# DRRN-pytorch

**Repository Path**: ctgulrh/DRRN-pytorch

## Basic Information

- **Project Name**: DRRN-pytorch
- **Description**: github上的超分辨率项目
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-23
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DRRN-pytorch
This is an unofficial implementation of "Deep Recursive Residual Network for Super Resolution (DRRN)", CVPR 2017 in Pytorch. [[Paper]](http://cvlab.cse.msu.edu/pdfs/Tai_Yang_Liu_CVPR2017.pdf) 

You can get the official Caffe implementation [here](https://github.com/tyshiwo/DRRN_CVPR17).

## 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           Training batch size
  --nEpochs             Number of epochs to train for
  --lr                  Learning rate. Default=0.1
  --step                Learning rate decay, Default: n=5 epochs
  --cuda                Use cuda?
  --resume              Path to checkpoint
  --clip                Clipping Gradients. Default=0.01
  --threads             Number of threads for data loader to use Default=1
  --momentum            Momentum, Default: 0.9
  --weight-decay        Weight decay, Default: 1e-4
  --pretrained          Path to the pretrained model, used for weight initialization (default: none)
```

### Evaluation
```
usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
               [--scale SCALE]

PyTorch DRRN Evaluation

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5
```
An example of training usage is shown as follows:
```
python eval.py --cuda
```

### Prepare Training dataset
  - the training data is generated with Matlab Bicubic Interpolation, please refer [Code for Data Generation](/data/generate_trainingset_x234.m) for creating training files.
  
### Performance
  - We provide a ***rough*** pre-trained DRRN_B1U25 [model](/model) trained on [291](/data/Train_291) images with data augmentation. The model can achieve a better performance with a smart optimization strategy. For the DRRN_B1U9 implementation, you can manually modify the number of recursive blocks [here](/drrn.py#L26:18).
  - The same adjustable gradient clipping's implementation as original paper.
  - No bias is used in this implementation.
  - No batch normalization is used in this implementation.
  - Performance in PSNR on Set5 
  
| Scale   | DRRN_B1U25 Paper | DRRN_B1U25 PyTorch|
| -------:| ----------------:| -----------------:|
| x2      | 37.74            | 37.69             |
| x3      | 34.03            | 34.02             |
| x4      | 31.68            | 31.70             |