pyiqa.archs.topiq_arch

TOP-IQ metric, proposed by

TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment. Chaofeng Chen, Jiadi Mo, Jingwen Hou, Haoning Wu, Liang Liao, Wenxiu Sun, Qiong Yan, Weisi Lin. Transactions on Image Processing, 2024.

Paper link: https://arxiv.org/abs/2308.03060

Module Contents

pyiqa.archs.topiq_arch.default_model_urls

class pyiqa.archs.topiq_arch.TransformerEncoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu', normalize_before=False)

Bases: torch.nn.Module

forward(src)

class pyiqa.archs.topiq_arch.TransformerDecoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu', normalize_before=False)

Bases: torch.nn.Module

forward(tgt, memory)

class pyiqa.archs.topiq_arch.TransformerEncoder(encoder_layer, num_layers)

Bases: torch.nn.Module

forward(src)

class pyiqa.archs.topiq_arch.TransformerDecoder(decoder_layer, num_layers)

Bases: torch.nn.Module

forward(tgt, memory)

class pyiqa.archs.topiq_arch.GatedConv(weightdim, ksz=3)

Bases: torch.nn.Module

forward(x)

class pyiqa.archs.topiq_arch.CFANet(semantic_model_name='resnet50', model_name='cfanet_nr_koniq_res50', backbone_pretrain=True, in_size=None, use_ref=True, num_class=1, num_crop=1, crop_size=256, inter_dim=256, num_heads=4, num_attn_layers=1, dprate=0.1, activation='gelu', pretrained=True, pretrained_model_path=None, out_act=False, block_pool='weighted_avg', test_img_size=None, align_crop_face=True, default_mean=IMAGENET_DEFAULT_MEAN, default_std=IMAGENET_DEFAULT_STD)

Bases: torch.nn.Module

preprocess(x)

fix_bn(model)

get_swin_feature(model, x)

dist_func(x, y, eps=1e-12)

forward_cross_attention(x, y=None)

preprocess_face(x)

forward(x, y=None, return_mos=True, return_dist=False)