SRNet

• institution：JMAI
• created by：fuchenli
• date：2023.11.22
• E-mail：[email protected]

This work creates a deep learning framework for low-level vision, using super-resolution networks as an example.

It is easy to change all different network for train,test,inference and so on.

The main network structure is shown in the figure, with the resnet series, unet series, and their improvements all under the codes path.

Configuration

‘’‘ pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple ‘’‘

test

• Modify dataroot_LQ and pretrain_model_G (you can also use the pretrained model which is provided in the ./pretrained_model) in ./codes/options/test/test_net.yml, then run python test.py

train

• Prepare the data. Modify input_folder and save_folder in ./scripts/extract_subimgs_single.py, then run

cd scripts
python extract_subimgs_single.py

This process is quite resource intensive, please make sure there are enough disks to carry it.

• Modify dataroot_LQ and dataroot_GT in ./codes/options/train/train_net.yml, then run

cd codes
python train.py -opt options/train/train_net.yml

The models and training states will be saved to ./experiments/name.

Acknowledgment

The code is inspired by BasicSR.