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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>
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<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>
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<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 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#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>
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<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-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="pretrained_models.html">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>
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<li class="toctree-l4"><a class="reference internal" href="../../../performance/compression/int8.html">Deploy with int-8</a></li>
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<!--- 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="Image-Augmentation">
<h1>Image Augmentation<a class="headerlink" href="#Image-Augmentation" title="Permalink to this headline"></a></h1>
<p>Image augmentation technology expands the scale of training data sets by making a series of random changes to the training images to produce similar, but different, training examples. Given its popularity in computer vision, the <code class="docutils literal notranslate"><span class="pre">mxnet.gluon.data.vision.transforms</span></code> model provides multiple pre-defined image augmentation methods. In this section we will briefly go through this module.</p>
<p>First, import the module required for this section.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">image</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">data</span> <span class="k">as</span> <span class="n">gdata</span><span class="p">,</span> <span class="n">utils</span>
</pre></div>
</div>
<p>Then read the sample <span class="math notranslate nohighlight">\(400\times 500\)</span> image.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">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/cat.jpg&#39;</span><span class="p">)</span>
<span class="n">img</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">&#39;cat.jpg&#39;</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">img</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>In addition, we define a function to draw a list of images.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">show_images</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
<span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="n">num_cols</span> <span class="o">*</span> <span class="n">scale</span><span class="p">,</span> <span class="n">num_rows</span> <span class="o">*</span> <span class="n">scale</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</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_rows</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_cols</span><span class="p">):</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">num_cols</span> <span class="o">+</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">axes</span>
</pre></div>
</div>
<p>Most image augmentation methods have a certain degree of randomness. To make it easier for us to observe the effect of image augmentation, we next define the auxiliary function <code class="docutils literal notranslate"><span class="pre">apply</span></code>. This function runs the image augmentation method <code class="docutils literal notranslate"><span class="pre">aug</span></code> multiple times on the input image <code class="docutils literal notranslate"><span class="pre">img</span></code> and shows all results.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">aug</span><span class="p">,</span> <span class="n">num_rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">aug</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span> <span class="o">*</span> <span class="n">num_cols</span><span class="p">)]</span>
<span class="n">show_images</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="Flip-and-Crop">
<h2>Flip and Crop<a class="headerlink" href="#Flip-and-Crop" title="Permalink to this headline"></a></h2>
<p>Flipping the image left and right usually does not change the category of the object. This is one of the earliest and most widely used methods of image augmentation. Next, we use the <code class="docutils literal notranslate"><span class="pre">transforms</span></code> module to create the <code class="docutils literal notranslate"><span class="pre">RandomFlipLeftRight</span></code> instance, which introduces a 50% chance that the image is flipped left and right.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">())</span>
</pre></div>
</div>
<p>Flipping up and down is not as commonly used as flipping left and right. However, at least for this example image, flipping up and down does not hinder recognition. Next, we create a <code class="docutils literal notranslate"><span class="pre">RandomFlipTopBottom</span></code> instance for a 50% chance of flipping the image up and down.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipTopBottom</span><span class="p">())</span>
</pre></div>
</div>
<p>In the example image we used, the cat is in the middle of the image, but this may not be the case for all images. In the <a class="reference external" href="https://d2l.ai/chapter_convolutional-neural-networks/pooling.html">Pooling Layer</a> section of the d2l.ai book, we explain that the pooling layer can reduce the sensitivity of the convolutional layer to the target location. In addition, we can make objects appear at different positions in the image in different proportions by randomly cropping the image. This can also reduce
the sensitivity of the model to the target position.</p>
<p>In the following code, we randomly crop a region with an area of 10% to 100% of the original area, and the ratio of width to height of the region is randomly selected from between 0.5 and 2. Then, the width and height of the region are both scaled to 200 pixels. Unless otherwise stated, the random number between <span class="math notranslate nohighlight">\(a\)</span> and <span class="math notranslate nohighlight">\(b\)</span> in this section refers to a continuous value obtained by uniform sampling in the interval <span class="math notranslate nohighlight">\([a,b]\)</span>.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
</pre></div>
</div>
<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="n">shape_aug</span> <span class="o">=</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span>
<span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">),</span> <span class="n">scale</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">ratio</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">shape_aug</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="Change-Color">
<h2>Change Color<a class="headerlink" href="#Change-Color" title="Permalink to this headline"></a></h2>
<p>Another augmentation method is changing colors. We can change four aspects of the image color: brightness, contrast, saturation, and hue. In the example below, we randomly change the brightness of the image to a value between 50% (<span class="math notranslate nohighlight">\(1-0.5\)</span>) and 150% (<span class="math notranslate nohighlight">\(1+0.5\)</span>) of the original image.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
</pre></div>
</div>
<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomBrightness</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
</div>
<p>Similarly, we can randomly change the hue of the image.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
</pre></div>
</div>
<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomHue</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
</div>
<p>We can also create a <code class="docutils literal notranslate"><span class="pre">RandomColorJitter</span></code> instance and set how to randomly change the <code class="docutils literal notranslate"><span class="pre">brightness</span></code>, <code class="docutils literal notranslate"><span class="pre">contrast</span></code>, <code class="docutils literal notranslate"><span class="pre">saturation</span></code>, and <code class="docutils literal notranslate"><span class="pre">hue</span></code> of the image at the same time.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
</pre></div>
</div>
<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="n">color_aug</span> <span class="o">=</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</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="mf">0.5</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">saturation</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">color_aug</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="Overlying-Multiple-Image-Augmentation-Methods">
<h2>Overlying Multiple Image Augmentation Methods<a class="headerlink" href="#Overlying-Multiple-Image-Augmentation-Methods" title="Permalink to this headline"></a></h2>
<p>In practice, we will overlay multiple image augmentation methods. We can overlay the different image augmentation methods defined above and apply them to each image by using a <code class="docutils literal notranslate"><span class="pre">Compose</span></code> instance.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
</pre></div>
</div>
<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="n">augs</span> <span class="o">=</span> <span class="n">gdata</span><span class="o">.</span><span class="n">vision</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">gdata</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> <span class="n">color_aug</span><span class="p">,</span> <span class="n">shape_aug</span><span class="p">])</span>
<span class="n">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">augs</span><span class="p">)</span>
</pre></div>
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<ul>
<li><a class="reference internal" href="#">Image Augmentation</a><ul>
<li><a class="reference internal" href="#Flip-and-Crop">Flip and Crop</a></li>
<li><a class="reference internal" href="#Change-Color">Change Color</a></li>
<li><a class="reference internal" href="#Overlying-Multiple-Image-Augmentation-Methods">Overlying Multiple Image Augmentation Methods</a></li>
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