VGG19_Usage.py

import torch
import torch.nn as nn
import torchvision

# VGG for perceptual loss
class VGG19(nn.Module):
    def __init__(self, require_grad=False):
        super(VGG19, self).__init__()

        vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
        self.slice1 = nn.Sequential()
        self.slice2 = nn.Sequential()
        self.slice3 = nn.Sequential()
        self.slice4 = nn.Sequential()
        self.slice5 = 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 require_grad:
            for param in self.parameters():
                param.require_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)

        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]

        return out