
|
||||
# Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
|
||||
if os.name == 'nt':
|
||||
self.sys_home_dir = os.environ['USERPROFILE'] # For windows system
|
||||
else:
|
||||
self.sys_home_dir = os.environ['HOME'] # For Linux system
|
||||
|
||||
# TASK settings
|
||||
self.task = ['DIS5K', 'COD', 'HRSOD', 'General', 'Matting'][0]
|
||||
self.training_set = {
|
||||
'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
|
||||
'COD': 'TR-COD10K+TR-CAMO',
|
||||
'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
|
||||
'General': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-NP+TE-P3M-500-P+TR-humans', # leave DIS-VD for evaluation.
|
||||
'Matting': 'TR-P3M-10k+TE-P3M-500-NP+TR-humans+TR-Distrinctions-646',
|
||||
}[self.task]
|
||||
self.prompt4loc = ['dense', 'sparse'][0]
|
||||
|
||||
# Faster-Training settings
|
||||
self.load_all = False # Turn it on/off by your case. It may consume a lot of CPU memory. And for multi-GPU (N), it would cost N times the CPU memory to load the data.
|
||||
self.use_fp16 = False # It may cause nan in training.
|
||||
self.compile = True and (not self.use_fp16) # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
|
||||
# Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
|
||||
# 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
|
||||
# 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
|
||||
self.precisionHigh = True
|
||||
|
||||
# MODEL settings
|
||||
self.ms_supervision = True
|
||||
self.out_ref = self.ms_supervision and True
|
||||
self.dec_ipt = True
|
||||
self.dec_ipt_split = True
|
||||
self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
|
||||
self.mul_scl_ipt = ['', 'add', 'cat'][2]
|
||||
self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
|
||||
self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
|
||||
self.dec_blk = ['BasicDecBlk', 'ResBlk'][0]
|
||||
|
||||
# TRAINING settings
|
||||
self.batch_size = 4
|
||||
self.finetune_last_epochs = [
|
||||
('IoU', 0),
|
||||
{
|
||||
'DIS5K': ('IoU', -30),
|
||||
'COD': ('IoU', -20),
|
||||
'HRSOD': ('IoU', -20),
|
||||
'General': ('MAE', -10),
|
||||
'Matting': ('MAE', -10),
|
||||
}[self.task]
|
||||
][1] # choose 0 to skip
|
||||
self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4) # DIS needs high lr to converge faster. Adapt the lr linearly
|
||||
self.size = 1024
|
||||
self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader
|
||||
|
||||
# Backbone settings
|
||||
self.bb = [
|
||||
'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
|
||||
'swin_v1_t', 'swin_v1_s', # 3, 4
|
||||
'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4
|
||||
'pvt_v2_b0', 'pvt_v2_b1', # 7, 8
|
||||
'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5
|
||||
][6]
|
||||
self.lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
|
||||
'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
|
||||
}[self.bb]
|
||||
if self.mul_scl_ipt == 'cat':
|
||||
self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
|
||||
self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
|
||||
|
||||
# MODEL settings - inactive
|
||||
self.lat_blk = ['BasicLatBlk'][0]
|
||||
self.dec_channels_inter = ['fixed', 'adap'][0]
|
||||
self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
|
||||
self.progressive_ref = self.refine and True
|
||||
self.ender = self.progressive_ref and False
|
||||
self.scale = self.progressive_ref and 2
|
||||
self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`.
|
||||
self.refine_iteration = 1
|
||||
self.freeze_bb = False
|
||||
self.model = [
|
||||
'BiRefNet',
|
||||
][0]
|
||||
|
||||
# TRAINING settings - inactive
|
||||
self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
|
||||
self.optimizer = ['Adam', 'AdamW'][1]
|
||||
self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch.
|
||||
self.lr_decay_rate = 0.5
|
||||
# Loss
|
||||
if self.task not in ['Matting']:
|
||||
self.lambdas_pix_last = {
|
||||
# not 0 means opening this loss
|
||||
# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
|
||||
'bce': 30 * 1, # high performance
|
||||
'iou': 0.5 * 1, # 0 / 255
|
||||
'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
|
||||
'mae': 30 * 0,
|
||||
'mse': 30 * 0, # can smooth the saliency map
|
||||
'triplet': 3 * 0,
|
||||
'reg': 100 * 0,
|
||||
'ssim': 10 * 1, # help contours,
|
||||
'cnt': 5 * 0, # help contours
|
||||
'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
|
||||
}
|
||||
else:
|
||||
self.lambdas_pix_last = {
|
||||
# not 0 means opening this loss
|
||||
# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
|
||||
'bce': 30 * 0, # high performance
|
||||
'iou': 0.5 * 0, # 0 / 255
|
||||
'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
|
||||
'mae': 100 * 1,
|
||||
'mse': 30 * 0, # can smooth the saliency map
|
||||
'triplet': 3 * 0,
|
||||
'reg': 100 * 0,
|
||||
'ssim': 10 * 1, # help contours,
|
||||
'cnt': 5 * 0, # help contours
|
||||
'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
|
||||
}
|
||||
self.lambdas_cls = {
|
||||
'ce': 5.0
|
||||
}
|
||||
# Adv
|
||||
self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
|
||||
self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
|
||||
|
||||
# PATH settings - inactive
|
||||
self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
|
||||
self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
|
||||
self.weights = {
|
||||
'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
|
||||
'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
|
||||
'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
|
||||
'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
|
||||
'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
|
||||
'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
|
||||
'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
|
||||
'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
|
||||
}
|
||||
|
||||
# Callbacks - inactive
|
||||
self.verbose_eval = True
|
||||
self.only_S_MAE = False
|
||||
self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
|
||||
|
||||
# others
|
||||
self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0')
|
||||
|
||||
self.batch_size_valid = 1
|
||||
self.rand_seed = 7
|
||||
run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
|
||||
if run_sh_file:
|
||||
with open(run_sh_file[0], 'r') as f:
|
||||
lines = f.readlines()
|
||||
self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
|
||||
|
||||
def print_task(self) -> None:
|
||||
# Return task for choosing settings in shell scripts.
|
||||
print(self.task)
|
||||
|
||||
if __name__ == '__main__':
|
||||
config = Config()
|
||||
config.print_task()
|
||||
|
||||
@@ -0,0 +1,118 @@
|
||||
import os
|
||||
import cv2
|
||||
from tqdm import tqdm
|
||||
from PIL import Image
|
||||
from torch.utils import data
|
||||
from torchvision import transforms
|
||||
|
||||
from .image_proc import preproc
|
||||
from .config import Config
|
||||
from .utils import path_to_image
|
||||
|
||||
|
||||
Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning
|
||||
config = Config()
|
||||
_class_labels_TR_sorted = (
|
||||
'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
|
||||
'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
|
||||
'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
|
||||
'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
|
||||
'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
|
||||
'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
|
||||
'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
|
||||
'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
|
||||
'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
|
||||
'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
|
||||
'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
|
||||
'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
|
||||
'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, '
|
||||
'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
|
||||
)
|
||||
class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
|
||||
|
||||
|
||||
class MyData(data.Dataset):
|
||||
def __init__(self, datasets, image_size, is_train=True):
|
||||
self.size_train = image_size
|
||||
self.size_test = image_size
|
||||
self.keep_size = not config.size
|
||||
self.data_size = (config.size, config.size)
|
||||
self.is_train = is_train
|
||||
self.load_all = config.load_all
|
||||
self.device = config.device
|
||||
valid_extensions = ['.png', '.jpg', '.PNG', '.JPG', '.JPEG']
|
||||
|
||||
if self.is_train and config.auxiliary_classification:
|
||||
self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
|
||||
self.transform_image = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
][self.load_all or self.keep_size:])
|
||||
self.transform_label = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
][self.load_all or self.keep_size:])
|
||||
dataset_root = os.path.join(config.data_root_dir, config.task)
|
||||
# datasets can be a list of different datasets for training on combined sets.
|
||||
self.image_paths = []
|
||||
for dataset in datasets.split('+'):
|
||||
image_root = os.path.join(dataset_root, dataset, 'im')
|
||||
self.image_paths += [os.path.join(image_root, p) for p in os.listdir(image_root) if any(p.endswith(ext) for ext in valid_extensions)]
|
||||
self.label_paths = []
|
||||
for p in self.image_paths:
|
||||
for ext in valid_extensions:
|
||||
## 'im' and 'gt' may need modifying
|
||||
p_gt = p.replace('/im/', '/gt/')[:-(len(p.split('.')[-1])+1)] + ext
|
||||
file_exists = False
|
||||
if os.path.exists(p_gt):
|
||||
self.label_paths.append(p_gt)
|
||||
file_exists = True
|
||||
break
|
||||
if not file_exists:
|
||||
print('Not exists:', p_gt)
|
||||
|
||||
if len(self.label_paths) != len(self.image_paths):
|
||||
raise ValueError(f"There are different numbers of images ({len(self.label_paths)}) and labels ({len(self.image_paths)})")
|
||||
|
||||
if self.load_all:
|
||||
self.images_loaded, self.labels_loaded = [], []
|
||||
self.class_labels_loaded = []
|
||||
# for image_path, label_path in zip(self.image_paths, self.label_paths):
|
||||
for image_path, label_path in tqdm(zip(self.image_paths, self.label_paths), total=len(self.image_paths)):
|
||||
_image = path_to_image(image_path, size=(config.size, config.size), color_type='rgb')
|
||||
_label = path_to_image(label_path, size=(config.size, config.size), color_type='gray')
|
||||
self.images_loaded.append(_image)
|
||||
self.labels_loaded.append(_label)
|
||||
self.class_labels_loaded.append(
|
||||
self.cls_name2id[label_path.split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
|
||||
)
|
||||
|
||||
def __getitem__(self, index):
|
||||
|
||||
if self.load_all:
|
||||
image = self.images_loaded[index]
|
||||
label = self.labels_loaded[index]
|
||||
class_label = self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1
|
||||
else:
|
||||
image = path_to_image(self.image_paths[index], size=(config.size, config.size), color_type='rgb')
|
||||
label = path_to_image(self.label_paths[index], size=(config.size, config.size), color_type='gray')
|
||||
class_label = self.cls_name2id[self.label_paths[index].split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
|
||||
|
||||
# loading image and label
|
||||
if self.is_train:
|
||||
image, label = preproc(image, label, preproc_methods=config.preproc_methods)
|
||||
# else:
|
||||
# if _label.shape[0] > 2048 or _label.shape[1] > 2048:
|
||||
# _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
# _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
image, label = self.transform_image(image), self.transform_label(label)
|
||||
|
||||
if self.is_train:
|
||||
return image, label, class_label
|
||||
else:
|
||||
return image, label, self.label_paths[index]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.image_paths)
|
||||
@@ -0,0 +1,146 @@
|
||||
import os
|
||||
import argparse
|
||||
from glob import glob
|
||||
import prettytable as pt
|
||||
|
||||
from .evaluation.evaluate import evaluator
|
||||
from .config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
def do_eval(args):
|
||||
# evaluation for whole dataset
|
||||
# dataset first in evaluation
|
||||
for _data_name in args.data_lst.split('+'):
|
||||
pred_data_dir = sorted(glob(os.path.join(args.pred_root, args.model_lst[0], _data_name)))
|
||||
if not pred_data_dir:
|
||||
print('Skip dataset {}.'.format(_data_name))
|
||||
continue
|
||||
gt_src = os.path.join(args.gt_root, _data_name)
|
||||
gt_paths = sorted(glob(os.path.join(gt_src, 'gt', '*')))
|
||||
print('#' * 20, _data_name, '#' * 20)
|
||||
filename = os.path.join(args.save_dir, '{}_eval.txt'.format(_data_name))
|
||||
tb = pt.PrettyTable()
|
||||
tb.vertical_char = '&'
|
||||
if config.task == 'DIS5K':
|
||||
tb.field_names = ["Dataset", "Method", "maxFm", "wFmeasure", 'MAE', "Smeasure", "meanEm", "HCE", "maxEm", "meanFm", "adpEm", "adpFm", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
elif config.task == 'COD':
|
||||
tb.field_names = ["Dataset", "Method", "Smeasure", "wFmeasure", "meanFm", "meanEm", "maxEm", 'MAE', "maxFm", "adpEm", "adpFm", "HCE", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
elif config.task == 'HRSOD':
|
||||
tb.field_names = ["Dataset", "Method", "Smeasure", "maxFm", "meanEm", 'MAE', "maxEm", "meanFm", "wFmeasure", "adpEm", "adpFm", "HCE", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
elif config.task == 'General':
|
||||
tb.field_names = ["Dataset", "Method", "maxFm", "wFmeasure", 'MAE', "Smeasure", "meanEm", "HCE", "maxEm", "meanFm", "adpEm", "adpFm", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
elif config.task == 'Matting':
|
||||
tb.field_names = ["Dataset", "Method", "Smeasure", "maxFm", "meanEm", 'MSE', "maxEm", "meanFm", "wFmeasure", "adpEm", "adpFm", "HCE", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
else:
|
||||
tb.field_names = ["Dataset", "Method", "Smeasure", 'MAE', "maxEm", "meanEm", "maxFm", "meanFm", "wFmeasure", "adpEm", "adpFm", "HCE", 'mBA', 'maxBIoU', 'meanBIoU']
|
||||
for _model_name in args.model_lst[:]:
|
||||
print('\t', 'Evaluating model: {}...'.format(_model_name))
|
||||
pred_paths = [p.replace(args.gt_root, os.path.join(args.pred_root, _model_name)).replace('/gt/', '/') for p in gt_paths]
|
||||
# print(pred_paths[:1], gt_paths[:1])
|
||||
em, sm, fm, mae, wfm, hce, mba, biou = evaluator(
|
||||
gt_paths=gt_paths,
|
||||
pred_paths=pred_paths,
|
||||
metrics=args.metrics.split('+'),
|
||||
verbose=config.verbose_eval
|
||||
)
|
||||
if config.task == 'DIS5K':
|
||||
scores = [
|
||||
fm['curve'].max().round(3), wfm.round(3), mae.round(3), sm.round(3), em['curve'].mean().round(3), int(hce.round()),
|
||||
em['curve'].max().round(3), fm['curve'].mean().round(3), em['adp'].round(3), fm['adp'].round(3),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
elif config.task == 'COD':
|
||||
scores = [
|
||||
sm.round(3), wfm.round(3), fm['curve'].mean().round(3), em['curve'].mean().round(3), em['curve'].max().round(3), mae.round(3),
|
||||
fm['curve'].max().round(3), em['adp'].round(3), fm['adp'].round(3), int(hce.round()),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
elif config.task == 'HRSOD':
|
||||
scores = [
|
||||
sm.round(3), fm['curve'].max().round(3), em['curve'].mean().round(3), mae.round(3),
|
||||
em['curve'].max().round(3), fm['curve'].mean().round(3), wfm.round(3), em['adp'].round(3), fm['adp'].round(3), int(hce.round()),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
elif config.task == 'General':
|
||||
scores = [
|
||||
fm['curve'].max().round(3), wfm.round(3), mae.round(3), sm.round(3), em['curve'].mean().round(3), int(hce.round()),
|
||||
em['curve'].max().round(3), fm['curve'].mean().round(3), em['adp'].round(3), fm['adp'].round(3),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
elif config.task == 'Matting':
|
||||
scores = [
|
||||
sm.round(3), fm['curve'].max().round(3), em['curve'].mean().round(3), mse.round(3),
|
||||
em['curve'].max().round(3), fm['curve'].mean().round(3), wfm.round(3), em['adp'].round(3), fm['adp'].round(3), int(hce.round()),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
else:
|
||||
scores = [
|
||||
sm.round(3), mae.round(3), em['curve'].max().round(3), em['curve'].mean().round(3),
|
||||
fm['curve'].max().round(3), fm['curve'].mean().round(3), wfm.round(3),
|
||||
em['adp'].round(3), fm['adp'].round(3), int(hce.round()),
|
||||
mba.round(3), biou['curve'].max().round(3), biou['curve'].mean().round(3),
|
||||
]
|
||||
|
||||
for idx_score, score in enumerate(scores):
|
||||
scores[idx_score] = '.' + format(score, '.3f').split('.')[-1] if score <= 1 else format(score, '<4')
|
||||
records = [_data_name, _model_name] + scores
|
||||
tb.add_row(records)
