pragneshbarik / svnit_ntnu_sisr

Submission for NTIRE SISR

NTIRE 2023 Challenge on Image Super-Resolution (x4) @ CVPR 2023

How to test the baseline model?

1. git clone https://github.com/zhengchen1999/NTIRE2023_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 CAT. The code and pretrained models of the three models are provided. 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_cat.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, and activations

    from utils.model_summary import get_model_flops, get_model_activation
    from models.team00_RFDN import RFDN
    model = RFDN()
    
    input_dim = (3, 256, 256)  # set the input dimension
    activations, num_conv = get_model_activation(model, input_dim)
    activations = activations / 10 ** 6
    print("{:>16s} : {:<.4f} [M]".format("#Activations", activations))
    print("{:>16s} : {:<d}".format("#Conv2d", num_conv))

    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.