README.md

NTIRE2023-ESR-RepRFN

Code, pre-train model and factsheet of RepRFN for NTIRE 2023 Challenge on Efficient Super-Resolution. Our team ID is 7.

How to test the model?

1. git clone https://github.com/laonafahaodange/NTIRE2023-ESR-RepRFN.git
2. Set the --model_id 0 / --model_id 7 to test the baseline / RepRFN model 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

   CUDA_VISIBLE_DEVICES=0 python test_demo.py --data_dir [path to your data dir] --save_dir [path to your save dir] --model_id 7

   • Be sure the change the directories --data_dir and --save_dir.

How to calculate the number of parameters, FLOPs, and activations

Code similar to the following is used.

    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))

Specifically you can run the models/team07_RepRFN.py to get the result, remember to uncomment the code.

Thank all organizers for their efforts!