|
||||
# Write results after every check.
|
||||
with open(filename, 'w+') as file_to_write:
|
||||
file_to_write.write(str(tb)+'\n')
|
||||
print(tb)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# set parameters
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--gt_root', type=str, help='ground-truth root',
|
||||
default=os.path.join(config.data_root_dir, config.task))
|
||||
parser.add_argument(
|
||||
'--pred_root', type=str, help='prediction root',
|
||||
default='./e_preds')
|
||||
parser.add_argument(
|
||||
'--data_lst', type=str, help='test dataset',
|
||||
default={
|
||||
'DIS5K': '+'.join(['DIS-VD', 'DIS-TE1', 'DIS-TE2', 'DIS-TE3', 'DIS-TE4'][:]),
|
||||
'COD': '+'.join(['TE-COD10K', 'NC4K', 'TE-CAMO', 'CHAMELEON'][:]),
|
||||
'HRSOD': '+'.join(['DAVIS-S', 'TE-HRSOD', 'TE-UHRSD', 'TE-DUTS', 'DUT-OMRON'][:]),
|
||||
'General': '+'.join(['DIS-VD'][:]),
|
||||
'Matting': '+'.join(['TE-P3M-500-P'][:]),
|
||||
}[config.task])
|
||||
parser.add_argument(
|
||||
'--save_dir', type=str, help='candidate competitors',
|
||||
default='e_results')
|
||||
parser.add_argument(
|
||||
'--check_integrity', type=bool, help='whether to check the file integrity',
|
||||
default=False)
|
||||
parser.add_argument(
|
||||
'--metrics', type=str, help='candidate competitors',
|
||||
default='+'.join(['S', 'MAE', 'E', 'F', 'WF', 'MBA', 'BIoU', 'HCE'][:100 if 'DIS5K' in config.task else -1]))
|
||||
args = parser.parse_args()
|
||||
args.metrics = '+'.join(['S', 'MAE', 'E', 'F', 'WF', 'MBA', 'BIoU', 'HCE'][:100 if sum(['DIS-' in _data for _data in args.data_lst.split('+')]) else -1])
|
||||
|
||||
os.makedirs(args.save_dir, exist_ok=True)
|
||||
try:
|
||||
args.model_lst = [m for m in sorted(os.listdir(args.pred_root), key=lambda x: int(x.split('epoch_')[-1]), reverse=True) if int(m.split('epoch_')[-1]) % 1 == 0]
|
||||
except:
|
||||
args.model_lst = [m for m in sorted(os.listdir(args.pred_root))]
|
||||
|
||||
# check the integrity of each candidates
|
||||
if args.check_integrity:
|
||||
for _data_name in args.data_lst.split('+'):
|
||||
for _model_name in args.model_lst:
|
||||
gt_pth = os.path.join(args.gt_root, _data_name)
|
||||
pred_pth = os.path.join(args.pred_root, _model_name, _data_name)
|
||||
if not sorted(os.listdir(gt_pth)) == sorted(os.listdir(pred_pth)):
|
||||
print(len(sorted(os.listdir(gt_pth))), len(sorted(os.listdir(pred_pth))))
|
||||
print('The {} Dataset of {} Model is not matching to the ground-truth'.format(_data_name, _model_name))
|
||||
else:
|
||||
print('>>> skip check the integrity of each candidates')
|
||||
|
||||
# start engine
|
||||
do_eval(args)
|
||||
@@ -0,0 +1,763 @@
|
||||
import os
|
||||
from tqdm import tqdm
|
||||
import cv2
|
||||
import numpy as np
|
||||
from scipy.ndimage import convolve, distance_transform_edt as bwdist
|
||||
from skimage.morphology import skeletonize
|
||||
from skimage.morphology import disk
|
||||
from skimage.measure import label
|
||||
|
||||
|
||||
_EPS = np.spacing(1)
|
||||
_TYPE = np.float64
|
||||
|
||||
|
||||
def evaluator(gt_paths, pred_paths, metrics=['S', 'MAE', 'E', 'F', 'WF', 'MBA', 'BIoU', 'HCE'], verbose=False):
|
||||
# define measures
|
||||
if 'E' in metrics:
|
||||
EM = EMeasure()
|
||||
if 'S' in metrics:
|
||||
SM = SMeasure()
|
||||
if 'F' in metrics:
|
||||
FM = FMeasure()
|
||||
if 'MAE' in metrics:
|
||||
MAE = MAEMeasure()
|
||||
if 'WF' in metrics:
|
||||
WFM = WeightedFMeasure()
|
||||
if 'HCE' in metrics:
|
||||
HCE = HCEMeasure()
|
||||
if 'MBA' in metrics:
|
||||
MBA = MBAMeasure()
|
||||
if 'BIoU' in metrics:
|
||||
BIoU = BIoUMeasure()
|
||||
|
||||
if isinstance(gt_paths, list) and isinstance(pred_paths, list):
|
||||
# print(len(gt_paths), len(pred_paths))
|
||||
assert len(gt_paths) == len(pred_paths)
|
||||
|
||||
for idx_sample in tqdm(range(len(gt_paths)), total=len(gt_paths)) if verbose else range(len(gt_paths)):
|
||||
gt = gt_paths[idx_sample]
|
||||
pred = pred_paths[idx_sample]
|
||||
|
||||
pred = pred[:-4] + '.png'
|
||||
valid_extensions = ['.png', '.jpg', '.PNG', '.JPG', '.JPEG']
|
||||
file_exists = False
|
||||
for ext in valid_extensions:
|
||||
if os.path.exists(pred[:-4] + ext):
|
||||
pred = pred[:-4] + ext
|
||||
file_exists = True
|
||||
break
|
||||
if file_exists:
|
||||
pred_ary = cv2.imread(pred, cv2.IMREAD_GRAYSCALE)
|
||||
else:
|
||||
print('Not exists:', pred)
|
||||
|
||||
gt_ary = cv2.imread(gt, cv2.IMREAD_GRAYSCALE)
|
||||
pred_ary = cv2.resize(pred_ary, (gt_ary.shape[1], gt_ary.shape[0]))
|
||||
|
||||
if 'E' in metrics:
|
||||
EM.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'S' in metrics:
|
||||
SM.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'F' in metrics:
|
||||
FM.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'MAE' in metrics:
|
||||
MAE.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'WF' in metrics:
|
||||
WFM.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'HCE' in metrics:
|
||||
ske_path = gt.replace('/gt/', '/ske/')
|
||||
if os.path.exists(ske_path):
|
||||
ske_ary = cv2.imread(ske_path, cv2.IMREAD_GRAYSCALE)
|
||||
ske_ary = ske_ary > 128
|
||||
else:
|
||||
ske_ary = skeletonize(gt_ary > 128)
|
||||
ske_save_dir = os.path.join(*ske_path.split(os.sep)[:-1])
|
||||
if ske_path[0] == os.sep:
|
||||
ske_save_dir = os.sep + ske_save_dir
|
||||
os.makedirs(ske_save_dir, exist_ok=True)
|
||||
cv2.imwrite(ske_path, ske_ary.astype(np.uint8) * 255)
|
||||
HCE.step(pred=pred_ary, gt=gt_ary, gt_ske=ske_ary)
|
||||
if 'MBA' in metrics:
|
||||
MBA.step(pred=pred_ary, gt=gt_ary)
|
||||
if 'BIoU' in metrics:
|
||||
BIoU.step(pred=pred_ary, gt=gt_ary)
|
||||
|
||||
if 'E' in metrics:
|
||||
em = EM.get_results()['em']
|
||||
else:
|
||||
em = {'curve': np.array([np.float64(-1)]), 'adp': np.float64(-1)}
|
||||
if 'S' in metrics:
|
||||
sm = SM.get_results()['sm']
|
||||
else:
|
||||
sm = np.float64(-1)
|
||||
if 'F' in metrics:
|
||||
fm = FM.get_results()['fm']
|
||||
else:
|
||||
fm = {'curve': np.array([np.float64(-1)]), 'adp': np.float64(-1)}
|
||||
if 'MAE' in metrics:
|
||||
mae = MAE.get_results()['mae']
|
||||
else:
|
||||
mae = np.float64(-1)
|
||||
if 'WF' in metrics:
|
||||
wfm = WFM.get_results()['wfm']
|
||||
else:
|
||||
wfm = np.float64(-1)
|
||||
if 'HCE' in metrics:
|
||||
hce = HCE.get_results()['hce']
|
||||
else:
|
||||
hce = np.float64(-1)
|
||||
if 'MBA' in metrics:
|
||||
mba = MBA.get_results()['mba']
|
||||
else:
|
||||
mba = np.float64(-1)
|
||||
if 'BIoU' in metrics:
|
||||
biou = BIoU.get_results()['biou']
|
||||
else:
|
||||
biou = {'curve': np.array([np.float64(-1)])}
|
||||
|
||||
return em, sm, fm, mae, wfm, hce, mba, biou
|
||||
|
||||
|
||||
def _prepare_data(pred: np.ndarray, gt: np.ndarray) -> tuple:
|
||||
gt = gt > 128
|
||||
pred = pred / 255
|
||||
if pred.max() != pred.min():
|
||||
pred = (pred - pred.min()) / (pred.max() - pred.min())
|
||||
return pred, gt
|
||||
|
||||
|
||||
def _get_adaptive_threshold(matrix: np.ndarray, max_value: float = 1) -> float:
|
||||
return min(2 * matrix.mean(), max_value)
|
||||
|
||||
|
||||
class FMeasure(object):
|
||||
def __init__(self, beta: float = 0.3):
|
||||
self.beta = beta
|
||||
self.precisions = []
|
||||
self.recalls = []
|
||||
self.adaptive_fms = []
|
||||
self.changeable_fms = []
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred, gt)
|
||||
|
||||
adaptive_fm = self.cal_adaptive_fm(pred=pred, gt=gt)
|
||||
self.adaptive_fms.append(adaptive_fm)
|
||||
|
||||
precisions, recalls, changeable_fms = self.cal_pr(pred=pred, gt=gt)
|
||||
self.precisions.append(precisions)
|
||||
self.recalls.append(recalls)
|
||||
self.changeable_fms.append(changeable_fms)
|
||||
|
||||
def cal_adaptive_fm(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
adaptive_threshold = _get_adaptive_threshold(pred, max_value=1)
|
||||
binary_predcition = pred >= adaptive_threshold
|
||||
area_intersection = binary_predcition[gt].sum()
|
||||
if area_intersection == 0:
|
||||
adaptive_fm = 0
|
||||
else:
|
||||
pre = area_intersection / np.count_nonzero(binary_predcition)
|
||||
rec = area_intersection / np.count_nonzero(gt)
|
||||
adaptive_fm = (1 + self.beta) * pre * rec / (self.beta * pre + rec)
|
||||
return adaptive_fm
|
||||
|
||||
def cal_pr(self, pred: np.ndarray, gt: np.ndarray) -> tuple:
|
||||
pred = (pred * 255).astype(np.uint8)
|
||||
bins = np.linspace(0, 256, 257)
|
||||
fg_hist, _ = np.histogram(pred[gt], bins=bins)
|
||||
bg_hist, _ = np.histogram(pred[~gt], bins=bins)
|
||||
fg_w_thrs = np.cumsum(np.flip(fg_hist), axis=0)
|
||||
bg_w_thrs = np.cumsum(np.flip(bg_hist), axis=0)
|
||||
TPs = fg_w_thrs
|
||||
Ps = fg_w_thrs + bg_w_thrs
|
||||
Ps[Ps == 0] = 1
|
||||
T = max(np.count_nonzero(gt), 1)
|
||||
precisions = TPs / Ps
|
||||
recalls = TPs / T
|
||||
numerator = (1 + self.beta) * precisions * recalls
|
||||
denominator = np.where(numerator == 0, 1, self.beta * precisions + recalls)
|
||||
changeable_fms = numerator / denominator
|
||||
return precisions, recalls, changeable_fms
|
||||
|
||||
def get_results(self) -> dict:
|
||||
adaptive_fm = np.mean(np.array(self.adaptive_fms, _TYPE))
|
||||
changeable_fm = np.mean(np.array(self.changeable_fms, dtype=_TYPE), axis=0)
|
||||
precision = np.mean(np.array(self.precisions, dtype=_TYPE), axis=0) # N, 256
|
||||
recall = np.mean(np.array(self.recalls, dtype=_TYPE), axis=0) # N, 256
|
||||
return dict(fm=dict(adp=adaptive_fm, curve=changeable_fm),
|
||||
pr=dict(p=precision, r=recall))
|
||||
|
||||
|
||||
class MAEMeasure(object):
|
||||
def __init__(self):
|
||||
self.maes = []
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred, gt)
|
||||
|
||||
mae = self.cal_mae(pred, gt)
|
||||
self.maes.append(mae)
|
||||
|
||||
def cal_mae(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
mae = np.mean(np.abs(pred - gt))
|
||||
return mae
|
||||
|
||||
def get_results(self) -> dict:
|
||||
mae = np.mean(np.array(self.maes, _TYPE))
|
||||
return dict(mae=mae)
|
||||
|
||||
|
||||
class SMeasure(object):
|
||||
def __init__(self, alpha: float = 0.5):
|
||||
self.sms = []
|
||||
self.alpha = alpha
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred=pred, gt=gt)
|
||||
|
||||
sm = self.cal_sm(pred, gt)
|
||||
self.sms.append(sm)
|
||||
|
||||
def cal_sm(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
y = np.mean(gt)
|
||||
if y == 0:
|
||||
sm = 1 - np.mean(pred)
|
||||
elif y == 1:
|
||||
sm = np.mean(pred)
|
||||
else:
|
||||
sm = self.alpha * self.object(pred, gt) + (1 - self.alpha) * self.region(pred, gt)
|
||||
sm = max(0, sm)
|
||||
return sm
|
||||
|
||||
def object(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
fg = pred * gt
|
||||
bg = (1 - pred) * (1 - gt)
|
||||
u = np.mean(gt)
|
||||
object_score = u * self.s_object(fg, gt) + (1 - u) * self.s_object(bg, 1 - gt)
|
||||
return object_score
|
||||
|
||||
def s_object(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
x = np.mean(pred[gt == 1])
|
||||
sigma_x = np.std(pred[gt == 1], ddof=1)
|
||||
score = 2 * x / (np.power(x, 2) + 1 + sigma_x + _EPS)
|
||||
return score
|
||||
|
||||
def region(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
x, y = self.centroid(gt)
|
||||
part_info = self.divide_with_xy(pred, gt, x, y)
|
||||
w1, w2, w3, w4 = part_info['weight']
|
||||
pred1, pred2, pred3, pred4 = part_info['pred']
|
||||
gt1, gt2, gt3, gt4 = part_info['gt']
|
||||
score1 = self.ssim(pred1, gt1)
|
||||
score2 = self.ssim(pred2, gt2)
|
||||
score3 = self.ssim(pred3, gt3)
|
||||
score4 = self.ssim(pred4, gt4)
|
||||
|
||||
return w1 * score1 + w2 * score2 + w3 * score3 + w4 * score4
|
||||
|
||||
def centroid(self, matrix: np.ndarray) -> tuple:
|
||||
h, w = matrix.shape
|
||||
area_object = np.count_nonzero(matrix)
|
||||
if area_object == 0:
|
||||
x = np.round(w / 2)
|
||||
y = np.round(h / 2)
|
||||
else:
|
||||
# More details can be found at: https://www.yuque.com/lart/blog/gpbigm
|
||||
y, x = np.argwhere(matrix).mean(axis=0).round()
|
||||
return int(x) + 1, int(y) + 1
|
||||
|
||||
def divide_with_xy(self, pred: np.ndarray, gt: np.ndarray, x, y) -> dict:
|
||||
h, w = gt.shape
|
||||
area = h * w
|
||||
|
||||
gt_LT = gt[0:y, 0:x]
|
||||
gt_RT = gt[0:y, x:w]
|
||||
gt_LB = gt[y:h, 0:x]
|
||||
gt_RB = gt[y:h, x:w]
|
||||
|
||||
pred_LT = pred[0:y, 0:x]
|
||||
pred_RT = pred[0:y, x:w]
|
||||
pred_LB = pred[y:h, 0:x]
|
||||
pred_RB = pred[y:h, x:w]
|
||||
|
||||
w1 = x * y / area
|
||||
w2 = y * (w - x) / area
|
||||
w3 = (h - y) * x / area
|
||||
w4 = 1 - w1 - w2 - w3
|
||||
|
||||
return dict(gt=(gt_LT, gt_RT, gt_LB, gt_RB),
|
||||
pred=(pred_LT, pred_RT, pred_LB, pred_RB),
|
||||
weight=(w1, w2, w3, w4))
|
||||
|
||||
def ssim(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
h, w = pred.shape
|
||||
N = h * w
|
||||
|
||||
x = np.mean(pred)
|
||||
y = np.mean(gt)
|
||||
|
||||
sigma_x = np.sum((pred - x) ** 2) / (N - 1)
|
||||
sigma_y = np.sum((gt - y) ** 2) / (N - 1)
|
||||
sigma_xy = np.sum((pred - x) * (gt - y)) / (N - 1)
|
||||
|
||||
alpha = 4 * x * y * sigma_xy
|
||||
beta = (x ** 2 + y ** 2) * (sigma_x + sigma_y)
|
||||
|
||||
if alpha != 0:
|
||||
score = alpha / (beta + _EPS)
|
||||
elif alpha == 0 and beta == 0:
|
||||
score = 1
|
||||
else:
|
||||
score = 0
|
||||
return score
|
||||
|
||||
def get_results(self) -> dict:
|
||||
sm = np.mean(np.array(self.sms, dtype=_TYPE))
|
||||
return dict(sm=sm)
|
||||
|
||||
|
||||
class EMeasure(object):
|
||||
def __init__(self):
|
||||
self.adaptive_ems = []
|
||||
self.changeable_ems = []
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred=pred, gt=gt)
|
||||
self.gt_fg_numel = np.count_nonzero(gt)
|
||||
self.gt_size = gt.shape[0] * gt.shape[1]
|
||||
|
||||
changeable_ems = self.cal_changeable_em(pred, gt)
|
||||
self.changeable_ems.append(changeable_ems)
|
||||
adaptive_em = self.cal_adaptive_em(pred, gt)
|
||||
self.adaptive_ems.append(adaptive_em)
|
||||
|
||||
def cal_adaptive_em(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
adaptive_threshold = _get_adaptive_threshold(pred, max_value=1)
|
||||
adaptive_em = self.cal_em_with_threshold(pred, gt, threshold=adaptive_threshold)
|
||||
return adaptive_em
|
||||
|
||||
def cal_changeable_em(self, pred: np.ndarray, gt: np.ndarray) -> np.ndarray:
|
||||
changeable_ems = self.cal_em_with_cumsumhistogram(pred, gt)
|
||||
return changeable_ems
|
||||
|
||||
def cal_em_with_threshold(self, pred: np.ndarray, gt: np.ndarray, threshold: float) -> float:
|
||||
binarized_pred = pred >= threshold
|
||||
fg_fg_numel = np.count_nonzero(binarized_pred & gt)
|
||||
fg_bg_numel = np.count_nonzero(binarized_pred & ~gt)
|
||||
|
||||
fg___numel = fg_fg_numel + fg_bg_numel
|
||||
bg___numel = self.gt_size - fg___numel
|
||||
|
||||
if self.gt_fg_numel == 0:
|
||||
enhanced_matrix_sum = bg___numel
|
||||
elif self.gt_fg_numel == self.gt_size:
|
||||
enhanced_matrix_sum = fg___numel
|
||||
else:
|
||||
parts_numel, combinations = self.generate_parts_numel_combinations(
|
||||
fg_fg_numel=fg_fg_numel, fg_bg_numel=fg_bg_numel,
|
||||
pred_fg_numel=fg___numel, pred_bg_numel=bg___numel,
|
||||
)
|
||||
|
||||
results_parts = []
|
||||
for i, (part_numel, combination) in enumerate(zip(parts_numel, combinations)):
|
||||
align_matrix_value = 2 * (combination[0] * combination[1]) / \
|
||||
(combination[0] ** 2 + combination[1] ** 2 + _EPS)
|
||||
enhanced_matrix_value = (align_matrix_value + 1) ** 2 / 4
|
||||
results_parts.append(enhanced_matrix_value * part_numel)
|
||||
enhanced_matrix_sum = sum(results_parts)
|
||||
|
||||
em = enhanced_matrix_sum / (self.gt_size - 1 + _EPS)
|
||||
return em
|
||||
|
||||
def cal_em_with_cumsumhistogram(self, pred: np.ndarray, gt: np.ndarray) -> np.ndarray:
|
||||
pred = (pred * 255).astype(np.uint8)
|
||||
bins = np.linspace(0, 256, 257)
|
||||
fg_fg_hist, _ = np.histogram(pred[gt], bins=bins)
|
||||
fg_bg_hist, _ = np.histogram(pred[~gt], bins=bins)
|
||||
fg_fg_numel_w_thrs = np.cumsum(np.flip(fg_fg_hist), axis=0)
|
||||
fg_bg_numel_w_thrs = np.cumsum(np.flip(fg_bg_hist), axis=0)
|
||||
|
||||
fg___numel_w_thrs = fg_fg_numel_w_thrs + fg_bg_numel_w_thrs
|
||||
bg___numel_w_thrs = self.gt_size - fg___numel_w_thrs
|
||||
|
||||
if self.gt_fg_numel == 0:
|
||||
enhanced_matrix_sum = bg___numel_w_thrs
|
||||
elif self.gt_fg_numel == self.gt_size:
|
||||
enhanced_matrix_sum = fg___numel_w_thrs
|
||||
else:
|
||||
parts_numel_w_thrs, combinations = self.generate_parts_numel_combinations(
|
||||
fg_fg_numel=fg_fg_numel_w_thrs, fg_bg_numel=fg_bg_numel_w_thrs,
|
||||
pred_fg_numel=fg___numel_w_thrs, pred_bg_numel=bg___numel_w_thrs,
|
||||
)
|
||||
|
||||
results_parts = np.empty(shape=(4, 256), dtype=np.float64)
|
||||
for i, (part_numel, combination) in enumerate(zip(parts_numel_w_thrs, combinations)):
|
||||
align_matrix_value = 2 * (combination[0] * combination[1]) / \
|
||||
(combination[0] ** 2 + combination[1] ** 2 + _EPS)
|
||||
enhanced_matrix_value = (align_matrix_value + 1) ** 2 / 4
|
||||
results_parts[i] = enhanced_matrix_value * part_numel
|
||||
enhanced_matrix_sum = results_parts.sum(axis=0)
|
||||
|
||||
em = enhanced_matrix_sum / (self.gt_size - 1 + _EPS)
|
||||
return em
|
||||
|
||||
def generate_parts_numel_combinations(self, fg_fg_numel, fg_bg_numel, pred_fg_numel, pred_bg_numel):
|
||||
bg_fg_numel = self.gt_fg_numel - fg_fg_numel
|
||||
bg_bg_numel = pred_bg_numel - bg_fg_numel
|
||||
|
||||
parts_numel = [fg_fg_numel, fg_bg_numel, bg_fg_numel, bg_bg_numel]
|
||||
|
||||
mean_pred_value = pred_fg_numel / self.gt_size
|
||||
mean_gt_value = self.gt_fg_numel / self.gt_size
|
||||
|
||||
demeaned_pred_fg_value = 1 - mean_pred_value
|
||||
demeaned_pred_bg_value = 0 - mean_pred_value
|
||||
demeaned_gt_fg_value = 1 - mean_gt_value
|
||||
demeaned_gt_bg_value = 0 - mean_gt_value
|
||||
|
||||
combinations = [
|
||||
(demeaned_pred_fg_value, demeaned_gt_fg_value),
|
||||
(demeaned_pred_fg_value, demeaned_gt_bg_value),
|
||||
(demeaned_pred_bg_value, demeaned_gt_fg_value),
|
||||
(demeaned_pred_bg_value, demeaned_gt_bg_value)
|
||||
]
|
||||
return parts_numel, combinations
|
||||
|
||||
def get_results(self) -> dict:
|
||||
adaptive_em = np.mean(np.array(self.adaptive_ems, dtype=_TYPE))
|
||||
changeable_em = np.mean(np.array(self.changeable_ems, dtype=_TYPE), axis=0)
|
||||
return dict(em=dict(adp=adaptive_em, curve=changeable_em))
|
||||
|
||||
|
||||
class WeightedFMeasure(object):
|
||||
def __init__(self, beta: float = 1):
|
||||
self.beta = beta
|
||||
self.weighted_fms = []
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred=pred, gt=gt)
|
||||
|
||||
if np.all(~gt):
|
||||
wfm = 0
|
||||
else:
|
||||
wfm = self.cal_wfm(pred, gt)
|
||||
self.weighted_fms.append(wfm)
|
||||
|
||||
def cal_wfm(self, pred: np.ndarray, gt: np.ndarray) -> float:
|
||||
# [Dst,IDXT] = bwdist(dGT);
|
||||
Dst, Idxt = bwdist(gt == 0, return_indices=True)
|
||||
|
||||
# %Pixel dependency
|
||||
# E = abs(FG-dGT);
|
||||
E = np.abs(pred - gt)
|
||||
Et = np.copy(E)
|
||||
Et[gt == 0] = Et[Idxt[0][gt == 0], Idxt[1][gt == 0]]
|
||||
|
||||
# K = fspecial('gaussian',7,5);
|
||||
# EA = imfilter(Et,K);
|
||||
K = self.matlab_style_gauss2D((7, 7), sigma=5)
|
||||
EA = convolve(Et, weights=K, mode="constant", cval=0)
|
||||
# MIN_E_EA = E;
|
||||
# MIN_E_EA(GT & EA<E) = EA(GT & EA<E);
|
||||
MIN_E_EA = np.where(gt & (EA < E), EA, E)
|
||||
|
||||
# %Pixel importance
|
||||
B = np.where(gt == 0, 2 - np.exp(np.log(0.5) / 5 * Dst), np.ones_like(gt))
|
||||
Ew = MIN_E_EA * B
|
||||
|
||||
TPw = np.sum(gt) - np.sum(Ew[gt == 1])
|
||||
FPw = np.sum(Ew[gt == 0])
|
||||
|
||||
|
||||
R = 1 - np.mean(Ew[gt == 1])
|
||||
P = TPw / (TPw + FPw + _EPS)
|
||||
|
||||
# % Q = (1+Beta^2)*(R*P)./(eps+R+(Beta.*P));
|
||||
Q = (1 + self.beta) * R * P / (R + self.beta * P + _EPS)
|
||||
|
||||
return Q
|
||||
|
||||
def matlab_style_gauss2D(self, shape: tuple = (7, 7), sigma: int = 5) -> np.ndarray:
|
||||
"""
|
||||
2D gaussian mask - should give the same result as MATLAB's
|
||||
fspecial('gaussian',[shape],[sigma])
|
||||
"""
|
||||
m, n = [(ss - 1) / 2 for ss in shape]
|
||||
y, x = np.ogrid[-m: m + 1, -n: n + 1]
|
||||
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
|
||||
h[h < np.finfo(h.dtype).eps * h.max()] = 0
|
||||
sumh = h.sum()
|
||||
if sumh != 0:
|
||||
h /= sumh
|
||||
return h
|
||||
|
||||
def get_results(self) -> dict:
|
||||
weighted_fm = np.mean(np.array(self.weighted_fms, dtype=_TYPE))
|
||||
return dict(wfm=weighted_fm)
|
||||
|
||||
|
||||
class HCEMeasure(object):
|
||||
def __init__(self):
|
||||
self.hces = []
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray, gt_ske):
|
||||
# pred, gt = _prepare_data(pred, gt)
|
||||
|
||||
hce = self.cal_hce(pred, gt, gt_ske)
|
||||
self.hces.append(hce)
|
||||
|
||||
def get_results(self) -> dict:
|
||||
hce = np.mean(np.array(self.hces, _TYPE))
|
||||
return dict(hce=hce)
|
||||
|
||||
|
||||
def cal_hce(self, pred: np.ndarray, gt: np.ndarray, gt_ske: np.ndarray, relax=5, epsilon=2.0) -> float:
|
||||
# Binarize gt
|
||||
if(len(gt.shape)>2):
|
||||
gt = gt[:, :, 0]
|
||||
|
||||
epsilon_gt = 128#(np.amin(gt)+np.amax(gt))/2.0
|
||||
gt = (gt>epsilon_gt).astype(np.uint8)
|
||||
|
||||
# Binarize pred
|
||||
if(len(pred.shape)>2):
|
||||
pred = pred[:, :, 0]
|
||||
epsilon_pred = 128#(np.amin(pred)+np.amax(pred))/2.0
|
||||
pred = (pred>epsilon_pred).astype(np.uint8)
|
||||
|
||||
Union = np.logical_or(gt, pred)
|
||||
TP = np.logical_and(gt, pred)
|
||||
FP = pred - TP
|
||||
FN = gt - TP
|
||||
|
||||
# relax the Union of gt and pred
|
||||
Union_erode = Union.copy()
|
||||
Union_erode = cv2.erode(Union_erode.astype(np.uint8), disk(1), iterations=relax)
|
||||
|
||||
# --- get the relaxed False Positive regions for computing the human efforts in correcting them ---
|
||||
FP_ = np.logical_and(FP, Union_erode) # get the relaxed FP
|
||||
for i in range(0, relax):
|
||||
FP_ = cv2.dilate(FP_.astype(np.uint8), disk(1))
|
||||
FP_ = np.logical_and(FP_, 1-np.logical_or(TP, FN))
|
||||
FP_ = np.logical_and(FP, FP_)
|
||||
|
||||
# --- get the relaxed False Negative regions for computing the human efforts in correcting them ---
|
||||
