| 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__()
|
|
|
|
|
| self.net = get_network(net_type)
|
|
|
|
|
| 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)
|
|
|
