.. blogpost::
    :title: A few tricks for tf2onnx
    :keywords: tips, tensorflow, tensorflow-onnx
    :date: 2021-08-12
    :categories: tf2onnx

    A few things I tend to forget. To run a specific
    test on a specific opset.

    ::

        python tests/test_backend.py --opset 12 BackendTests.test_rfft2d_ops_specific_dimension

    Optimisation of an onnx file. It applies the whole list of
    optimizers available in :epkg:`tensorflow-onnx`.

    ::

        import logging
        import onnx
        from onnx import helper
        from tf2onnx.graph import GraphUtil
        from tf2onnx import logging, optimizer, constants
        from tf2onnx.late_rewriters import rewrite_channels_first, rewrite_channels_last

        logging.basicConfig(level=logging.DEBUG)

        def load_graph(fname, target):
            model_proto = onnx.ModelProto()
            with open(fname, "rb") as f:
                data = f.read()
                model_proto.ParseFromString(data)
            g = GraphUtil.create_graph_from_onnx_model(model_proto, target)
            return g, model_proto

        def optimize(input, output):
            g, org_model_proto = load_graph(input, [])
            if g.is_target(constants.TARGET_CHANNELS_FIRST):
                g.reset_nodes(rewrite_channels_first(g, g.get_nodes()))
            if g.is_target(constants.TARGET_CHANNELS_LAST):
                g.reset_nodes(rewrite_channels_last(g, g.get_nodes()))
            g = optimizer.optimize_graph(g)
            onnx_graph = g.make_graph(
                org_model_proto.graph.doc_string + " (+tf2onnx/onnx-optimize)")
            kwargs = GraphUtil.get_onnx_model_properties(org_model_proto)
            model_proto = helper.make_model(onnx_graph, **kwargs)
            with open(output, "wb") as f:
                f.write(model_proto.SerializeToString())

        optimize("debug_noopt.onnx", "debug_opt.onnx")