1c9347c6666d0bb8b9793b504e9cb597b75f1401,torch/onnx/utils.py,,_model_to_graph,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,426

Before Change

    flatten_args, _ = torch._C._jit_flatten(args)
    assert len(params) + len(flatten_args) == sum(1 for _ in graph.inputs())

    input_and_param_names = [val.debugName() for val in graph.inputs()]
    param_names = input_and_param_names[len(input_and_param_names) - len(params):]
    params_dict = dict(zip(param_names, params))

After Change

                                                            _export_onnx_opset_version)
        torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)

    if _onnx_shape_inference:
        torch._C._jit_pass_onnx_graph_shape_type_inference(graph, params_dict, _export_onnx_opset_version)

    params_dict = torch._C._jit_pass_onnx_eliminate_unused_items(graph, params_dict)

    // For ONNX opset < 9, constants only have three data types: float16, float, double.
    // In this pass transform constants of other data types to float/double + cast operator.

Instances