def parse()

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

• 35 lines of code
• 8 McCabe index (conditional complexity)

def parse(graph):
    nodes_proto = []
    nodes = []
    import itertools
    for node in itertools.chain(graph.input, graph.output):
        nodes_proto.append(node)

    for node in nodes_proto:
        print(node.name)
        shapeproto = TensorShapeProto(
            dim=[TensorShapeProto.Dim(size=d.dim_value) for d in node.type.tensor_type.shape.dim])
        nodes.append(NodeDef(
            name=node.name.encode(encoding='utf_8'),
            op='Variable',
            input=[],
            attr={
                'dtype': AttrValue(type=node.type.tensor_type.elem_type),
                'shape': AttrValue(shape=shapeproto),
            })
        )

    for node in graph.node:
        attr = []
        for s in node.attribute:
            attr.append(' = '.join([str(f[1]) for f in s.ListFields()]))
        attr = ', '.join(attr).encode(encoding='utf_8')
        print(node.output[0])
        nodes.append(NodeDef(
            name=node.output[0].encode(encoding='utf_8'),
            op=node.op_type,
            input=node.input,
            attr={'parameters': AttrValue(s=attr)},
        ))

    # two pass token replacement, appends opname to object id
    mapping = {}
    for node in nodes:
        mapping[node.name] = node.op + '_' + node.name

    return GraphDef(node=nodes, versions=VersionDef(producer=22))