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| from collections import namedtuple | |
| import torch | |
| from torchvision import models | |
| class Vgg16(torch.nn.Module): | |
| def __init__(self, requires_grad=False): | |
| super(Vgg16, self).__init__() | |
| vgg_pretrained_features = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features | |
| self.slice1 = torch.nn.Sequential() | |
| self.slice2 = torch.nn.Sequential() | |
| self.slice3 = torch.nn.Sequential() | |
| self.slice4 = torch.nn.Sequential() | |
| for x in range(4): | |
| self.slice1.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(4, 9): | |
| self.slice2.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(9, 16): | |
| self.slice3.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(16, 23): | |
| self.slice4.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 = self.slice1(X) | |
| h_relu1_2 = h | |
| h = self.slice2(h) | |
| h_relu2_2 = h | |
| h = self.slice3(h) | |
| h_relu3_3 = h | |
| h = self.slice4(h) | |
| h_relu4_3 = h | |
| vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3']) | |
| out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3) | |
| return out | |