losses.py

import torch
import torch.nn as nn
import functools
from torch.autograd import Variable
import numpy as np

##############################################################################
# Losses
##############################################################################
class GANLoss(nn.Module):
    def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0):
        super(GANLoss, self).__init__()
        self.real_label = target_real_label
        self.fake_label = target_fake_label
        self.real_label_var = None
        self.fake_label_var = None
        if use_lsgan:
            self.loss = nn.MSELoss()
        else:
            self.loss = nn.BCELoss()

    def get_target_tensor(self, input, target_is_real):
        if target_is_real:
            target = self.real_label
        else:
            target = self.fake_label
        targets = torch.full_like(input, fill_value=target)
        return targets

    def __call__(self, input, target_is_real):
        if isinstance(input[0], list):
            loss = 0
            for input_i in input:
                pred = input_i[-1]
                target_tensor = self.get_target_tensor(pred, target_is_real)
                loss += self.loss(pred, target_tensor)
            return loss
        else:            
            target_tensor = self.get_target_tensor(input[-1], target_is_real)
            return self.loss(input[-1], target_tensor)

class VGGLoss(nn.Module):
    def __init__(self):
        super(VGGLoss, self).__init__()        
        self.vgg = Vgg19()
        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 
    
from torchvision import models
class Vgg19(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super(Vgg19, self).__init__()
        vgg_pretrained_features = models.vgg19(pretrained=True).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 requires_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