SPCT

SPCT: Soft Pattern Coding Transformer for Lightweight Image Super-Resolution

Qian Wang, Ruilong Guo, Yanyu Mao, Yao Tang and Jiulun Fan

💻Environment

• PyTorch >= 1.9
• Python 3.7
• Numpy
• BasicSR >= 1.3.4.9

🔧Installation

pip install -r requirements.txt

📜Data Preparation

The trainset uses the DIV2K (800). In order to effectively improve the training speed, images are cropped to 480 * 480 images by running script extract_subimages.py, and the dataloader will further randomly crop the images to the GT_size required for training. GT_size defaults to 128/192/256 (×2/×3/×4).

python extract_subimages.py

The input and output paths of cropped pictures can be modify in this script. Default location: ./datasets/DIV2K.

🚀Train

▶️ You can change the training strategy by modifying the configuration file. The default configuration files are included in ./options/train/SPCT. Take one GPU as the example.

### Train ###
### SPCT ###
python train.py -opt ./options/train/SPCT/train_SPCT_x2.yml --auto_resume  # ×2
python train.py -opt ./options/train/SPCT/train_SPCT_x3.yml --auto_resume  # ×3
python train.py -opt ./options/train/SPCT/train_SPCT_x4.yml --auto_resume  # ×4
python train.py -opt ./options/train/SPCT/train_SPCT_Tiny_x2.yml --auto_resume  # ×2
python train.py -opt ./options/train/SPCT/train_SPCT_Tiny_x3.yml --auto_resume  # ×3
python train.py -opt ./options/train/SPCT/train_SPCT_Tiny_x4.yml --auto_resume  # ×4

For more training commands, please check the docs in BasicSR.

🚀Test

▶️ You can modify the configuration file about the test, which is located in ./options/test/SPCT. At the same time, you can change the benchmark datasets and modify the path of the pre-train model.

▶️ We will publish all SPCT pre-trained models.

### Test ###
### SPCT for Lightweight Image Super-Resolution ###
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_x2.yml  # ×2
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_x3.yml  # ×3
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_x4.yml  # ×4
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_Tiny_x2.yml  # ×2
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_Tiny_x3.yml  # ×3
python basicsr/test.py -opt ./options/test/SPCT/test_SPCT_Tiny_x4.yml  # ×4

🚩Results

The inference results on benchmark datasets will be available at Google Drive.

📫Contact

If you have any questions, please feel free to contact us [email protected], [email protected] and [email protected].