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)