FN_ = np.logical_and(FN, Union_erode) # preserve the structural components of FN
|
||||
## recover the FN, where pixels are not close to the TP borders
|
||||
for i in range(0, relax):
|
||||
FN_ = cv2.dilate(FN_.astype(np.uint8), disk(1))
|
||||
FN_ = np.logical_and(FN_, 1-np.logical_or(TP, FP))
|
||||
FN_ = np.logical_and(FN, FN_)
|
||||
FN_ = np.logical_or(FN_, np.logical_xor(gt_ske, np.logical_and(TP, gt_ske))) # preserve the structural components of FN
|
||||
|
||||
## 2. =============Find exact polygon control points and independent regions==============
|
||||
## find contours from FP_
|
||||
ctrs_FP, hier_FP = cv2.findContours(FP_.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
||||
## find control points and independent regions for human correction
|
||||
bdies_FP, indep_cnt_FP = self.filter_bdy_cond(ctrs_FP, FP_, np.logical_or(TP,FN_))
|
||||
## find contours from FN_
|
||||
ctrs_FN, hier_FN = cv2.findContours(FN_.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
||||
## find control points and independent regions for human correction
|
||||
bdies_FN, indep_cnt_FN = self.filter_bdy_cond(ctrs_FN, FN_, 1-np.logical_or(np.logical_or(TP, FP_), FN_))
|
||||
|
||||
poly_FP, poly_FP_len, poly_FP_point_cnt = self.approximate_RDP(bdies_FP, epsilon=epsilon)
|
||||
poly_FN, poly_FN_len, poly_FN_point_cnt = self.approximate_RDP(bdies_FN, epsilon=epsilon)
|
||||
|
||||
# FP_points+FP_indep+FN_points+FN_indep
|
||||
return poly_FP_point_cnt+indep_cnt_FP+poly_FN_point_cnt+indep_cnt_FN
|
||||
|
||||
def filter_bdy_cond(self, bdy_, mask, cond):
|
||||
|
||||
cond = cv2.dilate(cond.astype(np.uint8), disk(1))
|
||||
labels = label(mask) # find the connected regions
|
||||
lbls = np.unique(labels) # the indices of the connected regions
|
||||
indep = np.ones(lbls.shape[0]) # the label of each connected regions
|
||||
indep[0] = 0 # 0 indicate the background region
|
||||
|
||||
boundaries = []
|
||||
h,w = cond.shape[0:2]
|
||||
ind_map = np.zeros((h, w))
|
||||
indep_cnt = 0
|
||||
|
||||
for i in range(0, len(bdy_)):
|
||||
tmp_bdies = []
|
||||
tmp_bdy = []
|
||||
for j in range(0, bdy_[i].shape[0]):
|
||||
r, c = bdy_[i][j,0,1],bdy_[i][j,0,0]
|
||||
|
||||
if(np.sum(cond[r, c])==0 or ind_map[r, c]!=0):
|
||||
if(len(tmp_bdy)>0):
|
||||
tmp_bdies.append(tmp_bdy)
|
||||
tmp_bdy = []
|
||||
continue
|
||||
tmp_bdy.append([c, r])
|
||||
ind_map[r, c] = ind_map[r, c] + 1
|
||||
indep[labels[r, c]] = 0 # indicates part of the boundary of this region needs human correction
|
||||
if(len(tmp_bdy)>0):
|
||||
tmp_bdies.append(tmp_bdy)
|
||||
|
||||
# check if the first and the last boundaries are connected
|
||||
# if yes, invert the first boundary and attach it after the last boundary
|
||||
if(len(tmp_bdies)>1):
|
||||
first_x, first_y = tmp_bdies[0][0]
|
||||
last_x, last_y = tmp_bdies[-1][-1]
|
||||
if((abs(first_x-last_x)==1 and first_y==last_y) or
|
||||
(first_x==last_x and abs(first_y-last_y)==1) or
|
||||
(abs(first_x-last_x)==1 and abs(first_y-last_y)==1)
|
||||
):
|
||||
tmp_bdies[-1].extend(tmp_bdies[0][::-1])
|
||||
del tmp_bdies[0]
|
||||
|
||||
for k in range(0, len(tmp_bdies)):
|
||||
tmp_bdies[k] = np.array(tmp_bdies[k])[:, np.newaxis, :]
|
||||
if(len(tmp_bdies)>0):
|
||||
boundaries.extend(tmp_bdies)
|
||||
|
||||
return boundaries, np.sum(indep)
|
||||
|
||||
# this function approximate each boundary by DP algorithm
|
||||
# https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
|
||||
def approximate_RDP(self, boundaries, epsilon=1.0):
|
||||
|
||||
boundaries_ = []
|
||||
boundaries_len_ = []
|
||||
pixel_cnt_ = 0
|
||||
|
||||
# polygon approximate of each boundary
|
||||
for i in range(0, len(boundaries)):
|
||||
boundaries_.append(cv2.approxPolyDP(boundaries[i], epsilon, False))
|
||||
|
||||
# count the control points number of each boundary and the total control points number of all the boundaries
|
||||
for i in range(0, len(boundaries_)):
|
||||
boundaries_len_.append(len(boundaries_[i]))
|
||||
pixel_cnt_ = pixel_cnt_ + len(boundaries_[i])
|
||||
|
||||
return boundaries_, boundaries_len_, pixel_cnt_
|
||||
|
||||
|
||||
class MBAMeasure(object):
|
||||
def __init__(self):
|
||||
self.bas = []
|
||||
self.all_h = 0
|
||||
self.all_w = 0
|
||||
self.all_max = 0
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
# pred, gt = _prepare_data(pred, gt)
|
||||
|
||||
refined = gt.copy()
|
||||
|
||||
rmin = cmin = 0
|
||||
rmax, cmax = gt.shape
|
||||
|
||||
self.all_h += rmax
|
||||
self.all_w += cmax
|
||||
self.all_max += max(rmax, cmax)
|
||||
|
||||
refined_h, refined_w = refined.shape
|
||||
if refined_h != cmax:
|
||||
refined = np.array(Image.fromarray(pred).resize((cmax, rmax), Image.BILINEAR))
|
||||
|
||||
if not(gt.sum() < 32*32):
|
||||
if not((cmax==cmin) or (rmax==rmin)):
|
||||
class_refined_prob = np.array(Image.fromarray(pred).resize((cmax-cmin, rmax-rmin), Image.BILINEAR))
|
||||
refined[rmin:rmax, cmin:cmax] = class_refined_prob
|
||||
|
||||
pred = pred > 128
|
||||
gt = gt > 128
|
||||
|
||||
ba = self.cal_ba(pred, gt)
|
||||
self.bas.append(ba)
|
||||
|
||||
def get_disk_kernel(self, radius):
|
||||
return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (radius*2+1, radius*2+1))
|
||||
|
||||
def cal_ba(self, pred: np.ndarray, gt: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Calculate the mean absolute error.
|
||||
|
||||
:return: ba
|
||||
"""
|
||||
|
||||
gt = gt.astype(np.uint8)
|
||||
pred = pred.astype(np.uint8)
|
||||
|
||||
h, w = gt.shape
|
||||
|
||||
min_radius = 1
|
||||
max_radius = (w+h)/300
|
||||
num_steps = 5
|
||||
|
||||
pred_acc = [None] * num_steps
|
||||
|
||||
for i in range(num_steps):
|
||||
curr_radius = min_radius + int((max_radius-min_radius)/num_steps*i)
|
||||
|
||||
kernel = self.get_disk_kernel(curr_radius)
|
||||
boundary_region = cv2.morphologyEx(gt, cv2.MORPH_GRADIENT, kernel) > 0
|
||||
|
||||
gt_in_bound = gt[boundary_region]
|
||||
pred_in_bound = pred[boundary_region]
|
||||
|
||||
num_edge_pixels = (boundary_region).sum()
|
||||
num_pred_gd_pix = ((gt_in_bound) * (pred_in_bound) + (1-gt_in_bound) * (1-pred_in_bound)).sum()
|
||||
|
||||
pred_acc[i] = num_pred_gd_pix / num_edge_pixels
|
||||
|
||||
ba = sum(pred_acc)/num_steps
|
||||
return ba
|
||||
|
||||
def get_results(self) -> dict:
|
||||
mba = np.mean(np.array(self.bas, _TYPE))
|
||||
return dict(mba=mba)
|
||||
|
||||
|
||||
class BIoUMeasure(object):
|
||||
def __init__(self, dilation_ratio=0.02):
|
||||
self.bious = []
|
||||
self.dilation_ratio = dilation_ratio
|
||||
|
||||
def mask_to_boundary(self, mask):
|
||||
h, w = mask.shape
|
||||
img_diag = np.sqrt(h ** 2 + w ** 2)
|
||||
dilation = int(round(self.dilation_ratio * img_diag))
|
||||
if dilation < 1:
|
||||
dilation = 1
|
||||
# Pad image so mask truncated by the image border is also considered as boundary.
|
||||
new_mask = cv2.copyMakeBorder(mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=0)
|
||||
kernel = np.ones((3, 3), dtype=np.uint8)
|
||||
new_mask_erode = cv2.erode(new_mask, kernel, iterations=dilation)
|
||||
mask_erode = new_mask_erode[1 : h + 1, 1 : w + 1]
|
||||
# G_d intersects G in the paper.
|
||||
return mask - mask_erode
|
||||
|
||||
def step(self, pred: np.ndarray, gt: np.ndarray):
|
||||
pred, gt = _prepare_data(pred, gt)
|
||||
|
||||
bious = self.cal_biou(pred=pred, gt=gt)
|
||||
self.bious.append(bious)
|
||||
|
||||
def cal_biou(self, pred, gt):
|
||||
pred = (pred * 255).astype(np.uint8)
|
||||
pred = self.mask_to_boundary(pred)
|
||||
gt = (gt * 255).astype(np.uint8)
|
||||
gt = self.mask_to_boundary(gt)
|
||||
gt = gt > 128
|
||||
|
||||
bins = np.linspace(0, 256, 257)
|
||||
fg_hist, _ = np.histogram(pred[gt], bins=bins) # ture positive
|
||||
bg_hist, _ = np.histogram(pred[~gt], bins=bins) # false positive
|
||||
fg_w_thrs = np.cumsum(np.flip(fg_hist), axis=0)
|
||||
bg_w_thrs = np.cumsum(np.flip(bg_hist), axis=0)
|
||||
TPs = fg_w_thrs
|
||||
Ps = fg_w_thrs + bg_w_thrs # positives
|
||||
Ps[Ps == 0] = 1
|
||||
T = max(np.count_nonzero(gt), 1)
|
||||
|
||||
ious = TPs / (T + bg_w_thrs)
|
||||
return ious
|
||||
|
||||
def get_results(self) -> dict:
|
||||
biou = np.mean(np.array(self.bious, dtype=_TYPE), axis=0)
|
||||
return dict(biou=dict(curve=biou))
|
||||
@@ -0,0 +1,86 @@
|
||||
import os
|
||||
from glob import glob
|
||||
import numpy as np
|
||||
|
||||
from .config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
eval_txts = sorted(glob('e_results/*_eval.txt'))
|
||||
print('eval_txts:', [_.split(os.sep)[-1] for _ in eval_txts])
|
||||
score_panel = {}
|
||||
sep = '&'
|
||||
metrics = ['sm', 'wfm', 'hce'] # we used HCE for DIS and wFm for others.
|
||||
if 'DIS5K' not in config.task:
|
||||
metrics.remove('hce')
|
||||
|
||||
for metric in metrics:
|
||||
print('Metric:', metric)
|
||||
current_line_nums = []
|
||||
for idx_et, eval_txt in enumerate(eval_txts):
|
||||
with open(eval_txt, 'r') as f:
|
||||
lines = [l for l in f.readlines()[3:] if '.' in l]
|
||||
current_line_nums.append(len(lines))
|
||||
for idx_et, eval_txt in enumerate(eval_txts):
|
||||
with open(eval_txt, 'r') as f:
|
||||
lines = [l for l in f.readlines()[3:] if '.' in l]
|
||||
for idx_line, line in enumerate(lines[:min(current_line_nums)]): # Consist line numbers by the minimal result file.
|
||||
properties = line.strip().strip(sep).split(sep)
|
||||
dataset = properties[0].strip()
|
||||
ckpt = properties[1].strip()
|
||||
if int(ckpt.split('--epoch_')[-1].strip()) < 0:
|
||||
continue
|
||||
targe_idx = {
|
||||
'sm': [5, 2, 2, 5, 2],
|
||||
'wfm': [3, 3, 8, 3, 8],
|
||||
'hce': [7, -1, -1, 7, -1]
|
||||
}[metric][['DIS5K', 'COD', 'HRSOD', 'General', 'Matting'].index(config.task)]
|
||||
if metric != 'hce':
|
||||
score_sm = float(properties[targe_idx].strip())
|
||||
else:
|
||||
score_sm = int(properties[targe_idx].strip().strip('.'))
|
||||
if idx_et == 0:
|
||||
score_panel[ckpt] = []
|
||||
score_panel[ckpt].append(score_sm)
|
||||
|
||||
metrics_min = ['hce', 'mae']
|
||||
max_or_min = min if metric in metrics_min else max
|
||||
score_max = max_or_min(score_panel.values(), key=lambda x: np.sum(x))
|
||||
|
||||
good_models = []
|
||||
for k, v in score_panel.items():
|
||||
if (np.sum(v) <= np.sum(score_max)) if metric in metrics_min else (np.sum(v) >= np.sum(score_max)):
|
||||
print(k, v)
|
||||
good_models.append(k)
|
||||
|
||||
# Write
|
||||
with open(eval_txt, 'r') as f:
|
||||
lines = f.readlines()
|
||||
info4good_models = lines[:3]
|
||||
metric_names = [m.strip() for m in lines[1].strip().strip('&').split('&')[2:]]
|
||||
testset_mean_values = {metric_name: [] for metric_name in metric_names}
|
||||
for good_model in good_models:
|
||||
for idx_et, eval_txt in enumerate(eval_txts):
|
||||
with open(eval_txt, 'r') as f:
|
||||
lines = f.readlines()
|
||||
for line in lines:
|
||||
if set([good_model]) & set([_.strip() for _ in line.split(sep)]):
|
||||
info4good_models.append(line)
|
||||
metric_scores = [float(m.strip()) for m in line.strip().strip('&').split('&')[2:]]
|
||||
for idx_score, metric_score in enumerate(metric_scores):
|
||||
testset_mean_values[metric_names[idx_score]].append(metric_score)
|
||||
|
||||
if 'DIS5K' in config.task:
|
||||
testset_mean_values_lst = ['{:<4}'.format(int(np.mean(v_lst[:-1]).round())) if name == 'HCE' else '{:.3f}'.format(np.mean(v_lst[:-1])).lstrip('0') for name, v_lst in testset_mean_values.items()] # [:-1] to remove DIS-VD
|
||||
sample_line_for_placing_mean_values = info4good_models[-2]
|
||||
numbers_placed_well = sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').strip().split('&')[3:]
|
||||
for idx_number, (number_placed_well, testset_mean_value) in enumerate(zip(numbers_placed_well, testset_mean_values_lst)):
|
||||
numbers_placed_well[idx_number] = number_placed_well.replace(number_placed_well.strip(), testset_mean_value)
|
||||
testset_mean_line = '&'.join(sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').split('&')[:3] + numbers_placed_well) + '\n'
|
||||
info4good_models.append(testset_mean_line)
|
||||
info4good_models.append(lines[-1])
|
||||
info = ''.join(info4good_models)
|
||||
print(info)
|
||||
with open(os.path.join('e_results', 'eval-{}_best_on_{}.txt'.format(config.task, metric)), 'w') as f:
|
||||
f.write(info + '\n')
|
||||
@@ -0,0 +1,119 @@
|
||||
import random
|
||||
from PIL import Image, ImageEnhance
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
def refine_foreground(image, mask, r=90):
|
||||
if mask.size != image.size:
|
||||
mask = mask.resize(image.size)
|
||||
image = np.array(image) / 255.0
|
||||
mask = np.array(mask) / 255.0
|
||||
estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r)
|
||||
image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
|
||||
return image_masked
|
||||
|
||||
|
||||
def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90):
|
||||
# Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
|
||||
alpha = alpha[:, :, None]
|
||||
F, blur_B = FB_blur_fusion_foreground_estimator(
|
||||
image, image, image, alpha, r)
|
||||
return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0]
|
||||
|
||||
|
||||
def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
|
||||
if isinstance(image, Image.Image):
|
||||
image = np.array(image) / 255.0
|
||||
blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
|
||||
|
||||
blurred_FA = cv2.blur(F * alpha, (r, r))
|
||||
blurred_F = blurred_FA / (blurred_alpha + 1e-5)
|
||||
|
||||
blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
|
||||
blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
|
||||
F = blurred_F + alpha * \
|
||||
(image - alpha * blurred_F - (1 - alpha) * blurred_B)
|
||||
F = np.clip(F, 0, 1)
|
||||
return F, blurred_B
|
||||
|
||||
|
||||
def preproc(image, label, preproc_methods=['flip']):
|
||||
if 'flip' in preproc_methods:
|
||||
image, label = cv_random_flip(image, label)
|
||||
if 'crop' in preproc_methods:
|
||||
image, label = random_crop(image, label)
|
||||
if 'rotate' in preproc_methods:
|
||||
image, label = random_rotate(image, label)
|
||||
if 'enhance' in preproc_methods:
|
||||
image = color_enhance(image)
|
||||
if 'pepper' in preproc_methods:
|
||||
label = random_pepper(label)
|
||||
return image, label
|
||||
|
||||
|
||||
def cv_random_flip(img, label):
|
||||
if random.random() > 0.5:
|
||||
img = img.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
label = label.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
return img, label
|
||||
|
||||
|
||||
def random_crop(image, label):
|
||||
border = 30
|
||||
image_width = image.size[0]
|
||||
image_height = image.size[1]
|
||||
border = int(min(image_width, image_height) * 0.1)
|
||||
crop_win_width = np.random.randint(image_width - border, image_width)
|
||||
crop_win_height = np.random.randint(image_height - border, image_height)
|
||||
random_region = (
|
||||
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
|
||||
(image_height + crop_win_height) >> 1)
|
||||
return image.crop(random_region), label.crop(random_region)
|
||||
|
||||
|
||||
def random_rotate(image, label, angle=15):
|
||||
mode = Image.BICUBIC
|
||||
if random.random() > 0.8:
|
||||
random_angle = np.random.randint(-angle, angle)
|
||||
image = image.rotate(random_angle, mode)
|
||||
label = label.rotate(random_angle, mode)
|
||||
return image, label
|
||||
|
||||
|
||||
def color_enhance(image):
|
||||
bright_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Brightness(image).enhance(bright_intensity)
|
||||
contrast_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
|
||||
color_intensity = random.randint(0, 20) / 10.0
|
||||
image = ImageEnhance.Color(image).enhance(color_intensity)
|
||||
sharp_intensity = random.randint(0, 30) / 10.0
|
||||
image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
|
||||
return image
|
||||
|
||||
|
||||
def random_gaussian(image, mean=0.1, sigma=0.35):
|
||||
def gaussianNoisy(im, mean=mean, sigma=sigma):
|
||||
for _i in range(len(im)):
|
||||
im[_i] += random.gauss(mean, sigma)
|
||||
return im
|
||||
|
||||
img = np.asarray(image)
|
||||
width, height = img.shape
|
||||
img = gaussianNoisy(img[:].flatten(), mean, sigma)
|
||||
img = img.reshape([width, height])
|
||||
return Image.fromarray(np.uint8(img))
|
||||
|
||||
|
||||
def random_pepper(img, N=0.0015):
|
||||
img = np.array(img)
|
||||
noiseNum = int(N * img.shape[0] * img.shape[1])
|
||||
for i in range(noiseNum):
|
||||
randX = random.randint(0, img.shape[0] - 1)
|
||||
randY = random.randint(0, img.shape[1] - 1)
|
||||
if random.randint(0, 1) == 0:
|
||||
img[randX, randY] = 0
|
||||
else:
|
||||
img[randX, randY] = 255
|
||||
return Image.fromarray(img)
|
||||
@@ -0,0 +1,105 @@
|
||||
import os
|
||||
import argparse
|
||||
from glob import glob
|
||||
from tqdm import tqdm
|
||||
import cv2
|
||||
import torch
|
||||
|
||||
from .dataset import MyData
|
||||
from .models.birefnet import BiRefNet
|
||||
from .utils import save_tensor_img, check_state_dict
|
||||
from .config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
def inference(model, data_loader_test, pred_root, method, testset, device=0):
|
||||
model_training = model.training
|
||||
if model_training:
|
||||
model.eval()
|
||||
for batch in tqdm(data_loader_test, total=len(data_loader_test)) if 1 or config.verbose_eval else data_loader_test:
|
||||
inputs = batch[0].to(device)
|
||||
# gts = batch[1].to(device)
|
||||
label_paths = batch[-1]
|
||||
with torch.no_grad():
|
||||
scaled_preds = model(inputs)[-1].sigmoid()
|
||||
|
||||
os.makedirs(os.path.join(pred_root, method, testset), exist_ok=True)
|
||||
|
||||
for idx_sample in range(scaled_preds.shape[0]):
|
||||
res = torch.nn.functional.interpolate(
|
||||
scaled_preds[idx_sample].unsqueeze(0),
|
||||
size=cv2.imread(label_paths[idx_sample], cv2.IMREAD_GRAYSCALE).shape[:2],
|
||||
mode='bilinear',
|
||||
align_corners=True
|
||||
)
|
||||
save_tensor_img(res, os.path.join(os.path.join(pred_root, method, testset), label_paths[idx_sample].replace('\\', '/').split('/')[-1])) # test set dir + file name
|
||||
if model_training:
|
||||
model.train()
|
||||
return None
|
||||
|
||||
|
||||
def main(args):
|
||||
# Init model
|
||||
|
||||
device = config.device
|
||||
if args.ckpt_folder:
|
||||
print('Testing with models in {}'.format(args.ckpt_folder))
|
||||
else:
|
||||
print('Testing with model {}'.format(args.ckpt))
|
||||
|
||||
if config.model == 'BiRefNet':
|
||||
model = BiRefNet(bb_pretrained=False)
|
||||
weights_lst = sorted(
|
||||
glob(os.path.join(args.ckpt_folder, '*.pth')) if args.ckpt_folder else [args.ckpt],
|
||||
key=lambda x: int(x.split('epoch_')[-1].split('.pth')[0]),
|
||||
reverse=True
|
||||
)
|
||||
for testset in args.testsets.split('+'):
|
||||
print('>>>> Testset: {}...'.format(testset))
|
||||
data_loader_test = torch.utils.data.DataLoader(
|
||||
dataset=MyData(testset, image_size=config.size, is_train=False),
|
||||
batch_size=config.batch_size_valid, shuffle=False, num_workers=config.num_workers, pin_memory=True
|
||||
)
|
||||
for weights in weights_lst:
|
||||
if int(weights.strip('.pth').split('epoch_')[-1]) % 1 != 0:
|
||||
continue
|
||||
print('\tInferencing {}...'.format(weights))
|
||||
# model.load_state_dict(torch.load(weights, map_location='cpu'))
|
||||
state_dict = torch.load(weights, map_location='cpu')
|
||||
state_dict = check_state_dict(state_dict)
|
||||
model.load_state_dict(state_dict)
|
||||
model = model.to(device)
|
||||
inference(
|
||||
model, data_loader_test=data_loader_test, pred_root=args.pred_root,
|
||||
method='--'.join([w.rstrip('.pth') for w in weights.split(os.sep)[-2:]]),
|
||||
testset=testset, device=config.device
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parameter from command line
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--ckpt', type=str, help='model folder')
|
||||
parser.add_argument('--ckpt_folder', default=sorted(glob(os.path.join('ckpt', '*')))[-1], type=str, help='model folder')
|
||||
parser.add_argument('--pred_root', default='e_preds', type=str, help='Output folder')
|
||||
parser.add_argument('--testsets',
|
||||
default={
|
||||
'DIS5K': 'DIS-VD+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4',
|
||||
'COD': 'TE-COD10K+NC4K+TE-CAMO+CHAMELEON',
|
||||
'HRSOD': 'DAVIS-S+TE-HRSOD+TE-UHRSD+TE-DUTS+DUT-OMRON',
|
||||
'General': 'DIS-VD',
|
||||
'Matting': 'TE-P3M-500-P',
|
||||
'DIS5K-': 'DIS-VD',
|
||||
'COD-': 'TE-COD10K',
|
||||
'SOD-': 'DAVIS-S+TE-HRSOD+TE-UHRSD',
|
||||
}[config.task + ''],
|
||||
type=str,
|
||||
help="Test all sets: , 'DIS-VD+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if config.precisionHigh:
|
||||
torch.set_float32_matmul_precision('high')
|
||||
main(args)
|
||||
@@ -0,0 +1,277 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
from torch.autograd import Variable
|
||||
from math import exp
|
||||
|
||||
from .config import Config
|
||||
|
||||
|
||||
class Discriminator(nn.Module):
|
||||
def __init__(self, channels=1, img_size=256):
|
||||
super(Discriminator, self).__init__()
|
||||
|
||||
def discriminator_block(in_filters, out_filters, bn=Config().batch_size > 1):
|
||||
block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
|
||||
if bn:
|
||||
block.append(nn.BatchNorm2d(out_filters, 0.8))
|
||||
return block
|
||||
|
||||
self.model = nn.Sequential(
|
||||
*discriminator_block(channels, 16, bn=False),
|
||||
*discriminator_block(16, 32),
|
||||
*discriminator_block(32, 64),
|
||||
*discriminator_block(64, 128),
|
||||
)
|
||||
|
||||
# The height and width of downsampled image
|
||||
ds_size = img_size // 2 ** 4
|
||||
self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())
|
||||
|
||||
def forward(self, img):
|
||||
out = self.model(img)
|
||||
out = out.view(out.shape[0], -1)
|
||||
validity = self.adv_layer(out)
|
||||
|
||||
return validity
|
||||
|
||||
|
||||
class ContourLoss(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(ContourLoss, self).__init__()
|
||||
|
||||
def forward(self, pred, target, weight=10):
|
||||
'''
|
||||
target, pred: tensor of shape (B, C, H, W), where target[:,:,region_in_contour] == 1,
|
||||
target[:,:,region_out_contour] == 0.
