pyiqa.archs.stlpips_arch

ST-LPIPS Model

github repo link: https://github.com/abhijay9/ShiftTolerant-LPIPS

Cite as: @inproceedings{ghildyal2022stlpips,

title={Shift-tolerant Perceptual Similarity Metric}, author={Abhijay Ghildyal and Feng Liu}, booktitle={European Conference on Computer Vision}, year={2022}

}

Module Contents

pyiqa.archs.stlpips_arch.default_model_urls

pyiqa.archs.stlpips_arch.spatial_average(in_tens, keepdim=True)

pyiqa.archs.stlpips_arch.upsample(in_tens, out_HW=(64, 64))

pyiqa.archs.stlpips_arch.normalize_tensor(in_feat, eps=1e-10)

class pyiqa.archs.stlpips_arch.STLPIPS(pretrained=True, net='alex', variant='shift_tolerant', lpips=True, spatial=False, pnet_tune=False, use_dropout=True, pretrained_model_path=None, eval_mode=True, blur_filter_size=3)

Bases: torch.nn.Module

ST-LPIPS model. :param lpips: Whether to use linear layers on top of base/trunk network. :type lpips: Boolean :param pretrained: Whether means linear layers are calibrated with human

perceptual judgments.

Parameters:

• net (String) – [‘alex’,’vgg’,’squeeze’] are the base/trunk networks available.

• pretrained_model_path (String) – Petrained model path.

• network (The following parameters should only be changed if training the)

• eval_mode (Boolean) – choose the mode; True is for test mode (default).

• pnet_tune (Boolean) – Whether to tune the base/trunk network.

• use_dropout (Boolean) – Whether to use dropout when training linear layers.

forward(in0, in1, retPerLayer=False, normalize=True)

Computation IQA using LPIPS. :param in1: An input tensor. Shape \((N, C, H, W)\). :param in0: A reference tensor. Shape \((N, C, H, W)\). :param retPerLayer: return result contains result of

each layer or not. Default: False.

Parameters:

normalize (Boolean) – Whether to normalize image data range in [0,1] to [-1,1]. Default: True.

Returns:

Quality score.

class pyiqa.archs.stlpips_arch.ScalingLayer

Bases: torch.nn.Module

forward(inp)

class pyiqa.archs.stlpips_arch.NetLinLayer(chn_in, chn_out=1, use_dropout=False)

Bases: torch.nn.Module

A single linear layer which does a 1x1 conv

forward(x)

class pyiqa.archs.stlpips_arch.alexnet(requires_grad=False, variant='shift_tolerant', filter_size=3)

Bases: torch.nn.Module

forward(X)

class pyiqa.archs.stlpips_arch.vggnet(requires_grad=False, variant='shift_tolerant', filter_size=3)

Bases: torch.nn.Module

forward(X)

class pyiqa.archs.stlpips_arch.Downsample(pad_type='reflect', filt_size=3, stride=2, channels=None, pad_off=0, pad_size='', pad_more=False)

Bases: torch.nn.Module

forward(inp)

pyiqa.archs.stlpips_arch.get_pad_layer(pad_type)

class pyiqa.archs.stlpips_arch.VGG(features, num_classes=1000, init_weights=True)

Bases: torch.nn.Module

forward(x)

pyiqa.archs.stlpips_arch.make_layers(cfg, batch_norm=False, filter_size=1, pad_more=False, fconv=False)

pyiqa.archs.stlpips_arch.cfg

pyiqa.archs.stlpips_arch.vgg16(pretrained=False, filter_size=1, pad_more=False, fconv=False, **kwargs)

VGG 16-layer model (configuration “D”)

Parameters:

pretrained (bool) – If True, returns a model pre-trained on ImageNet