Graph Scope

Hi, I’m a Pytorch beginner (previously working on th and tf) using your tensorboard-pytorch bridge to get some info during neural net training. It works like a charm for everything i’ve needed since now 😃. However i’m having some troubles with the graph visualization for some ConvNet and i’ve a few questions:

• Is it possible to define scope for module? (i.e. if I’have a nested module can i give it a name to obtain a compress visualization of everything inside it?) ;
• using the torch.cat() method i get some strange behaviours related to the order of the elements in the list of the first argument of cat() (see attached images, where the second one is correct for the code reported) .

Btw great work 😃

def forward(self,i):
        # i stand as the input
        # conv_j is a module
        x = self.conv_0(i)
        x = self.conv_1(x)
        y = self.conv_r1(i)
        z = torch.cat((y,x),1)
        z = z.view(len(z),-1)
        z = self.fc1(z)
        z = F.relu(z)
        z = self.fc2(z)
        z = F.log_softmax(z)
        return z

[miguelvr]

I think the activations should be outside if you define them outside, anyway! By doing that the graph would be much cleaner

[lucabergamini]

I completely forgot it, that makes a lot of sense 😃

Yeah that could explains it all…unfortunately autograd is used only in Pytorch as far as i know, so we can’t just compile the same network under TF to check if the graphs match. Thank you in advance anyway, I’ll try to look at the code behind in my spare time 😃