图像超分辨率算法EDSR的Pytorch版本复现代码，适合新手，修改后无bug，含制作好的h5数据集和最优PSNR的模型权重文件

# PyTorch EDSR Implementation of CVPR2017 workshop Paper: "Enhanced Deep Residual Networks for Single Image Super-Resolution"(https://arxiv.org/pdf/1707.02921.pdf) in PyTorch ## Usage ### Training ``` usage: main_edsr.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR] [--step STEP] [--cuda] [--resume RESUME] [--start-epoch START_EPOCH] [--threads THREADS] [--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY] optional arguments: -h, --help show this help message and exit --batchSize BATCHSIZE training batch size --nEpochs NEPOCHS number of epochs to train for --lr LR Learning Rate. Default=1e-4 --step STEP Sets the learning rate to the initial LR decayed by momentum every n epochs, Default: n=10 --cuda use cuda? --resume RESUME path to latest checkpoint (default: none) --start-epoch START_EPOCH manual epoch number (useful on restarts) --threads THREADS number of threads for data loader to use --momentum MOMENTUM momentum --weight-decay WEIGHT_DECAY, --wd WEIGHT_DECAY weight decay, Default: 0 ``` ### Test ``` usage: test.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE] PyTorch EDSR Test optional arguments: -h, --help show this help message and exit --cuda use cuda? --model MODEL model path --image IMAGE image name --scale SCALE scale factor, Default: 4 ``` - We convert Set5 test set images to mat format using Matlab, for best PSNR performance, please use Matlab - An example of usage is shown as follows: ``` python test.py --model model/model_edsr.pth --image butterfly_GT --scale 4 --cuda ``` ### Evaluation ``` usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET] [--scale SCALE] PyTorch ByNet Eval optional arguments: -h, --help Show this help message and exit --cuda use cuda? --model MODEL Model path. Default=model/model_epoch_40.pth --dataset DATASET Dataset name, Default: Set5 ``` - An trained model on [291](https://drive.google.com/open?id=1Rt3asDLuMgLuJvPA1YrhyjWhb97Ly742) images can be downloaded at [google_drive](https://drive.google.com/file/d/1DsvRuKMe91Rxhy7_r6t9mrZeMxARa-fT/view?usp=shar5ng), which could achieve 31.94dB PNSR on Set5 dataset. - An example of training usage is shown as follows: ``` python eval.py --cuda --model model/model_edsr.pth --dataset Set5 ``` ### Prepare Training dataset - Please refer [Code for Data Generation](https://github.com/twtygqyy/pytorch-edsr/tree/master/data) for creating training files. - Data augmentations including flipping, rotation, downsizing are adopted.