|
import torch |
|
import torch.nn as nn |
|
|
|
from criteria.lpips.networks import get_network, LinLayers |
|
from criteria.lpips.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).to("cuda") |
|
|
|
|
|
self.lin = LinLayers(self.net.n_channels_list).to("cuda") |
|
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)) / x.shape[0] |
|
|
