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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>
<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-l5"><a class="reference internal" href="../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="../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="../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="../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="../../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>
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<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-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../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 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-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>
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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>
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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="../blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5 current"><a class="current reference internal" href="#">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../loss/loss.html">Loss functions</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../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="../training/normalization/index.html">Normalization Blocks</a></li>
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<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"><a class="reference internal" href="../../onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../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-l3"><a class="reference internal" href="../../viz/index.html">Visualization</a><ul>
<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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<!--- 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="Using-pre-trained-models-in-MXNet">
<h1>Using pre-trained models in MXNet<a class="headerlink" href="#Using-pre-trained-models-in-MXNet" title="Permalink to this headline"></a></h1>
<p>In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let’s download three image classification models from the Apache MXNet <a class="reference external" href="https://mxnet.apache.org/api/python/gluon/model_zoo.html">Gluon model zoo</a>. * <strong>DenseNet-121</strong> (<a class="reference external" href="https://arxiv.org/abs/1608.06993">research paper</a>), improved state of the art on <a class="reference external" href="http://image-net.org/challenges/LSVRC">ImageNet dataset</a> in 2016. * <strong>MobileNet</strong> (<a class="reference external" href="https://arxiv.org/abs/1704.04861">research paper</a>),
MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks, suitable for mobile applications. * <strong>ResNet-18</strong> (<a class="reference external" href="https://arxiv.org/abs/1512.03385v1">research paper</a>), the -152 version is the 2015 winner in multiple categories.</p>
<p>Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the tutorial, even though these models have been trained on the same dataset and optimized for maximum accuracy, they do behave slightly differently on specific images. In addition, prediction speed and memory footprints can vary, and that is an important factor for many applications. By trying a few pretrained models, you have an opportunity to find a model that can be a good fit
for solving your business problem.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">json</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">mxnet</span> <span class="k">as</span> <span class="nn">mx</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="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
</pre></div>
</div>
<div class="section" id="Loading-the-model">
<h2>Loading the model<a class="headerlink" href="#Loading-the-model" title="Permalink to this headline"></a></h2>
<p>The <a class="reference external" href="https://mxnet.apache.org/api/python/gluon/model_zoo.html">Gluon Model Zoo</a> provides a collection of off-the-shelf models. You can get the ImageNet pre-trained model by using <code class="docutils literal notranslate"><span class="pre">pretrained=True</span></code>. If you want to train on your own classification problem from scratch, you can get an untrained network with a specific number of classes using the <code class="docutils literal notranslate"><span class="pre">classes</span></code> parameter: for example <code class="docutils literal notranslate"><span class="pre">net</span> <span class="pre">=</span> <span class="pre">vision.resnet18_v1(classes=10)</span></code>. However note that you cannot use the <code class="docutils literal notranslate"><span class="pre">pretrained</span></code> and <code class="docutils literal notranslate"><span class="pre">classes</span></code>
parameter at the same time. If you want to use pre-trained weights as initialization of your network except for the last layer, have a look at the last section of this tutorial.</p>
<p>We can specify the <em>context</em> where we want to run the model: the default behavior is to use a CPU context. There are two reasons for this: * First, this will allow you to test the notebook even if your machine is not equipped with a GPU :) * Second, we’re going to predict a single image and we don’t have any specific performance requirements. For production applications where you’d want to predict large batches of images with the best possible throughput, a GPU could definitely be the way to
go. * If you want to use a GPU, make sure you have pip installed the right version of mxnet, or you will get an error when using the <code class="docutils literal notranslate"><span class="pre">mx.gpu()</span></code> context. Refer to the <a class="reference external" href="/get_started">install instructions</a></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># We set the context to CPU, you can switch to GPU if you have one and installed a compatible version of MXNet</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>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># We can load three the three models</span>
<span class="n">densenet121</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</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">mobileNet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">mobilenet0_5</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</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">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
</pre></div>
</div>
