Esempio n. 1
0
    def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict):
        """ Convert ONNX graph to TensorflowRep.

    :param graph_def: ONNX GraphProto object.
    :param opset: ONNX OperatorSetIdProto list.
    :param strict: whether to enforce semantic equivalence between the original model
      and the converted tensorflow model.
    :return: TensorflowRep object.
    """
        handlers = cls._get_handlers(opset)

        # initializer: TensorProtos representing the values to initialize
        # a given tensor.
        # initialized: A list of names of the initialized tensors.

        if graph_def.initializer:
            initialized = {init.name for init in graph_def.initializer}
        else:
            initialized = set()

        module = BackendTFModule(handlers, opset, strict, graph_def, cls)
        signatures = dict()

        for value_info in graph_def.input:
            if value_info.name in initialized:
                continue
            shape = list(d.dim_value if (
                d.dim_value > 0 and d.dim_param == "") else None
                         for d in value_info.type.tensor_type.shape.dim)
            value_info_name = value_info.name.replace(
                ":", "_tf_") + "_" + get_unique_suffix(
                ) if ":" in value_info.name else value_info.name

            tf_spec = tf.TensorSpec(
                shape,
                data_type.onnx2tf(value_info.type.tensor_type.elem_type),
                value_info_name)
            signatures[value_info.name] = tf_spec

        tf_rep = TensorflowRep()
        tf_rep.inputs = [
            value_info.name for value_info in graph_def.input
            if value_info.name not in initialized
        ]
        tf_rep.outputs = [value_info.name for value_info in graph_def.output]
        module.outputs = tf_rep.outputs
        tf_rep.tf_module = module
        tf_rep.signatures = signatures
        return tf_rep
Esempio n. 2
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    def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict, **kwargs):
        """ Convert ONNX graph to TensorflowRep.

    :param graph_def: ONNX GraphProto object.
    :param opset: ONNX OperatorSetIdProto list.
    :param strict: whether to enforce semantic equivalence between the original model
      and the converted tensorflow model.
    :kwargs: additional arguements to generate tensor_dict for model debugging
    :return: TensorflowRep object.
    """
        # To generate tensor_dict or not, default is False
        gen_tensor_dict = kwargs[
            'gen_tensor_dict'] if 'gen_tensor_dict' in kwargs else False
        # User provided input tensors, in the case the model inputs have unknown shapes
        input_tensor_dict = kwargs[
            'input_tensor_dict'] if 'input_tensor_dict' in kwargs else dict()

        handlers = cls._get_handlers(opset)

        # initializer: TensorProtos representing the values to initialize
        # a given tensor.
        # initialized: A list of names of the initialized tensors.

        if graph_def.initializer:
            initialized = {init.name for init in graph_def.initializer}
        else:
            initialized = set()

        input_dict = dict()

        module = BackendTFModule(handlers, opset, strict, graph_def, cls)
        signatures = dict()
        for value_info in graph_def.input:
            if value_info.name in initialized:
                continue
            shape = list(d.dim_value if (
                d.dim_value > 0 and d.dim_param == "") else None
                         for d in value_info.type.tensor_type.shape.dim)
            value_info_name = value_info.name.replace(
                ":", "_tf_") + "_" + get_unique_suffix(
                ) if ":" in value_info.name else value_info.name

            tf_spec = tf.TensorSpec(
                shape,
                data_type.onnx2tf(value_info.type.tensor_type.elem_type),
                value_info_name)
            signatures[value_info.name] = tf_spec

            if gen_tensor_dict:
                x = tf.constant(
                    0,
                    dtype=data_type.onnx2tf(
                        value_info.type.tensor_type.elem_type),
                    name=value_info_name,
                    shape=shape
                ) if value_info.name not in input_tensor_dict else input_tensor_dict[
                    value_info.name]
                input_dict[value_info.name] = x

        tf_rep = TensorflowRep()
        tf_rep.inputs = [
            value_info.name for value_info in graph_def.input
            if value_info.name not in initialized
        ]
        tf_rep.outputs = [value_info.name for value_info in graph_def.output]
        module.outputs = tf_rep.outputs
        tf_rep.tf_module = module
        tf_rep.signatures = signatures
        tf_rep.tensor_dict = module.gen_tensor_dict(
            input_dict) if gen_tensor_dict else None
        tf_rep.onnx_op_list = cls._get_onnx_op_list(graph_def)
        return tf_rep
Esempio n. 3
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    def _onnx_graph_to_tensorflow_rep(cls, graph_def, opset, strict, **kwargs):
        """ Convert ONNX graph to TensorflowRep.

