python>=3.5
onnx>=1.8.0
onnx-simplifier>=0.3.3
onnxoptimizer>=0.2.5
onnxruntime>=1.6.0
oneflow>=0.3.4

pytorch>=1.7.0
paddlepaddle>=2.0.0
tensorflow>=2.0.0

pip install oneflow_onnx

git clone https://github.com/Oneflow-Inc/oneflow_convert_tools
cd oneflow_onnx
python3 setup.py install

def test_alexnet():
    @flow.global_function()
    def alexnet_eval_job(x: tp.Numpy.Placeholder((1, 227, 227, 3))):
        return alexnet(x, None, False)

    convert_to_onnx_and_check(alexnet_eval_job, flow_weight_dir=None, onnx_model_path="/tmp")

 while not os.path.exists(os.path.join(flow_weight_dir, "snapshot_done")):
     pass
 onnx_model_dir = onnx_model_path
 onnx_model_path = os.path.join(onnx_model_dir, "model.onnx")
 flow.onnx.export(
     job_func,
     flow_weight_dir,
     onnx_model_path,
     opset=opset,
     external_data=external_data,
 )

def Export(
    job_func: Callable,
    model_save_dir: Text,
    onnx_filename: Text,
    continue_on_error: bool = False,
    opset: Optional[int] = None,
    extra_opset: Optional[int] = None,
    shape_override: Optional[Dict[Text, List[int]]] = None,
    external_data: bool = False,
):
    r"""Export a oneflow model into ONNX format.
    Args:
        job_func: OneFlow的作业函数
        model_save_dir: 包含OneFlow定义的模型权重的文件夹. 这个模型权重是用oneflow的check_point.save接口保存的。
        onnx_filename: 输出ONNX模型文件名，字符串类型
        continue_on_error: 如果某个OP无法处理（即没有映射），是否继续
        opset: ONNX Opset版本号，默认为10
        extra_opset: 额外Opset的列表，例如自定义操作使用的Opset
        shape_override: 带有输入信息的字典，覆盖OneFlow给定的输入形状
        external_data: 将权重另存为ONNX外部数据，通常是为了绕过protobuf的2GB文件大小限制。
    """
    assert os.getenv("ENABLE_USER_OP") != "False"
    # 确定模型的路径是存在的
    assert os.path.isdir(model_save_dir)
    # 通过c_api_util.GetJobSet()方法获取当前的所有job
    job_set = c_api_util.GetJobSet()
    # 我们要转的模型被定义在job_func中，所以我们先记录下它的名字
    job_name = job_func.__name__
    # 编译job_set，找到定义模型的job
    for job in job_set.job:
        # TODO(OYY) Modify the interface before modifying it
        if job.job_conf.job_name == job_name:
            # job找到了，可以开始进行下面的步骤，我们在外面详细解释
            onnx_graph = ProcessFlowGraph(
                job,
                model_save_dir,
                continue_on_error=continue_on_error,
                opset=opset,
                extra_opset=extra_opset,
                shape_override=shape_override,
            )
            onnx_graph = optimizer.OptimizeGraph(onnx_graph)
            model_proto = onnx_graph.MakeModel(
                job_name, onnx_filename, external_data=external_data
            )
            with open(onnx_filename, "wb") as f:
                try:
                    f.write(model_proto.SerializeToString())
                except ValueError as e:
                    raise ValueError(
                        "Error occured when running model_proto.SerializeToString(). If the model is larger than 2GB, please specify external_data=True when calling flow.onnx.export. Original error message:\n{}".format(
                            e
                        )
                    )
            return
    raise ValueError('Cannot find job "{}" in jobset'.format(job_name))

def ProcessFlowGraph(
    flow_graph,
    model_save_dir,
    continue_on_error=False,
    opset=None,
    extra_opset=None,
    shape_override=None,
):
    # 这个函数用来获取导出的ONNX的Opset Version，OneFlow里面最高为10
    opset = util.FindOpset(opset)
    logger.info("Using opset <onnx, %s>", opset)
    # 判断当前的ONNX版本是否支持上面的Opset Version
    if opset > schemas.get_max_supported_opset_version():
        logger.warning(
            "Currently installed onnx package %s is too low to support opset %s, "
            "please upgrade onnx package to avoid potential conversion issue.",
            util.get_onnx_version(),
            opset,
        )

