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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))
self.eval()
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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