Loss_contrast

import numpy
import torch
import torch.nn.functional as F
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)  # relu1_1
        h_relu2 = self.slice2(h_relu1)  # relu2_1
        h_relu3 = self.slice3(h_relu2)  # relu3_1
        h_relu4 = self.slice4(h_relu3)  # relu4_1
        h_relu5 = self.slice5(h_relu4)  # relu5_1
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out


class LossNetwork(torch.nn.Module):
    def __init__(self, device):
        super(LossNetwork, self).__init__()
        self.vgg = Vgg19().to(device)
        self.L1 = torch.nn.L1Loss()
        self.weight = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]

    def forward(self, pred, gt, input):
        loss = []
        pred_features = self.vgg(pred)
        gt_features = self.vgg(gt)
        input_features = self.vgg(input)

        for i in range(len(pred_features)):
            pred_gt = self.L1(pred_features[i], gt_features[i])
            pred_input = self.L1(pred_features[i], input_features[i])
            per_loss = pred_gt / (pred_input + 1e-7)
            loss.append(self.weight[i] * per_loss)

            # loss.append(self.weight[i] * pred_gt)

        return sum(loss)