    if shape_override is None:
        shape_override = {}
    # 用于将oneflow 的各个 node 转换为 onnx node 的格式，保持 op 类型、输入输出和属性值不变，这一步产生的还不是合法的 onnx 模型
    (onnx_nodes, op_cnt, attr_cnt, dtypes, output_shapes,) = FlowToOnnxNaive(
        flow_graph, shape_override
    )
    # 构造一个 Graph 类，用于后续方便的修改 onnx 网络结构
    g = Graph(onnx_nodes, model_save_dir, output_shapes, dtypes, opset, extra_opset,)

    # create ops mapping for the desired opsets
    ops_mapping = handler.flow_op.CreateMapping(g.opset, g.extra_opset)

    # some nodes may already copied into inner Graph, so remove them from main Graph.
    TopologicalSort(g, continue_on_error)
    # FlowOnnxMapping 函数调用各个转换函数（通过 @flow_op 注册）逐个转换 op，转换后产生的是合法的 onnx 模型
    mapped_op, unmapped_op, exceptions = FlowOnnxMapping(g, ops_mapping)
    if unmapped_op:
        logger.error("Unsupported ops: %s", unmapped_op)
    if exceptions and not continue_on_error:
        raise exceptions[0]

    # onnx requires topological sorting
    TopologicalSort(g, continue_on_error)

    g.UpdateProto()

    logger.debug(
        "Summay Stats:\n"
        "\toneflow ops: {}\n"
        "\toneflow attr: {}\n"
        "\tonnx mapped: {}\n"
        "\tonnx unmapped: {}".format(op_cnt, attr_cnt, mapped_op, unmapped_op)
    )

    return g

def CreateMapping(max_onnx_opset_version, extra_opsets):
        """Create the final mapping dictionary by stacking domains and opset versions.

        :param max_onnx_opset_version: The highest onnx opset the resulting graph may use.
        :param extra_opsets: Extra opsets the resulting graph may use.
        """
        mapping = {constants.ONNX_DOMAIN: max_onnx_opset_version}
        if extra_opsets:
            for extra_opset in extra_opsets:
                mapping[extra_opset.domain] = extra_opset.version
        ops_mapping = {}
        for domain, opsets in flow_op.get_opsets().items():
            for target_opset, op_map in enumerate(opsets):
                print('='*100)
                print(target_opset)
                print(op_map)
                m = mapping.get(domain)
                if m:
                    if target_opset <= m and op_map:
                        ops_mapping.update(op_map)

