model.py

import torch
import torch.nn as nn

cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M',
       512, 512, 512, 'M']

class VGG(nn.Module):
    def __init__(self):
        super(VGG, self).__init__()
        self.classifier = nn.Linear(512, 10)
        self.features = self._make_layers(cfg)

    def forward(self, x):
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)

# input --> hidden layer
class VGG2(torch.nn.Module):
    def __init__(self, model1, target_layer):
        super(VGG2, self).__init__()
        features = list(model1.features)
        self.features = nn.ModuleList(features).eval()
        self.target_layer = int(target_layer)

    def forward(self, x):
        if self.target_layer == -1:
            return x

        for ii, model in enumerate(self.features):
            x = model(x)
            if ii == self.target_layer:
                return x