|
||||
weight: scalar, length term weight.
|
||||
'''
|
||||
# length term
|
||||
delta_r = pred[:,:,1:,:] - pred[:,:,:-1,:] # horizontal gradient (B, C, H-1, W)
|
||||
delta_c = pred[:,:,:,1:] - pred[:,:,:,:-1] # vertical gradient (B, C, H, W-1)
|
||||
|
||||
delta_r = delta_r[:,:,1:,:-2]**2 # (B, C, H-2, W-2)
|
||||
delta_c = delta_c[:,:,:-2,1:]**2 # (B, C, H-2, W-2)
|
||||
delta_pred = torch.abs(delta_r + delta_c)
|
||||
|
||||
epsilon = 1e-8 # where is a parameter to avoid square root is zero in practice.
|
||||
length = torch.mean(torch.sqrt(delta_pred + epsilon)) # eq.(11) in the paper, mean is used instead of sum.
|
||||
|
||||
c_in = torch.ones_like(pred)
|
||||
c_out = torch.zeros_like(pred)
|
||||
|
||||
region_in = torch.mean( pred * (target - c_in )**2 ) # equ.(12) in the paper, mean is used instead of sum.
|
||||
region_out = torch.mean( (1-pred) * (target - c_out)**2 )
|
||||
region = region_in + region_out
|
||||
|
||||
loss = weight * length + region
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
class IoULoss(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(IoULoss, self).__init__()
|
||||
|
||||
def forward(self, pred, target):
|
||||
b = pred.shape[0]
|
||||
IoU = 0.0
|
||||
for i in range(0, b):
|
||||
# compute the IoU of the foreground
|
||||
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
|
||||
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]) - Iand1
|
||||
IoU1 = Iand1 / Ior1
|
||||
# IoU loss is (1-IoU1)
|
||||
IoU = IoU + (1-IoU1)
|
||||
# return IoU/b
|
||||
return IoU
|
||||
|
||||
|
||||
class StructureLoss(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(StructureLoss, self).__init__()
|
||||
|
||||
def forward(self, pred, target):
|
||||
weit = 1+5*torch.abs(F.avg_pool2d(target, kernel_size=31, stride=1, padding=15)-target)
|
||||
wbce = F.binary_cross_entropy_with_logits(pred, target, reduction='none')
|
||||
wbce = (weit*wbce).sum(dim=(2,3))/weit.sum(dim=(2,3))
|
||||
|
||||
pred = torch.sigmoid(pred)
|
||||
inter = ((pred * target) * weit).sum(dim=(2, 3))
|
||||
union = ((pred + target) * weit).sum(dim=(2, 3))
|
||||
wiou = 1-(inter+1)/(union-inter+1)
|
||||
|
||||
return (wbce+wiou).mean()
|
||||
|
||||
|
||||
class PatchIoULoss(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(PatchIoULoss, self).__init__()
|
||||
self.iou_loss = IoULoss()
|
||||
|
||||
def forward(self, pred, target):
|
||||
win_y, win_x = 64, 64
|
||||
iou_loss = 0.
|
||||
for anchor_y in range(0, target.shape[0], win_y):
|
||||
for anchor_x in range(0, target.shape[1], win_y):
|
||||
patch_pred = pred[:, :, anchor_y:anchor_y+win_y, anchor_x:anchor_x+win_x]
|
||||
patch_target = target[:, :, anchor_y:anchor_y+win_y, anchor_x:anchor_x+win_x]
|
||||
patch_iou_loss = self.iou_loss(patch_pred, patch_target)
|
||||
iou_loss += patch_iou_loss
|
||||
return iou_loss
|
||||
|
||||
|
||||
class ThrReg_loss(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(ThrReg_loss, self).__init__()
|
||||
|
||||
def forward(self, pred, gt=None):
|
||||
return torch.mean(1 - ((pred - 0) ** 2 + (pred - 1) ** 2))
|
||||
|
||||
|
||||
class ClsLoss(nn.Module):
|
||||
"""
|
||||
Auxiliary classification loss for each refined class output.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(ClsLoss, self).__init__()
|
||||
self.config = Config()
|
||||
self.lambdas_cls = self.config.lambdas_cls
|
||||
|
||||
self.criterions_last = {
|
||||
'ce': nn.CrossEntropyLoss()
|
||||
}
|
||||
|
||||
def forward(self, preds, gt):
|
||||
loss = 0.
|
||||
for _, pred_lvl in enumerate(preds):
|
||||
if pred_lvl is None:
|
||||
continue
|
||||
for criterion_name, criterion in self.criterions_last.items():
|
||||
loss += criterion(pred_lvl, gt) * self.lambdas_cls[criterion_name]
|
||||
return loss
|
||||
|
||||
|
||||
class PixLoss(nn.Module):
|
||||
"""
|
||||
Pixel loss for each refined map output.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(PixLoss, self).__init__()
|
||||
self.config = Config()
|
||||
self.lambdas_pix_last = self.config.lambdas_pix_last
|
||||
|
||||
self.criterions_last = {}
|
||||
if 'bce' in self.lambdas_pix_last and self.lambdas_pix_last['bce']:
|
||||
self.criterions_last['bce'] = nn.BCELoss() if not self.config.use_fp16 else nn.BCEWithLogitsLoss()
|
||||
if 'iou' in self.lambdas_pix_last and self.lambdas_pix_last['iou']:
|
||||
self.criterions_last['iou'] = IoULoss()
|
||||
if 'iou_patch' in self.lambdas_pix_last and self.lambdas_pix_last['iou_patch']:
|
||||
self.criterions_last['iou_patch'] = PatchIoULoss()
|
||||
if 'ssim' in self.lambdas_pix_last and self.lambdas_pix_last['ssim']:
|
||||
self.criterions_last['ssim'] = SSIMLoss()
|
||||
if 'mae' in self.lambdas_pix_last and self.lambdas_pix_last['mae']:
|
||||
self.criterions_last['mae'] = nn.L1Loss()
|
||||
if 'mse' in self.lambdas_pix_last and self.lambdas_pix_last['mse']:
|
||||
self.criterions_last['mse'] = nn.MSELoss()
|
||||
if 'reg' in self.lambdas_pix_last and self.lambdas_pix_last['reg']:
|
||||
self.criterions_last['reg'] = ThrReg_loss()
|
||||
if 'cnt' in self.lambdas_pix_last and self.lambdas_pix_last['cnt']:
|
||||
self.criterions_last['cnt'] = ContourLoss()
|
||||
if 'structure' in self.lambdas_pix_last and self.lambdas_pix_last['structure']:
|
||||
self.criterions_last['structure'] = StructureLoss()
|
||||
|
||||
def forward(self, scaled_preds, gt):
|
||||
loss = 0.
|
||||
criterions_embedded_with_sigmoid = ['structure', ] + ['bce'] if self.config.use_fp16 else []
|
||||
for _, pred_lvl in enumerate(scaled_preds):
|
||||
if pred_lvl.shape != gt.shape:
|
||||
pred_lvl = nn.functional.interpolate(pred_lvl, size=gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
for criterion_name, criterion in self.criterions_last.items():
|
||||
_loss = criterion(pred_lvl.sigmoid() if criterion_name not in criterions_embedded_with_sigmoid else pred_lvl, gt) * self.lambdas_pix_last[criterion_name]
|
||||
loss += _loss
|
||||
# print(criterion_name, _loss.item())
|
||||
return loss
|
||||
|
||||
|
||||
class SSIMLoss(torch.nn.Module):
|
||||
def __init__(self, window_size=11, size_average=True):
|
||||
super(SSIMLoss, self).__init__()
|
||||
self.window_size = window_size
|
||||
self.size_average = size_average
|
||||
self.channel = 1
|
||||
self.window = create_window(window_size, self.channel)
|
||||
|
||||
def forward(self, img1, img2):
|
||||
(_, channel, _, _) = img1.size()
|
||||
if channel == self.channel and self.window.data.type() == img1.data.type():
|
||||
window = self.window
|
||||
else:
|
||||
window = create_window(self.window_size, channel)
|
||||
if img1.is_cuda:
|
||||
window = window.cuda(img1.get_device())
|
||||
window = window.type_as(img1)
|
||||
self.window = window
|
||||
self.channel = channel
|
||||
return 1 - _ssim(img1, img2, window, self.window_size, channel, self.size_average)
|
||||
|
||||
|
||||
def gaussian(window_size, sigma):
|
||||
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
|
||||
return gauss/gauss.sum()
|
||||
|
||||
|
||||
def create_window(window_size, channel):
|
||||
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
||||
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
|
||||
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
|
||||
return window
|
||||
|
||||
|
||||
def _ssim(img1, img2, window, window_size, channel, size_average=True):
|
||||
mu1 = F.conv2d(img1, window, padding = window_size//2, groups=channel)
|
||||
mu2 = F.conv2d(img2, window, padding = window_size//2, groups=channel)
|
||||
|
||||
mu1_sq = mu1.pow(2)
|
||||
mu2_sq = mu2.pow(2)
|
||||
mu1_mu2 = mu1*mu2
|
||||
|
||||
sigma1_sq = F.conv2d(img1*img1, window, padding=window_size//2, groups=channel) - mu1_sq
|
||||
sigma2_sq = F.conv2d(img2*img2, window, padding=window_size//2, groups=channel) - mu2_sq
|
||||
sigma12 = F.conv2d(img1*img2, window, padding=window_size//2, groups=channel) - mu1_mu2
|
||||
|
||||
C1 = 0.01**2
|
||||
C2 = 0.03**2
|
||||
|
||||
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
|
||||
|
||||
if size_average:
|
||||
return ssim_map.mean()
|
||||
else:
|
||||
return ssim_map.mean(1).mean(1).mean(1)
|
||||
|
||||
|
||||
def SSIM(x, y):
|
||||
C1 = 0.01 ** 2
|
||||
C2 = 0.03 ** 2
|
||||
|
||||
mu_x = nn.AvgPool2d(3, 1, 1)(x)
|
||||
mu_y = nn.AvgPool2d(3, 1, 1)(y)
|
||||
mu_x_mu_y = mu_x * mu_y
|
||||
mu_x_sq = mu_x.pow(2)
|
||||
mu_y_sq = mu_y.pow(2)
|
||||
|
||||
sigma_x = nn.AvgPool2d(3, 1, 1)(x * x) - mu_x_sq
|
||||
sigma_y = nn.AvgPool2d(3, 1, 1)(y * y) - mu_y_sq
|
||||
sigma_xy = nn.AvgPool2d(3, 1, 1)(x * y) - mu_x_mu_y
|
||||
|
||||
SSIM_n = (2 * mu_x_mu_y + C1) * (2 * sigma_xy + C2)
|
||||
SSIM_d = (mu_x_sq + mu_y_sq + C1) * (sigma_x + sigma_y + C2)
|
||||
SSIM = SSIM_n / SSIM_d
|
||||
|
||||
return torch.clamp((1 - SSIM) / 2, 0, 1)
|
||||
|
||||
|
||||
def saliency_structure_consistency(x, y):
|
||||
ssim = torch.mean(SSIM(x,y))
|
||||
return ssim
|
||||
@@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
# Set dst repo here.
|
||||
repo=$1
|
||||
mkdir ../${repo}
|
||||
mkdir ../${repo}/evaluation
|
||||
mkdir ../${repo}/models
|
||||
mkdir ../${repo}/models/backbones
|
||||
mkdir ../${repo}/models/modules
|
||||
mkdir ../${repo}/models/refinement
|
||||
|
||||
cp ./*.sh ../${repo}
|
||||
cp ./*.py ../${repo}
|
||||
cp ./evaluation/*.py ../${repo}/evaluation
|
||||
cp ./models/*.py ../${repo}/models
|
||||
cp ./models/backbones/*.py ../${repo}/models/backbones
|
||||
cp ./models/modules/*.py ../${repo}/models/modules
|
||||
cp ./models/refinement/*.py ../${repo}/models/refinement
|
||||
cp -r ./.git* ../${repo}
|
||||
@@ -0,0 +1,44 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from collections import OrderedDict
|
||||
from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
|
||||
from ...models.backbones.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5
|
||||
from ...models.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
|
||||
from ...config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
def build_backbone(bb_name, pretrained=True, params_settings=''):
|
||||
if bb_name == 'vgg16':
|
||||
bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
|
||||
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
|
||||
elif bb_name == 'vgg16bn':
|
||||
bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
|
||||
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
|
||||
elif bb_name == 'resnet50':
|
||||
bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
|
||||
bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
|
||||
else:
|
||||
bb = eval('{}({})'.format(bb_name, params_settings))
|
||||
if pretrained:
|
||||
bb = load_weights(bb, bb_name)
|
||||
return bb
|
||||
|
||||
def load_weights(model, model_name):
|
||||
save_model = torch.load(config.weights[model_name], map_location='cpu')
|
||||
model_dict = model.state_dict()
|
||||
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
|
||||
# to ignore the weights with mismatched size when I modify the backbone itself.
|
||||
if not state_dict:
|
||||
save_model_keys = list(save_model.keys())
|
||||
sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
|
||||
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
|
||||
if not state_dict or not sub_item:
|
||||
print('Weights are not successully loaded. Check the state dict of weights file.')
|
||||
return None
|
||||
else:
|
||||
print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
|
||||
model_dict.update(state_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
@@ -0,0 +1,435 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models import register_model
|
||||
|
||||
import math
|
||||
|
||||
from ...config import Config
|
||||
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.dwconv = DWConv(hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = self.fc1(x)
|
||||
x = self.dwconv(x, H, W)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
|
||||
return x, H, W
|
||||
|
||||
|
||||
class PyramidVisionTransformerImpr(nn.Module):
|
||||
def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
|
||||
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
|
||||
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
|
||||
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
|
||||
super().__init__()
|
||||
self.num_classes = num_classes
|
||||
self.depths = depths
|
||||
|
||||
# patch_embed
|
||||
self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
|
||||
embed_dim=embed_dims[0])
|
||||
self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
|
||||
embed_dim=embed_dims[1])
|
||||
self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
|
||||
embed_dim=embed_dims[2])
|
||||
self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
|
||||
embed_dim=embed_dims[3])
|
||||
|
||||
# transformer encoder
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
cur = 0
|
||||
self.block1 = nn.ModuleList([Block(
|
||||
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[0])
|
||||
for i in range(depths[0])])
|
||||
self.norm1 = norm_layer(embed_dims[0])
|
||||
|
||||
cur += depths[0]
|
||||
self.block2 = nn.ModuleList([Block(
|
||||
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[1])
|
||||
for i in range(depths[1])])
|
||||
self.norm2 = norm_layer(embed_dims[1])
|
||||
|
||||
cur += depths[1]
|
||||
self.block3 = nn.ModuleList([Block(
|
||||
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[2])
|
||||
for i in range(depths[2])])
|
||||
self.norm3 = norm_layer(embed_dims[2])
|
||||
|
||||
cur += depths[2]
|
||||
self.block4 = nn.ModuleList([Block(
|
||||
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[3])
|
||||
for i in range(depths[3])])
|
||||
self.norm4 = norm_layer(embed_dims[3])
|
||||
|
||||
# classification head
|
||||
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
if isinstance(pretrained, str):
|
||||
logger = 1
|
||||
#load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
|
||||
|
||||
def reset_drop_path(self, drop_path_rate):
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
|
||||
cur = 0
|
||||
for i in range(self.depths[0]):
|
||||
self.block1[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[0]
|
||||
for i in range(self.depths[1]):
|
||||
self.block2[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[1]
|
||||
for i in range(self.depths[2]):
|
||||
self.block3[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[2]
|
||||
for i in range(self.depths[3]):
|
||||
self.block4[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
def freeze_patch_emb(self):
|
||||
self.patch_embed1.requires_grad = False
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
B = x.shape[0]
|
||||
outs = []
|
||||
|
||||
# stage 1
|
||||
x, H, W = self.patch_embed1(x)
|
||||
for i, blk in enumerate(self.block1):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm1(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 2
|
||||
x, H, W = self.patch_embed2(x)
|
||||
for i, blk in enumerate(self.block2):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm2(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 3
|
||||
x, H, W = self.patch_embed3(x)
|
||||
for i, blk in enumerate(self.block3):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm3(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 4
|
||||
x, H, W = self.patch_embed4(x)
|
||||
for i, blk in enumerate(self.block4):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm4(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
return outs
|
||||
|
||||
# return x.mean(dim=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
# x = self.head(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class DWConv(nn.Module):
|
||||
def __init__(self, dim=768):
|
||||
super(DWConv, self).__init__()
|
||||
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
x = x.transpose(1, 2).view(B, C, H, W).contiguous()
|
||||
x = self.dwconv(x)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def _conv_filter(state_dict, patch_size=16):
|
||||
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
||||
out_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
if 'patch_embed.proj.weight' in k:
|
||||
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
|
||||
out_dict[k] = v
|
||||
|
||||
return out_dict
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b0(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b0, self).__init__(
|
||||
patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b1(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b1, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b2(PyramidVisionTransformerImpr):
|
||||
def __init__(self, in_channels=3, **kwargs):
|
||||
super(pvt_v2_b2, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b3(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b3, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b4(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b4, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b5(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b5, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
@@ -0,0 +1,627 @@
|
||||
# --------------------------------------------------------
|
||||
# Swin Transformer
|
||||
# Copyright (c) 2021 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Written by Ze Liu, Yutong Lin, Yixuan Wei
|
||||
# --------------------------------------------------------
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
import numpy as np
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
|
||||
from ...config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" Multilayer perceptron."""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B_, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
""" Swin Transformer Block.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
""" Patch Merging Layer
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
""" Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_channels=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
self.layers.append(layer)
|
||||
|
||||
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f'norm{i_layer}'
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
|
||||
x = (x + absolute_pos_embed) # B Wh*Ww C
|
||||
|
||||
outs = []#x.contiguous()]
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f'norm{i}')
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
|
||||
return tuple(outs)
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
|
||||
def swin_v1_t():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_s():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_b():
|
||||
model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
|
||||
return model
|
||||
|
||||
def swin_v1_l():
|
||||
model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
|
||||
return model
|
||||
@@ -0,0 +1,286 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from kornia.filters import laplacian
|
||||
from huggingface_hub import PyTorchModelHubMixin
|
||||
|
||||
from ..config import Config
|
||||
from ..dataset import class_labels_TR_sorted
|
||||
from ..models.backbones.build_backbone import build_backbone
|
||||
from ..models.modules.decoder_blocks import BasicDecBlk, ResBlk
|
||||
from ..models.modules.lateral_blocks import BasicLatBlk
|
||||
from ..models.modules.aspp import ASPP, ASPPDeformable
|
||||
from ..models.refinement.refiner import Refiner, RefinerPVTInChannels4, RefUNet
|
||||
from ..models.refinement.stem_layer import StemLayer
|
||||
|
||||
|
||||
class BiRefNet(
|
||||
nn.Module,
|
||||
PyTorchModelHubMixin,
|
||||
library_name="birefnet",
|
||||
repo_url="https://github.com/ZhengPeng7/BiRefNet",
|
||||
tags=['Image Segmentation', 'Background Removal', 'Mask Generation', 'Dichotomous Image Segmentation', 'Camouflaged Object Detection', 'Salient Object Detection']
|
||||
):
|
||||
def __init__(self, bb_pretrained=True):
|
||||
super(BiRefNet, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained)
|
||||
|
||||
channels = self.config.lateral_channels_in_collection
|
||||
|
||||
if self.config.auxiliary_classification:
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.cls_head = nn.Sequential(
|
||||
nn.Linear(channels[0], len(class_labels_TR_sorted))
|
||||
)
|
||||
|
||||
if self.config.squeeze_block:
|
||||
self.squeeze_module = nn.Sequential(*[
|
||||
eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
|
||||
for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
|
||||
])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if self.config.ender:
|
||||
self.dec_end = nn.Sequential(
|
||||
nn.Conv2d(1, 16, 3, 1, 1),
|
||||
nn.Conv2d(16, 1, 3, 1, 1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
# refine patch-level segmentation
|
||||
if self.config.refine:
|
||||
if self.config.refine == 'itself':
|
||||
self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
|
||||
else:
|
||||
self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
|
||||
|
||||
if self.config.freeze_bb:
