图像超分辨率算法LapSRN的Pytorch版本复现代码，适合新手入门，修改后无bug，含最优PSNR的模型权重文件

# PyTorch LapSRN Implementation of CVPR2017 Paper: "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution"(http://vllab.ucmerced.edu/wlai24/LapSRN/) in PyTorch ## Usage ### Training ``` usage: main.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] [--pretrained PRETRAINED] PyTorch LapSRN 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 checkpoint (default: none) --start-epoch START_EPOCH Manual epoch number (useful on restarts) --threads THREADS Number of threads for data loader to use, Default: 1 --momentum MOMENTUM Momentum, Default: 0.9 --weight-decay WEIGHT_DECAY, --wd WEIGHT_DECAY weight decay, Default: 1e-4 --pretrained PRETRAINED path to pretrained model (default: none) ``` An example of training usage is shown as follows: ``` python main_lapsrn.py --cuda ``` ### Evaluation ``` usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET] [--scale SCALE] PyTorch LapSRN Eval optional arguments: -h, --help show this help message and exit --cuda use cuda? --model MODEL model path --dataset DATASET dataset name, Default: Set5 --scale SCALE scale factor, Default: 4 ``` ### Demo ``` usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE] PyTorch LapSRN Demo 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 ### Prepare Training dataset - We provide a simple hdf5 format training sample in data folder with 'data', 'label_x2', and 'label_x4' keys, the training data is generated with Matlab Bicubic Interplotation, please refer [Code for Data Generation](https://github.com/twtygqyy/pytorch-LapSRN/tree/master/data) for creating training files. ### Performance - We provide a pretrained LapSRN x4 model trained on T91 and BSDS200 images from [SR_training_datasets](http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_testing_datasets.zip) with data augmentation as mentioned in the paper - No bias is used in this implementation, and another difference from paper is that Adam optimizer with 1e-4 learning is applied instead of SGD - Performance in PSNR on Set5, Set14, and BSD100 | DataSet/Method | LapSRN Paper | LapSRN PyTorch| | ------------- |:-------------:| -----:| | Set5 | 31.54 | **31.65** | | Set14 | 28.19 | **28.27** | | BSD100 | 27.32 | **27.36** | ### ToDos - LapSRN x8 - LapGAN Evaluation ### Citation If you find the code and datasets useful in your research, please cite: @inproceedings{LapSRN, author = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}, title = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution}, booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition}, year = {2017} }