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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import numpy as np
class VGG19Loss(nn.Module):
def __init__(self):
super().__init__()
self.vgg = VGG19Model()
self.criterion = nn.L1Loss()
self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
self.downsample = nn.AvgPool2d(2, stride=2, count_include_pad=False)
def forward(self, x, y):
while x.size()[3] > 1024:
x, y = self.downsample(x), self.y.downsample(y)
x_vgg = self.vgg(x)
y_vgg = 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
class VGG19Model(torch.nn.Module):
"""
Vgg19 network for perceptual loss.
"""
def __init__(self, requires_grad=False):
super(VGG19Model, self).__init__()
# load pretrained weights from torchvision
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])
self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
requires_grad=False)
self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
requires_grad=False)
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X, is_normalize=True):
if is_normalize: # x is within [-1,1]
X = (X + 1) / 2
assert torch.all(X <= 1+1e-6)
assert torch.all(X >= -1e-6)
X = (X - self.mean) / self.std
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
if __name__ == '__main__':
vgg_loss_fn = VGG19Loss()
x1 = torch.randn([4, 3, 512, 512]).clamp(-1,1)
x2 = torch.randn([4, 3, 512, 512]).clamp(-1,1)
loss = vgg_loss_fn(x1, x2)
print(loss) |