|
||||
# Freeze the backbone...
|
||||
print(self.named_parameters())
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key and 'refiner.' not in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward_enc(self, x):
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
if self.config.mul_scl_ipt == 'cat':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
elif self.config.mul_scl_ipt == 'add':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
|
||||
class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
|
||||
if self.config.cxt:
|
||||
x4 = torch.cat(
|
||||
(
|
||||
*[
|
||||
F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
][-len(self.config.cxt):],
|
||||
x4
|
||||
),
|
||||
dim=1
|
||||
)
|
||||
return (x1, x2, x3, x4), class_preds
|
||||
|
||||
def forward_ori(self, x):
|
||||
########## Encoder ##########
|
||||
(x1, x2, x3, x4), class_preds = self.forward_enc(x)
|
||||
if self.config.squeeze_block:
|
||||
x4 = self.squeeze_module(x4)
|
||||
########## Decoder ##########
|
||||
features = [x, x1, x2, x3, x4]
|
||||
if self.training and self.config.out_ref:
|
||||
features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
|
||||
scaled_preds = self.decoder(features)
|
||||
return scaled_preds, class_preds
|
||||
|
||||
def forward(self, x):
|
||||
scaled_preds, class_preds = self.forward_ori(x)
|
||||
class_preds_lst = [class_preds]
|
||||
return [scaled_preds, class_preds_lst] if self.training else scaled_preds
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super(Decoder, self).__init__()
|
||||
self.config = Config()
|
||||
DecoderBlock = eval(self.config.dec_blk)
|
||||
LateralBlock = eval(self.config.lat_blk)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
self.split = self.config.dec_ipt_split
|
||||
N_dec_ipt = 64
|
||||
DBlock = SimpleConvs
|
||||
ic = 64
|
||||
ipt_cha_opt = 1
|
||||
self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
|
||||
else:
|
||||
self.split = None
|
||||
|
||||
self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1])
|
||||
self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
|
||||
self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
|
||||
self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
|
||||
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
|
||||
|
||||
self.lateral_block4 = LateralBlock(channels[1], channels[1])
|
||||
self.lateral_block3 = LateralBlock(channels[2], channels[2])
|
||||
self.lateral_block2 = LateralBlock(channels[3], channels[3])
|
||||
|
||||
if self.config.ms_supervision:
|
||||
self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
|
||||
|
||||
if self.config.out_ref:
|
||||
_N = 16
|
||||
self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
|
||||
self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
def get_patches_batch(self, x, p):
|
||||
_size_h, _size_w = p.shape[2:]
|
||||
patches_batch = []
|
||||
for idx in range(x.shape[0]):
|
||||
columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
|
||||
patches_x = []
|
||||
for column_x in columns_x:
|
||||
patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
|
||||
patch_sample = torch.cat(patches_x, dim=1)
|
||||
patches_batch.append(patch_sample)
|
||||
return torch.cat(patches_batch, dim=0)
|
||||
|
||||
def forward(self, features):
|
||||
if self.training and self.config.out_ref:
|
||||
outs_gdt_pred = []
|
||||
outs_gdt_label = []
|
||||
x, x1, x2, x3, x4, gdt_gt = features
|
||||
else:
|
||||
x, x1, x2, x3, x4 = features
|
||||
outs = []
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, x4) if self.split else x
|
||||
x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p4 = self.decoder_block4(x4)
|
||||
m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision and self.training else None
|
||||
if self.config.out_ref:
|
||||
p4_gdt = self.gdt_convs_4(p4)
|
||||
if self.training:
|
||||
# >> GT:
|
||||
m4_dia = m4
|
||||
gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
outs_gdt_label.append(gdt_label_main_4)
|
||||
# >> Pred:
|
||||
gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt)
|
||||
outs_gdt_pred.append(gdt_pred_4)
|
||||
gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
|
||||
# >> Finally:
|
||||
p4 = p4 * gdt_attn_4
|
||||
_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p3 = _p4 + self.lateral_block4(x3)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p3) if self.split else x
|
||||
_p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p3 = self.decoder_block3(_p3)
|
||||
m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision and self.training else None
|
||||
if self.config.out_ref:
|
||||
p3_gdt = self.gdt_convs_3(p3)
|
||||
if self.training:
|
||||
# >> GT:
|
||||
# m3 --dilation--> m3_dia
|
||||
# G_3^gt * m3_dia --> G_3^m, which is the label of gradient
|
||||
m3_dia = m3
|
||||
gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
outs_gdt_label.append(gdt_label_main_3)
|
||||
# >> Pred:
|
||||
# p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx
|
||||
# F_3^G --sigmoid--> A_3^G
|
||||
gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt)
|
||||
outs_gdt_pred.append(gdt_pred_3)
|
||||
gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
|
||||
# >> Finally:
|
||||
# p3 = p3 * A_3^G
|
||||
p3 = p3 * gdt_attn_3
|
||||
_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p2 = _p3 + self.lateral_block3(x2)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p2) if self.split else x
|
||||
_p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p2 = self.decoder_block2(_p2)
|
||||
m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision and self.training else None
|
||||
if self.config.out_ref:
|
||||
p2_gdt = self.gdt_convs_2(p2)
|
||||
if self.training:
|
||||
# >> GT:
|
||||
m2_dia = m2
|
||||
gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
outs_gdt_label.append(gdt_label_main_2)
|
||||
# >> Pred:
|
||||
gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt)
|
||||
outs_gdt_pred.append(gdt_pred_2)
|
||||
gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
|
||||
# >> Finally:
|
||||
p2 = p2 * gdt_attn_2
|
||||
_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p1 = _p2 + self.lateral_block2(x1)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
_p1 = self.decoder_block1(_p1)
|
||||
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p1_out = self.conv_out1(_p1)
|
||||
|
||||
if self.config.ms_supervision and self.training:
|
||||
outs.append(m4)
|
||||
outs.append(m3)
|
||||
outs.append(m2)
|
||||
outs.append(p1_out)
|
||||
return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs)
|
||||
|
||||
|
||||
class SimpleConvs(nn.Module):
|
||||
def __init__(
|
||||
self, in_channels: int, out_channels: int, inter_channels=64
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv_out(self.conv1(x))
|
||||
@@ -0,0 +1,120 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from ...models.modules.deform_conv import DeformableConv2d
|
||||
from ...config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class _ASPPModule(nn.Module):
|
||||
def __init__(self, in_channels, planes, kernel_size, padding, dilation):
|
||||
super(_ASPPModule, self).__init__()
|
||||
self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size,
|
||||
stride=1, padding=padding, dilation=dilation, bias=False)
|
||||
self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.atrous_conv(x)
|
||||
x = self.bn(x)
|
||||
|
||||
return self.relu(x)
|
||||
|
||||
|
||||
class ASPP(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, output_stride=16):
|
||||
super(ASPP, self).__init__()
|
||||
self.down_scale = 1
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channelster = 256 // self.down_scale
|
||||
if output_stride == 16:
|
||||
dilations = [1, 6, 12, 18]
|
||||
elif output_stride == 8:
|
||||
dilations = [1, 12, 24, 36]
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
|
||||
self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
|
||||
self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
|
||||
self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
|
||||
|
||||
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
|
||||
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
|
||||
nn.ReLU(inplace=True))
|
||||
self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.dropout = nn.Dropout(0.5)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.aspp1(x)
|
||||
x2 = self.aspp2(x)
|
||||
x3 = self.aspp3(x)
|
||||
x4 = self.aspp4(x)
|
||||
x5 = self.global_avg_pool(x)
|
||||
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
|
||||
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
return self.dropout(x)
|
||||
|
||||
|
||||
##################### Deformable
|
||||
class _ASPPModuleDeformable(nn.Module):
|
||||
def __init__(self, in_channels, planes, kernel_size, padding):
|
||||
super(_ASPPModuleDeformable, self).__init__()
|
||||
self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
|
||||
stride=1, padding=padding, bias=False)
|
||||
self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.atrous_conv(x)
|
||||
x = self.bn(x)
|
||||
|
||||
return self.relu(x)
|
||||
|
||||
|
||||
class ASPPDeformable(nn.Module):
|
||||
def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7]):
|
||||
super(ASPPDeformable, self).__init__()
|
||||
self.down_scale = 1
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channelster = 256 // self.down_scale
|
||||
|
||||
self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0)
|
||||
self.aspp_deforms = nn.ModuleList([
|
||||
_ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2)) for conv_size in parallel_block_sizes
|
||||
])
|
||||
|
||||
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
|
||||
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
|
||||
nn.ReLU(inplace=True))
|
||||
self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.dropout = nn.Dropout(0.5)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.aspp1(x)
|
||||
x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
|
||||
x5 = self.global_avg_pool(x)
|
||||
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
|
||||
x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
return self.dropout(x)
|
||||
@@ -0,0 +1,66 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ...models.modules.aspp import ASPP, ASPPDeformable
|
||||
from ...config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class BasicDecBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
|
||||
super(BasicDecBlk, self).__init__()
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
|
||||
self.relu_in = nn.ReLU(inplace=True)
|
||||
if config.dec_att == 'ASPP':
|
||||
self.dec_att = ASPP(in_channels=inter_channels)
|
||||
elif config.dec_att == 'ASPPDeformable':
|
||||
self.dec_att = ASPPDeformable(in_channels=inter_channels)
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
|
||||
self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
|
||||
self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_in(x)
|
||||
x = self.bn_in(x)
|
||||
x = self.relu_in(x)
|
||||
if hasattr(self, 'dec_att'):
|
||||
x = self.dec_att(x)
|
||||
x = self.conv_out(x)
|
||||
x = self.bn_out(x)
|
||||
return x
|
||||
|
||||
|
||||
class ResBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
|
||||
super(ResBlk, self).__init__()
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
|
||||
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
|
||||
self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
|
||||
self.relu_in = nn.ReLU(inplace=True)
|
||||
|
||||
if config.dec_att == 'ASPP':
|
||||
self.dec_att = ASPP(in_channels=inter_channels)
|
||||
elif config.dec_att == 'ASPPDeformable':
|
||||
self.dec_att = ASPPDeformable(in_channels=inter_channels)
|
||||
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
|
||||
self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
|
||||
|
||||
self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
|
||||
|
||||
def forward(self, x):
|
||||
_x = self.conv_resi(x)
|
||||
x = self.conv_in(x)
|
||||
x = self.bn_in(x)
|
||||
x = self.relu_in(x)
|
||||
if hasattr(self, 'dec_att'):
|
||||
x = self.dec_att(x)
|
||||
x = self.conv_out(x)
|
||||
x = self.bn_out(x)
|
||||
return x + _x
|
||||
@@ -0,0 +1,66 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torchvision.ops import deform_conv2d
|
||||
|
||||
|
||||
class DeformableConv2d(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
bias=False):
|
||||
|
||||
super(DeformableConv2d, self).__init__()
|
||||
|
||||
assert type(kernel_size) == tuple or type(kernel_size) == int
|
||||
|
||||
kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
|
||||
self.stride = stride if type(stride) == tuple else (stride, stride)
|
||||
self.padding = padding
|
||||
|
||||
self.offset_conv = nn.Conv2d(in_channels,
|
||||
2 * kernel_size[0] * kernel_size[1],
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=True)
|
||||
|
||||
nn.init.constant_(self.offset_conv.weight, 0.)
|
||||
nn.init.constant_(self.offset_conv.bias, 0.)
|
||||
|
||||
self.modulator_conv = nn.Conv2d(in_channels,
|
||||
1 * kernel_size[0] * kernel_size[1],
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=True)
|
||||
|
||||
nn.init.constant_(self.modulator_conv.weight, 0.)
|
||||
nn.init.constant_(self.modulator_conv.bias, 0.)
|
||||
|
||||
self.regular_conv = nn.Conv2d(in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=bias)
|
||||
|
||||
def forward(self, x):
|
||||
#h, w = x.shape[2:]
|
||||
#max_offset = max(h, w)/4.
|
||||
|
||||
offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
|
||||
modulator = 2. * torch.sigmoid(self.modulator_conv(x))
|
||||
|
||||
x = deform_conv2d(
|
||||
input=x,
|
||||
offset=offset,
|
||||
weight=self.regular_conv.weight,
|
||||
bias=self.regular_conv.bias,
|
||||
padding=self.padding,
|
||||
mask=modulator,
|
||||
stride=self.stride,
|
||||
)
|
||||
return x
|
||||
@@ -0,0 +1,21 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from functools import partial
|
||||
|
||||
from ...config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class BasicLatBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
|
||||
super(BasicLatBlk, self).__init__()
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv(x)
|
||||
return x
|
||||
@@ -0,0 +1,118 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models import register_model
|
||||
|
||||
import math
|
||||
|
||||
|
||||
class MLPLayer(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
return x, H, W
|
||||
|
||||
@@ -0,0 +1,222 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Any, Optional, Tuple, Type
|
||||
|
||||
|
||||
class PromptEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim=256,
|
||||
image_embedding_size=1024,
|
||||
input_image_size=(1024, 1024),
|
||||
mask_in_chans=16,
|
||||
activation=nn.GELU
|
||||
) -> None:
|
||||
super().__init__()
|
||||
"""
|
||||
Codes are partially from SAM: https://github.com/facebookresearch/segment-anything/blob/6fdee8f2727f4506cfbbe553e23b895e27956588/segment_anything/modeling/prompt_encoder.py.
|
||||
|
||||
Arguments:
|
||||
embed_dim (int): The prompts' embedding dimension
|
||||
image_embedding_size (tuple(int, int)): The spatial size of the
|
||||
image embedding, as (H, W).
|
||||
input_image_size (int): The padded size of the image as input
|
||||
to the image encoder, as (H, W).
|
||||
mask_in_chans (int): The number of hidden channels used for
|
||||
encoding input masks.
|
||||
activation (nn.Module): The activation to use when encoding
|
||||
input masks.
|
||||
"""
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.input_image_size = input_image_size
|
||||
self.image_embedding_size = image_embedding_size
|
||||
self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
|
||||
|
||||
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
|
||||
point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
|
||||
self.point_embeddings = nn.ModuleList(point_embeddings)
|
||||
self.not_a_point_embed = nn.Embedding(1, embed_dim)
|
||||
|
||||
self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
|
||||
self.mask_downscaling = nn.Sequential(
|
||||
nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
|
||||
LayerNorm2d(mask_in_chans // 4),
|
||||
activation(),
|
||||
nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
|
||||
LayerNorm2d(mask_in_chans),
|
||||
activation(),
|
||||
nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
|
||||
)
|
||||
self.no_mask_embed = nn.Embedding(1, embed_dim)
|
||||
|
||||
def get_dense_pe(self) -> torch.Tensor:
|
||||
"""
|
||||
Returns the positional encoding used to encode point prompts,
|
||||
applied to a dense set of points the shape of the image encoding.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Positional encoding with shape
|
||||
1x(embed_dim)x(embedding_h)x(embedding_w)
|
||||
"""
|
||||
return self.pe_layer(self.image_embedding_size).unsqueeze(0)
|
||||
|
||||
def _embed_points(
|
||||
self,
|
||||
points: torch.Tensor,
|
||||
labels: torch.Tensor,
|
||||
pad: bool,
|
||||
) -> torch.Tensor:
|
||||
"""Embeds point prompts."""
|
||||
points = points + 0.5 # Shift to center of pixel
|
||||
if pad:
|
||||
padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
|
||||
padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
|
||||
points = torch.cat([points, padding_point], dim=1)
|
||||
labels = torch.cat([labels, padding_label], dim=1)
|
||||
point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
|
||||
point_embedding[labels == -1] = 0.0
|
||||
point_embedding[labels == -1] += self.not_a_point_embed.weight
|
||||
point_embedding[labels == 0] += self.point_embeddings[0].weight
|
||||
point_embedding[labels == 1] += self.point_embeddings[1].weight
|
||||
return point_embedding
|
||||
|
||||
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
|
||||
"""Embeds box prompts."""
|
||||
boxes = boxes + 0.5 # Shift to center of pixel
|
||||
coords = boxes.reshape(-1, 2, 2)
|
||||
corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
|
||||
corner_embedding[:, 0, :] += self.point_embeddings[2].weight
|
||||
corner_embedding[:, 1, :] += self.point_embeddings[3].weight
|
||||
return corner_embedding
|
||||
|
||||
def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
|
||||
"""Embeds mask inputs."""
|
||||
mask_embedding = self.mask_downscaling(masks)
|
||||
return mask_embedding
|
||||
|
||||
def _get_batch_size(
|
||||
self,
|
||||
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
||||
boxes: Optional[torch.Tensor],
|
||||
masks: Optional[torch.Tensor],
|
||||
) -> int:
|
||||
"""
|
||||
Gets the batch size of the output given the batch size of the input prompts.
|
||||
"""
|
||||
if points is not None:
|
||||
return points[0].shape[0]
|
||||
elif boxes is not None:
|
||||
return boxes.shape[0]
|
||||
elif masks is not None:
|
||||
return masks.shape[0]
|
||||
else:
|
||||
return 1
|
||||
|
||||
def _get_device(self) -> torch.device:
|
||||
return self.point_embeddings[0].weight.device
|
||||
|
||||
def forward(
|
||||
self,
|
||||
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
||||
boxes: Optional[torch.Tensor],
|
||||
masks: Optional[torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Embeds different types of prompts, returning both sparse and dense
|
||||
embeddings.
|
||||
|
||||
Arguments:
|
||||
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
|
||||
and labels to embed.
|
||||
boxes (torch.Tensor or none): boxes to embed
|
||||
masks (torch.Tensor or none): masks to embed
|
||||
|
||||
Returns:
|
||||
torch.Tensor: sparse embeddings for the points and boxes, with shape
|
||||
BxNx(embed_dim), where N is determined by the number of input points
|
||||
and boxes.
|
||||
torch.Tensor: dense embeddings for the masks, in the shape
|
||||
Bx(embed_dim)x(embed_H)x(embed_W)
|
||||
"""
|
||||
bs = self._get_batch_size(points, boxes, masks)
|
||||
sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
|
||||
if points is not None:
|
||||
coords, labels = points
|
||||
point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
|
||||
sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
|
||||
if boxes is not None:
|
||||
box_embeddings = self._embed_boxes(boxes)
|
||||
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
|
||||
|
||||
if masks is not None:
|
||||
dense_embeddings = self._embed_masks(masks)
|
||||
else:
|
||||
dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
|
||||
bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
|
||||
)
|
||||
|
||||
return sparse_embeddings, dense_embeddings
|
||||
|
||||
|
||||
class PositionEmbeddingRandom(nn.Module):
|
||||
"""
|
||||
Positional encoding using random spatial frequencies.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
|
||||
super().__init__()
|
||||
if scale is None or scale <= 0.0:
|
||||
scale = 1.0
|
||||
self.register_buffer(
|
||||
"positional_encoding_gaussian_matrix",
|
||||
scale * torch.randn((2, num_pos_feats)),
|
||||
)
|
||||
|
||||
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
|
||||
"""Positionally encode points that are normalized to [0,1]."""
|
||||
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
|
||||
coords = 2 * coords - 1
|
||||
coords = coords @ self.positional_encoding_gaussian_matrix
|
||||
coords = 2 * np.pi * coords
|
||||
# outputs d_1 x ... x d_n x C shape
|
||||
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
|
||||
|
||||
def forward(self, size: Tuple[int, int]) -> torch.Tensor:
|
||||
"""Generate positional encoding for a grid of the specified size."""
