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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
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<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul>
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<!--- 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. --><div class="section" id="Exporting-to-ONNX-format">
<h1>Exporting to ONNX format<a class="headerlink" href="#Exporting-to-ONNX-format" title="Permalink to this headline"></a></h1>
<p><a class="reference external" href="https://github.com/onnx/onnx">Open Neural Network Exchange (ONNX)</a> provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. In the MXNet 1.9 release, the MXNet-to-ONNX export module (mx2onnx) has received a major update with new features such as dynamic input shapes and better operator and model coverages. Please visit the <a class="reference external" href="https://github.com/apache/mxnet/tree/v1.x/python/mxnet/onnx#onnx-export-support-for-mxnet">ONNX Export Support for
MXNet</a> page for more information.</p>
<p>In this tutorial, we will learn how to use the mx2onnx exporter on pre-trained models.</p>
<div class="section" id="Prerequisites">
<h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline"></a></h2>
<p>To run the tutorial we will need to have installed the following python modules: - <a class="reference external" href="/get_started">MXNet &gt;= 1.9.0</a> <em>OR</em> an earlier MXNet version + <a class="reference external" href="https://github.com/apache/mxnet/tree/v1.x/python/mxnet/onnx#installation">the mx2onnx wheel</a> - <a class="reference external" href="https://github.com/onnx/onnx#installation">onnx &gt;= 1.7.0</a></p>
<p><em>Note:</em> The latest mx2onnx exporting module is tested with ONNX op set version 12 or later, which corresponds to ONNX version 1.7 or later. Use of ealier ONNX versions may still work on some simple models, but again this is not tested.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="Download-a-model-from-the-MXNet-model-zoo">
<h2>Download a model from the MXNet model zoo<a class="headerlink" href="#Download-a-model-from-the-MXNet-model-zoo" title="Permalink to this headline"></a></h2>
<p>We can download a pre-trained ResNet-18 <a class="reference external" href="http://www.image-net.org/">ImageNet</a> model from the <a class="reference external" href="/api/python/docs/api/gluon/model_zoo/index.html">MXNet Model Zoo</a>. We will also download a synset file to match the labels.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Download pre-trained resnet model - json and params by running following code.</span>
<span class="n">path</span><span class="o">=</span><span class="s1">&#39;http://data.mxnet.io/models/imagenet/&#39;</span>
<span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;resnet/18-layers/resnet-18-0000.params&#39;</span><span class="p">),</span>
<span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;resnet/18-layers/resnet-18-symbol.json&#39;</span><span class="p">),</span>
<span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;synset.txt&#39;</span><span class="p">)]</span>
</pre></div>
</div>
</div>
<div class="section" id="MXNet-to-ONNX-exporter-(mx2onnx)-API">
<h2>MXNet to ONNX exporter (mx2onnx) API<a class="headerlink" href="#MXNet-to-ONNX-exporter-(mx2onnx)-API" title="Permalink to this headline"></a></h2>
<p>Now let’s check MXNet’s <code class="docutils literal notranslate"><span class="pre">export_model</span></code> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export_model</span><span class="p">)</span>
</pre></div>
</div>
<p>Output:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>Help on function export_model in module mxnet.onnx.mx2onnx._export_model:
export_model(sym, params, in_shapes=None, in_types=&lt;class &#39;numpy.float32&#39;&gt;, onnx_file_path=&#39;model.onnx&#39;, verbose=False, dynamic=False, dynamic_input_shapes=None, run_shape_inference=False, input_type=None, input_shape=None)
Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
Operator support and coverage -
https://github.com/apache/mxnet/tree/v1.x/python/mxnet/onnx#operator-support-matrix
Parameters
----------
sym : str or symbol object
Path to the json file or Symbol object
params : str or dict or list of dict
str - Path to the params file
dict - params dictionary (Including both arg_params and aux_params)
list - list of length 2 that contains arg_params and aux_params
in_shapes : List of tuple
Input shape of the model e.g [(1,3,224,224)]
in_types : data type or list of data types
Input data type e.g. np.float32, or [np.float32, np.int32]
onnx_file_path : str
Path where to save the generated onnx file
verbose : Boolean
If True will print logs of the model conversion
dynamic: Boolean
If True will allow for dynamic input shapes to the model
dynamic_input_shapes: list of tuple
Specifies the dynamic input_shapes. If None then all dimensions are set to None
run_shape_inference : Boolean
If True will run shape inference on the model
input_type : data type or list of data types
This is the old name of in_types. We keep this parameter name for backward compatibility
input_shape : List of tuple
This is the old name of in_shapes. We keep this parameter name for backward compatibility
Returns
-------
onnx_file_path : str
Onnx file path
Notes
-----
This method is available when you ``import mxnet.onnx``
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">export_model</span></code> API can accept a MXNet model in one of the following ways.</p>
<ol class="arabic simple">
<li><p>MXNet’s exported json and params files:</p>
<ul class="simple">
<li><p>This is useful if we have pre-trained models and we want to convert them to ONNX format.</p></li>
</ul>
</li>
<li><p>MXNet sym, params objects:</p>
<ul class="simple">
<li><p>This is useful if we are training a model. At the end of training, we just need to invoke the <code class="docutils literal notranslate"><span class="pre">export_model</span></code> function and provide the sym and params objects as inputs to save the model in ONNX format. The params can be either a single object that contains both argument and auxiliary parameters, or a list that includes arg_parmas and aux_params objects</p></li>
</ul>
</li>
</ol>
<p>Since we have downloaded pre-trained model files, we will use the <code class="docutils literal notranslate"><span class="pre">export_model</span></code> API by passing in the paths of the symbol and params files.</p>
</div>
<div class="section" id="Use-mx2onnx-to-export-the-model">
<h2>Use mx2onnx to export the model<a class="headerlink" href="#Use-mx2onnx-to-export-the-model" title="Permalink to this headline"></a></h2>
