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 images can be downloaded at google_drive, 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 for creating training files.
• Data augmentations including flipping, rotation, downsizing are adopted.