/NTIRE2024_ImageSR_x4_LVGroup_HFUT

LVGroup's solution of the NTIRE 2024 Image Super-Resolution x4 Challenge

Primary LanguagePythonMIT LicenseMIT

NTIRE 2024 Challenge on Image Super-Resolution (x4) @ CVPR 2024

How to test the baseline model?

  1. git clone https://github.com/zhengchen1999/NTIRE2024_ImageSR_x4.git
  2. Select the model you would like to test from run.sh 
    CUDA_VISIBLE_DEVICES=0 python test_demo.py --data_dir [path to your data dir] --save_dir [path to your save dir] --model_id 0
    • Be sure the change the directories --data_dir and --save_dir.
    • We provide three baselines (team00): RFDN (default), SwinIR, and DAT. The code and pretrained models of the three models are provided. Switch models (default is DAT) through commenting the code in test_demo.py. Three baselines are all test normally with run.sh.

How to add your model to this baseline?

  1. Register your team in the Google Spreadsheet and get your team ID.
  2. Put your the code of your model in ./models/[Your_Team_ID]_[Your_Model_Name].py
    • Please add only one file in the folder ./models. Please do not add other submodules.
    • Please zero pad [Your_Team_ID] into two digits: e.g. 00, 01, 02
  3. Put the pretrained model in ./model_zoo/[Your_Team_ID]_[Your_Model_Name].[pth or pt or ckpt]
    • Please zero pad [Your_Team_ID] into two digits: e.g. 00, 01, 02
    • Note: Please provide a download link for the pretrained model, if the file size exceeds 100 MB. Put the link in ./model_zoo/[Your_Team_ID]_[Your_Model_Name].txt: e.g. team00_dat.txt
  4. Add your model to the model loader ./test_demo/select_model as follows: 
        elif model_id == [Your_Team_ID]:
        # define your model and load the checkpoint
    • Note: Please set the correct data_range, either 255.0 or 1.0
  5. Send us the command to download your code, e.g,
    • git clone [Your repository link]
    • We will do the following steps to add your code and model checkpoint to the repository.

How to calculate the number of parameters, FLOPs

    from utils.model_summary import get_model_flops
    from models.team00_DAT import DAT
    model = DAT()
    
    input_dim = (3, 256, 256)  # set the input dimension
    flops = get_model_flops(model, input_dim, False)
    flops = flops / 10 ** 9
    print("{:>16s} : {:<.4f} [G]".format("FLOPs", flops))

    num_parameters = sum(map(lambda x: x.numel(), model.parameters()))
    num_parameters = num_parameters / 10 ** 6
    print("{:>16s} : {:<.4f} [M]".format("#Params", num_parameters))

License and Acknowledgement

This code repository is release under MIT License.