<p>We will use the downloaded pre-trained model files (sym, params) and define a few more parameters.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Downloaded input symbol and params files</span>
<span class="n">sym</span> <span class="o">=</span> <span class="s1">&#39;./resnet-18-symbol.json&#39;</span>
<span class="n">params</span> <span class="o">=</span> <span class="s1">&#39;./resnet-18-0000.params&#39;</span>
<span class="c1"># Standard Imagenet input - 3 channels, 224 * 224</span>
<span class="n">in_shapes</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)]</span>
<span class="n">in_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">]</span>
<span class="c1"># Path of the output file</span>
<span class="n">onnx_file</span> <span class="o">=</span> <span class="s1">&#39;./mxnet_exported_resnet18.onnx&#39;</span>
</pre></div>
</div>
<p>We have defined the input parameters required for the <code class="docutils literal notranslate"><span class="pre">export_model</span></code> API. Now, we are ready to covert the MXNet model into ONNX format.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Invoke export model API. It returns path of the converted onnx model</span>
<span class="n">converted_model_path</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export_model</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_types</span><span class="p">,</span> <span class="n">onnx_file</span><span class="p">)</span>
</pre></div>
</div>
<p>This API returns the path of the converted model which you can later use to run inference with or import the model into other frameworks. Please refer to <a class="reference external" href="https://github.com/apache/mxnet/tree/v1.x/python/mxnet/onnx#apis">mx2onnx</a> for more details about the API.</p>
</div>
<div class="section" id="Dynamic-input-shapes">
<h2>Dynamic input shapes<a class="headerlink" href="#Dynamic-input-shapes" title="Permalink to this headline"></a></h2>
<p>The mx2onnx module also supports dynamic input shapes. We can set <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code> to turn it on. Note that even with dynamic shapes, a set of static input shapes still need to be specified in <code class="docutils literal notranslate"><span class="pre">in_shapes</span></code>; on top of that, we’ll also need to specify which dimensions of the input shapes are dynamic in <code class="docutils literal notranslate"><span class="pre">dynamic_input_shapes</span></code>. We can simply set the dynamic dimensions as <code class="docutils literal notranslate"><span class="pre">None</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">3,</span> <span class="pre">None,</span> <span class="pre">None)</span></code>, or use strings in place of the <code class="docutils literal notranslate"><span class="pre">None</span></code>’s for better understandability in the exported
onnx graph, e.g. <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">3,</span> <span class="pre">'Height',</span> <span class="pre">'Width')</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The first input dimension will be dynamic in this case</span>
<span class="n">dynamic_input_shapes</span> <span class="o">=</span> <span class="p">[(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)]</span>
<span class="n">converted_model_path</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export_model</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_types</span><span class="p">,</span> <span class="n">onnx_file</span><span class="p">,</span>
<span class="n">dynamic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dynamic_input_shapes</span><span class="o">=</span><span class="n">dynamic_input_shapes</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="Validate-the-exported-ONNX-model">
<h2>Validate the exported ONNX model<a class="headerlink" href="#Validate-the-exported-ONNX-model" title="Permalink to this headline"></a></h2>
<p>Now that we have the converted model, we can validate its correctness with the ONNX checker tool.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnx</span> <span class="kn">import</span> <span class="n">checker</span>
<span class="kn">import</span> <span class="nn">onnx</span>
<span class="c1"># Load the ONNX model</span>
<span class="n">model_proto</span> <span class="o">=</span> <span class="n">onnx</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">converted_model_path</span><span class="p">)</span>
<span class="c1"># Check if the converted ONNX protobuf is valid</span>
<span class="n">checker</span><span class="o">.</span><span class="n">check_graph</span><span class="p">(</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p>Now that the model passes the check (hopefully :)), we can run it with inference frameworks or import it into other deep learning frameworks!</p>
</div>
<div class="section" id="Simplify-the-exported-ONNX-model">
<h2>Simplify the exported ONNX model<a class="headerlink" href="#Simplify-the-exported-ONNX-model" title="Permalink to this headline"></a></h2>
<p>Okay, we already have the exported ONNX model now, but it may not be the end of the story. Due to differences in MXNet’s and ONNX’s operator specifications, sometimes helper operators/nodes will need to be created to help construct the ONNX graph from the MXNet blueprint. In that sense, we recommend our users to checkout <a class="reference external" href="https://github.com/daquexian/onnx-simplifier">onnx-simplifier</a>, which can greatly simplify the exported ONNX model by techniques such as constant folding, operator fusion
and more.</p>
</div>
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<span class="caption-text">Table Of Contents</span>
</p>
<ul>
<li><a class="reference internal" href="#">Exporting to ONNX format</a><ul>
<li><a class="reference internal" href="#Prerequisites">Prerequisites</a></li>
<li><a class="reference internal" href="#Download-a-model-from-the-MXNet-model-zoo">Download a model from the MXNet model zoo</a></li>
<li><a class="reference internal" href="#MXNet-to-ONNX-exporter-(mx2onnx)-API">MXNet to ONNX exporter (mx2onnx) API</a></li>
<li><a class="reference internal" href="#Use-mx2onnx-to-export-the-model">Use mx2onnx to export the model</a></li>
<li><a class="reference internal" href="#Dynamic-input-shapes">Dynamic input shapes</a></li>
<li><a class="reference internal" href="#Validate-the-exported-ONNX-model">Validate the exported ONNX model</a></li>
<li><a class="reference internal" href="#Simplify-the-exported-ONNX-model">Simplify the exported ONNX model</a></li>
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