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<title>Running inference on MXNet/Gluon from an ONNX model &#8212; Apache MXNet documentation</title>
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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
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<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>
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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>
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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>
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<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-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="../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="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../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="../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l4"><a class="reference internal" href="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-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>
</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-l2 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="../gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../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="../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
</ul>
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<li class="toctree-l3 current"><a class="reference internal" href="index.html">ONNX</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4 current"><a class="current reference internal" href="#">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="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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<!--- 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="Running-inference-on-MXNet/Gluon-from-an-ONNX-model">
<h1>Running inference on MXNet/Gluon from an ONNX model<a class="headerlink" href="#Running-inference-on-MXNet/Gluon-from-an-ONNX-model" 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.</p>
<p>In this tutorial we will:</p>
<ul class="simple">
<li><p>learn how to load a pre-trained .onnx model file into MXNet/Gluon</p></li>
<li><p>learn how to test this model using the sample input/output</p></li>
<li><p>learn how to test the model on custom images</p></li>
</ul>
<div class="section" id="Pre-requisite">
<h2>Pre-requisite<a class="headerlink" href="#Pre-requisite" title="Permalink to this headline"></a></h2>
<p>To run the tutorial you will need to have installed the following python modules: - <a class="reference external" href="/get_started">MXNet &gt; 1.1.0</a> - <a class="reference external" href="https://github.com/onnx/onnx">onnx</a> (follow the install guide) - matplotlib</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">from</span> <span class="nn">mxnet.contrib</span> <span class="kn">import</span> <span class="n">onnx</span> <span class="k">as</span> <span class="n">onnx_mxnet</span>
<span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">nd</span>
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">tarfile</span><span class="o">,</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">json</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 class="section" id="Downloading-supporting-files">
<h3>Downloading supporting files<a class="headerlink" href="#Downloading-supporting-files" title="Permalink to this headline"></a></h3>
<p>These are images and a vizualisation script</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">image_folder</span> <span class="o">=</span> <span class="s2">&quot;images&quot;</span>
<span class="n">utils_file</span> <span class="o">=</span> <span class="s2">&quot;utils.py&quot;</span> <span class="c1"># contain utils function to plot nice visualization</span>
<span class="n">image_net_labels_file</span> <span class="o">=</span> <span class="s2">&quot;image_net_labels.json&quot;</span>
<span class="n">images</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;apron.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;hammerheadshark.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;wrench.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dolphin.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;lotus.jpg&#39;</span><span class="p">]</span>
<span class="n">base_url</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/</span><span class="si">{}</span><span class="s2">?raw=true&quot;</span>
<span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</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">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">image_folder</span><span class="p">,</span> <span class="n">image</span><span class="p">)),</span> <span class="n">fname</span><span class="o">=</span><span class="n">image</span><span class="p">,</span><span class="n">dirname</span><span class="o">=</span><span class="n">image_folder</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">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">utils_file</span><span class="p">),</span> <span class="n">fname</span><span class="o">=</span><span class="n">utils_file</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">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">image_net_labels_file</span><span class="p">),</span> <span class="n">fname</span><span class="o">=</span><span class="n">image_net_labels_file</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="o">*</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="Downloading-a-model-from-the-ONNX-model-zoo">
