basicsr 1.3.3.1

Open Source Image and Video Super-Resolution Toolbox

:rocket: BasicSR

English | 简体中文   GitHub | Gitee码云

Google Colab: GitHub Link | Google Drive Link
:m: Model Zoo :arrow_double_down: Google Drive: Pretrained Models | Reproduced Experiments :arrow_double_down: 百度网盘: 预训练模型 | 复现实验
:file_folder: Datasets :arrow_double_down: Google Drive :arrow_double_down: 百度网盘 (提取码:basr)
:chart_with_upwards_trend: Training curves in wandb
:computer: Commands for training and testing
:zap: HOWTOs

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BasicSR (Basic Super Restoration) is an open source image and video restoration toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, etc.
_{(ESRGAN, EDVR, DNI, SFTGAN) (HandyView, HandyFigure, HandyCrawler, HandyWriting)}

:sparkles: New Features

• Nov 29, 2020. Add ESRGAN and DFDNet colab demo.
• Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
• Aug 27, 2020. Add StyleGAN2 training and testing codes: StyleGAN2.

More

• Sep 8, 2020. Add blind face restoration inference codes: DFDNet.
  ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries
  Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang
• Aug 27, 2020. Add StyleGAN2 training and testing codes.
  CVPR20: Analyzing and Improving the Image Quality of StyleGAN
  Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila
• Aug 19, 2020. A brand-new BasicSR v1.0.0 online.

: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	Inference
Face Restoration
DFDNet	-	Inference
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 or Miniconda)
• PyTorch >= 1.7
• NVIDIA GPU + CUDA

Pip install

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

   git clone https://github.com/xinntao/BasicSR.git
   

2. Install dependent packages

   cd BasicSR
   pip install -r requirements.txt
   

3. Install BasicSR

   Please run the following commands in the BasicSR root path to install BasicSR:
   (Make sure that your GCC version: gcc >= 5)
   If you do need the cuda extensions:
    dcn for EDVR
    upfirdn2d and fused_act for StyleGAN2
   please add --cuda_ext when installing.
   If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary.

   python setup.py develop --cuda_ext
   

   Otherwise, install without compiling cuda extensions

   python setup.py develop
   

   You may also want to specify the CUDA paths:

   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 :-) (It is now only available for insider build with Fast ring).

:hourglass_flowing_sand: TODO List

Please see project boards.

:turtle: Dataset Preparation

• Please refer to DatasetPreparation.md for more details.
• The descriptions of currently supported datasets (torch.utils.data.Dataset classes) are in Datasets.md.

:computer: Train and Test

• Training and testing commands: Please see TrainTest.md for the basic usage.
• Options/Configs: Please refer to Config.md.
• Logging: Please refer to Logging.md.

:european_castle: Model Zoo and Baselines

• The descriptions of currently supported models are in Models.md.
• Pre-trained models and log examples are available in ModelZoo.md.
• We also provide training curves in wandb:

:memo: Codebase Designs and Conventions

Please see DesignConvention.md for the designs and conventions of the BasicSR codebase.
The figure below shows the overall framework. More descriptions for each component:
Datasets.md | Models.md | Config.md | Logging.md

:scroll: License and Acknowledgement

This project is released under the Apache 2.0 license.
More details about license and acknowledgement are in LICENSE.

:earth_asia: Citations

If BasicSR helps your research or work, please consider citing BasicSR.
The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@misc{wang2020basicsr,
  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
                  Chao Dong and Chen Change Loy},
  title =        {BasicSR},
  howpublished = {\url{https://github.com/xinntao/BasicSR}},
  year =         {2020}
}

Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.

:e-mail: Contact

If you have any question, please email [e-mail].