<p>We can look at the description of the MobileNet network for example, which has a relatively simple yet deep architecture</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">mobileNet</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s have a closer look at the first convolution layer:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">mobileNet</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
</pre></div>
</div>
<p>The first layer applies <strong>``16``</strong> different convolutional masks, of size <strong>``InputChannels x 3 x 3``</strong>. For the first convolution, there are <strong>``3``</strong> input channels, the <code class="docutils literal notranslate"><span class="pre">R</span></code>, <code class="docutils literal notranslate"><span class="pre">G</span></code>, <code class="docutils literal notranslate"><span class="pre">B</span></code> channels of the input image. That gives us the weight matrix of shape <strong>``16 x 3 x 3 x 3``</strong>. There is no bias applied in this convolution.</p>
<p>Let’s have a look at the output layer now:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">mobileNet</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
</pre></div>
</div>
<p>Did you notice the shape of layer? The weight matrix is <strong>1000 x 512</strong>. This layer contains 1,000 neurons: each of them will store an activation representative of the probability of the image belonging to a specific category. Each neuron is also fully connected to all 512 neurons in the previous layer.</p>
<p>OK, enough exploring! Now let’s use these models to classify our own images.</p>
</div>
<div class="section" id="Loading-the-data">
<h2>Loading the data<a class="headerlink" href="#Loading-the-data" title="Permalink to this headline"></a></h2>
<p>All three models have been pre-trained on the ImageNet data set which includes over 1.2 million pictures of objects and animals sorted in 1,000 categories. We get the imageNet list of labels. That way we have the mapping so when the model predicts for example category index <code class="docutils literal notranslate"><span class="pre">4</span></code>, we know it is predicting <code class="docutils literal notranslate"><span class="pre">hammerhead,</span> <span class="pre">hammerhead</span> <span class="pre">shark</span></code></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="s1">&#39;https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json&#39;</span><span class="p">)</span>
<span class="n">categories</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">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="s1">&#39;image_net_labels.json&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">categories</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
</pre></div>
</div>
<p>Get a test image</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</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="s1">&#39;https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/dog.jpg?raw=true&#39;</span><span class="p">,</span> <span class="n">fname</span><span class="o">=</span><span class="s1">&#39;dog.jpg&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>If you want to use your own image for the test, copy the image to the same folder that contains the notebook and change the following line:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;dog.jpg&#39;</span>
</pre></div>
</div>
<p>Load the image as a NDArray</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p>Neural network expects input in a specific format. Usually images comes in the <code class="docutils literal notranslate"><span class="pre">Width</span> <span class="pre">x</span> <span class="pre">Height</span> <span class="pre">x</span> <span class="pre">Channels</span></code> format. Where channels are the RGB channels. This network accepts images in the <code class="docutils literal notranslate"><span class="pre">BatchSize</span> <span class="pre">x</span> <span class="pre">3</span> <span class="pre">x</span> <span class="pre">224</span> <span class="pre">x</span> <span class="pre">224</span></code>. <code class="docutils literal notranslate"><span class="pre">224</span> <span class="pre">x</span> <span class="pre">224</span></code> is the image resolution, that’s how the model was trained. <code class="docutils literal notranslate"><span class="pre">3</span></code> is the number of channels : Red, Green and Blue (in this order). In this case we use a <code class="docutils literal notranslate"><span class="pre">BatchSize</span></code> of <code class="docutils literal notranslate"><span class="pre">1</span></code> since we are predicting one image at a time. Here are the transformation steps: * Read the
image: this will return a NDArray shaped as (image height, image width, 3), with the three channels in RGB order. * Resize the shorter edge of the image 224. * Crop, using a size of 224x224 from the center of the image. * Shift the mean and standard deviation of our color channels to match the ones of the dataset the network has been trained on. * Transpose the array from (Height, Width, 3) to (3, Height, Width). * Add a fourth dimension, the batch dimension.</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">image</span><span class="p">):</span>
<span class="n">resized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">resize_short</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span> <span class="c1">#minimum 224x224 images</span>
<span class="n">cropped</span><span class="p">,</span> <span class="n">crop_info</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">center_crop</span><span class="p">(</span><span class="n">resized</span><span class="p">,</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">normalized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">color_normalize</span><span class="p">(</span><span class="n">cropped</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><span class="o">/</span><span class="mi">255</span><span class="p">,</span>
<span class="n">mean</span><span class="o">=</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="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]),</span>
<span class="n">std</span><span class="o">=</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="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]))</span>
<span class="c1"># the network expect batches of the form (N,3,224,224)</span>
<span class="n">transposed</span> <span class="o">=</span> <span class="n">normalized</span><span class="o">.</span><span class="n">transpose</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="c1"># Transposing from (224, 224, 3) to (3, 224, 224)</span>
