Training on batches of GraphsTuples? about graph_nets HOT 5 OPEN

Thanks for your message!

There are two options here and hopefully at least one of them would work for you:

• Option 1: if the graphs change structure over time, define the input as a sequence of graphs tuples as you mention. Then you can use this method to get a signature as in the TF demo, but instead of getting a single signature, you can get getting a signature for each graph in the sequence, and passing this list of signatures as the input_signature to the tf.function, because specs passed to tf.function can be arbitrarily nested this should work.
• Option 2: If all graphs in the sequence share the same structure (nodes and edges), you can make any node features and edge features have shape [total_num_nodes/edges, sequence_length, feature_size], and just give a single GraphsTuple as the input to your update function. Then inside your update function you can do something like this to build graphs at each step:

def update_fn(input_graph_sequence, ...)

  def loop_body(step_i,...):
     graph_step_i = input_graph_sequence.replace(
        nodes=input_graph_sequence.nodes[:, step_i], 
        edges=input_graph_sequence.edges[:, step_i])

  num_steps = input_graph_sequence.nodes.shape.as_list()[1]
  tf.scan(loop_body, tf.range(num_steps), ...)  
 ...

Hope this helps!

from graph_nets.

This worked. Thanks @alvarosg !

from graph_nets.

Follow-on issue:

I am passing batches to the model during training like so:

outputs = tf.convert_to_tensor([model(graphs_tuple) for graphs_tuple in inputs])

As a reminder, each batch is an iterable of GaphsTuples, and each GraphsTuple represents a sequence of graphs for one training data point.

The GraphIndependent object of the encoder block (EncodeProcessDecode) throws the error: AttributeError: 'GraphsTuple' object has no attribute 'replace'. Location of the error according to the stack trace is here in modules.GraphIndependent``._build().

Any ideas on how to solve?

from graph_nets.

Could you check the type of the object being passed to the model?

My guess is that the GraphsTuple input that your are passing is not actually a graphs.GraphsTuple, but some serialization library or something like that has transformed it into a namedtuple that looks the same, but is not actually the same class, and does not have the extra methods

A simple fix to get the right type just do:

graphs_tuple = graphs.GraphsTuple(*graphs_tuple) before passing it to EncodeProcessDecode, but it may be good to understand where the type gets messed up.

from graph_nets.

Your hunch was correct! It was being transformed into collections.GraphsTuple during the list comprehension:

outputs = tf.convert_to_tensor([model(inputs) for inputs in inputs_train])

Is that^^ the preferred way to feed a batch of graph sequences during the update step? Wondering if it isn't, since when run graphs.GraphsTuple(*inputs) during training, I receive the following error:

OperatorNotAllowedInGraphError: iterating over 'tf.Tensor' is not allowed: AutGraph did convert this function. This might indicate you are trying to use an unsupported feature.

Checking a bit more, type(inputs) when this error is thrown is a symbolic tensor, not a GraphsTuple, which I guess means this happens before the backend actually runs the first batch through the model.

from graph_nets.