mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-22 16:28:32 +00:00
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
|
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)
|