[python 3.8]
[Ubuntu 20.04]
PyTorch 1.13.0, Torchvision 0.14.0, Cuda 11.7
git clone https://github.com/Supereeeee/MSDAN.git
pip install -r requirements.txt
python setup.py develop
· Refer to ./options/test for the configuration file of the model to be tested and prepare the testing data.
· The pre-trained models have been palced in ./experiments/pretrained_models/
· Then run the follwing codes (taking MSDAN_x4.pth as an example):
python basicsr/test.py -opt options/test/test_MSDAN_x4.yml
The testing results will be saved in the ./results folder.
· Refer to ./options/train for the configuration file of the model to train.
· Preparation of training data can refer to this page. All datasets can be downloaded at the official website.
· Note that the default training dataset is based on lmdb, refer to docs in BasicSR to learn how to generate the training datasets.
· The training command is like
python basicsr/train.py -opt options/train/train_MSDAN_x4.yml
For more training commands and details, please check the docs in BasicSR
· The network structure of MSDAN is palced at ./basicsr/archs/MSDAN_arch.py
· We adopt thop tool to calculate model complexity, see ./basicsr/archs/model_complexity.py
· We test the inference time on multiple benchmark datasets on a 140W fully powered 3060 laptop.
· You can run ./inference/inference_MSDAN.py on your decive.
This code is based on BasicSR toolbox. Thanks for the awesome work.
If you have any question, please email 1051823707@qq.com.