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import torch
import torch.nn as nn
import torchvision
# VGG architecter, used for the perceptual loss using a pretrained VGG network
class VGG19(torch.nn.Module):
def __init__(self, requires_grad=False):
super().__init__()
vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.slice6 = torch.nn.Sequential()
for x in range(2):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(2, 7):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(7, 12):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 21):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(21, 32):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
for x in range(32, 36):
self.slice6.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
self.pool = nn.AdaptiveAvgPool2d(output_size=1)
self.mean = torch.tensor([0.485, 0.456, 0.406]).view(1,-1, 1, 1).cuda() * 2 - 1
self.std = torch.tensor([0.229, 0.224, 0.225]).view(1,-1, 1, 1).cuda() * 2
def forward(self, X): # relui_1
X = (X-self.mean)/self.std
h_relu1 = self.slice1(X)
h_relu2 = self.slice2(h_relu1)
h_relu3 = self.slice3(h_relu2)
h_relu4 = self.slice4(h_relu3)
h_relu5 = self.slice5[:-2](h_relu4)
out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
return out
# Perceptual loss that uses a pretrained VGG network
class VGGLoss(nn.Module):
def __init__(self):
super(VGGLoss, self).__init__()
self.vgg = VGG19().cuda()
self.criterion = nn.L1Loss()
self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]
def forward(self, x, y):
x_vgg, y_vgg = self.vgg(x), self.vgg(y)
loss = 0
for i in range(len(x_vgg)):
loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
return loss |