# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 """Functions for importing ONNX models to MXNet and for checking metadata""" # pylint: disable=no-member from .import_onnx import GraphProto def import_model(model_file): """Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters. Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration Parameters ---------- model_file : str ONNX model file name Returns ------- sym : :class:`~mxnet.symbol.Symbol` MXNet symbol object arg_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format aux_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format """ graph = GraphProto() try: import onnx except ImportError: raise ImportError("Onnx and protobuf need to be installed. " + "Instructions to install - https://github.com/onnx/onnx") # loads model file and returns ONNX protobuf object model_proto = onnx.load_model(model_file) sym, arg_params, aux_params = graph.from_onnx(model_proto.graph) return sym, arg_params, aux_params def get_model_metadata(model_file): """ Returns the name and shape information of input and output tensors of the given ONNX model file. Parameters ---------- model_file : str ONNX model file name Returns ------- model_metadata : dict A dictionary object mapping various metadata to its corresponding value. The dictionary will have the following template. { 'input_tensor_data' : , 'output_tensor_data' : } """ graph = GraphProto() try: import onnx except ImportError: raise ImportError("Onnx and protobuf need to be installed. " + "Instructions to install - https://github.com/onnx/onnx") model_proto = onnx.load_model(model_file) metadata = graph.get_graph_metadata(model_proto.graph) return metadata