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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="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="crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="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="crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3 current"><a class="current reference internal" href="#">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="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> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference 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> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <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-l1 current"><a class="reference internal" href="../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3"><a class="reference internal" href="crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="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="crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="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="crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3 current"><a class="current reference internal" href="#">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="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> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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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="Gluon:-from-experiment-to-deployment"> |
| <h1>Gluon: from experiment to deployment<a class="headerlink" href="#Gluon:-from-experiment-to-deployment" title="Permalink to this headline">¶</a></h1> |
| <div class="section" id="Overview"> |
| <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet Gluon API comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference. This tutorial covers training and inference in Python, please continue to <a class="reference external" href="/api/cpp/docs/tutorials/cpp_inference">C++ inference part</a> after you finish.</p> |
| <p>Let’s say you need to build a service that provides flower species recognition. A common problem is that you don’t have enough data to train a good model. In such cases, a technique called Transfer Learning can be used to make a more robust model. In Transfer Learning we make use of a pre-trained model that solves a related task, and was trained on a very large standard dataset, such as ImageNet. ImageNet is from a different domain, but we can utilize the knowledge in this pre-trained model to |
| perform the new task at hand.</p> |
| <p>Gluon provides State of the Art models for many of the standard tasks such as Classification, Object Detection, Segmentation, etc. In this tutorial we will use the pre-trained model <a class="reference external" href="https://arxiv.org/abs/1603.05027">ResNet50 V2</a> trained on ImageNet dataset. This model achieves 77.11% top-1 accuracy on ImageNet. We seek to transfer as much knowledge as possible for our task of recognizing different species of flowers.</p> |
| </div> |
| <div class="section" id="Prerequisites"> |
| <h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline">¶</a></h2> |
| <p>To complete this tutorial, you need:</p> |
| <ul class="simple"> |
| <li><p><a class="reference external" href="https://mxnet.apache.org/get_started/ubuntu_setup#build-mxnet-from-source">Build MXNet from source</a> with Python(Gluon) and C++ Packages</p></li> |
| <li><p>Learn the basics about Gluon with <a class="reference external" href="https://gluon-crash-course.mxnet.io/">A 60-minute Gluon Crash Course</a></p></li> |
| </ul> |
| </div> |
| <div class="section" id="The-Data"> |
| <h2>The Data<a class="headerlink" href="#The-Data" title="Permalink to this headline">¶</a></h2> |
| <p>We will use the <a class="reference external" href="http://www.robots.ox.ac.uk/~vgg/data/flowers/102/">Oxford 102 Category Flower Dataset</a> as an example to show you the steps. We have prepared a utility file to help you download and organize your data into train, test, and validation sets. Run the following Python code to download and prepare the data:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="n">data_util_file</span> <span class="o">=</span> <span class="s2">"oxford_102_flower_dataset.py"</span> |
| <span class="n">base_url</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/apache/mxnet/master/docs/tutorial_utils/data/</span><span class="si">{}</span><span class="s2">?raw=true"</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">data_util_file</span><span class="p">),</span> <span class="n">fname</span><span class="o">=</span><span class="n">data_util_file</span><span class="p">)</span> |
| <span class="kn">import</span> <span class="nn">oxford_102_flower_dataset</span> |
| |
| <span class="c1"># download and move data to train, test, valid folders</span> |
| <span class="n">path</span> <span class="o">=</span> <span class="s1">'./data'</span> |
| <span class="n">oxford_102_flower_dataset</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Now your data will be organized into train, test, and validation sets, images belong to the same class are moved to the same folder.</p> |
| </div> |
| <div class="section" id="Training-using-Gluon"> |
| <h2>Training using Gluon<a class="headerlink" href="#Training-using-Gluon" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="Define-Hyper-parameters"> |
| <h3>Define Hyper-parameters<a class="headerlink" href="#Define-Hyper-parameters" title="Permalink to this headline">¶</a></h3> |
| <p>Now let’s first import necessary packages:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">autograd</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">init</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo.vision</span> <span class="kn">import</span> <span class="n">resnet50_v2</span> |
| </pre></div> |
| </div> |
| <p>Next, we define the hyper-parameters that we will use for fine-tuning. We will use the <a class="reference external" href="/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">MXNet learning rate scheduler</a> to adjust learning rates during training. Here we set the <code class="docutils literal notranslate"><span class="pre">epochs</span></code> to 1 for quick demonstration, please change to 40 for actual training.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">classes</span> <span class="o">=</span> <span class="mi">102</span> |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001</span> |
| <span class="n">per_device_batch_size</span> <span class="o">=</span> <span class="mi">32</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="mf">0.9</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="mf">0.0001</span> |
| |
| <span class="n">lr_factor</span> <span class="o">=</span> <span class="mf">0.75</span> |
| <span class="c1"># learning rate change at following epochs</span> |