    :param graph_def: ONNX GraphProto object.
    :param opset: ONNX OperatorSetIdProto list.
    :param strict: whether to enforce semantic equivalence between the original model
      and the converted tensorflow model.
    :kwargs: additional arguements to generate tensor_dict for model debugging
    :return: TensorflowRep object.
    """
        # To generate tensor_dict or not, default is False
        gen_tensor_dict = kwargs[
            'gen_tensor_dict'] if 'gen_tensor_dict' in kwargs else False
        # User provided input tensors, in the case the model inputs have unknown shapes
        input_tensor_dict = kwargs[
            'input_tensor_dict'] if 'input_tensor_dict' in kwargs else dict()
        training_mode = kwargs[
            'training_mode'] if 'training_mode' in kwargs else False

        handlers = cls._get_handlers(opset)

        # initializer: TensorProtos representing the values to initialize
        # a given tensor.
        # initialized: A list of names of the initialized tensors.

        if graph_def.initializer:
            initialized = {init.name for init in graph_def.initializer}
        else:
            initialized = set()

        input_dict = dict()

        module = BackendTFModule(handlers, opset, strict, graph_def, cls)
        signatures = dict()
        tf_rep_graph = tf.Graph()
        with tf_rep_graph.as_default():
            for value_info in graph_def.input:
                if value_info.name in initialized:
                    continue
                shape = list(d.dim_value if (
                    d.dim_value > 0 and d.dim_param == "") else None
                             for d in value_info.type.tensor_type.shape.dim)
                value_info_name = value_info.name.replace(
                    ":", "_tf_") + "_" + get_unique_suffix(
                    ) if ":" in value_info.name else value_info.name

                tf_spec = tf.TensorSpec(
                    shape,
                    data_type.onnx2tf(value_info.type.tensor_type.elem_type),
                    value_info_name)
                signatures[value_info.name] = tf_spec

                if gen_tensor_dict or training_mode:
                    x = tf.compat.v1.placeholder(
                        data_type.onnx2tf(
                            value_info.type.tensor_type.elem_type),
                        name=value_info_name,
                        shape=shape
                    ) if value_info.name not in input_tensor_dict else input_tensor_dict[
                        value_info.name]
                    input_dict[value_info.name] = x

            if gen_tensor_dict or training_mode:
                input_dict_items = cls._onnx_initializer_to_input_dict_items(
                    graph_def.initializer, training_mode=True)
                tensor_dict = dict(input_dict)
                tensor_dict.update(input_dict_items)
                tensor_dict[
                    training_flag_name] = tf.compat.v1.placeholder_with_default(
                        False, shape=[])
                for node in graph_def.node:
                    onnx_node = OnnxNode(node)
                    output_ops = cls._onnx_node_to_tensorflow_op(onnx_node,
                                                                 tensor_dict,
                                                                 handlers,
                                                                 opset=opset,
                                                                 strict=strict)
                    curr_node_output_map = dict(
                        zip(onnx_node.outputs, output_ops))
                    tensor_dict.update(curr_node_output_map)

        tf_rep = TensorflowRep()
        tf_rep.inputs = [
            value_info.name for value_info in graph_def.input
            if value_info.name not in initialized
        ]
        tf_rep.outputs = [value_info.name for value_info in graph_def.output]
        module.outputs = tf_rep.outputs
        tf_rep.tf_module = module
        tf_rep.signatures = signatures
        if gen_tensor_dict or training_mode:
            tf_rep.tensor_dict = tensor_dict
        if training_mode:
            tf_rep.graph = tf_rep_graph
        tf_rep.onnx_op_list = cls._get_onnx_op_list(graph_def)
        return tf_rep