        flow_op._MAPPING = ops_mapping
        return ops_mapping

====================================================================================================
0
{}
====================================================================================================
1
{'add_n': (<bound method AddN.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.AddN'>>, 'Sum', {}), 'leaky_relu': (<bound method DirectOpSinceOpset1.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOpSinceOpset1'>>, 'LeakyRelu', {}), 'softplus': (<bound method DirectOpSinceOpset1.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOpSinceOpset1'>>, 'Softplus', {}), 'abs': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Abs', {}), 'ceil': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Ceil', {}), 'elu': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Elu', {}), 'exp': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Exp', {}), 'floor': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Floor', {}), 'log': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Log', {}), 'neg': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Neg', {}), 'sigmoid': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sigmoid_v2': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sqrt': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sqrt', {}), 'tanh': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Tanh', {}), 'reciprocal': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Reciprocal', {}), 'relu': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Relu', {}), 'broadcast_maximum': (<bound method MinMaxOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MinMaxOp'>>, 'Max', {}), 'broadcast_minimum': (<bound method MinMaxOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MinMaxOp'>>, 'Min', {}), 'clip_by_scalar': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_min': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_max': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'softmax': (<bound method Softmax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Softmax'>>, 'Softmax', {}), 'square': (<bound method Square.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Square'>>, None, {}), 'rsqrt': (<bound method Rsqrt.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Rsqrt'>>, None, {}), 'squared_difference': (<bound method SquaredDifference.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.SquaredDifference'>>, None, {}), 'sign': (<bound method Sign.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Sign'>>, 'Sign', {}), 'matmul': (<bound method MatMul.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MatMul'>>, 'MatMul', {}), 'batch_matmul': (<bound method MatMul.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MatMul'>>, 'MatMul', {}), 'erf': (<bound method Erf.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Erf'>>, 'Erf', {}), 'logical_not': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Not', {}), 'broadcast_logical_or': (<bound method BroadcastOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Or', {}), 'broadcast_logical_and': (<bound method BroadcastOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'And', {}), 'input': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'return': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'variable': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'distribute_split': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_concat': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_clone': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_add': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'conv2d': (<bound method ConvOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.ConvOp'>>, None, {}), 'max_pool_2d': (<bound method PoolOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'reduce_prod': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceProd', {}), 'reduce_sum': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceSum', {}), 'reduce_min': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceMin', {}), 'argmax': (<bound method ArgMax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMax', {}), 'argmin': (<bound method ArgMax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMin', {}), 'squeeze': (<bound method Squeeze.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Squeeze'>>, 'Squeeze', {}), 'transpose': (<bound method Transpose.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Transpose'>>, 'Transpose', {}), 'concat': (<bound method Concat.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Concat'>>, 'Concat', {}), 'identity': (<bound method Identity.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Identity'>>, 'Identity', {})}
====================================================================================================
2
{'pad': (<bound method Pad.Version_2 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.Pad'>>, 'Pad', {})}
====================================================================================================
3
{}
====================================================================================================
4
{}
====================================================================================================
5
{'reshape': (<bound method Reshape.Version_5 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Reshape'>>, 'Reshape', {})}
====================================================================================================
6
{'broadcast_div': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Div', {}), 'scalar_div_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Div', {}), 'multiply': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'broadcast_mul': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'scalar_mul_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'broadcast_sub': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Sub', {}), 'scalar_sub_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Sub', {}), 'broadcast_add': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Add', {}), 'scalar_add_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Add', {}), 'scalar_add': (<bound method ScalarBinaryOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ScalarBinaryOp'>>, 'Add', {}), 'scalar_mul': (<bound method ScalarBinaryOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ScalarBinaryOp'>>, 'Mul', {}), 'bias_add': (<bound method BiasAdd.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BiasAdd'>>, 'Add', {}), 'abs': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Abs', {}), 'ceil': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Ceil', {}), 'elu': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Elu', {}), 'exp': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Exp', {}), 'floor': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Floor', {}), 'log': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Log', {}), 'neg': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Neg', {}), 'sigmoid': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sigmoid_v2': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sqrt': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sqrt', {}), 'tanh': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Tanh', {}), 'reciprocal': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Reciprocal', {}), 'relu': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Relu', {}), 'broadcast_logical_or': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Or', {}), 'broadcast_logical_and': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'And', {}), 'normalization': (<bound method BatchNorm.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.BatchNorm'>>, None, {}), 'cast': (<bound method Cast.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Cast'>>, 'Cast', {})}
====================================================================================================
7
{'acos': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Acos', {}), 'asin': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Asin', {}), 'atan': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Atan', {}), 'cos': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Cos', {}), 'sin': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Sin', {}), 'tan': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Tan', {}), 'broadcast_floor_mod': (<bound method FloorMod.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.FloorMod'>>, 'FloorMod', {}), 'broadcast_equal': (<bound method Equal.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'Equal', {}), 'broadcast_not_equal': (<bound method Equal.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'NotEqual', {}), 'broadcast_greater': (<bound method GreaterLess.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLess'>>, 'Greater', {}), 'broadcast_less': (<bound method GreaterLess.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLess'>>, 'Less', {}), 'broadcast_less_equal': (<bound method GreaterLessEqual.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLessEqual'>>, 'Greater', {}), 'broadcast_greater_equal': (<bound method GreaterLessEqual.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLessEqual'>>, 'Less', {})}
====================================================================================================
8
{}
====================================================================================================
9
{'acosh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Acosh', {}), 'asinh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Asinh', {}), 'atanh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Atanh', {}), 'cosh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Cosh', {}), 'sinh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Sinh', {}), 'sign': (<bound method Sign.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Sign'>>, 'Sign', {}), 'erf': (<bound method Erf.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Erf'>>, 'Erf', {}), 'normalization': (<bound method BatchNorm.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.BatchNorm'>>, None, {}), 'cast': (<bound method Cast.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Cast'>>, 'Cast', {})}
====================================================================================================
10
{'max_pool_2d': (<bound method PoolOp.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'min_max_observer': (<bound method MinMaxObserver.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MinMaxObserver'>>, None, {}), 'moving_average_min_max_observer': (<bound method MovingAverageMinMaxObserver.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MovingAverageMinMaxObserver'>>, None, {}), 'fake_quantization': (<bound method FakeQuantization.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.FakeQuantization'>>, 'QuantizeLinear', {})}
====================================================================================================
11
{'clip_by_scalar': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_min': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_max': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'softmax': (<bound method Softmax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Softmax'>>, 'Softmax', {}), 'round': (<bound method Round.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Round'>>, 'Round', {}), 'broadcast_equal': (<bound method Equal.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'Equal', {}), 'broadcast_not_equal': (<bound method Equal.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'NotEqual', {}), 'conv2d': (<bound method ConvOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.ConvOp'>>, None, {}), 'max_pool_2d': (<bound method PoolOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'pad': (<bound method Pad.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.Pad'>>, 'Pad', {}), 'reduce_prod': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceProd', {}), 'reduce_sum': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceSum', {}), 'reduce_min': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceMin', {}), 'argmax': (<bound method ArgMax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMax', {}), 'argmin': (<bound method ArgMax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMin', {}), 'squeeze': (<bound method Squeeze.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Squeeze'>>, 'Squeeze', {}), 'concat': (<bound method Concat.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Concat'>>, 'Concat', {}), 'gather_nd': (<bound method GatherND.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.GatherND'>>, 'GatherND', {})}
====================================================================================================
12
{}
====================================================================================================
13
{'min_max_observer': (<bound method MinMaxObserver.Version_13 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MinMaxObserver'>>, None, {}), 'fake_quantization': (<bound method FakeQuantization.Version_13 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.FakeQuantization'>>, 'QuantizeLinear', {})}

