| import torch.nn as nn |
| import torch |
| from torch.nn import functional as F |
| import torch.nn.functional as fnn |
| from torch.autograd import Variable |
| import numpy as np |
| 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_vgg, p_vgg, n_vgg = self.vgg(a), self.vgg(p), self.vgg(n) |
| 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 |
|
|
