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loss.py
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loss.py
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import torch
from torch import nn
from torchvision.models.vgg import vgg16
from skimage.measure import compare_psnr, compare_ssim
import pytorch_ssim
from torch.autograd import Variable
class GeneratorLoss_adv(nn.Module):
def __init__(self):
super(GeneratorLoss_adv, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_labels, out_images, target_images):
# Adversarial Loss
adversarial_loss = torch.mean(1 - out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))
# Image Loss
image_loss = self.mse_loss(out_images, target_images)
# TV Loss
tv_loss = self.tv_loss(out_images)
# return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss + 2e-8 * tv_loss
return adversarial_loss
class GeneratorLoss_ssim(nn.Module):
def __init__(self):
super(GeneratorLoss_ssim, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_labels, out_images, target_images):
# Adversarial Loss
adversarial_loss = torch.mean(1 - out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))
# Image Loss
image_loss = self.mse_loss(out_images, target_images)
# TV Loss
tv_loss = self.tv_loss(out_images)
#ssim Loss
#print("cal ssim loss !!!")
ssim_loss = pytorch_ssim.ssim(Variable(target_images), Variable(out_images))
# out_batch = out_images.cpu().detach().numpy()
# label_batch = target_images.cpu().detach().numpy()
# #print("to cpu !!!")
# N, _, _, _ = out_batch.shape
# #print(type(label_batch[0]))
# ssim_loss = 0
# for i in range(N):
# ssim_loss += compare_ssim(label_batch[i],out_batch[i], win_size=3, multichannel=True)
# ssim_loss /= N
# #print("get ssim_loss ------")
return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss - 1e-6 * ssim_loss
class GeneratorLoss(nn.Module):
def __init__(self):
super(GeneratorLoss, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_labels, out_images, target_images):
# Adversarial Loss
adversarial_loss = torch.mean(1 - out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))
# Image Loss
image_loss = self.mse_loss(out_images, target_images)
# TV Loss
tv_loss = self.tv_loss(out_images)
return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss + 2e-8 * tv_loss
class GeneratorLoss_notv(nn.Module):
def __init__(self):
super(GeneratorLoss_notv, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_labels, out_images, target_images):
# Adversarial Loss
adversarial_loss = torch.mean(1 - out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))
# Image Loss
image_loss = self.mse_loss(out_images, target_images)
# TV Loss
tv_loss = self.tv_loss(out_images)
return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self.tensor_size(x[:, :, 1:, :])
count_w = self.tensor_size(x[:, :, :, 1:])
h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, :h_x - 1, :]), 2).sum()
w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, :w_x - 1]), 2).sum()
return self.tv_loss_weight * 2 * (h_tv / count_h + w_tv / count_w) / batch_size
@staticmethod
def tensor_size(t):
return t.size()[1] * t.size()[2] * t.size()[3]
if __name__ == "__main__":
g_loss = GeneratorLoss()
print(g_loss)