| <span class="n">lr_epochs</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">]</span> |
| |
| <span class="n">num_gpus</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> |
| <span class="c1"># you can replace num_workers with the number of cores on you device</span> |
| <span class="n">num_workers</span> <span class="o">=</span> <span class="mi">8</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_gpus</span><span class="p">)]</span> <span class="k">if</span> <span class="n">num_gpus</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()]</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">per_device_batch_size</span> <span class="o">*</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_gpus</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon <a class="reference external" href="/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset">DataSet API</a>, <a class="reference external" href="/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader">DataLoader API</a>, and <a class="reference external" href="/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset.transform">Transform |
| API</a> to load the images and apply the following data augmentations: 1. Randomly crop the image and resize it to 224x224 2. Randomly flip the image horizontally 3. Randomly jitter color and add noise 4. Transpose the data from <code class="docutils literal notranslate"><span class="pre">[height,</span> <span class="pre">width,</span> <span class="pre">num_channels]</span></code> to <code class="docutils literal notranslate"><span class="pre">[num_channels,</span> <span class="pre">height,</span> <span class="pre">width]</span></code>, and map values from [0, 255] to [0, 1] 5. Normalize with the mean and standard deviation from the ImageNet dataset.</p> |
| <p>For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for <a class="reference external" href="/api/cpp/docs/tutorials/cpp_inference">inference using C++</a>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">jitter_param</span> <span class="o">=</span> <span class="mf">0.4</span> |
| <span class="n">lighting_param</span> <span class="o">=</span> <span class="mf">0.1</span> |
| |
| <span class="c1"># mean and std for normalizing image value in range (0,1)</span> |
| <span class="n">mean</span> <span class="o">=</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="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="n">training_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">brightness</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">,</span> |
| <span class="n">saturation</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomLighting</span><span class="p">(</span><span class="n">lighting_param</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="n">validation_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="c1"># save mean and std NDArray values for inference</span> |
| <span class="n">mean_img</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">stack</span><span class="p">(</span><span class="o">*</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">full</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">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mean</span><span class="p">])</span> |
| <span class="n">std_img</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">stack</span><span class="p">(</span><span class="o">*</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">full</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">s</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">std</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">save</span><span class="p">(</span><span class="s1">'mean_std_224.nd'</span><span class="p">,</span> <span class="p">{</span><span class="s2">"mean_img"</span><span class="p">:</span> <span class="n">mean_img</span><span class="p">,</span> <span class="s2">"std_img"</span><span class="p">:</span> <span class="n">std_img</span><span class="p">})</span> |
| |
| <span class="n">train_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">path</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">)</span> |
| <span class="n">val_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">path</span><span class="p">,</span> <span class="s1">'valid'</span><span class="p">)</span> |
| <span class="n">test_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">path</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">)</span> |
| |
| <span class="c1"># loading the data and apply pre-processing(transforms) on images</span> |
| <span class="n">train_data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span> |
| <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">train_path</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">training_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span> |
| |
| <span class="n">val_data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span> |
| <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">val_path</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span> |
| |
| <span class="n">test_data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span> |
| <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">test_path</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Loading-pre-trained-model"> |
| <h3>Loading pre-trained model<a class="headerlink" href="#Loading-pre-trained-model" title="Permalink to this headline">¶</a></h3> |
| <p>We will use pre-trained ResNet50_v2 model which was pre-trained on the <a class="reference external" href="http://www.image-net.org/">ImageNet Dataset</a> with 1000 classes. To match the classes in the Flower dataset, we must redefine the last softmax (output) layer to be 102, then initialize the parameters.</p> |
| <p>Before we go to training, one unique Gluon feature you should be aware of is hybridization. It allows you to convert your imperative code to a static symbolic graph, which is much more efficient to execute. There are two main benefits of hybridizing your model: better performance and easier serialization for deployment. The best part is that it’s as simple as just calling <code class="docutils literal notranslate"><span class="pre">net.hybridize()</span></code>. To know more about Gluon hybridization, please follow the <a class="reference external" href="/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html">hybridization |
| tutorial</a>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># load pre-trained resnet50_v2 from model zoo</span> |
| <span class="n">finetune_net</span> <span class="o">=</span> <span class="n">resnet50_v2</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="c1"># change last softmax layer since number of classes are different</span> |
| <span class="k">with</span> <span class="n">finetune_net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="n">finetune_net</span><span class="o">.</span><span class="n">output</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">classes</span><span class="p">)</span> |
