BasicSR:用于超分辨率，去噪，去模糊等的开源图像和视频恢复工具箱。当前，它包括EDSR，RCAN，SRResNet，SRG...

# :rocket: BasicSR [English](README.md) **|** [简体中文](README_CN.md) &emsp; [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR) <a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br> :m: [Model Zoo](docs/ModelZoo.md) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) <br> :file_folder: [Datasets](docs/DatasetPreparation.md) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)<br> :chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) <br> :computer: [Commands for training and testing](docs/TrainTest.md) <br> :zap: [HOWTOs](#zap-howtos) --- BasicSR (**Basic** **S**uper **R**estoration) is an open source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.<br> _{([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))} _{([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/HandyWriting))} ## :sparkles: New Features - Nov 29, 2020. Add **ESRGAN** and **DFDNet** [colab demo](colab). - Sep 8, 2020. Add **blind face restoration** inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet). - Aug 27, 2020. Add **StyleGAN2 training and testing** codes: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch). <details> <summary>More</summary> <ul> <li> Sep 8, 2020. Add <b>blind face restoration</b> inference codes: <b>DFDNet</b>. <br> <i><font color="#DCDCDC">ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries</font></i> <br> <i><font color="#DCDCDC">Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang</font></i> </li> <li> Aug 27, 2020. Add <b>StyleGAN2</b> training and testing codes. <br> <i><font color="#DCDCDC">CVPR20: Analyzing and Improving the Image Quality of StyleGAN</font></i> <br> <i><font color="#DCDCDC">Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila</font></i> </li> <li>Aug 19, 2020. A <b>brand-new</b> BasicSR v1.0.0 online.</li> </ul> </details> ## :zap: HOWTOs We provides simple pipelines to train/test/inference models for quick start. These pipelines/commands cannot cover all the cases and more details are in the following sections. | GAN | | | | | | | :--- | :---: | :---: | :--- | :---: | :---: | | StyleGAN2 | [Train](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | | | **Face Restoration** | | | | | | | DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | | | **Super Resolution** | | | | | | | ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO*| | EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO*| | RCAN | *TODO* | *TODO* | | | | | EDVR | *TODO* | *TODO* | DUF | - | *TODO* | | BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* | | **Deblurring** | | | | | | | DeblurGANv2 | - | *TODO* | | | | | **Denoise** | | | | | | | RIDNet | - | *TODO* | CBDNet | - | *TODO*| ## :wrench: Dependencies and Installation - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) - [PyTorch >= 1.3](https://pytorch.org/) - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) ### Pip install ```bash pip install basicsr ``` - pip installation does not compile cuda extensions. - If you want to use cuda extensions, set environment variable `BASICSR_JIT=True`. Note that every time you run the model, it will compile the extensions just time. - Example: StyleGAN2 inference colab. ### Git clone and compile 1. Clone repo ```bash git clone https://github.com/xinntao/BasicSR.git ``` 1. Install dependent packages ```bash cd BasicSR pip install -r requirements.txt ``` 1. Install BasicSR Please run the following commands in the **BasicSR root path** to install BasicSR:<br> (Make sure that your GCC version: gcc >= 5) <br> If you do need the cuda extensions: <br> &emsp;[*dcn* for EDVR](basicsr/ops)<br> &emsp;[*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/ops)<br> please add `--cuda_ext` when installing.<br> If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary. ```bash python setup.py develop --cuda_ext ``` Otherwise, install without compiling cuda extensions ```bash python setup.py develop ``` You may also want to specify the CUDA paths: ```bash CUDA_HOME=/usr/local/cuda \ CUDNN_INCLUDE_DIR=/usr/local/cuda \ CUDNN_LIB_DIR=/usr/local/cuda \ python setup.py develop ``` Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try [Windows WSL with CUDA supports](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) :-) (It is now only available for insider build with Fast ring). ## :hourglass_flowing_sand: TODO List Please see [project boards](https://github.com/xinntao/BasicSR/projects). ## :turtle: Dataset Preparation - Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details. - The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md). ## :computer: Train and Test - **Training and testing commands**: Please see **[TrainTest.md](docs/TrainTest.md)** for the basic usage. - **Options/Configs**: Please refer to [Config.md](docs/Config.md). - **Logging**: Please refer to [Logging.md](docs/Logging.md). ## :european_castle: Model Zoo and Baselines - The descriptions of currently supported models are in [Models.md](docs/Models.md). - **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**. - We also provide **training curves** in [wandb](https://app.wandb.ai/xintao/basicsr): <p align="center"> <a href="https://app.wandb.ai/xintao/basicsr" target="_blank"> <img src="./assets/wandb.jpg" height="280"> </a></p> ## :memo: Codebase Designs and Conventions Please see [DesignConvention.md](docs/DesignConvention.md) for the designs and conventions of the BasicSR codebase.<br> The figure below shows the overall framework. More descriptions for each component: <br> **[Datasets.md](docs/Datasets.md)**&emsp;|&emsp;**[Models.md](docs/Models.md)**&emsp;|&emsp;**[Config.md](Config.md)**&emsp;|&emsp;**[Logging.md](docs/Logging.md)**  ## :scroll: License and Acknowledgement This project is released under the Apache 2.0 license.<br> More details about **license** and **acknowledgement** are in [LICENSE](LICENSE/README.md). ## :earth_asia: Citations If BasicSR helps your research or work, please consider citing BasicSR.<br> The following is a BibTeX reference.