MemNet-pytorch

An implementation of MemNet: A Persistent Memory Network for Image Restoration, ICCV2017

This is an unofficial implementation of "MemNet: A Persistent Memory Network for Image Restoration (MemNet)", ICCV 2017 in Pytorch. [Paper]

You can get the official Caffe implementation here.

Usage

Training

usage: train_memnet.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]

PyTorch MemNet

optional arguments:
  -h, --help            show this help message and exit
  --batchSize BATCHSIZE
                        Training batch size
  --nEpochs NEPOCHS     Number of epochs to train
  --lr LR               the initial learning rate is 0.1
  --step STEP           Sets the learning rate is divided 10 every 20 epochs
  --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=0.4
  --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)
  --gpus GPUS           gpu ids (default: 0,1,2,3)

Evaluation

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

PyTorch MemNet 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

Demo

usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE]
               [--gpus GPUS]

PyTorch Memnet Demo

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
  --gpus GPUS    gpu ids (default: 0)

An example of training usage is shown as follows:

python demo.py --cuda

Prepare Training dataset

the training data is generated with Matlab Bicubic Interpolation, please refer Code for Data Generation for creating training files train_291_31_x234.h5.

Performance

We provide a pre-trained MemNet_M6R6 model trained on 291 images with data augmentation. For the MemNet_M6R6 implementation, you can manually modify the number of Memory blocks and Residual Blocks here.
Performance in PSNR on Set5 (train: 50 epochs.)

| Scale |Bicubic | MemNet(M6R6) Paper | MemNet(M6R6) PyTorch| | -------:| ----------:|-------------------:| -------------------:| | x2 | 33.66 |37.78 | 36.83 | | x3 | 30.39 |34.09 | 33.26 | | x4 | 28.42 |31.74 | 30.95 |

Note:

This implementation is modified from the implementation of VDSR by @Jiu XU.
I use the default initialization methods for layers, but author uses "msra" on Paper, which results in performance difference.

MemNet

Install / Use

README