Accessing intermediate layers of a pretrained network forward?

1

Hi, I want to get outputs from multiple layers of a pretrained VGG-19 network. I have already done that with this approach, that I found on this board:

class AlexNetConv4(nn.Module):
            def __init__(self):
                super(AlexNetConv4, self).__init__()
                self.features = nn.Sequential(
                    # stop at conv4
                    *list(original_model.features.children())[:-3]
                )
            def forward(self, x):
                x = self.features(x)
                return x

Initializing a new net for every output I am interested in, occupies a lot space on my GPU, therefore I would like to follow this approach, via the forward method:

def forward(self, x):
    out1 = F.relu(self.conv1(x))
    out2 = F.relu(self.conv2(out1))
    out3 = F.relu(self.conv3(out2))
    return out1, out2, out3

The problem is, that I don’t know how to get the names of the convolutions in a pretrained VGG-Net that I got from the torch vision models.
Hope someone can help me out with that! :slight_smile:

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