| <span class="n">finetune_net</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</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="c1"># hybridize for better performance</span> |
| <span class="n">finetune_net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">num_batch</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span> |
| |
| <span class="c1"># setup learning rate scheduler</span> |
| <span class="n">iterations_per_epoch</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">num_batch</span><span class="p">)</span> |
| <span class="c1"># learning rate change at following steps</span> |
| <span class="n">lr_steps</span> <span class="o">=</span> <span class="p">[</span><span class="n">epoch</span> <span class="o">*</span> <span class="n">iterations_per_epoch</span> <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">lr_epochs</span><span class="p">]</span> |
| <span class="n">schedule</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">MultiFactorScheduler</span><span class="p">(</span><span class="n">step</span><span class="o">=</span><span class="n">lr_steps</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="n">lr_factor</span><span class="p">,</span> <span class="n">base_lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span> |
| |
| <span class="c1"># setup optimizer with learning rate scheduler, metric, and loss function</span> |
| <span class="n">sgd_optimizer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">schedule</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">)</span> |
| <span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">softmax_cross_entropy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Fine-tuning-model-on-your-custom-dataset"> |
| <h3>Fine-tuning model on your custom dataset<a class="headerlink" href="#Fine-tuning-model-on-your-custom-dataset" title="Permalink to this headline">¶</a></h3> |
| <p>Now let’s define the test metrics and start fine-tuning.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">val_data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span> |
| <span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">val_data</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">even_split</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">even_split</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">finetune_net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">sgd_optimizer</span><span class="p">)</span> |
| |
| <span class="c1"># start with epoch 1 for easier learning rate calculation</span> |
| <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> |
| |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">train_loss</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_data</span><span class="p">):</span> |
| <span class="c1"># get the images and labels</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">even_split</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">even_split</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">finetune_net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="p">[</span><span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">yhat</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">label</span><span class="p">)]</span> |
| <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">loss</span><span class="p">:</span> |
| <span class="n">l</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="n">train_loss</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">l</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">loss</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">train_acc</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="n">train_loss</span> <span class="o">/=</span> <span class="n">num_batch</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">val_acc</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">finetune_net</span><span class="p">,</span> <span class="n">val_data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'[Epoch </span><span class="si">%d</span><span class="s1">] Train-acc: </span><span class="si">%.3f</span><span class="s1">, loss: </span><span class="si">%.3f</span><span class="s1"> | Val-acc: </span><span class="si">%.3f</span><span class="s1"> | learning-rate: </span><span class="si">%.3E</span><span class="s1"> | time: </span><span class="si">%.1f</span><span class="s1">'</span> <span class="o">%</span> |
| <span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">train_acc</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">val_acc</span><span class="p">,</span> <span class="n">trainer</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">finetune_net</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'[Finished] Test-acc: </span><span class="si">%.3f</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">test_acc</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>Following is the training result:</p> |
| <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>[Epoch 40] Train-acc: 0.945, loss: 0.354 | Val-acc: 0.955 | learning-rate: 4.219E-04 | time: 17.8 |
| [Finished] Test-acc: 0.952 |
| </pre></div> |
| </div> |
| <p>In the previous example output, we trained the model using an <a class="reference external" href="https://aws.amazon.com/ec2/instance-types/p3/">AWS p3.8xlarge instance</a> with 4 Tesla V100 GPUs. We were able to reach a test accuracy of 95.5% with 40 epochs in around 12 minutes. This was really fast because our model was pre-trained on a much larger dataset, ImageNet, with around 1.3 million images. It worked really well to capture features on our small dataset.</p> |
| </div> |
| <div class="section" id="Save-the-fine-tuned-model"> |
| <h3>Save the fine-tuned model<a class="headerlink" href="#Save-the-fine-tuned-model" title="Permalink to this headline">¶</a></h3> |
| <p>We now have a trained our custom model. This can be serialized into model files using the export function. The export function will export the model architecture into a <code class="docutils literal notranslate"><span class="pre">.json</span></code> file and model parameters into a <code class="docutils literal notranslate"><span class="pre">.params</span></code> file.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">finetune_net</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s2">"flower-recognition"</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">export</span></code> creates <code class="docutils literal notranslate"><span class="pre">flower-recognition-symbol.json</span></code> and <code class="docutils literal notranslate"><span class="pre">flower-recognition-0040.params</span></code> (<code class="docutils literal notranslate"><span class="pre">0040</span></code> is for 40 epochs we ran) in the current directory. These files can be used for model deployment in the next section.</p> |
