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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 |