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_srresnet.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS]
                        [--lr LR] [--step STEP] [--cuda] [--resume RESUME]
                        [--start-epoch START_EPOCH] [--threads THREADS]
                        [--pretrained PRETRAINED] [--vgg_loss] [--gpus GPUS]

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)
  --threads THREADS     Number of threads for data loader to use, Default: 1
  --pretrained PRETRAINED
                        path to pretrained model (default: none)
  --vgg_loss            Use content loss?
  --gpus GPUS           gpu ids (default: 0)

An example of training usage is shown as follows:

python main_srresnet.py --cuda --vgg_loss --gpus 0

demo

usage: demo.py [-h] [--device DEVICE] [--model MODEL] [--image IMAGE]
               [--dataset DATASET] [--scale SCALE]

optional arguments:
  -h, --help         show this help message and exit
  --device DEVICE    device to use, e.g. 'cpu', 'cuda' or 'cuda:0'
  --model MODEL      local model path (optional)
  --image IMAGE      image name
  --dataset DATASET  dataset name
  --scale SCALE      scale factor, Default: 4

We converted Set5 test set images to mat format using Matlab, for simple image reading. An example of usage is shown as follows:

python demo.py --dataset Set5 --image butterfly_GT --scale 4

Eval

usage: eval.py [-h] [--device DEVICE] [--model MODEL] [--dataset DATASET]
               [--scale SCALE]

optional arguments:
  -h, --help         show this help message and exit
  --device DEVICE    device to use, e.g. 'cpu', 'cuda' (default) or 'cuda:0'
  --model MODEL      local model path (optional)
  --dataset DATASET  dataset name, default: Set5
  --scale SCALE      scale factor, default: 4

We converted Set5 test set images to mat format using Matlab. An example of usage is shown as follows:

python eval.py --dataset Set5

Prepare Training dataset

• Download and extract testsets.tar.gz.
• 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
• Instance Normalization is applied instead of Batch Normalization for better performance
• So far performance in PSNR is not as good as paper, any suggestion is welcome

Dataset	SRResNet Paper	SRResNet PyTorch
Set5	32.05	31.80
Set14	28.49	28.25
BSD100	27.58	27.51

Result

From left to right are ground truth, bicubic and SRResNet.