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import torch
import torch.nn as nn
import torchvision
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
vgg16 = torchvision.models.vgg16(pretrained=True)
self.enc_1 = nn.Sequential(*vgg16.features[:5])
self.enc_2 = nn.Sequential(*vgg16.features[5:10])
self.enc_3 = nn.Sequential(*vgg16.features[10:17])
for i in range(3):
for param in getattr(self, f'enc_{i+1:d}').parameters():
param.requires_grad = False
def forward(self, image):
results = [image]
for i in range(3):
func = getattr(self, f'enc_{i+1:d}')
results.append(func(results[-1]))
return results[1:]
class ContentPerceptualLoss(nn.Module):
def __init__(self):
super().__init__()
self.VGG = VGG16()
def calculate_loss(self, generated_images, target_images, device):
self.VGG = self.VGG.to(device)
generated_features = self.VGG(generated_images)
target_features = self.VGG(target_images)
perceptual_loss = 0
perceptual_loss += torch.mean((target_features[0] - generated_features[0]) ** 2)
perceptual_loss += torch.mean((target_features[1] - generated_features[1]) ** 2)
perceptual_loss += torch.mean((target_features[2] - generated_features[2]) ** 2)
perceptual_loss /= 3
return perceptual_loss
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