PyTorch SRResNet

Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs/1609.04802) in PyTorch

Usage

Training

usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
               [--step STEP] [--cuda] [--resume RESUME]
               [--start-epoch START_EPOCH] [--clip CLIP] [--threads THREADS]
               [--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
               [--pretrained PRETRAINED]
               
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=500
  --cuda                Use cuda?
  --resume RESUME       Path to checkpoint (default: none)
  --start-epoch START_EPOCH
                        Manual epoch number (useful on restarts)
  --clip CLIP           Clipping Gradients. Default=0.1
  --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: 0
  --pretrained PRETRAINED
                        path to pretrained model (default: none)

Test

usage: test.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE]

PyTorch SRResNet 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_epoch_415.pth --image butterfly_GT --scale 4 --cuda

Prepare Training dataset

Please refer Code for Data Generation for creating training files.
Data augmentations including flipping, rotation, downsizing are adopted.

Performance

We provide a pretrained model trained on 291 images with data augmentation
So far performance in PSNR is not as good as paper, not even comparable. Any suggestion is welcome

Dataset	SRResNet Paper	SRResNet PyTorch
Set5	32.05	30.84
Set14	28.49	27.71
BSD100	27.58	26.21

Result

From left to right are ground truth, bicubic and SRResNet

mchong6/pytorch-SRResNet