Awesome

PyTorch VDSR

Implementation of CVPR2016 Paper: "Accurate Image Super-Resolution Using Very Deep Convolutional Networks"(http://cv.snu.ac.kr/research/VDSR/) in PyTorch

Usage

Training

usage: main_vdsr.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] [--gpus GPUS]
               
optional arguments:
  -h, --help            Show this help message and exit
  --batchSize           Training batch size
  --nEpochs             Number of epochs to train for
  --lr                  Learning rate. Default=0.01
  --step                Learning rate decay, Default: n=10 epochs
  --cuda                Use cuda
  --resume              Path to checkpoint
  --clip                Clipping Gradients. Default=0.4
  --threads             Number of threads for data loader to use Default=1
  --momentum            Momentum, Default: 0.9
  --weight-decay        Weight decay, Default: 1e-4
  --pretrained PRETRAINED
                        path to pretrained model (default: none)
  --gpus GPUS           gpu ids (default: 0)

An example of training usage is shown as follows:

python main_vdsr.py --cuda --gpus 0

Evaluation

usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
               [--scale SCALE] [--gpus GPUS]

PyTorch VDSR Eval

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5
  --gpus GPUS        gpu ids (default: 0)

An example of training usage is shown as follows:

python eval.py --cuda --dataset Set5

Demo

usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE] [--gpus GPUS]
               
optional arguments:
  -h, --help            Show this help message and exit
  --cuda                Use cuda
  --model               Model path. Default=model/model_epoch_50.pth
  --image               Image name. Default=butterfly_GT
  --scale               Scale factor, Default: 4
  --gpus GPUS           gpu ids (default: 0)

An example of usage is shown as follows:

python eval.py --model model/model_epoch_50.pth --dataset Set5 --cuda

Prepare Training dataset

• We provide a simple hdf5 format training sample in data folder with 'data' and 'label' keys, the training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.

Performance

• We provide a pretrained VDSR model trained on 291 images with data augmentation
• No bias is used in this implementation, and the gradient clipping's implementation is different from paper
• Performance in PSNR on Set5

Result

From left to right are ground truth, bicubic and vdsr