|
||||
h, w = size
|
||||
device: Any = self.positional_encoding_gaussian_matrix.device
|
||||
grid = torch.ones((h, w), device=device, dtype=torch.float32)
|
||||
y_embed = grid.cumsum(dim=0) - 0.5
|
||||
x_embed = grid.cumsum(dim=1) - 0.5
|
||||
y_embed = y_embed / h
|
||||
x_embed = x_embed / w
|
||||
|
||||
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
|
||||
return pe.permute(2, 0, 1) # C x H x W
|
||||
|
||||
def forward_with_coords(
|
||||
self, coords_input: torch.Tensor, image_size: Tuple[int, int]
|
||||
) -> torch.Tensor:
|
||||
"""Positionally encode points that are not normalized to [0,1]."""
|
||||
coords = coords_input.clone()
|
||||
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
|
||||
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
|
||||
return self._pe_encoding(coords.to(torch.float)) # B x N x C
|
||||
|
||||
|
||||
class LayerNorm2d(nn.Module):
|
||||
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(num_channels))
|
||||
self.bias = nn.Parameter(torch.zeros(num_channels))
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
u = x.mean(1, keepdim=True)
|
||||
s = (x - u).pow(2).mean(1, keepdim=True)
|
||||
x = (x - u) / torch.sqrt(s + self.eps)
|
||||
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
return x
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def build_act_layer(act_layer):
|
||||
if act_layer == 'ReLU':
|
||||
return nn.ReLU(inplace=True)
|
||||
elif act_layer == 'SiLU':
|
||||
return nn.SiLU(inplace=True)
|
||||
elif act_layer == 'GELU':
|
||||
return nn.GELU()
|
||||
|
||||
raise NotImplementedError(f'build_act_layer does not support {act_layer}')
|
||||
|
||||
|
||||
def build_norm_layer(dim,
|
||||
norm_layer,
|
||||
in_format='channels_last',
|
||||
out_format='channels_last',
|
||||
eps=1e-6):
|
||||
layers = []
|
||||
if norm_layer == 'BN':
|
||||
if in_format == 'channels_last':
|
||||
layers.append(to_channels_first())
|
||||
layers.append(nn.BatchNorm2d(dim))
|
||||
if out_format == 'channels_last':
|
||||
layers.append(to_channels_last())
|
||||
elif norm_layer == 'LN':
|
||||
if in_format == 'channels_first':
|
||||
layers.append(to_channels_last())
|
||||
layers.append(nn.LayerNorm(dim, eps=eps))
|
||||
if out_format == 'channels_first':
|
||||
layers.append(to_channels_first())
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f'build_norm_layer does not support {norm_layer}')
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
class to_channels_first(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x.permute(0, 3, 1, 2)
|
||||
|
||||
|
||||
class to_channels_last(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x.permute(0, 2, 3, 1)
|
||||
@@ -0,0 +1,252 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from collections import OrderedDict
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision.models import vgg16, vgg16_bn
|
||||
from torchvision.models import resnet50
|
||||
|
||||
from ...config import Config
|
||||
from ...dataset import class_labels_TR_sorted
|
||||
from ...models.backbones.build_backbone import build_backbone
|
||||
from ...models.modules.decoder_blocks import BasicDecBlk
|
||||
from ...models.modules.lateral_blocks import BasicLatBlk
|
||||
from ...models.refinement.stem_layer import StemLayer
|
||||
|
||||
|
||||
class RefinerPVTInChannels4(nn.Module):
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(RefinerPVTInChannels4, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
|
||||
|
||||
lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}
|
||||
channels = lateral_channels_in_collection[self.config.bb]
|
||||
self.squeeze_module = BasicDecBlk(channels[0], channels[0])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if 0:
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
########## Encoder ##########
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x)
|
||||
x2 = self.bb.conv2(x1)
|
||||
x3 = self.bb.conv3(x2)
|
||||
x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
|
||||
x4 = self.squeeze_module(x4)
|
||||
|
||||
########## Decoder ##########
|
||||
|
||||
features = [x, x1, x2, x3, x4]
|
||||
scaled_preds = self.decoder(features)
|
||||
|
||||
return scaled_preds
|
||||
|
||||
|
||||
class Refiner(nn.Module):
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(Refiner, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
|
||||
self.bb = build_backbone(self.config.bb)
|
||||
|
||||
lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}
|
||||
channels = lateral_channels_in_collection[self.config.bb]
|
||||
self.squeeze_module = BasicDecBlk(channels[0], channels[0])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if 0:
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
x = self.stem_layer(x)
|
||||
########## Encoder ##########
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x)
|
||||
x2 = self.bb.conv2(x1)
|
||||
x3 = self.bb.conv3(x2)
|
||||
x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
|
||||
x4 = self.squeeze_module(x4)
|
||||
|
||||
########## Decoder ##########
|
||||
|
||||
features = [x, x1, x2, x3, x4]
|
||||
scaled_preds = self.decoder(features)
|
||||
|
||||
return scaled_preds
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super(Decoder, self).__init__()
|
||||
self.config = Config()
|
||||
DecoderBlock = eval('BasicDecBlk')
|
||||
LateralBlock = eval('BasicLatBlk')
|
||||
|
||||
self.decoder_block4 = DecoderBlock(channels[0], channels[1])
|
||||
self.decoder_block3 = DecoderBlock(channels[1], channels[2])
|
||||
self.decoder_block2 = DecoderBlock(channels[2], channels[3])
|
||||
self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
|
||||
|
||||
self.lateral_block4 = LateralBlock(channels[1], channels[1])
|
||||
self.lateral_block3 = LateralBlock(channels[2], channels[2])
|
||||
self.lateral_block2 = LateralBlock(channels[3], channels[3])
|
||||
|
||||
if self.config.ms_supervision:
|
||||
self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
|
||||
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
|
||||
|
||||
def forward(self, features):
|
||||
x, x1, x2, x3, x4 = features
|
||||
outs = []
|
||||
p4 = self.decoder_block4(x4)
|
||||
_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p3 = _p4 + self.lateral_block4(x3)
|
||||
|
||||
p3 = self.decoder_block3(_p3)
|
||||
_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p2 = _p3 + self.lateral_block3(x2)
|
||||
|
||||
p2 = self.decoder_block2(_p2)
|
||||
_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p1 = _p2 + self.lateral_block2(x1)
|
||||
|
||||
_p1 = self.decoder_block1(_p1)
|
||||
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
p1_out = self.conv_out1(_p1)
|
||||
|
||||
if self.config.ms_supervision:
|
||||
outs.append(self.conv_ms_spvn_4(p4))
|
||||
outs.append(self.conv_ms_spvn_3(p3))
|
||||
outs.append(self.conv_ms_spvn_2(p2))
|
||||
outs.append(p1_out)
|
||||
return outs
|
||||
|
||||
|
||||
class RefUNet(nn.Module):
|
||||
# Refinement
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(RefUNet, self).__init__()
|
||||
self.encoder_1 = nn.Sequential(
|
||||
nn.Conv2d(in_channels, 64, 3, 1, 1),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_2 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_3 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_4 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
|
||||
#####
|
||||
self.decoder_5 = nn.Sequential(
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
#####
|
||||
self.decoder_4 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_3 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_2 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_1 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
|
||||
|
||||
self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
|
||||
|
||||
def forward(self, x):
|
||||
outs = []
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
hx = x
|
||||
|
||||
hx1 = self.encoder_1(hx)
|
||||
hx2 = self.encoder_2(hx1)
|
||||
hx3 = self.encoder_3(hx2)
|
||||
hx4 = self.encoder_4(hx3)
|
||||
|
||||
hx = self.decoder_5(self.pool4(hx4))
|
||||
hx = torch.cat((self.upscore2(hx), hx4), 1)
|
||||
|
||||
d4 = self.decoder_4(hx)
|
||||
hx = torch.cat((self.upscore2(d4), hx3), 1)
|
||||
|
||||
d3 = self.decoder_3(hx)
|
||||
hx = torch.cat((self.upscore2(d3), hx2), 1)
|
||||
|
||||
d2 = self.decoder_2(hx)
|
||||
hx = torch.cat((self.upscore2(d2), hx1), 1)
|
||||
|
||||
d1 = self.decoder_1(hx)
|
||||
|
||||
x = self.conv_d0(d1)
|
||||
outs.append(x)
|
||||
return outs
|
||||
@@ -0,0 +1,45 @@
|
||||
import torch.nn as nn
|
||||
from ...models.modules.utils import build_act_layer, build_norm_layer
|
||||
|
||||
|
||||
class StemLayer(nn.Module):
|
||||
r""" Stem layer of InternImage
|
||||
Args:
|
||||
in_channels (int): number of input channels
|
||||
out_channels (int): number of output channels
|
||||
act_layer (str): activation layer
|
||||
norm_layer (str): normalization layer
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
in_channels=3+1,
|
||||
inter_channels=48,
|
||||
out_channels=96,
|
||||
act_layer='GELU',
|
||||
norm_layer='BN'):
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels,
|
||||
inter_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
self.norm1 = build_norm_layer(
|
||||
inter_channels, norm_layer, 'channels_first', 'channels_first'
|
||||
)
|
||||
self.act = build_act_layer(act_layer)
|
||||
self.conv2 = nn.Conv2d(inter_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
self.norm2 = build_norm_layer(
|
||||
out_channels, norm_layer, 'channels_first', 'channels_first'
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.act(x)
|
||||
x = self.conv2(x)
|
||||
x = self.norm2(x)
|
||||
return x
|
||||
@@ -0,0 +1,15 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torch==2.0.1
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torchvision==0.15.2
|
||||
numpy<2
|
||||
opencv-python
|
||||
timm
|
||||
scipy
|
||||
scikit-image
|
||||
kornia
|
||||
|
||||
tqdm
|
||||
prettytable
|
||||
|
||||
huggingface_hub
|
||||
@@ -0,0 +1,20 @@
|
||||
#!/bin/bash
|
||||
rm -rf __pycache__ */__pycache__
|
||||
|
||||
# Val
|
||||
rm -r tmp*
|
||||
|
||||
# Train
|
||||
rm slurm*
|
||||
rm -r ckpt
|
||||
rm nohup.out*
|
||||
|
||||
# Eval
|
||||
rm -r evaluation/eval-*
|
||||
rm -r tmp*
|
||||
rm -r e_logs/
|
||||
|
||||
# System
|
||||
rm core-*-python-*
|
||||
|
||||
clear
|
||||
@@ -0,0 +1,17 @@
|
||||
#!/bin/sh
|
||||
# Example: ./sub.sh tmp_proj 0,1,2,3 3 --> Use 0,1,2,3 for training, release GPUs, use GPU:3 for inference.
|
||||
|
||||
# module load gcc/11.2.0 cuda/11.8 cudnn/8.6.0_cu11x && cpu_core_num=6
|
||||
module load compilers/cuda/11.8 compilers/gcc/12.2.0 cudnn/8.4.0.27_cuda11.x && cpu_core_num=32
|
||||
|
||||
export PYTHONUNBUFFERED=1
|
||||
|
||||
method=${1:-"BSL"}
|
||||
devices=${2:-0}
|
||||
gpu_num=$(($(echo ${devices%%,} | grep -o "," | wc -l)+1))
|
||||
|
||||
sbatch --nodes=1 -p vip_gpu_ailab -A ai4bio \
|
||||
--gres=gpu:${gpu_num} --ntasks-per-node=1 --cpus-per-task=$((gpu_num*cpu_core_num)) \
|
||||
./train_test.sh ${method} ${devices}
|
||||
|
||||
hostname
|
||||
@@ -0,0 +1,29 @@
|
||||
devices=${1:-0}
|
||||
pred_root=${2:-e_preds}
|
||||
|
||||
# Inference
|
||||
|
||||
CUDA_VISIBLE_DEVICES=${devices} python inference.py --pred_root ${pred_root}
|
||||
|
||||
echo Inference finished at $(date)
|
||||
|
||||
# Evaluation
|
||||
log_dir=e_logs && mkdir ${log_dir}
|
||||
|
||||
task=$(python3 config.py)
|
||||
case "${task}" in
|
||||
"DIS5K") testsets='DIS-VD,DIS-TE1,DIS-TE2,DIS-TE3,DIS-TE4' ;;
|
||||
"COD") testsets='CHAMELEON,NC4K,TE-CAMO,TE-COD10K' ;;
|
||||
"HRSOD") testsets='DAVIS-S,TE-HRSOD,TE-UHRSD,DUT-OMRON,TE-DUTS' ;;
|
||||
"General") testsets='DIS-VD' ;;
|
||||
"Matting") testsets='TE-P3M-500-P' ;;
|
||||
esac
|
||||
testsets=(`echo ${testsets} | tr ',' ' '`) && testsets=${testsets[@]}
|
||||
|
||||
for testset in ${testsets}; do
|
||||
python eval_existingOnes.py --pred_root ${pred_root} --data_lst ${testset} > ${log_dir}/eval_${testset}.out
|
||||
# nohup python eval_existingOnes.py --pred_root ${pred_root} --data_lst ${testset} > ${log_dir}/eval_${testset}.out 2>&1 &
|
||||
done
|
||||
|
||||
|
||||
echo Evaluation started at $(date)
|
||||
@@ -0,0 +1,333 @@
|
||||
import os
|
||||
import datetime
|
||||
import argparse
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.autograd import Variable
|
||||
|
||||
from .config import Config
|
||||
from .loss import PixLoss, ClsLoss
|
||||
from .dataset import MyData
|
||||
from .models.birefnet import BiRefNet
|
||||
from .utils import Logger, AverageMeter, set_seed, check_state_dict
|
||||
|
||||
from torch.utils.data.distributed import DistributedSampler
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.distributed import init_process_group, destroy_process_group, get_rank
|
||||
from torch.cuda import amp
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--resume', default=None, type=str, help='path to latest checkpoint')
|
||||
parser.add_argument('--epochs', default=120, type=int)
|
||||
parser.add_argument('--trainset', default='DIS5K', type=str, help="Options: 'DIS5K'")
|
||||
parser.add_argument('--ckpt_dir', default=None, help='Temporary folder')
|
||||
parser.add_argument('--testsets', default='DIS-VD+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4', type=str)
|
||||
parser.add_argument('--dist', default=False, type=lambda x: x == 'True')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
config = Config()
|
||||
if config.rand_seed:
|
||||
set_seed(config.rand_seed)
|
||||
|
||||
if config.use_fp16:
|
||||
# Half Precision
|
||||
scaler = amp.GradScaler(enabled=config.use_fp16)
|
||||
|
||||
# DDP
|
||||
to_be_distributed = args.dist
|
||||
if to_be_distributed:
|
||||
init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=3600*10))
|
||||
device = int(os.environ["LOCAL_RANK"])
|
||||
else:
|
||||
device = config.device
|
||||
|
||||
epoch_st = 1
|
||||
# make dir for ckpt
|
||||
os.makedirs(args.ckpt_dir, exist_ok=True)
|
||||
|
||||
# Init log file
|
||||
logger = Logger(os.path.join(args.ckpt_dir, "log.txt"))
|
||||
logger_loss_idx = 1
|
||||
|
||||
# log model and optimizer params
|
||||
# logger.info("Model details:"); logger.info(model)
|
||||
logger.info("datasets: load_all={}, compile={}.".format(config.load_all, config.compile))
|
||||
logger.info("Other hyperparameters:"); logger.info(args)
|
||||
print('batch size:', config.batch_size)
|
||||
|
||||
|
||||
if os.path.exists(os.path.join(config.data_root_dir, config.task, args.testsets.strip('+').split('+')[0])):
|
||||
args.testsets = args.testsets.strip('+').split('+')
|
||||
else:
|
||||
args.testsets = []
|
||||
|
||||
# Init model
|
||||
def prepare_dataloader(dataset: torch.utils.data.Dataset, batch_size: int, to_be_distributed=False, is_train=True):
|
||||
if to_be_distributed:
|
||||
return torch.utils.data.DataLoader(
|
||||
dataset=dataset, batch_size=batch_size, num_workers=min(config.num_workers, batch_size), pin_memory=True,
|
||||
shuffle=False, sampler=DistributedSampler(dataset), drop_last=True
|
||||
)
|
||||
else:
|
||||
return torch.utils.data.DataLoader(
|
||||
dataset=dataset, batch_size=batch_size, num_workers=min(config.num_workers, batch_size, 0), pin_memory=True,
|
||||
shuffle=is_train, drop_last=True
|
||||
)
|
||||
|
||||
|
||||
def init_data_loaders(to_be_distributed):
|
||||
# Prepare dataset
|
||||
train_loader = prepare_dataloader(
|
||||
MyData(datasets=config.training_set, image_size=config.size, is_train=True),
|
||||
config.batch_size, to_be_distributed=to_be_distributed, is_train=True
|
||||
)
|
||||
print(len(train_loader), "batches of train dataloader {} have been created.".format(config.training_set))
|
||||
test_loaders = {}
|
||||
for testset in args.testsets:
|
||||
_data_loader_test = prepare_dataloader(
|
||||
MyData(datasets=testset, image_size=config.size, is_train=False),
|
||||
config.batch_size_valid, is_train=False
|
||||
)
|
||||
print(len(_data_loader_test), "batches of valid dataloader {} have been created.".format(testset))
|
||||
test_loaders[testset] = _data_loader_test
|
||||
return train_loader, test_loaders
|
||||
|
||||
|
||||
def init_models_optimizers(epochs, to_be_distributed):
|
||||
model = BiRefNet(bb_pretrained=True)
|
||||
if args.resume:
|
||||
if os.path.isfile(args.resume):
|
||||
logger.info("=> loading checkpoint '{}'".format(args.resume))
|
||||
state_dict = torch.load(args.resume, map_location='cpu')
|
||||
state_dict = check_state_dict(state_dict)
|
||||
model.load_state_dict(state_dict)
|
||||
global epoch_st
|
||||
epoch_st = int(args.resume.rstrip('.pth').split('epoch_')[-1]) + 1
|
||||
else:
|
||||
logger.info("=> no checkpoint found at '{}'".format(args.resume))
|
||||
if to_be_distributed:
|
||||
model = model.to(device)
|
||||
model = DDP(model, device_ids=[device])
|
||||
else:
|
||||
model = model.to(device)
|
||||
if config.compile:
|
||||
model = torch.compile(model, mode=['default', 'reduce-overhead', 'max-autotune'][0])
|
||||
if config.precisionHigh:
|
||||
torch.set_float32_matmul_precision('high')
|
||||
|
||||
|
||||
# Setting optimizer
|
||||
if config.optimizer == 'AdamW':
|
||||
optimizer = optim.AdamW(params=model.parameters(), lr=config.lr, weight_decay=1e-2)
|
||||
elif config.optimizer == 'Adam':
|
||||
optimizer = optim.Adam(params=model.parameters(), lr=config.lr, weight_decay=0)
|
||||
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
|
||||
optimizer,
|
||||
milestones=[lde if lde > 0 else epochs + lde + 1 for lde in config.lr_decay_epochs],
|
||||
gamma=config.lr_decay_rate
|
||||
)
|
||||
logger.info("Optimizer details:"); logger.info(optimizer)
|
||||
logger.info("Scheduler details:"); logger.info(lr_scheduler)
|
||||
|
||||
return model, optimizer, lr_scheduler
|
||||
|
||||
|
||||
class Trainer:
|
||||
def __init__(
|
||||
self, data_loaders, model_opt_lrsch,
|
||||
):
|
||||
self.model, self.optimizer, self.lr_scheduler = model_opt_lrsch
|
||||
self.train_loader, self.test_loaders = data_loaders
|
||||
if config.out_ref:
|
||||
self.criterion_gdt = nn.BCELoss() if not config.use_fp16 else nn.BCEWithLogitsLoss()
|
||||
|
||||
# Setting Losses
|
||||
self.pix_loss = PixLoss()
|
||||
self.cls_loss = ClsLoss()
|
||||
|
||||
# Others
|
||||
self.loss_log = AverageMeter()
|
||||
if config.lambda_adv_g:
|
||||
self.optimizer_d, self.lr_scheduler_d, self.disc, self.adv_criterion = self._load_adv_components()
|
||||
self.disc_update_for_odd = 0
|
||||
|
||||
def _load_adv_components(self):
|
||||
# AIL
|
||||
from loss import Discriminator
|
||||
disc = Discriminator(channels=3, img_size=config.size)
|
||||
if to_be_distributed:
|
||||
disc = disc.to(device)
|
||||
disc = DDP(disc, device_ids=[device], broadcast_buffers=False)
|
||||
else:
|
||||
disc = disc.to(device)
|
||||
if config.compile:
|
||||
disc = torch.compile(disc, mode=['default', 'reduce-overhead', 'max-autotune'][0])
|
||||
adv_criterion = nn.BCELoss() if not config.use_fp16 else nn.BCEWithLogitsLoss()
|
||||
if config.optimizer == 'AdamW':
|
||||
optimizer_d = optim.AdamW(params=disc.parameters(), lr=config.lr, weight_decay=1e-2)
|
||||
elif config.optimizer == 'Adam':
|
||||
optimizer_d = optim.Adam(params=disc.parameters(), lr=config.lr, weight_decay=0)
|
||||
lr_scheduler_d = torch.optim.lr_scheduler.MultiStepLR(
|
||||
optimizer_d,
|
||||
milestones=[lde if lde > 0 else args.epochs + lde + 1 for lde in config.lr_decay_epochs],
|
||||
gamma=config.lr_decay_rate
|
||||
)
|
||||
return optimizer_d, lr_scheduler_d, disc, adv_criterion
|
||||
|
||||
def _train_batch(self, batch):
|
||||
inputs = batch[0].to(device)
|
||||
gts = batch[1].to(device)
|
||||
class_labels = batch[2].to(device)
|
||||
if config.use_fp16:
|
||||
with amp.autocast(enabled=config.use_fp16):
|
||||
scaled_preds, class_preds_lst = self.model(inputs)
|
||||
if config.out_ref:
|
||||
(outs_gdt_pred, outs_gdt_label), scaled_preds = scaled_preds
|
||||
for _idx, (_gdt_pred, _gdt_label) in enumerate(zip(outs_gdt_pred, outs_gdt_label)):
|
||||
_gdt_pred = nn.functional.interpolate(_gdt_pred, size=_gdt_label.shape[2:], mode='bilinear', align_corners=True)#.sigmoid()
|
||||
# _gdt_label = _gdt_label.sigmoid()
|
||||
loss_gdt = self.criterion_gdt(_gdt_pred, _gdt_label) if _idx == 0 else self.criterion_gdt(_gdt_pred, _gdt_label) + loss_gdt
|
||||
# self.loss_dict['loss_gdt'] = loss_gdt.item()
|
||||
if None in class_preds_lst:
|
||||
loss_cls = 0.
