fromutils.utils_modelsummaryimportget_model_activation,get_model_flopsinput_dim= (3,256,256)# set the input dimensionactivations,num_conv2d=get_model_activation(model,input_dim)logger.info('{:>16s} : {:<.4f} [M]'.format('#Activations',activations/10**6))logger.info('{:>16s} : {:<d}'.format('#Conv2d',num_conv2d))flops=get_model_flops(model,input_dim,False)logger.info('{:>16s} : {:<.4f} [G]'.format('FLOPs',flops/10**9))num_parameters=sum(map(lambdax:x.numel(),model.parameters()))logger.info('{:>16s} : {:<.4f} [M]'.format('#Params',num_parameters/10**6))

Clone repo

git clone https://github.com/cszn/KAIR.git

pip install -r requirement.txt

Training

pythonmain_train_psnr.py--optoptions/train_msrresnet_psnr.json

pythonmain_train_gan.py--optoptions/train_msrresnet_gan.json

python-mtorch.distributed.launch--nproc_per_node=4--master_port=1234main_train_psnr.py--optoptions/train_msrresnet_psnr.json--distTrue

python-mtorch.distributed.launch--nproc_per_node=8--master_port=1234main_train_psnr.py--optoptions/train_msrresnet_psnr.json--distTrue

python-mtorch.distributed.launch--nproc_per_node=4--master_port=1234main_train_gan.py--optoptions/train_msrresnet_gan.json--distTrue

python-mtorch.distributed.launch--nproc_per_node=8--master_port=1234main_train_gan.py--optoptions/train_msrresnet_gan.json--distTrue

kill $(psaux|grepmain_train_gan.py|grep-vgrep|awk'{print $2}')

Network architectures

Testing

model_zoo

trainsets

testsets

References

@inproceedings{zhu2023denoising, % DiffPIRtitle={Denoising Diffusion Models for Plug-and-Play Image Restoration},author={Yuanzhi Zhu and Kai Zhang and Jingyun Liang and Jiezhang Cao and Bihan Wen and Radu Timofte and Luc Van Gool},booktitle={IEEE Conference on Computer Vision and Pattern Recognition Workshops},year={2023}}@article{liang2022vrt,title={VRT: A Video Restoration Transformer},author={Liang, Jingyun and Cao, Jiezhang and Fan, Yuchen and Zhang, Kai and Ranjan, Rakesh and Li, Yawei and Timofte, Radu and Van Gool, Luc},journal={arXiv preprint arXiv:2022.00000},year={2022}}@inproceedings{liang2021swinir,title={SwinIR: Image Restoration Using Swin Transformer},author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},booktitle={IEEE International Conference on Computer Vision Workshops},pages={1833--1844},year={2021}}@inproceedings{zhang2021designing,title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},booktitle={IEEE International Conference on Computer Vision},pages={4791--4800},year={2021}}@article{zhang2021plug, % DPIR & DRUNet & IRCNNtitle={Plug-and-Play Image Restoration with Deep Denoiser Prior},author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},year={2021}}@inproceedings{zhang2020aim, % efficientSR_challengetitle={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},booktitle={European Conference on Computer Vision Workshops},year={2020}}@inproceedings{zhang2020deep, % USRNettitle={Deep unfolding network for image super-resolution},author={Zhang, Kai and Van Gool, Luc and Timofte, Radu},booktitle={IEEE Conference on Computer Vision and Pattern Recognition},pages={3217--3226},year={2020}}@article{zhang2017beyond, % DnCNNtitle={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising},author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},journal={IEEE Transactions on Image Processing},volume={26},number={7},pages={3142--3155},year={2017}}@inproceedings{zhang2017learning, % IRCNNtitle={Learning deep CNN denoiser prior for image restoration},author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},booktitle={IEEE conference on computer vision and pattern recognition},pages={3929--3938},year={2017}}@article{zhang2018ffdnet, % FFDNet, FDnCNNtitle={FFDNet: Toward a fast and flexible solution for CNN-based image denoising},author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},journal={IEEE Transactions on Image Processing},volume={27},number={9},pages={4608--4622},year={2018}}@inproceedings{zhang2018learning, % SRMDtitle={Learning a single convolutional super-resolution network for multiple degradations},author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},booktitle={IEEE Conference on Computer Vision and Pattern Recognition},pages={3262--3271},year={2018}}@inproceedings{zhang2019deep, % DPSRtitle={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},booktitle={IEEE Conference on Computer Vision and Pattern Recognition},pages={1671--1681},year={2019}}@InProceedings{wang2018esrgan, % ESRGAN, MSRResNetauthor ={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},title ={ESRGAN: Enhanced super-resolution generative adversarial networks},booktitle ={The European Conference on Computer Vision Workshops (ECCVW)},month ={September},year ={2018}}@inproceedings{hui2019lightweight, % IMDNtitle={Lightweight Image Super-Resolution with Information Multi-distillation Network},author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},booktitle={Proceedings of the 27th ACM International Conference on Multimedia (ACM MM)},pages={2024--2032},year={2019}}@inproceedings{zhang2019aim, % IMDNtitle={AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results},author={Kai Zhang and Shuhang Gu and Radu Timofte and others},booktitle={IEEE International Conference on Computer Vision Workshops},year={2019}}@inproceedings{yang2021gan,title={GAN Prior Embedded Network for Blind Face Restoration in the Wild},author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang},booktitle={IEEE Conference on Computer Vision and Pattern Recognition},year={2021}}