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| import torch.nn as nn | |
| import torch | |
| 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) | |
| h_relu2 = self.slice2(h_relu1) | |
| h_relu3 = self.slice3(h_relu2) | |
| h_relu4 = self.slice4(h_relu3) | |
| h_relu5 = self.slice5(h_relu4) | |
| return [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] | |
| class ContrastLoss(nn.Module): | |
| def __init__(self, ablation=False): | |
| super(ContrastLoss, self).__init__() | |
| self.vgg = Vgg19().cuda() | |
| self.l1 = nn.L1Loss() | |
| self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] | |
| self.ab = ablation | |
| def forward(self, a, p, n): | |
| a_re = a.repeat(1, 3, 1, 1) | |
| p_re = p.repeat(1, 3, 1, 1) | |
| n_re = n.repeat(1, 3, 1, 1) | |
| a_vgg, p_vgg, n_vgg = self.vgg(a_re), self.vgg(p_re), self.vgg(n_re) | |
| loss = 0 | |
| d_ap, d_an = 0, 0 | |
| for i in range(len(a_vgg)): | |
| d_ap = self.l1(a_vgg[i], p_vgg[i].detach()) | |
| if not self.ab: | |
| d_an = self.l1(a_vgg[i], n_vgg[i].detach()) | |
| contrastive = d_ap / (d_an + 1e-7) | |
| else: | |
| contrastive = d_ap | |
| loss += self.weights[i] * contrastive | |
| return loss | |