<h2>Downloading a model from the ONNX model zoo<a class="headerlink" href="#Downloading-a-model-from-the-ONNX-model-zoo" title="Permalink to this headline"></a></h2>
<p>We download a pre-trained model, in our case the <a class="reference external" href="https://arxiv.org/abs/1409.4842">GoogleNet</a> model, trained on <a class="reference external" href="http://www.image-net.org/">ImageNet</a> from the <a class="reference external" href="https://github.com/onnx/models">ONNX model zoo</a>. The model comes packaged in an archive <code class="docutils literal notranslate"><span class="pre">tar.gz</span></code> file containing an <code class="docutils literal notranslate"><span class="pre">model.onnx</span></code> model file.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">base_url</span> <span class="o">=</span> <span class="s2">&quot;https://s3.amazonaws.com/download.onnx/models/opset_3/&quot;</span>
<span class="n">current_model</span> <span class="o">=</span> <span class="s2">&quot;bvlc_googlenet&quot;</span>
<span class="n">model_folder</span> <span class="o">=</span> <span class="s2">&quot;model&quot;</span>
<span class="n">archive</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.tar.gz&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">current_model</span><span class="p">)</span>
<span class="n">archive_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">archive</span><span class="p">)</span>
<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">base_url</span><span class="p">,</span> <span class="n">archive</span><span class="p">)</span>
</pre></div>
</div>
<p>Download and extract pre-trained model</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">url</span><span class="p">,</span> <span class="n">dirname</span> <span class="o">=</span> <span class="n">model_folder</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">current_model</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Extracting model...&#39;</span><span class="p">)</span>
<span class="n">tar</span> <span class="o">=</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">archive_file</span><span class="p">,</span> <span class="s2">&quot;r:gz&quot;</span><span class="p">)</span>
<span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">model_folder</span><span class="p">)</span>
<span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Extracted&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>The models have been pre-trained on ImageNet, let’s load the label mapping of the 1000 classes.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">categories</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">image_net_labels_file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="Loading-the-model-into-MXNet-Gluon">
<h2>Loading the model into MXNet Gluon<a class="headerlink" href="#Loading-the-model-into-MXNet-Gluon" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">current_model</span><span class="p">,</span> <span class="s2">&quot;model.onnx&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>We get the symbol and parameter objects</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">onnx_mxnet</span><span class="o">.</span><span class="n">import_model</span><span class="p">(</span><span class="n">onnx_path</span><span class="p">)</span>
</pre></div>
</div>
<p>We pick a context, CPU is fine for inference, switch to mx.gpu() if you want to use your GPU.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
</pre></div>
</div>
<p>We obtain the data names of the inputs to the model by using the model metadata API:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model_metadata</span> <span class="o">=</span> <span class="n">onnx_mxnet</span><span class="o">.</span><span class="n">get_model_metadata</span><span class="p">(</span><span class="n">onnx_path</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_metadata</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">&#39;output_tensor_data&#39;</span><span class="p">:</span> <span class="p">[(</span><span class="sa">u</span><span class="s1">&#39;gpu_0/softmax_1&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="n">L</span><span class="p">,</span> <span class="mi">1000</span><span class="n">L</span><span class="p">))],</span>
<span class="s1">&#39;input_tensor_data&#39;</span><span class="p">:</span> <span class="p">[(</span><span class="sa">u</span><span class="s1">&#39;gpu_0/data_0&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="n">L</span><span class="p">,</span> <span class="mi">3</span><span class="n">L</span><span class="p">,</span> <span class="mi">224</span><span class="n">L</span><span class="p">,</span> <span class="mi">224</span><span class="n">L</span><span class="p">))]}</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="n">model_metadata</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;input_tensor_data&#39;</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-[u'data_0']```&lt;!--notebook-skip-line--&gt; notranslate"><div class="highlight"><pre><span></span>And load them into a MXNet Gluon symbol block.