<span class="n">batchified</span> <span class="o">=</span> <span class="n">transposed</span><span class="o">.</span><span class="n">expand_dims</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="c1"># change the shape from (3, 224, 224) to (1, 3, 224, 224)</span>
<span class="k">return</span> <span class="n">batchified</span>
</pre></div>
</div>
</div>
<div class="section" id="Testing-the-different-networks">
<h2>Testing the different networks<a class="headerlink" href="#Testing-the-different-networks" title="Permalink to this headline"></a></h2>
<p>We run the image through each pre-trained network. The models output a <em>NDArray</em> holding 1,000 activation values, which we convert to probabilities using the <code class="docutils literal notranslate"><span class="pre">softmax()</span></code> function, corresponding to the 1,000 categories it has been trained on. The output prediction NDArray has only one row since batch size is equal to 1</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">resnet18</span><span class="p">(</span><span class="n">transform</span><span class="p">(</span><span class="n">image</span><span class="p">))</span><span class="o">.</span><span class="n">softmax</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">predictions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>
</div>
<p>We then take the top <code class="docutils literal notranslate"><span class="pre">k</span></code> predictions for our image, here the top <code class="docutils literal notranslate"><span class="pre">3</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">top_pred</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>And we print the categories predicted with their corresponding probabilities:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">top_pred</span><span class="p">:</span>
<span class="n">probability</span> <span class="o">=</span> <span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="nb">int</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">categories</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: </span><span class="si">{:.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">probability</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
</pre></div>
</div>
<p>Let’s turn this into a function. Our parameters are an image, a model, a list of categories and the number of top categories we’d like to print.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">transform</span><span class="p">(</span><span class="n">image</span><span class="p">))</span><span class="o">.</span><span class="n">softmax</span><span class="p">()</span>
<span class="n">top_pred</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">top_pred</span><span class="p">:</span>
<span class="n">probability</span> <span class="o">=</span> <span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="nb">int</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">categories</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: </span><span class="si">{:.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">probability</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="DenseNet121">
<h3>DenseNet121<a class="headerlink" href="#DenseNet121" title="Permalink to this headline"></a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="n">time</span>
<span class="n">predict</span><span class="p">(</span><span class="n">densenet121</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="MobileNet">
<h3>MobileNet<a class="headerlink" href="#MobileNet" title="Permalink to this headline"></a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="n">time</span>
<span class="n">predict</span><span class="p">(</span><span class="n">mobileNet</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="Resnet-18">
<h3>Resnet-18<a class="headerlink" href="#Resnet-18" title="Permalink to this headline"></a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="n">time</span>
<span class="n">predict</span><span class="p">(</span><span class="n">resnet18</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<p>As you can see, pre-trained networks produce slightly different predictions, and have different run-time. In this case, MobileNet is almost <strong>5 times faster</strong> than DenseNet!</p>
</div>
</div>
<div class="section" id="Fine-tuning-pre-trained-models">
<h2>Fine-tuning pre-trained models<a class="headerlink" href="#Fine-tuning-pre-trained-models" title="Permalink to this headline"></a></h2>
<p>You can replace the output layer of your pre-trained model to fit the right number of classes for your own image classification task like this, for example for 10 classes:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">NUM_CLASSES</span> <span class="o">=</span> <span class="mi">10</span>
<span class="k">with</span> <span class="n">resnet18</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">resnet18</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">NUM_CLASSES</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">resnet18</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
</pre></div>
</div>
<p>Now you can train your model on your new data using the pre-trained weights as initialization. This is called transfer learning and it has proved to be very useful especially in the cases where you only have access to a small dataset. Your network will have already learned how to perform general pattern detection and feature extraction on the larger dataset. You can learn more about transfer learning and fine-tuning with MXNet in these tutorials: - <a class="reference external" href="http://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html">Transferring knowledge through
fine-tuning</a> - <a class="reference external" href="/api/python/docs/tutorials/packages/onnx/fine_tuning_gluon.html">Fine Tuning an ONNX Model</a></p>
<p>That’s it! Explore the model zoo, have fun with pre-trained models!</p>
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<span class="caption-text">Table Of Contents</span>
</p>
<ul>
<li><a class="reference internal" href="#">Using pre-trained models in MXNet</a><ul>
<li><a class="reference internal" href="#Loading-the-model">Loading the model</a></li>
<li><a class="reference internal" href="#Loading-the-data">Loading the data</a></li>
<li><a class="reference internal" href="#Testing-the-different-networks">Testing the different networks</a><ul>
<li><a class="reference internal" href="#DenseNet121">DenseNet121</a></li>
<li><a class="reference internal" href="#MobileNet">MobileNet</a></li>
<li><a class="reference internal" href="#Resnet-18">Resnet-18</a></li>
</ul>
</li>
<li><a class="reference internal" href="#Fine-tuning-pre-trained-models">Fine-tuning pre-trained models</a></li>
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