import torch.nn as nn
import torch
def gram_matrix(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) 
class ContentLoss(nn.Module):
    def __init__(self, target):
        super().__init__()
        self.target = target.detach()
    def forward(self, input):
        self.loss = nn.functional.mse_loss(input, self.target)
        return input
    
class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super().__init__()
        self.target = gram_matrix(target_feature).detach()
    def forward(self, input):
        G = gram_matrix(input)
        self.loss = nn.functional.mse_loss(G, self.target)
        return input
    
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super().__init__()
        self.mean = mean.view(-1, 1, 1)
        self.std = std.view(-1, 1, 1)
    def forward(self, img):
        return (img - self.mean) / self.std