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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>
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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-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
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<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>
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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-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-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li>
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<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>
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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"><a class="reference internal" href="inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4 current"><a class="current reference internal" href="#">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 external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul>
<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>
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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>
</ul>
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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"><a class="reference internal" href="inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4 current"><a class="current reference internal" href="#">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="Importing-an-ONNX-model-into-MXNet">
<h1>Importing an ONNX model into MXNet<a class="headerlink" href="#Importing-an-ONNX-model-into-MXNet" title="Permalink to this headline"></a></h1>
<p>In this tutorial we will:</p>
<ul class="simple">
<li><p>learn how to load a pre-trained ONNX model file into MXNet.</p></li>
<li><p>run inference in MXNet.</p></li>
</ul>
<div class="section" id="Prerequisites">
<h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline"></a></h2>
<p>This example assumes that the following python packages are installed: - <a class="reference external" href="/get_started">mxnet</a> - <a class="reference external" href="https://github.com/onnx/onnx">onnx</a> (follow the install guide) - Pillow - A Python Image Processing package and is required for input pre-processing. It can be installed with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">Pillow</span></code>. - matplotlib</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</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">import</span> <span class="nn">mxnet.contrib.onnx</span> <span class="k">as</span> <span class="nn">onnx_mxnet</span>
<span class="kn">from</span> <span class="nn">mxnet.test_utils</span> <span class="kn">import</span> <span class="n">download</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">import</span> <span class="n">imshow</span>
</pre></div>
</div>
<div class="section" id="Fetching-the-required-files">
<h3>Fetching the required files<a class="headerlink" href="#Fetching-the-required-files" title="Permalink to this headline"></a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">img_url</span> <span class="o">=</span> <span class="s1">&#39;https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg&#39;</span>
<span class="n">download</span><span class="p">(</span><span class="n">img_url</span><span class="p">,</span> <span class="s1">&#39;super_res_input.jpg&#39;</span><span class="p">)</span>
<span class="n">model_url</span> <span class="o">=</span> <span class="s1">&#39;https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx&#39;</span>
<span class="n">onnx_model_file</span> <span class="o">=</span> <span class="n">download</span><span class="p">(</span><span class="n">model_url</span><span class="p">,</span> <span class="s1">&#39;super_resolution.onnx&#39;</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="Loading-the-model-into-MXNet">
<h2>Loading the model into MXNet<a class="headerlink" href="#Loading-the-model-into-MXNet" title="Permalink to this headline"></a></h2>
<p>To completely describe a pre-trained model in MXNet, we need two elements: a symbolic graph, containing the model’s network definition, and a binary file containing the model weights. You can import the ONNX model and get the symbol and parameters objects using <code class="docutils literal notranslate"><span class="pre">import_model</span></code> API. The paameter object is split into argument parameters and auxilliary parameters.</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</span><span class="p">,</span> <span class="n">aux</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_model_file</span><span class="p">)</span>
</pre></div>
</div>
<p>We can now visualize the imported model (graphviz needs to be 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">viz</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="svg" src="https://s3.amazonaws.com/onnx-mxnet/examples/super_res_mxnet_model.png" /></p>
</div>
<div class="section" id="Input-Pre-processing">
<h2>Input Pre-processing<a class="headerlink" href="#Input-Pre-processing" title="Permalink to this headline"></a></h2>
<p>We will transform the previously downloaded input image into an input tensor.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;super_res_input.jpg&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">resize</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">img_ycbcr</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;YCbCr&quot;</span><span class="p">)</span>
<span class="n">img_y</span><span class="p">,</span> <span class="n">img_cb</span><span class="p">,</span> <span class="n">img_cr</span> <span class="o">=</span> <span class="n">img_ycbcr</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">test_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img_y</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
</pre></div>
</div>
</div>
<div class="section" id="Run-Inference-using-MXNet’s-Module-API">
<h2>Run Inference using MXNet’s Module API<a class="headerlink" href="#Run-Inference-using-MXNet’s-Module-API" title="Permalink to this headline"></a></h2>
<p>We will use MXNet’s Module API to run the inference. For this we will need to create the module, bind it to the input data and assign the loaded weights from the two parameter objects - argument parameters and auxilliary parameters.</p>
<p>To obtain the input data names we run the following line, which picks all the inputs of the symbol graph excluding the argument and auxiliary parameters:</p>
<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">graph_input</span> <span class="k">for</span> <span class="n">graph_input</span> <span class="ow">in</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span>
<span class="k">if</span> <span class="n">graph_input</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">arg</span> <span class="ow">and</span> <span class="n">graph_input</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">aux</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-['1']``` notranslate"><div class="highlight"><pre><span></span>```python
mod = mx.mod.Module(symbol=sym, data_names=data_names, context=mx.cpu(), label_names=None)
mod.bind(for_training=False, data_shapes=[(data_names[0],test_image.shape)], label_shapes=None)
mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
</pre></div>
</div>
<p>Module API’s forward method requires batch of data as input. We will prepare the data in that format and feed it to the forward method.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="n">Batch</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;Batch&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">])</span>
<span class="c1"># forward on the provided data batch</span>
<span class="n">mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Batch</span><span class="p">([</span><span class="n">mx</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">test_image</span><span class="p">)]))</span>
</pre></div>
</div>
<p>To get the output of previous forward computation, you use <code class="docutils literal notranslate"><span class="pre">module.get_outputs()</span></code> method. It returns an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> that we convert to a <code class="docutils literal notranslate"><span class="pre">numpy</span></code> array and then to Pillow’s image format</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">output</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">img_out_y</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">((</span><span class="n">output</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;L&#39;</span><span class="p">))</span>
<span class="n">result_img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
<span class="s2">&quot;YCbCr&quot;</span><span class="p">,</span> <span class="p">[</span>
<span class="n">img_out_y</span><span class="p">,</span>
<span class="n">img_cb</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">img_out_y</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">BICUBIC</span><span class="p">),</span>
<span class="n">img_cr</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">img_out_y</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">BICUBIC</span><span class="p">)</span>
<span class="p">])</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;RGB&quot;</span><span class="p">)</span>
<span class="n">result_img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;super_res_output.jpg&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>You can now compare the input image and the resulting output image. As you will notice, the model was able to increase the spatial resolution from <code class="docutils literal notranslate"><span class="pre">256x256</span></code> to <code class="docutils literal notranslate"><span class="pre">672x672</span></code>.</p>
<div class="line-block">
<div class="line">Input Image | Output Image |</div>
<div class="line">———– | ———— |</div>
<div class="line"><img alt="input" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_input.jpg?raw=true" /> | <img alt="output" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/images/super_res_output.jpg?raw=true" /> |</div>
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<ul>
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