```python
import warnings
with warnings.catch_warnings():
warnings.simplefilter(&quot;ignore&quot;)
net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var(&#39;data_0&#39;))
net_params = net.collect_params()
for param in arg_params:
if param in net_params:
net_params[param]._load_init(arg_params[param], ctx=ctx)
for param in aux_params:
if param in net_params:
net_params[param]._load_init(aux_params[param], ctx=ctx)
</pre></div>
</div>
<p>We can now cache the computational graph through <a class="reference external" href="https://mxnet.apache.org/tutorials/gluon/hybrid.html">hybridization</a> to gain some performance</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span>
</pre></div>
</div>
<p>We can visualize the network (requires graphviz installed)</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">visualization</span><span class="o">.</span><span class="n">plot_network</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">node_attrs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shape&quot;</span><span class="p">:</span><span class="s2">&quot;oval&quot;</span><span class="p">,</span><span class="s2">&quot;fixedsize&quot;</span><span class="p">:</span><span class="s2">&quot;false&quot;</span><span class="p">})</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/caltech101.png?raw=true" /></p>
<p>This is a helper function to run M batches of data of batch-size N through the net and collate the outputs into an array of shape (K, 1000) where K=MxN is the total number of examples (mumber of batches x batch-size) run through the network.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_batch</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">o</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">outputs</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()])</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="Test-using-real-images">
<h2>Test using real images<a class="headerlink" href="#Test-using-real-images" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">TOP_P</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># How many top guesses we show in the visualization</span>
</pre></div>
</div>
<p>Transform function to set the data into the format the network expects, (N, 3, 224, 224) where N is the batch size.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">img</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
</pre></div>
</div>
<p>We load two sets of images in memory</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">image_net_images</span> <span class="o">=</span> <span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">.jpg&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">image_folder</span><span class="p">,</span> <span class="n">path</span><span class="p">))</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;apron&#39;</span><span class="p">,</span> <span class="s1">&#39;hammerheadshark&#39;</span><span class="p">,</span><span class="s1">&#39;dog&#39;</span><span class="p">]]</span>
<span class="n">caltech101_images</span> <span class="o">=</span> <span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">.jpg&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">image_folder</span><span class="p">,</span> <span class="n">path</span><span class="p">))</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;wrench&#39;</span><span class="p">,</span> <span class="s1">&#39;dolphin&#39;</span><span class="p">,</span><span class="s1">&#39;lotus&#39;</span><span class="p">]]</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">image_net_images</span> <span class="o">+</span> <span class="n">caltech101_images</span>
</pre></div>
</div>
<p>And run them as a batch through the network to get the predictions</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">batch</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">run_batch</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="p">[</span><span class="n">batch</span><span class="p">])</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plot_predictions</span><span class="p">(</span><span class="n">image_net_images</span><span class="p">,</span> <span class="n">result</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">categories</span><span class="p">,</span> <span class="n">TOP_P</span><span class="p">)</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/caltech101.png?raw=true" /></p>
<p><strong>Well done!</strong> Looks like it is doing a pretty good job at classifying pictures when the category is a ImageNet label</p>
<p>Let’s now see the results on the 3 other images</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plot_predictions</span><span class="p">(</span><span class="n">caltech101_images</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">categories</span><span class="p">,</span> <span class="n">TOP_P</span><span class="p">)</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/caltech101.png?raw=true" /></p>
<p><strong>Hmm, not so good…</strong> Even though predictions are close, they are not accurate, which is due to the fact that the ImageNet dataset does not contain <code class="docutils literal notranslate"><span class="pre">wrench</span></code>, <code class="docutils literal notranslate"><span class="pre">dolphin</span></code>, or <code class="docutils literal notranslate"><span class="pre">lotus</span></code> categories and our network has been trained on ImageNet.</p>
<p>Lucky for us, the <a class="reference external" href="http://www.vision.caltech.edu/Image_Datasets/Caltech101/">Caltech101 dataset</a> has them, let’s see how we can fine-tune our network to classify these categories correctly.</p>
<p>We show that in our next tutorial:</p>
<ul class="simple">
<li><p><a class="reference external" href="http://mxnet.apache.org/tutorials/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX Model using the modern imperative MXNet/Gluon</a></p></li>
</ul>
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<span class="caption-text">Table Of Contents</span>
</p>
<ul>
<li><a class="reference internal" href="#">Running inference on MXNet/Gluon from an ONNX model</a><ul>
<li><a class="reference internal" href="#Pre-requisite">Pre-requisite</a><ul>
<li><a class="reference internal" href="#Downloading-supporting-files">Downloading supporting files</a></li>
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<li><a class="reference internal" href="#Downloading-a-model-from-the-ONNX-model-zoo">Downloading a model from the ONNX model zoo</a></li>
<li><a class="reference internal" href="#Loading-the-model-into-MXNet-Gluon">Loading the model into MXNet Gluon</a></li>
<li><a class="reference internal" href="#Test-using-real-images">Test using real images</a></li>
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