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<div class="section" id="methods-of-applying-data-augmentation-gluon-api">
<span id="methods-of-applying-data-augmentation-gluon-api"></span><h1>Methods of applying data augmentation (Gluon API)<a class="headerlink" href="#methods-of-applying-data-augmentation-gluon-api" title="Permalink to this headline"></a></h1>
<p>Data Augmentation is a regularization technique that’s used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it’s very common in Computer Vision. Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, cropping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trained without data augmentation might unhelpfully learn that faces can only be of this size.</p>
<p>In this tutorial we demonstrate a method of applying data augmentation with Gluon <a class="reference external" href="/api/python/gluon/data.html#mxnet.gluon.data.Dataset"><code class="docutils literal"><span class="pre">mxnet.gluon.data.Dataset</span></code></a>s, specifically the <a class="reference external" href="/api/python/gluon/data.html#mxnet.gluon.data.vision.datasets.ImageFolderDataset"><code class="docutils literal"><span class="pre">ImageFolderDataset</span></code></a>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
<span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span> <span class="c1"># used version '1.0.0' at time of writing</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">import</span> <span class="n">imshow</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
<span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span> <span class="c1"># set seed for repeatability</span>
</pre></div>
</div>
<p>We define a utility function below, that will be used for visualising the augmentations in the tutorial.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_mx_array</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Array expected to be height x width x 3 (channels), and values are floats between 0 and 255.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"RGB Channel should be last"</span>
<span class="n">imshow</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span>!mkdir -p data/images
!wget https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg -P ./data/images/
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">example_image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">"./data/images/0.jpg"</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">plot_mx_array</span><span class="p">(</span><span class="n">example_image</span><span class="p">)</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png"/></p>
<div class="section" id="quick-start-with-imagefolderdataset">
<span id="quick-start-with-imagefolderdataset"></span><h2>Quick start with <a class="reference external" href="/api/python/gluon/data.html#mxnet.gluon.data.vision.datasets.ImageFolderDataset"><code class="docutils literal"><span class="pre">ImageFolderDataset</span></code></a><a class="headerlink" href="#quick-start-with-imagefolderdataset" title="Permalink to this headline"></a></h2>
<p>Using Gluon, it’s simple to add data augmentation to your training pipeline. When creating either <a class="reference external" href="/api/python/gluon/data.html#mxnet.gluon.data.vision.datasets.ImageFolderDataset"><code class="docutils literal"><span class="pre">ImageFolderDataset</span></code></a> or <a class="reference external" href="/api/python/gluon/data.html#mxnet.gluon.data.vision.datasets.ImageRecordDataset"><code class="docutils literal"><span class="pre">ImageRecordDataset</span></code></a>, you can pass a <code class="docutils literal"><span class="pre">transform</span></code> function that will be applied to each image in the dataset, every time it’s loaded from disk. Augmentations are intended to be random, so you’ll pass a slightly different version of the image to the network on each epoch.</p>
<p>We define <code class="docutils literal"><span class="pre">aug_transform</span></code> below to perform a selection of augmentation steps and pass it to our dataset. It’s worth noting that augmentations should only be applied to the training data (and not the test data), so you don’t want to pass this augmentation transform function to the testing dataset.</p>
<p><a class="reference external" href="/api/python/image/image.html?highlight=createaugmenter#mxnet.image.CreateAugmenter"><code class="docutils literal"><span class="pre">mxnet.image.CreateAugmenter</span></code></a> is a useful function for creating a diverse set of augmentations at once. Despite the singular <code class="docutils literal"><span class="pre">CreateAugmenter</span></code>, this function actually returns a list of Augmenters. We can then loop through this list and apply each type of augmentation one after another. Although the parameters of <code class="docutils literal"><span class="pre">CreateAugmenter</span></code> are fixed, the random augmentations (such as <code class="docutils literal"><span class="pre">rand_mirror</span></code> and <code class="docutils literal"><span class="pre">brightness</span></code>) will be different each time <code class="docutils literal"><span class="pre">aug_transform</span></code> is called.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">aug_transform</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="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
<span class="n">augs</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">CreateAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">),</span>
<span class="n">rand_crop</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">rand_mirror</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">inter_method</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">brightness</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">saturation</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span>
<span class="n">pca_noise</span><span class="o">=</span><span class="mf">0.02</span><span class="p">)</span>
<span class="k">for</span> <span class="n">aug</span> <span class="ow">in</span> <span class="n">augs</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">aug</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span>
<span class="n">training_dataset</span> <span class="o">=</span> <span class="n">mx</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">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">aug_transform</span><span class="p">)</span>
</pre></div>
</div>
<p>We can quickly inspect the augmentations by indexing the dataset (which calls the <code class="docutils literal"><span class="pre">__getitem__</span></code> method of the dataset). When this method is called (with an index) the correct image is read from disk, and the <code class="docutils literal"><span class="pre">transform</span></code> is applied. We can see the result of the augmentations when comparing the image below with the original image above.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sample</span> <span class="o">=</span> <span class="n">training_dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">sample_data</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">plot_mx_array</span><span class="p">(</span><span class="n">sample_data</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_10_0.png"/></p>
<p>In practice you should load images from a dataset with a <a class="reference external" href="/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader"><code class="docutils literal"><span class="pre">mxnet.gluon.data.DataLoader</span></code></a> to take advantage of automatic batching and shuffling. Under the hood the <code class="docutils literal"><span class="pre">DataLoader</span></code> calls <code class="docutils literal"><span class="pre">__getitem__</span></code>, but you shouldn’t need to call directly for anything other than debugging. Some practitioners pre-augment their datasets by applying a fixed number of augmentations to each image and saving the outputs to disk with the aim of increased throughput. With the <code class="docutils literal"><span class="pre">num_workers</span></code> parameter of <code class="docutils literal"><span class="pre">DataLoader</span></code> you can use all CPU cores to apply the augmentations, which often mitigates the need to perform pre-augmentation; reducing complexity and saving disk space.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">training_data_loader</span> <span class="o">=</span> <span class="n">mx</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">training_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">data_batch</span><span class="p">,</span> <span class="n">label_batch</span> <span class="ow">in</span> <span class="n">training_data_loader</span><span class="p">:</span>
<span class="n">plot_mx_array</span><span class="p">(</span><span class="n">data_batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">255</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">label_batch</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
<span class="k">break</span>
</pre></div>
</div>
<p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_12_1.png"/></p>
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<li><a class="reference internal" href="#">Methods of applying data augmentation (Gluon API)</a><ul>
<li><a class="reference internal" href="#quick-start-with-imagefolderdataset">Quick start with <code class="docutils literal"><span class="pre">ImageFolderDataset</span></code></a></li>
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