void TopologicalSort(Graph G){
    InitStack(S);
    for(i = 0;i < G.vexnum; i++){
        if(indegrdd[i]==0)
            Push(S, i);
    }

    int count =0;
    while(!Empty(S)){
        Pop(S,i);
        print[count++] = i;
        for(p = G.vertices[i].firstarc; p; p = p->nextarc){
            v = p->adjvex;
            if(!(--indegree[v]))
                Push(S, v);
        }
    }

    if(count < G.vexnum)
        return false;
    else
        return true;
}

def FlowOnnxMapping(g, ops_mapping):
    logger.debug("Mapping Oneflow node to ONNX node(s)")
    mapped_op = collections.Counter()
    unmapped_op = collections.Counter()
    exceptions = []

    ops = list(g.get_nodes())
    for node in ops:
        logger.debug("Process node: %s\n%s", node.name, node.summary)

        if node.skip_conversion:
            logger.debug("explicitly skip node " + node.name)
            continue

        op = node.op_type
        map_info = ops_mapping.get(op)
        if map_info is None:
            unmapped_op[op] += 1
            logger.error("oneflow op [%s: %s] is not supported", node.name, op)
            continue
        mapped_op[op] += 1

        func, onnx_op, kwargs = map_info
        if onnx_op is not None:
            node.op_type = onnx_op
        try:
            func(g, node, **kwargs)
            node.skip_conversion = True
        except Exception as ex:
            logger.error(
                "Failed to convert node %s\n%s", node.name, node.summary, exc_info=1
            )
            exceptions.append(ex)

    return mapped_op, unmapped_op, exceptions

model_proto = onnx_graph.MakeModel(
                job_name, onnx_filename, external_data=external_data
            )