waleedka
commented
5 years ago This would be a useful feature. It's not available right now, but you can probably do it with a bit of additional code. I'll tag this issue as an enhancement so hopefully we add it later, or maybe someone adds it and submits a pull request.
HiddenLayer stores the neural network as a graph (a series of nodes stored as a dict, and a series of edges stored as a list, in the Graph class). You can easily manipulate that graph anyway you like. For example, you can delete all the nodes except the ones you want to print.
Take a look at the Prune transform in transforms.py. It's a simple transform (~15 lines of code) that removes all nodes that match a pattern. You need to do the opposite: delete all nodes that do not match the pattern. Let's call it the Subgraph transform. If you write that, then you can use it as follows:
graph = hl.build_graph(pytorch_graph, inputs)
sub_graph = Subgraph(pattern_of_nodes_to_keep).apply(graph)If you can't describe the nodes you want to keep with a graph expression pattern, then you can do it with node IDs (see the FoldId transform for an example).
noklam
The graph is useful for visualizing model, but when the model is big, printing the entire model is not too helpful. Say I already have a structure of the model(a OrderDict or groups of layer), is that possible to print only part of a model graph.
I am using PyTorch with a pre-trained Resnet, I am only interested in the layer after the Resnet Encoder part. Ideally, I would like to print something like ResNet(Maybe just a manual input argument) + details of the interested layer groups.