def _operator_to_node_simp()

in tensorboardX/tensorboardX/caffe2_graph.py [0:0]

• 50 lines of code
• 11 McCabe index (conditional complexity)

def _operator_to_node_simp(op, inter_blobs, seen):
    '''
    Convert the operators to nodes.

    Args:
        op: Caffe2 operator to convert to node
        inter_blobs: Set of intermediate blobs
        seen: Names that have already been used and are not unique

    Returns:
        nodes: Nodes representing 'op' and the outputs of 'op'
    '''
    assert op
    nodes = []
    outputs = [o for o in op.output if o not in inter_blobs]
    seen.update(outputs)
    len_outputs = len(outputs)
    if len_outputs == 1:
        n = NodeDef()
        n.name = outputs[0]
        # Here we are sure the name is unique.
        n.input.extend(op.input)
        n.op = op.type
        n.device = _tf_device(op.device_option)
        for arg in op.arg:
            _set_tf_attr(n.attr, arg)
        nodes.append(n)
    elif len_outputs > 1:
        # Create a name that is likely unique
        if op.name:
            name = op.name
        else:
            name_list = [name for name in outputs]
            scope = os.path.commonprefix(name_list)
            name = os.path.join(scope, op.type)
        assert(name)
        op.name = _make_unique_name(seen, name)
        device = _tf_device(op.device_option)

        # Create additional output nodes
        for output in outputs:
            n = NodeDef()
            n.name = output
            n.input.extend([op.name])
            n.op = 'Blob'
            n.device = device
            nodes.append(n)

        # Node for the current op
        n = NodeDef()
        n.name = op.name
        n.input.extend(op.input)
        n.op = op.type
        n.device = device
        for arg in op.arg:
            _set_tf_attr(n.attr, arg)
        nodes.append(n)

    return nodes