vgg_loss.py

import torch.nn as nn
import torch
import torchvision
# import config

class VGG19(nn.Module):
    def __init__(self, required_grad=False):
        super(VGG19, self).__init__()
        vgg_pretrained_features = torchvision.models.vgg19(weights=torchvision.models.VGG19_Weights.DEFAULT).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()
        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, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not required_grad:
            for param in self.parameters():
                param.requires_grad = False
    
    def forward(self, X):
        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(h_relu4)
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out
    
class VGGLoss(nn.Module):
    def __init__(self):
        super().__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