|
||||
else:
|
||||
loss_cls = self.cls_loss(class_preds_lst, class_labels) * 1.0
|
||||
self.loss_dict['loss_cls'] = loss_cls.item()
|
||||
|
||||
# Loss
|
||||
loss_pix = self.pix_loss(scaled_preds, torch.clamp(gts, 0, 1)) * 1.0
|
||||
self.loss_dict['loss_pix'] = loss_pix.item()
|
||||
# since there may be several losses for sal, the lambdas for them (lambdas_pix) are inside the loss.py
|
||||
loss = loss_pix + loss_cls
|
||||
if config.out_ref:
|
||||
loss = loss + loss_gdt * 1.0
|
||||
|
||||
if config.lambda_adv_g:
|
||||
# gen
|
||||
valid = Variable(torch.cuda.FloatTensor(scaled_preds[-1].shape[0], 1).fill_(1.0), requires_grad=False).to(device)
|
||||
adv_loss_g = self.adv_criterion(self.disc(scaled_preds[-1] * inputs), valid) * config.lambda_adv_g
|
||||
loss += adv_loss_g
|
||||
self.loss_dict['loss_adv'] = adv_loss_g.item()
|
||||
self.disc_update_for_odd += 1
|
||||
# self.loss_log.update(loss.item(), inputs.size(0))
|
||||
# self.optimizer.zero_grad()
|
||||
# loss.backward()
|
||||
# self.optimizer.step()
|
||||
self.optimizer.zero_grad()
|
||||
scaler.scale(loss).backward()
|
||||
scaler.step(self.optimizer)
|
||||
scaler.update()
|
||||
|
||||
if config.lambda_adv_g and self.disc_update_for_odd % 2 == 0:
|
||||
# disc
|
||||
fake = Variable(torch.cuda.FloatTensor(scaled_preds[-1].shape[0], 1).fill_(0.0), requires_grad=False).to(device)
|
||||
adv_loss_real = self.adv_criterion(self.disc(gts * inputs), valid)
|
||||
adv_loss_fake = self.adv_criterion(self.disc(scaled_preds[-1].detach() * inputs.detach()), fake)
|
||||
adv_loss_d = (adv_loss_real + adv_loss_fake) / 2 * config.lambda_adv_d
|
||||
self.loss_dict['loss_adv_d'] = adv_loss_d.item()
|
||||
# self.optimizer_d.zero_grad()
|
||||
# adv_loss_d.backward()
|
||||
# self.optimizer_d.step()
|
||||
self.optimizer_d.zero_grad()
|
||||
scaler.scale(adv_loss_d).backward()
|
||||
scaler.step(self.optimizer_d)
|
||||
scaler.update()
|
||||
else:
|
||||
scaled_preds, class_preds_lst = self.model(inputs)
|
||||
if config.out_ref:
|
||||
(outs_gdt_pred, outs_gdt_label), scaled_preds = scaled_preds
|
||||
for _idx, (_gdt_pred, _gdt_label) in enumerate(zip(outs_gdt_pred, outs_gdt_label)):
|
||||
_gdt_pred = nn.functional.interpolate(_gdt_pred, size=_gdt_label.shape[2:], mode='bilinear', align_corners=True).sigmoid()
|
||||
_gdt_label = _gdt_label.sigmoid()
|
||||
loss_gdt = self.criterion_gdt(_gdt_pred, _gdt_label) if _idx == 0 else self.criterion_gdt(_gdt_pred, _gdt_label) + loss_gdt
|
||||
# self.loss_dict['loss_gdt'] = loss_gdt.item()
|
||||
if None in class_preds_lst:
|
||||
loss_cls = 0.
|
||||
else:
|
||||
loss_cls = self.cls_loss(class_preds_lst, class_labels) * 1.0
|
||||
self.loss_dict['loss_cls'] = loss_cls.item()
|
||||
|
||||
# Loss
|
||||
loss_pix = self.pix_loss(scaled_preds, torch.clamp(gts, 0, 1)) * 1.0
|
||||
self.loss_dict['loss_pix'] = loss_pix.item()
|
||||
# since there may be several losses for sal, the lambdas for them (lambdas_pix) are inside the loss.py
|
||||
loss = loss_pix + loss_cls
|
||||
if config.out_ref:
|
||||
loss = loss + loss_gdt * 1.0
|
||||
|
||||
if config.lambda_adv_g:
|
||||
# gen
|
||||
valid = Variable(torch.cuda.FloatTensor(scaled_preds[-1].shape[0], 1).fill_(1.0), requires_grad=False).to(device)
|
||||
adv_loss_g = self.adv_criterion(self.disc(scaled_preds[-1] * inputs), valid) * config.lambda_adv_g
|
||||
loss += adv_loss_g
|
||||
self.loss_dict['loss_adv'] = adv_loss_g.item()
|
||||
self.disc_update_for_odd += 1
|
||||
self.loss_log.update(loss.item(), inputs.size(0))
|
||||
self.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
self.optimizer.step()
|
||||
|
||||
if config.lambda_adv_g and self.disc_update_for_odd % 2 == 0:
|
||||
# disc
|
||||
fake = Variable(torch.cuda.FloatTensor(scaled_preds[-1].shape[0], 1).fill_(0.0), requires_grad=False).to(device)
|
||||
adv_loss_real = self.adv_criterion(self.disc(gts * inputs), valid)
|
||||
adv_loss_fake = self.adv_criterion(self.disc(scaled_preds[-1].detach() * inputs.detach()), fake)
|
||||
adv_loss_d = (adv_loss_real + adv_loss_fake) / 2 * config.lambda_adv_d
|
||||
self.loss_dict['loss_adv_d'] = adv_loss_d.item()
|
||||
self.optimizer_d.zero_grad()
|
||||
adv_loss_d.backward()
|
||||
self.optimizer_d.step()
|
||||
|
||||
def train_epoch(self, epoch):
|
||||
global logger_loss_idx
|
||||
self.model.train()
|
||||
self.loss_dict = {}
|
||||
if epoch > args.epochs + config.finetune_last_epochs[1]:
|
||||
for k in self.pix_loss.lambdas_pix_last.keys():
|
||||
if k.lower() == config.finetune_last_epochs[0].lower():
|
||||
self.pix_loss.lambdas_pix_last[k] = config.lambdas_pix_last[k] * 0.5
|
||||
else:
|
||||
self.pix_loss.lambdas_pix_last[k] = 0
|
||||
|
||||
for batch_idx, batch in enumerate(self.train_loader):
|
||||
self._train_batch(batch)
|
||||
# Logger
|
||||
if batch_idx % 20 == 0:
|
||||
info_progress = 'Epoch[{0}/{1}] Iter[{2}/{3}].'.format(epoch, args.epochs, batch_idx, len(self.train_loader))
|
||||
info_loss = 'Training Losses'
|
||||
for loss_name, loss_value in self.loss_dict.items():
|
||||
info_loss += ', {}: {:.3f}'.format(loss_name, loss_value)
|
||||
logger.info(' '.join((info_progress, info_loss)))
|
||||
info_loss = '@==Final== Epoch[{0}/{1}] Training Loss: {loss.avg:.3f} '.format(epoch, args.epochs, loss=self.loss_log)
|
||||
logger.info(info_loss)
|
||||
|
||||
self.lr_scheduler.step()
|
||||
if config.lambda_adv_g:
|
||||
self.lr_scheduler_d.step()
|
||||
return self.loss_log.avg
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
trainer = Trainer(
|
||||
data_loaders=init_data_loaders(to_be_distributed),
|
||||
model_opt_lrsch=init_models_optimizers(args.epochs, to_be_distributed)
|
||||
)
|
||||
|
||||
for epoch in range(epoch_st, args.epochs+1):
|
||||
train_loss = trainer.train_epoch(epoch)
|
||||
# Save checkpoint
|
||||
# DDP
|
||||
if epoch >= args.epochs - config.save_last and epoch % config.save_step == 0:
|
||||
torch.save(
|
||||
trainer.model.module.state_dict() if to_be_distributed else trainer.model.state_dict(),
|
||||
os.path.join(args.ckpt_dir, 'epoch_{}.pth'.format(epoch))
|
||||
)
|
||||
if to_be_distributed:
|
||||
destroy_process_group()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
# Run script
|
||||
# Settings of training & test for different tasks.
|
||||
method="$1"
|
||||
task=$(python3 config.py)
|
||||
case "${task}" in
|
||||
"DIS5K") epochs=600 && val_last=50 && step=5 ;;
|
||||
"COD") epochs=150 && val_last=50 && step=5 ;;
|
||||
"HRSOD") epochs=150 && val_last=50 && step=5 ;;
|
||||
"General") epochs=250 && val_last=20 && step=2 ;;
|
||||
"Matting") epochs=100 && val_last=20 && step=2 ;;
|
||||
esac
|
||||
testsets=NO # Non-existing folder to skip.
|
||||
# testsets=TE-COD10K # for COD
|
||||
|
||||
# Train
|
||||
devices=$2
|
||||
nproc_per_node=$(echo ${devices%%,} | grep -o "," | wc -l)
|
||||
|
||||
to_be_distributed=`echo ${nproc_per_node} | awk '{if($e > 0) print "True"; else print "False";}'`
|
||||
|
||||
echo Training started at $(date)
|
||||
if [ ${to_be_distributed} == "True" ]
|
||||
then
|
||||
# Adapt the nproc_per_node by the number of GPUs. Give 8989 as the default value of master_port.
|
||||
echo "Multi-GPU mode received..."
|
||||
CUDA_VISIBLE_DEVICES=${devices} \
|
||||
torchrun --nproc_per_node $((nproc_per_node+1)) --master_port=${3:-8999} \
|
||||
train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
|
||||
--testsets ${testsets} \
|
||||
--dist ${to_be_distributed} \
|
||||
--resume xx/xx-epoch_244.pth
|
||||
else
|
||||
echo "Single-GPU mode received..."
|
||||
CUDA_VISIBLE_DEVICES=${devices} \
|
||||
python train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
|
||||
--testsets ${testsets} \
|
||||
--dist ${to_be_distributed} \
|
||||
--resume xx/xx-epoch_244.pth
|
||||
fi
|
||||
|
||||
echo Training finished at $(date)
|
||||
@@ -0,0 +1,11 @@
|
||||
#!/bin/sh
|
||||
|
||||
method=${1:-"BSL"}
|
||||
devices=${2:-"0,1,2,3,4,5,6,7"}
|
||||
|
||||
bash train.sh ${method} ${devices}
|
||||
|
||||
devices_test=${3:-0}
|
||||
bash test.sh ${devices_test}
|
||||
|
||||
hostname
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,312 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "LTj2A0RUQFNo"
|
||||
},
|
||||
"source": [
|
||||
"# Convert our BiRefNet weights to onnx format.\n",
|
||||
"\n",
|
||||
"> This colab file is modified from [Kazuhito00](https://github.com/Kazuhito00)'s nice work.\n",
|
||||
"\n",
|
||||
"> Repo: https://github.com/Kazuhito00/BiRefNet-ONNX-Sample \n",
|
||||
"> Original Colab: https://colab.research.google.com/github/Kazuhito00/BiRefNet-ONNX-Sample/blob/main/Convert2ONNX.ipynb\n",
|
||||
"\n",
|
||||
"+ Currently, Colab with 12.7GB RAM / 15GB GPU Mem cannot hold the transformation of BiRefNet in default setting. So, I take BiRefNet with swin_v1_tiny backbone as an example."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Online Colab version: https://colab.research.google.com/drive/1z6OruR52LOvDDpnp516F-N4EyPGrp5om"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "781JHjLJmveh"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"weights_file = 'BiRefNet-general-bb_swin_v1_tiny-epoch_232.pth' # https://github.com/ZhengPeng7/BiRefNet/releases/download/v1/BiRefNet-general-bb_swin_v1_tiny-epoch_232.pth\n",
|
||||
"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('config.py') as fp:\n",
|
||||
" file_lines = fp.read()\n",
|
||||
"if 'swin_v1_tiny' in weights_file:\n",
|
||||
" print('Set `swin_v1_tiny` as the backbone.')\n",
|
||||
" file_lines = file_lines.replace(\n",
|
||||
" '''\n",
|
||||
" 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5\n",
|
||||
" ][6]\n",
|
||||
" ''',\n",
|
||||
" '''\n",
|
||||
" 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5\n",
|
||||
" ][3]\n",
|
||||
" ''',\n",
|
||||
" )\n",
|
||||
" with open('config.py', mode=\"w\") as fp:\n",
|
||||
" fp.write(file_lines)\n",
|
||||
"else:\n",
|
||||
" file_lines = file_lines.replace(\n",
|
||||
" '''\n",
|
||||
" 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5\n",
|
||||
" ][3]\n",
|
||||
" ''',\n",
|
||||
" '''\n",
|
||||
" 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5\n",
|
||||
" ][6]\n",
|
||||
" ''',\n",
|
||||
" )\n",
|
||||
" with open('config.py', mode=\"w\") as fp:\n",
|
||||
" fp.write(file_lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "7lFgKfPS8Icy"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from utils import check_state_dict\n",
|
||||
"from models.birefnet import BiRefNet\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"birefnet = BiRefNet(bb_pretrained=False)\n",
|
||||
"state_dict = torch.load('./{}'.format(weights_file), map_location=device)\n",
|
||||
"state_dict = check_state_dict(state_dict)\n",
|
||||
"birefnet.load_state_dict(state_dict)\n",
|
||||
"\n",
|
||||
"torch.set_float32_matmul_precision(['high', 'highest'][0])\n",
|
||||
"\n",
|
||||
"birefnet.to(device)\n",
|
||||
"_ = birefnet.eval()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "JVgJAdgxQVJW"
|
||||
},
|
||||
"source": [
|
||||
"# Process deform_conv2d in the conversion to ONNX"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "vJiZv0L75kTe"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torchvision.ops.deform_conv import DeformConv2d\n",
|
||||
"import deform_conv2d_onnx_exporter\n",
|
||||
"\n",
|
||||
"# register deform_conv2d operator\n",
|
||||
"deform_conv2d_onnx_exporter.register_deform_conv2d_onnx_op()\n",
|
||||
"\n",
|
||||
"def convert_to_onnx(net, file_name='output.onnx', input_shape=(1024, 1024), device=device):\n",
|
||||
" input = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device)\n",
|
||||
"\n",
|
||||
" input_layer_names = ['input_image']\n",
|
||||
" output_layer_names = ['output_image']\n",
|
||||
"\n",
|
||||
" torch.onnx.export(\n",
|
||||
" net,\n",
|
||||
" input,\n",
|
||||
" file_name,\n",
|
||||
" verbose=False,\n",
|
||||
" opset_version=17,\n",
|
||||
" input_names=input_layer_names,\n",
|
||||
" output_names=output_layer_names,\n",
|
||||
" )\n",
|
||||
"convert_to_onnx(birefnet, weights_file.replace('.pth', '.onnx'), input_shape=(1024, 1024), device=device)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "-eU-g40P1zS-"
|
||||
},
|
||||
"source": [
|
||||
"# Load ONNX weights and do the inference."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "LZ4HVqcoDvto"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from PIL import Image\n",
|
||||
"from torchvision import transforms\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"transform_image = transforms.Compose([\n",
|
||||
" transforms.Resize((1024, 1024)),\n",
|
||||
" transforms.ToTensor(),\n",
|
||||
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"imagepath = './Helicopter-HR.jpg'\n",
|
||||
"image = Image.open(imagepath)\n",
|
||||
"input_images = transform_image(image).unsqueeze(0).to(device)\n",
|
||||
"input_images_numpy = input_images.cpu().numpy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "rwzdKX1EfYkd"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import onnxruntime\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider']\n",
|
||||
"onnx_session = onnxruntime.InferenceSession(\n",
|
||||
" weights_file.replace('.pth', '.onnx'),\n",
|
||||
" providers=providers\n",
|
||||
")\n",
|
||||
"input_name = onnx_session.get_inputs()[0].name\n",
|
||||
"print(onnxruntime.get_device(), onnx_session.get_providers())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "DJVtxZUZum4-"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from time import time\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"time_st = time()\n",
|
||||
"pred_onnx = torch.tensor(\n",
|
||||
" onnx_session.run(None, {input_name: input_images_numpy if device == 'cpu' else input_images_numpy})[-1]\n",
|
||||
").squeeze(0).sigmoid().cpu()\n",
|
||||
"print(time() - time_st)\n",
|
||||
"\n",
|
||||
"plt.imshow(pred_onnx.squeeze(), cmap='gray'); plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with torch.no_grad():\n",
|
||||
" preds = birefnet(input_images)[-1].sigmoid().cpu()\n",
|
||||
"plt.imshow(preds.squeeze(), cmap='gray'); plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"diff = abs(preds - pred_onnx)\n",
|
||||
"print('sum(diff):', diff.sum())\n",
|
||||
"plt.imshow((diff).squeeze(), cmap='gray'); plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "qzYHflt92Bjd"
|
||||
},
|
||||
"source": [
|
||||
"# Efficiency Comparison between .pth and .onnx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "A5IYfT-uzphA",
|
||||
"outputId": "2999e345-950e-41b3-ddd3-9f58a71a3f21"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%timeit\n",
|
||||
"with torch.no_grad():\n",
|
||||
" preds = birefnet(input_images)[-1].sigmoid().cpu()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "G0Ul4rfNg1za"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%timeit\n",
|
||||
"pred_onnx = torch.tensor(\n",
|
||||
" onnx_session.run(None, {input_name: input_images_numpy})[-1]\n",
|
||||
").squeeze(0).sigmoid().cpu()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"gpuType": "T4",
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.14"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,97 @@
|
||||
import logging
|
||||
import os
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
import numpy as np
|
||||
import random
|
||||
import cv2
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def path_to_image(path, size=(1024, 1024), color_type=['rgb', 'gray'][0]):
|
||||
if color_type.lower() == 'rgb':
|
||||
image = cv2.imread(path)
|
||||
elif color_type.lower() == 'gray':
|
||||
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
|
||||
else:
|
||||
print('Select the color_type to return, either to RGB or gray image.')
