SrSENet.pytorch

Implement of our paper "Single Image Super-Resolution UsingSqueeze-and-Excitation Networks"

Usage

Prepare

For more fair comparison with the state-of-art method using MATLAB, we use same script to do datasets generate and then export them with HDF5 file. So the model in PyTorch could receive same date as previous method done.

• For training, download DIV2K dataset and place the folder into prepare. Then run the generate_train.m using MATLAB. A big file in train.h5 will appear after it down.

• For testing, download Testing datasets, and change the variable folder to the place where test datasets in. Then run it in Matlab. A folder named testdatasets will appear.

• For real time loss monitor, we use tensorboardX

Training

usage: train.py [-h] [--batchSize BATCHSIZE] [--blocks BLOCKS]
                [--nEpochs NEPOCHS] [--lr LR] [--step STEP] [--cuda]
                [--resume RESUME] [--start-epoch START_EPOCH]
                [--threads THREADS] [--momentum MOMENTUM]
                [--weight-decay WEIGHT_DECAY] [--pretrained PRETRAINED]

PyTorch SrSENet

optional arguments:
  -h, --help            show this help message and exit
  --batchSize BATCHSIZE
                        training batch size
  --blocks BLOCKS       Blocks nums of SrSEBlock
  --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)

Testing

PyTorch SrSENet

optional arguments:
  -h, --help            show this help message and exit
  --checkpoint CHECKPOINT
                        path to load model checkpoint
  --test TEST           path to load test images

Performance

Datasets	VDSR	LapSRN	SrSENet
BSDS100(x8)	24.37dB	24.54dB	24.59dB
Urban100(x8)	21.54dB	21.81dB	21.88dB
Manga109(x8)	22.83dB	23.39dB	23.54dB

Result

VDSR	LapSRN	SrSENet

References

https://github.com/twtygqyy/pytorch-SRResNet

https://github.com/moskomule/senet.pytorch