NTIRE 2022 Workshop and Challenge @ CVPR 2022

Efficient Super-Resolution Challenge

Jointly with NTIRE workshop we have a challenge on Efficient Super-Resolution, that is, the task of super-resolving (increasing the resolution) an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The challenge has three tracks.

Track 1: Inference Runtime, the aim is to obtain a network design / solution with the lowest inference time (runtime) on a common GPU (ie. Titan Xp) while being constrained to maintain or improve over IMDN (Hui et al, 2019) in terms of number of parameters and the PSNR result.

Track 2: Model Complexity (Parameters and FLOPs), the aim is to obtain a network design / solution with the lowest amount of parameters and FLOPs while being constrained to maintain or improve the PSNR result and the inference time (runtime) of IMDN (Hui et al, 2019).

Track 3: Overall Performance (Runtime, Parameters, FLOPs, Activation, Memory), the aim is to obtain a network design / solution with the best overall performance in terms of number of parameters, FLOPS, activations, and inference time and GPU memory on a common GPU (ie. Titan Xp).

Baseline model (IMDN)

• Number of parameters: 893,936 (0.89M)

  number_parameters = sum(map(lambda x: x.numel(), model.parameters()))

• Average PSNR on validation data: 29.13 dB

• Average inference time (Titan Xp) on validation data: 0.049 second

Run test_demo.py to test the model

How to use the code during test phase.

1. git clone https://github.com/ofsoundof/IMDN
2. Put your model script under the models folder.
3. Put your pretrained model under the model_zoo folder.
4. Modify model_path in test_demo.py. Modify the imported models.
5. python test_demo.py

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

    from utils.model_summary import get_model_flops, get_model_activation

    input_dim = (3, 256, 256)  # set the input dimension
    activations, num_conv = get_model_activation(model, input_dim)
    activations = activations / 10 ** 6
    logger.info("{:>16s} : {:<.4f} [M]".format("#Activations", activations))
    logger.info("{:>16s} : {:<d}".format("#Conv2d", num_conv))

    flops = get_model_flops(model, input_dim, False)
    flops = flops / 10 ** 9
    logger.info("{:>16s} : {:<.4f} [G]".format("FLOPs", flops))

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