| """Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
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|
|
| from collections import namedtuple
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|
|
| import torch
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| import torch.nn as nn
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| from torchvision import models
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|
|
| from ..util import get_ckpt_path
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|
|
|
|
| class LPIPS(nn.Module):
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|
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| def __init__(self, use_dropout=True):
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| super().__init__()
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| self.scaling_layer = ScalingLayer()
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| self.chns = [64, 128, 256, 512, 512]
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| self.net = vgg16(pretrained=True, requires_grad=False)
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| self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
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| self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
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| self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
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| self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
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| self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
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| self.load_from_pretrained()
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| for param in self.parameters():
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| param.requires_grad = False
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|
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| def load_from_pretrained(self, name="vgg_lpips"):
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| ckpt = get_ckpt_path(name, "sgm/modules/autoencoding/lpips/loss")
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| self.load_state_dict(
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| torch.load(ckpt, map_location=torch.device("cpu")), strict=False
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| )
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| print("loaded pretrained LPIPS loss from {}".format(ckpt))
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|
|
| @classmethod
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| def from_pretrained(cls, name="vgg_lpips"):
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| if name != "vgg_lpips":
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| raise NotImplementedError
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| model = cls()
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| ckpt = get_ckpt_path(name)
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| model.load_state_dict(
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| torch.load(ckpt, map_location=torch.device("cpu")), strict=False
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| )
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| return model
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|
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| def forward(self, input, target):
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| in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
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| outs0, outs1 = self.net(in0_input), self.net(in1_input)
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| feats0, feats1, diffs = {}, {}, {}
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| lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
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| for kk in range(len(self.chns)):
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| feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(
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| outs1[kk]
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| )
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| diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
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|
|
| res = [
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| spatial_average(lins[kk].model(diffs[kk]), keepdim=True)
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| for kk in range(len(self.chns))
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| ]
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| val = res[0]
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| for l in range(1, len(self.chns)):
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| val += res[l]
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| return val
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|
|
|
|
| class ScalingLayer(nn.Module):
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| def __init__(self):
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| super(ScalingLayer, self).__init__()
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| self.register_buffer(
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| "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
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| )
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| self.register_buffer(
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| "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None]
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| )
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|
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| def forward(self, inp):
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| return (inp - self.shift) / self.scale
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|
|
|
|
| class NetLinLayer(nn.Module):
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| """A single linear layer which does a 1x1 conv"""
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|
|
| def __init__(self, chn_in, chn_out=1, use_dropout=False):
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| super(NetLinLayer, self).__init__()
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| layers = (
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| [
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| nn.Dropout(),
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| ]
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| if (use_dropout)
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| else []
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| )
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| layers += [
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| nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),
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| ]
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| self.model = nn.Sequential(*layers)
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|
|
|
|
| class vgg16(torch.nn.Module):
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| def __init__(self, requires_grad=False, pretrained=True):
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| super(vgg16, self).__init__()
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| vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
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| self.slice1 = torch.nn.Sequential()
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| self.slice2 = torch.nn.Sequential()
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| self.slice3 = torch.nn.Sequential()
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| self.slice4 = torch.nn.Sequential()
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| self.slice5 = torch.nn.Sequential()
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| self.N_slices = 5
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| for x in range(4):
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| self.slice1.add_module(str(x), vgg_pretrained_features[x])
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| for x in range(4, 9):
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| self.slice2.add_module(str(x), vgg_pretrained_features[x])
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| for x in range(9, 16):
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| self.slice3.add_module(str(x), vgg_pretrained_features[x])
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| for x in range(16, 23):
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| self.slice4.add_module(str(x), vgg_pretrained_features[x])
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| for x in range(23, 30):
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| self.slice5.add_module(str(x), vgg_pretrained_features[x])
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| if not requires_grad:
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| for param in self.parameters():
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| param.requires_grad = False
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|
|
| def forward(self, X):
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| h = self.slice1(X)
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| h_relu1_2 = h
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| h = self.slice2(h)
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| h_relu2_2 = h
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| h = self.slice3(h)
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| h_relu3_3 = h
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| h = self.slice4(h)
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| h_relu4_3 = h
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| h = self.slice5(h)
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| h_relu5_3 = h
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| vgg_outputs = namedtuple(
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| "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
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| )
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| out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
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| return out
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|
|
|
|
| def normalize_tensor(x, eps=1e-10):
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| norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
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| return x / (norm_factor + eps)
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|
|
|
|
| def spatial_average(x, keepdim=True):
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| return x.mean([2, 3], keepdim=keepdim)
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|
|
