WDSR

This repository is implementation of the "Wide Activation for Efficient and Accurate Image Super-Resolution".

参考：https://github.com/yjn870/WDSR-pytorch

Requirements

• paddlepaddle 2.4.0
• Numpy 1.15.4
• Pillow-SIMD 5.3.0.post1
• h5py 2.8.0
• tqdm 4.30.0

Prepare dataset

To prepare dataset used in experiments, first download dataset files from this link and organize it as shown below.

/YOUR_STORAGE_PATH/DIV2K
├── DIV2K_train_HR
├── DIV2K_train_LR_bicubic
│   └── X2
│   └── X3
│   └── X4
├── DIV2K_valid_HR
├── DIV2K_valid_LR_bicubic
│   └── X2
│   └── X3
│   └── X4
├── DIV2K_train_HR.zip
├── DIV2K_train_LR_bicubic_X2.zip
├── DIV2K_train_LR_bicubic_X3.zip
├── DIV2K_train_LR_bicubic_X4.zip
├── DIV2K_valid_HR.zip
├── DIV2K_valid_LR_bicubic_X2.zip
├── DIV2K_valid_LR_bicubic_X3.zip
└── DIV2K_valid_LR_bicubic_X4.zip

By default, we use "0001-0800.png" images to train the model and "0801-0900.png" images to validate the training. All experiments also use images with BICUBIC degradation on RGB space.

Training

WDSR Baseline (=WDSR-A) Example

python train.py --dataset-dir "/root/autodl-tmp/paddle_SR/SR/DATA/DIV2K" \
                --output-dir "/root/autodl-tmp/paddle_SR/SR/WDSR/outputs_a" \
                --model "WDSR-A" \
                --scale 2 \
                --n-feats 32 \
                --n-res-blocks 16 \
                --expansion-ratio 4 \
                --res-scale 1.0 \
                --lr 1e-3

If you want to modify more options, see the core/option.py file.

Evaluation

Trained model is evaluated on DIV2K validation 100 images. Pre-trained Model can be found in outputs_a

WDSR-A
python eval.py --model "WDSR-A" \
               --dataset-dir "/root/autodl-tmp/paddle_SR/SR/DATA/DIV2K" \
               --checkpoint-file "/root/autodl-tmp/paddle_SR/SR/WDSR/outputs_a/WDSR-A-f32-b16-r4-x2-best.pdiparams.tar"
               

Model	Scale	Residual Blocks	Parameters	PSNR
WDSR	x2	16	1.19M	34.36 dB