pyiqa.archs.hypernet_arch

HyperNet Metric

@InProceedings{hyperiqa,

author = {Su, Shaolin and Yan, Qingsen and Zhu, Yu and Zhang, Cheng and Ge, Xin and Sun, Jinqiu and Zhang, Yanning}, title = {Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020}

}

Ref url: https://github.com/SSL92/hyperIQA Re-implemented by: Chaofeng Chen (https://github.com/chaofengc)

Module Contents

pyiqa.archs.hypernet_arch.default_model_urls

class pyiqa.archs.hypernet_arch.HyperNet(base_model_name='resnet50', num_crop=25, pretrained=True, pretrained_model_path=None, default_mean=[0.485, 0.456, 0.406], default_std=[0.229, 0.224, 0.225])

Bases: torch.nn.Module

HyperNet Model. :param - base_model_name: pretrained model to extract features, :type - base_model_name: String :param can be any models supported by timm. Default: resnet50. :param - pretrained_model_path: Pretrained model path. :type - pretrained_model_path: String :param - default_mean: Default mean value. :type - default_mean: list :param - default_std: Default std value. :type - default_std: list

Reference:

Su, Shaolin, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. “Blindly assess image quality in the wild guided by a self-adaptive hyper network.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3667-3676. 2020.

preprocess(x)

forward_patch(x)

forward(x)

HYPERNET model. :param x: A distortion tensor. Shape \((N, C, H, W)\).