| </div> |
| </div> |
| <div class="section" id="Load-the-model-and-run-inference-using-the-MXNet-Module-API"> |
| <h2>Load the model and run inference using the MXNet Module API<a class="headerlink" href="#Load-the-model-and-run-inference-using-the-MXNet-Module-API" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet provides various useful tools and interfaces for deploying your model for inference. For example, you can use <a class="reference external" href="https://github.com/awslabs/mxnet-model-server">MXNet Model Server</a> to start a service and host your trained model easily. Besides that, you can also use MXNet’s different language APIs to integrate your model with your existing service. We provide <a class="reference external" href="/api/python.html">Python</a>, <a class="reference external" href="/api/java.html">Java</a>, <a class="reference external" href="/api/scala.html">Scala</a>, and <a class="reference external" href="/api/cpp">C++</a> APIs.</p> |
| <p>Here we will briefly introduce how to run inference using Module API in Python. In general, prediction consists of the following steps: 1. Load the model architecture (symbol file) and trained parameter values (params file) 2. Load the synset file for label names 3. Load the image and apply the same transformation we did on validation dataset during training 4. Run a forward pass on the image data 5. Convert output probabilities to predicted label name</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">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</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> |
| <span class="c1"># load model symbol and params</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">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s1">'flower-recognition'</span><span class="p">,</span> <span class="n">epochs</span><span class="p">)</span> |
| <span class="n">mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">for_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_shapes</span><span class="o">=</span><span class="p">[(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))],</span> <span class="n">label_shapes</span><span class="o">=</span><span class="n">mod</span><span class="o">.</span><span class="n">_label_shapes</span><span class="p">)</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="c1"># load synset for label names</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'synset.txt'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">l</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">f</span><span class="p">]</span> |
| |
| <span class="c1"># load an image for prediction</span> |
| <span class="n">img</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="s1">'./data/test/lotus/image_01832.jpg'</span><span class="p">)</span> |
| <span class="c1"># apply transform we did during training</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">validation_transformer</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="c1"># batchify</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">img</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="n">Batch</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">'Batch'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">])</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">img</span><span class="p">]))</span> |
| <span class="n">prob</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="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'probability=</span><span class="si">%f</span><span class="s1">, class=</span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">prob</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span> |
| </pre></div> |
| </div> |
| <p>Following is the output, you can see the image has been classified as lotus correctly.</p> |
| <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>probability=9.798435, class=lotus |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="What’s-next"> |
| <h2>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h2> |
| <p>You can continue to the <a class="reference external" href="/api/cpp/docs/tutorials/cpp_inference">next tutorial</a> on how to load the model we just trained and run inference using MXNet C++ API.</p> |
| <p>You can also find more ways to run inference and deploy your models here: 1. <a class="reference external" href="https://github.com/apache/mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer">Java Inference examples</a> 2. <a class="reference external" href="/api/scala/docs/tutorials/infer">Scala Inference examples</a> 4. <a class="reference external" href="https://github.com/awslabs/mxnet-model-server/tree/master/examples">MXNet Model Server Examples</a></p> |
| </div> |
| <div class="section" id="References"> |
| <h2>References<a class="headerlink" href="#References" title="Permalink to this headline">¶</a></h2> |
| <ol class="arabic simple"> |
| <li><p><a class="reference external" href="https://github.com/Arsey/keras-transfer-learning-for-oxford102">Transfer Learning for Oxford102 Flower Dataset</a></p></li> |
| <li><p><a class="reference external" href="https://www.d2l.ai/chapter_computer-vision/fine-tuning.html">Gluon book on fine-tuning</a></p></li> |
| <li><p><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_classification/transfer_learning_minc.html">Gluon CV transfer learning tutorial</a></p></li> |
| <li><p><a class="reference external" href="https://gluon-crash-course.mxnet.io/">Gluon crash course</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/mxnet/blob/master/cpp-package/example/inference/">Gluon CPP inference example</a></p></li> |
| </ol> |
| </div> |
| </div> |
| |
| |
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| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| <div class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Gluon: from experiment to deployment</a><ul> |
| <li><a class="reference internal" href="#Overview">Overview</a></li> |
| <li><a class="reference internal" href="#Prerequisites">Prerequisites</a></li> |
| <li><a class="reference internal" href="#The-Data">The Data</a></li> |
| <li><a class="reference internal" href="#Training-using-Gluon">Training using Gluon</a><ul> |
| <li><a class="reference internal" href="#Define-Hyper-parameters">Define Hyper-parameters</a></li> |
| <li><a class="reference internal" href="#Loading-pre-trained-model">Loading pre-trained model</a></li> |
| <li><a class="reference internal" href="#Fine-tuning-model-on-your-custom-dataset">Fine-tuning model on your custom dataset</a></li> |
| <li><a class="reference internal" href="#Save-the-fine-tuned-model">Save the fine-tuned model</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Load-the-model-and-run-inference-using-the-MXNet-Module-API">Load the model and run inference using the MXNet Module API</a></li> |
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