|
||||
return
|
||||
if size:
|
||||
image = cv2.resize(image, size, interpolation=cv2.INTER_LINEAR)
|
||||
if color_type.lower() == 'rgb':
|
||||
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).convert('RGB')
|
||||
else:
|
||||
image = Image.fromarray(image).convert('L')
|
||||
return image
|
||||
|
||||
|
||||
|
||||
def check_state_dict(state_dict, unwanted_prefix='_orig_mod.'):
|
||||
for k, v in list(state_dict.items()):
|
||||
if k.startswith(unwanted_prefix):
|
||||
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
|
||||
return state_dict
|
||||
|
||||
|
||||
def generate_smoothed_gt(gts):
|
||||
epsilon = 0.001
|
||||
new_gts = (1-epsilon)*gts+epsilon/2
|
||||
return new_gts
|
||||
|
||||
|
||||
class Logger():
|
||||
def __init__(self, path="log.txt"):
|
||||
self.logger = logging.getLogger('BiRefNet')
|
||||
self.file_handler = logging.FileHandler(path, "w")
|
||||
self.stdout_handler = logging.StreamHandler()
|
||||
self.stdout_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
|
||||
self.file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
|
||||
self.logger.addHandler(self.file_handler)
|
||||
self.logger.addHandler(self.stdout_handler)
|
||||
self.logger.setLevel(logging.INFO)
|
||||
self.logger.propagate = False
|
||||
|
||||
def info(self, txt):
|
||||
self.logger.info(txt)
|
||||
|
||||
def close(self):
|
||||
self.file_handler.close()
|
||||
self.stdout_handler.close()
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0.0
|
||||
self.avg = 0.0
|
||||
self.sum = 0.0
|
||||
self.count = 0.0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
|
||||
def save_checkpoint(state, path, filename="latest.pth"):
|
||||
torch.save(state, os.path.join(path, filename))
|
||||
|
||||
|
||||
def save_tensor_img(tenor_im, path):
|
||||
im = tenor_im.cpu().clone()
|
||||
im = im.squeeze(0)
|
||||
tensor2pil = transforms.ToPILImage()
|
||||
im = tensor2pil(im)
|
||||
im.save(path)
|
||||
|
||||
|
||||
def set_seed(seed):
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
np.random.seed(seed)
|
||||
random.seed(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
@@ -1 +1,66 @@
|
||||
# ComfyUI-RemoveBackgroundSuite
|
||||
# ComfyUI-RemoveBackgroundSuite
|
||||
|
||||
这是一个 ComfyUI 插件，专注于实现各类高质量背景移除功能，支持多种 SOTA 算法和细节处理。
|
||||
|
||||
## 节点说明
|
||||
|
||||
### 1. LoadBiRefNetModel_RBS
|
||||
- **功能**：加载本地 BiRefNet 模型权重。
|
||||
- **参数**：
|
||||
- `model`：选择本地模型文件（.pth）。
|
||||
- **输出**：`birefnet_model`（供后续节点使用）
|
||||
|
||||
### 2. LoadBiRefNetModelV2_RBS
|
||||
- **功能**：自动下载并加载 BiRefNet 新版模型（支持 Huggingface 仓库）。
|
||||
- **参数**：
|
||||
- `version`：选择模型版本（如 BiRefNet-General、RMBG-2.0）。
|
||||
- **输出**：`birefnet_model`（供后续节点使用）
|
||||
|
||||
### 3. BiRefNetUltraV2_RBS
|
||||
- **功能**：使用 BiRefNet Ultra V2 进行高质量背景移除。
|
||||
- **参数**：
|
||||
- `image`：输入图片（支持批量）。
|
||||
- `birefnet_model`：已加载的模型。
|
||||
- `detail_method`：细节处理方式（VITMatte、PyMatting、GuidedFilter等）。
|
||||
- `detail_erode`/`detail_dilate`：腐蚀/膨胀参数，影响边缘细节。
|
||||
- `black_point`/`white_point`：黑白场，调整掩码对比度。
|
||||
- `process_detail`：是否进行细节处理。
|
||||
- `device`：推理设备（cuda/cpu）。
|
||||
- `max_megapixels`：最大处理分辨率。
|
||||
- **输出**：
|
||||
- `image`：去背景后的 RGBA 图片
|
||||
- `mask`：前景掩码
|
||||
|
||||
### 4. TransparentBackgroundUltra_RBS
|
||||
- **功能**：将图片背景转换为透明，支持多种细节处理。
|
||||
- **参数**：
|
||||
- `image`：输入图片。
|
||||
- `model`：选择本地模型。
|
||||
- 其余参数同上。
|
||||
- **输出**：
|
||||
- `image`：透明背景图片
|
||||
- `mask`：前景掩码
|
||||
|
||||
## 典型用法
|
||||
1. 用 `LoadBiRefNetModel_RBS` 或 `LoadBiRefNetModelV2_RBS` 加载模型。
|
||||
2. 用 `BiRefNetUltraV2_RBS` 进行背景移除。
|
||||
3. 可选：用 `TransparentBackgroundUltra_RBS` 进一步处理透明背景。
|
||||
|
||||
## 注意事项
|
||||
- 请将模型文件放在 `ComfyUI/models/BiRefNet/pth/` 目录下，或使用新版节点自动下载。
|
||||
- 推荐使用 CUDA 设备以获得更快推理速度。
|
||||
- 细节处理方法对边缘质量有显著影响，可根据实际需求调整。
|
||||
- 插件所有节点均归类于 `RemoveBackgroundSuite`，便于统一管理。
|
||||
|
||||
## 依赖安装
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 常见问题
|
||||
- **模型下载失败**：请检查网络连接或手动下载模型放入指定目录。
|
||||
- **推理慢/显存不足**：可适当降低 `max_megapixels` 或切换到 CPU。
|
||||
- **节点不显示**：请确认插件已放入 `custom_nodes` 目录并重启 ComfyUI。
|
||||
|
||||
---
|
||||
如有更多问题请参考原项目文档或在 Issues 区反馈。
|
||||
@@ -0,0 +1,3 @@
|
||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
+107
@@ -0,0 +1,107 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image, ImageEnhance, ImageFilter, ImageOps
|
||||
import os
|
||||
import folder_paths
|
||||
import cv2
|
||||
from scipy.ndimage import gaussian_filter
|
||||
from scipy.sparse import diags, csr_matrix
|
||||
from scipy.sparse.linalg import spsolve
|
||||
import torch.nn.functional as F
|
||||
from torchvision import transforms
|
||||
from transformers import AutoModelForImageSegmentation
|
||||
import sys
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), 'BiRefNet_v2'))
|
||||
|
||||
def get_files(path, extensions):
|
||||
files = {}
|
||||
for file in os.listdir(path):
|
||||
if file.endswith(extensions):
|
||||
files[file] = os.path.join(path, file)
|
||||
return files
|
||||
|
||||
def scan_model():
|
||||
model_path = os.path.join(folder_paths.models_dir, 'BiRefNet')
|
||||
model_ext = [".pth"]
|
||||
model_dict = get_files(model_path, model_ext)
|
||||
return model_dict
|
||||
|
||||
def tensor2pil(image):
|
||||
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
def pil2tensor(image):
|
||||
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
|
||||
def image2mask(image):
|
||||
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
|
||||
def RGB2RGBA(image, mask):
|
||||
image = image.convert('RGBA')
|
||||
image.putalpha(mask)
|
||||
return image
|
||||
|
||||
def adjust_levels(image, black_point, white_point):
|
||||
image = np.array(image)
|
||||
image = np.clip((image - black_point) / (white_point - black_point), 0, 1)
|
||||
return Image.fromarray((image * 255).astype(np.uint8))
|
||||
|
||||
def guided_filter_alpha(image, mask, radius):
|
||||
image = tensor2pil(image)
|
||||
mask = tensor2pil(mask)
|
||||
image = np.array(image)
|
||||
mask = np.array(mask)
|
||||
mask = cv2.ximgproc.guidedFilter(image, mask, radius, 1e-6)
|
||||
return torch.from_numpy(mask).unsqueeze(0)
|
||||
|
||||
def mask_edge_detail(image, mask, radius, black_point, white_point):
|
||||
image = tensor2pil(image)
|
||||
mask = tensor2pil(mask)
|
||||
image = np.array(image)
|
||||
mask = np.array(mask)
|
||||
mask = cv2.ximgproc.guidedFilter(image, mask, radius, 1e-6)
|
||||
mask = adjust_levels(Image.fromarray(mask), black_point, white_point)
|
||||
return torch.from_numpy(np.array(mask)).unsqueeze(0)
|
||||
|
||||
def generate_VITMatte_trimap(mask, erode, dilate):
|
||||
mask = tensor2pil(mask)
|
||||
mask = np.array(mask)
|
||||
kernel = np.ones((erode, erode), np.uint8)
|
||||
erode_mask = cv2.erode(mask, kernel, iterations=1)
|
||||
kernel = np.ones((dilate, dilate), np.uint8)
|
||||
dilate_mask = cv2.dilate(mask, kernel, iterations=1)
|
||||
trimap = np.zeros_like(mask)
|
||||
trimap[erode_mask > 0.5] = 1
|
||||
trimap[dilate_mask > 0.5] = 0.5
|
||||
return Image.fromarray(trimap)
|
||||
|
||||
def generate_VITMatte(image, trimap, local_files_only=False, device='cuda', max_megapixels=2.0):
|
||||
from transformers import AutoModelForImageSegmentation
|
||||
model_path = os.path.join(folder_paths.models_dir, 'BiRefNet', 'VITMatte')
|
||||
if not os.path.exists(model_path):
|
||||
os.makedirs(model_path, exist_ok=True)
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="ZhengPeng7/VITMatte", local_dir=model_path, ignore_patterns=["*.md", "*.txt"])
|
||||
model = AutoModelForImageSegmentation.from_pretrained(model_path, trust_remote_code=True)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
image = np.array(image)
|
||||
trimap = np.array(trimap)
|
||||
image = cv2.resize(image, (1024, 1024))
|
||||
trimap = cv2.resize(trimap, (1024, 1024))
|
||||
image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float() / 255.0
|
||||
trimap = torch.from_numpy(trimap).unsqueeze(0).unsqueeze(0).float()
|
||||
image = image.to(device)
|
||||
trimap = trimap.to(device)
|
||||
with torch.no_grad():
|
||||
pred = model(image, trimap)
|
||||
pred = pred.cpu().numpy().squeeze()
|
||||
pred = cv2.resize(pred, (image.shape[3], image.shape[2]))
|
||||
return Image.fromarray((pred * 255).astype(np.uint8))
|
||||
|
||||
def histogram_remap(mask, black_point, white_point):
|
||||
mask = tensor2pil(mask)
|
||||
mask = adjust_levels(mask, black_point, white_point)
|
||||
return torch.from_numpy(np.array(mask)).unsqueeze(0)
|
||||
|
||||
def log(message, message_type='info'):
|
||||
print(f"[{message_type.upper()}] {message}")
|
||||
+123
@@ -0,0 +1,123 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
class BiRefNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(BiRefNet, self).__init__()
|
||||
# 编码器
|
||||
self.enc1 = nn.Sequential(
|
||||
nn.Conv2d(3, 64, 3, padding=1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.enc2 = nn.Sequential(
|
||||
nn.Conv2d(64, 128, 3, padding=1),
|
||||
nn.BatchNorm2d(128),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.enc3 = nn.Sequential(
|
||||
nn.Conv2d(128, 256, 3, padding=1),
|
||||
nn.BatchNorm2d(256),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
# 解码器
|
||||
self.dec3 = nn.Sequential(
|
||||
nn.Conv2d(256, 128, 3, padding=1),
|
||||
nn.BatchNorm2d(128),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.dec2 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, padding=1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.dec1 = nn.Sequential(
|
||||
nn.Conv2d(64, 1, 3, padding=1),
|
||||
nn.Sigmoid()
|
||||
)
|
||||
|
||||
# 跳跃连接
|
||||
self.skip1 = nn.Conv2d(64, 64, 1)
|
||||
self.skip2 = nn.Conv2d(128, 128, 1)
|
||||
|
||||
def forward(self, x):
|
||||
# 编码
|
||||
e1 = self.enc1(x)
|
||||
e2 = self.enc2(F.max_pool2d(e1, 2))
|
||||
e3 = self.enc3(F.max_pool2d(e2, 2))
|
||||
|
||||
# 解码
|
||||
d3 = self.dec3(e3)
|
||||
d3 = F.interpolate(d3, size=e2.shape[2:], mode='bilinear', align_corners=True)
|
||||
d3 = d3 + self.skip2(e2)
|
||||
|
||||
d2 = self.dec2(d3)
|
||||
d2 = F.interpolate(d2, size=e1.shape[2:], mode='bilinear', align_corners=True)
|
||||
d2 = d2 + self.skip1(e1)
|
||||
|
||||
d1 = self.dec1(d2)
|
||||
return d1
|
||||
|
||||
def load_model(model_path):
|
||||
"""加载模型"""
|
||||
model = BiRefNet()
|
||||
model.load_state_dict(torch.load(model_path, map_location='cpu'))
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
def preprocess_image(image):
|
||||
"""预处理图像"""
|
||||
if isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image)
|
||||
|
||||
# 转换为RGB模式
|
||||
if image.mode != 'RGB':
|
||||
image = image.convert('RGB')
|
||||
|
||||
# 调整大小为32的倍数
|
||||
width, height = image.size
|
||||
new_width = ((width + 31) // 32) * 32
|
||||
new_height = ((height + 31) // 32) * 32
|
||||
if new_width != width or new_height != height:
|
||||
image = image.resize((new_width, new_height), Image.LANCZOS)
|
||||
|
||||
# 转换为tensor
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0)
|
||||
return image
|
||||
|
||||
def postprocess_mask(mask, original_size):
|
||||
"""后处理掩码"""
|
||||
mask = mask.squeeze().cpu().numpy()
|
||||
mask = (mask > 0.5).astype(np.float32)
|
||||
|
||||
# 调整回原始大小
|
||||
if mask.shape != original_size:
|
||||
mask = Image.fromarray(mask)
|
||||
mask = mask.resize(original_size, Image.LANCZOS)
|
||||
mask = np.array(mask)
|
||||
|
||||
return mask
|
||||
|
||||
def apply_transparency(image, mask, alpha=1.0):
|
||||
"""应用透明效果"""
|
||||
if isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image)
|
||||
|
||||
# 确保图像是RGBA模式
|
||||
if image.mode != 'RGBA':
|
||||
image = image.convert('RGBA')
|
||||
|
||||
# 创建alpha通道
|
||||
alpha_channel = (mask * 255 * alpha).astype(np.uint8)
|
||||
alpha_channel = Image.fromarray(alpha_channel)
|
||||
|
||||
# 应用alpha通道
|
||||
r, g, b, _ = image.split()
|
||||
image = Image.merge('RGBA', (r, g, b, alpha_channel))
|
||||
|
||||
return image
|
||||
@@ -0,0 +1,308 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import folder_paths
|
||||
import os
|
||||
from .model_utils import load_model, preprocess_image, postprocess_mask, apply_transparency
|
||||
from .imagefunc import *
|
||||
from comfy.utils import ProgressBar
|
||||
import tqdm
|
||||
from torchvision import transforms
|
||||
from transformers import AutoModelForImageSegmentation
|
||||
import sys
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), 'BiRefNet_v2'))
|
||||
|
||||
# 获取本地所有BiRefNet模型文件
|
||||
# 返回字典：{模型文件名: 路径}
|
||||
def get_models():
|
||||
model_path = os.path.join(folder_paths.models_dir, 'BiRefNet', 'pth')
|
||||
model_ext = [".pth"]
|
||||
model_dict = get_files(model_path, model_ext)
|
||||
return model_dict
|
||||
|
||||
# 加载本地BiRefNet模型节点
|
||||
class LoadBiRefNetModel_RBS:
|
||||
def __init__(self):
|
||||
self.birefnet = None
|
||||
self.state_dict = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
# 自动扫描本地模型文件，优先显示推荐模型
|
||||
tmp_list = list(get_models().keys())
|
||||
model_list = []
|
||||
if 'BiRefNet-general-epoch_244.pth' in tmp_list:
|
||||
model_list.append('BiRefNet-general-epoch_244.pth')
|
||||
tmp_list.remove('BiRefNet-general-epoch_244.pth')
|
||||
model_list.extend(tmp_list)
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"model": (model_list,), # 选择模型文件
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BIREFNET_MODEL",)
|
||||
RETURN_NAMES = ("birefnet_model",)
|
||||
FUNCTION = "load_birefnet_model"
|
||||
CATEGORY = 'RemoveBackgroundSuite'
|
||||
|
||||
# 加载模型权重并返回模型对象
|
||||
def load_birefnet_model(self, model):
|
||||
from .BiRefNet_v2.models.birefnet import BiRefNet
|
||||
from .BiRefNet_v2.utils import check_state_dict
|
||||
model_dict = get_models()
|
||||
self.birefnet = BiRefNet(bb_pretrained=False)
|
||||
self.state_dict = torch.load(model_dict[model], map_location='cpu', weights_only=True)
|
||||
self.state_dict = check_state_dict(self.state_dict)
|
||||
self.birefnet.load_state_dict(self.state_dict)
|
||||
return (self.birefnet,)
|
||||
|
||||
# 自动下载并加载BiRefNet新版模型节点
|
||||
class LoadBiRefNetModelV2_RBS:
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
# 支持的模型版本列表
|
||||
model_list = list(s.birefnet_model_repos.keys())
|
||||
return {
|
||||
"required": {
|
||||
"version": (model_list,{"default": model_list[0]}), # 选择模型版本
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BIREFNET_MODEL",)
|
||||
RETURN_NAMES = ("birefnet_model",)
|
||||
FUNCTION = "load_birefnet_model"
|
||||
CATEGORY = 'RemoveBackgroundSuite'
|
||||
|
||||
# Huggingface仓库映射
|
||||
birefnet_model_repos = {
|
||||
"BiRefNet-General": "ZhengPeng7/BiRefNet",
|
||||
"RMBG-2.0": "briaai/RMBG-2.0"
|
||||
}
|
||||
|
||||
# 自动下载并加载模型
|
||||
def load_birefnet_model(self, version):
|
||||
birefnet_path = os.path.join(folder_paths.models_dir, 'BiRefNet')
|
||||
os.makedirs(birefnet_path, exist_ok=True)
|
||||
|
||||
model_path = os.path.join(birefnet_path, version)
|
||||
|
||||
# 兼容老模型
|
||||
if version == "BiRefNet-General":
|
||||
old_birefnet_path = os.path.join(birefnet_path, 'pth')
|
||||
old_model = "BiRefNet-general-epoch_244.pth"
|
||||
old_model_path = os.path.join(old_birefnet_path, old_model)
|
||||
if os.path.exists(old_model_path):
|
||||
from .BiRefNet_v2.models.birefnet import BiRefNet
|
||||
from .BiRefNet_v2.utils import check_state_dict
|
||||
self.birefnet = BiRefNet(bb_pretrained=False)
|
||||
self.state_dict = torch.load(old_model_path, map_location='cpu', weights_only=True)
|
||||
self.state_dict = check_state_dict(self.state_dict)
|
||||
self.birefnet.load_state_dict(self.state_dict)
|
||||
return (self.birefnet,)
|
||||
# 若本地无模型则自动下载
|
||||
elif not os.path.exists(model_path):
|
||||
log(f"Downloading {version} model...")
|
||||
repo_id = self.birefnet_model_repos[version]
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt"])
|
||||
|
||||
self.model = AutoModelForImageSegmentation.from_pretrained(model_path, trust_remote_code=True)
|
||||
return (self.model,)
|
||||
|
||||
# BiRefNet Ultra V2 背景移除主节点
|
||||
class BiRefNetUltraV2_RBS:
|
||||
def __init__(self):
|
||||
self.NODE_NAME = 'BiRefNetUltraV2_RBS'
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
# 支持的细节处理方法和设备
|
||||
method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]
|
||||
device_list = ['cuda', 'cpu']
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",), # 输入图片
|
||||
"birefnet_model": ("BIREFNET_MODEL",), # 已加载的模型
|
||||
"detail_method": (method_list,), # 细节处理方式
|
||||
"detail_erode": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}), # 腐蚀参数
|
||||
"detail_dilate": ("INT", {"default": 2, "min": 1, "max": 255, "step": 1}), # 膨胀参数
|
||||
"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}), # 黑场
|
||||
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}), # 白场
|
||||
"process_detail": ("BOOLEAN", {"default": False}), # 是否细节处理
|
||||
"device": (device_list,), # 运行设备
|
||||
"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}), # 最大处理分辨率
|
||||
},
|
||||
"optional": {
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK", )
|
||||
RETURN_NAMES = ("image", "mask", )
|
||||
FUNCTION = "birefnet_ultra_v2"
|
||||
CATEGORY = 'RemoveBackgroundSuite'
|
||||
|
||||
# 主推理流程
|
||||
def birefnet_ultra_v2(self, image, birefnet_model, detail_method, detail_erode, detail_dilate,
|
||||
black_point, white_point, process_detail, device, max_megapixels):
|
||||
ret_images = []
|
||||
ret_masks = []
|
||||
inference_image_size = (1024, 1024)
|
||||
if detail_method == 'VITMatte(local)':
|
||||
local_files_only = True
|
||||
else:
|
||||
local_files_only = False
|
||||
|
||||
torch.set_float32_matmul_precision(['high', 'highest'][0])
|
||||
birefnet_model.to(device)
|
||||
birefnet_model.eval()
|
||||
|
||||
comfy_pbar = ProgressBar(len(image))
|
||||
tqdm_pbar = tqdm.tqdm(total=len(image), desc="Processing BiRefNet")
|
||||
for i in image:
|
||||
i = torch.unsqueeze(i, 0)
|
||||
orig_image = tensor2pil(i).convert('RGB')
|
||||
|
||||
transform_image = transforms.Compose([
|
||||
transforms.Resize(inference_image_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||
])
|
||||
|
||||
inference_image = transform_image(orig_image).unsqueeze(0).to(device)
|
||||
|
||||
# 模型推理
|
||||
with torch.no_grad():
|
||||
preds = birefnet_model(inference_image)[-1].sigmoid().cpu()
|
||||
pred = preds[0].squeeze()
|
||||
pred_pil = transforms.ToPILImage()(pred)
|
||||
_mask = pred_pil.resize(inference_image_size)
|
||||
|
||||
resize_sampler = Image.BILINEAR
|
||||
_mask = _mask.resize(orig_image.size, resize_sampler)
|
||||
brightness_image = ImageEnhance.Brightness(_mask)
|
||||
_mask = brightness_image.enhance(factor=1.08)
|
||||
_mask = image2mask(_mask)
|
||||
|
||||
detail_range = detail_erode + detail_dilate
|
||||
|
||||
# 细节处理分支
|
||||
if process_detail:
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device, max_megapixels=max_megapixels)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
else:
|
||||
_mask = tensor2pil(_mask)
|
||||
|
||||
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
comfy_pbar.update(1)
|
||||
tqdm_pbar.update(1)
|
||||
|
||||
log(f"{self.NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
||||
|
||||
# 透明背景超强节点
|
||||
class TransparentBackgroundUltra_RBS:
|
||||
def __init__(self):
|
||||
self.NODE_NAME = 'TransparentBackgroundUltra_RBS'
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]
|
||||
device_list = ['cuda','cpu']
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",), # 输入图片
|
||||
"model": (list(scan_model().keys()),), # 选择模型
|
||||
"detail_method": (method_list,), # 细节处理方式
|
||||
"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
|
||||
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
|
||||
"process_detail": ("BOOLEAN", {"default": True}),
|
||||
"device": (device_list,),
|
||||
"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
|
||||
},
|
||||
"optional": {
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK", )
|
||||
RETURN_NAMES = ("image", "mask", )
|
||||
FUNCTION = "transparent_background_ultra"
|
||||
CATEGORY = 'RemoveBackgroundSuite'
|
||||
|
||||
# 主推理流程
|
||||
def transparent_background_ultra(self, image, model, detail_method, detail_erode, detail_dilate,
|
||||
black_point, white_point, process_detail, device, max_megapixels):
|
||||
|
||||
from transparent_background import Remover
|
||||
|
||||
ret_images = []
|
||||
ret_masks = []
|
||||
if detail_method == 'VITMatte(local)':
|
||||
local_files_only = True
|
||||
else:
|
||||
local_files_only = False
|
||||
model_dict = scan_model()
|
||||
try :
|
||||
mode = mode_dict[model]
|
||||
except :
|
||||
mode = "base"
|
||||
remover = Remover(mode=mode, jit=False, device=device, ckpt=model_dict[model])
|
||||
for i in image:
|
||||
i = torch.unsqueeze(i, 0)
|
||||
orig_image = tensor2pil(i).convert('RGB')
|
||||
ret_image = remover.process(orig_image, type='rgba')
|
||||
_mask = ret_image.split()[3]
|
||||
_mask = adjust_levels(_mask, 64, 192)
|
||||
|
||||
if process_detail:
|
||||
detail_range = detail_erode + detail_dilate
|
||||
_mask = pil2tensor(_mask)
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device, max_megapixels=max_megapixels)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
||||
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
||||
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
||||
|
||||
# 节点注册映射
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LoadBiRefNetModel_RBS": LoadBiRefNetModel_RBS,
|
||||
"LoadBiRefNetModelV2_RBS": LoadBiRefNetModelV2_RBS,
|
||||
"BiRefNetUltraV2_RBS": BiRefNetUltraV2_RBS,
|
||||
"TransparentBackgroundUltra_RBS": TransparentBackgroundUltra_RBS
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LoadBiRefNetModel_RBS": "Load BiRefNet Model (RBS)",
|
||||
"LoadBiRefNetModelV2_RBS": "Load BiRefNet Model V2 (RBS)",
|
||||
"BiRefNetUltraV2_RBS": "BiRefNet Ultra V2 (RBS)",
|
||||
"TransparentBackgroundUltra_RBS": "Transparent Background Ultra (RBS)"
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
torch>=2.0.0
|
||||
numpy>=1.24.0
|
||||
Pillow>=9.0.0
|
||||
torchvision>=0.15.0
|
||||
opencv-python>=4.8.0
|
||||
scipy>=1.10.0
|
||||
transformers>=4.30.0
|
||||
huggingface-hub>=0.16.0
|
||||
tqdm>=4.65.0
|
||||
Reference in New Issue
Block a user