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- import torch
- import torch.nn as nn
- from .networks import get_network, LinLayers
- from .utils import get_state_dict
- class LPIPS(nn.Module):
- r"""Creates a criterion that measures
- Learned Perceptual Image Patch Similarity (LPIPS).
- Arguments:
- net_type (str): the network type to compare the features:
- 'alex' | 'squeeze' | 'vgg'. Default: 'alex'.
- version (str): the version of LPIPS. Default: 0.1.
- """
- def __init__(self, net_type: str = 'alex', version: str = '0.1'):
- assert version in ['0.1'], 'v0.1 is only supported now'
- super(LPIPS, self).__init__()
- # pretrained network
- self.net = get_network(net_type)
- # linear layers
- self.lin = LinLayers(self.net.n_channels_list)
- self.lin.load_state_dict(get_state_dict(net_type, version))
- def forward(self, x: torch.Tensor, y: torch.Tensor):
- feat_x, feat_y = self.net(x), self.net(y)
- diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)]
- res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)]
- return torch.sum(torch.cat(res, 0), 0, True)
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