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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) |