loss_functions.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class ContentLoss(nn.Module):
    def __init__(self, target,):
        super().__init__()
        self.target = target.detach()

    def forward(self, input):
        self.loss = F.mse_loss(input, self.target)
        return input

    
class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super().__init__()
        self.target = self.gram_matrix(target_feature).detach()
        
    def gram_matrix(self,input):
        a, b, c, d = input.size()
        features = input.view(a * b, c * d)
        G = torch.mm(features, features.t())
        return G.div(a * b * c * d)

    def forward(self, input):
        G = self.gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input

    
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super().__init__()
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = torch.tensor(std).view(-1, 1, 1)

    def forward(self, img):
        return (img - self.mean) / self.std