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<div class="section" id="data-loading-api">
<span id="data-loading-api"></span><h1>Data Loading API<a class="headerlink" href="#data-loading-api" title="Permalink to this headline"></a></h1>
<div class="section" id="overview">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline"></a></h2>
<p>A data iterator reads data batch by batch.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="nv">$data</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$nd_iter</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">NDArrayIter</span><span class="p">(</span><span class="nv">$data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=></span><span class="mi">25</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="k">for</span> <span class="k">my</span> <span class="nv">$batch</span> <span class="p">(</span><span class="nv">@</span><span class="p">{</span> <span class="nv">$nd_iter</span> <span class="p">})</span> <span class="p">{</span> <span class="k">print</span> <span class="nv">$batch</span><span class="o">-></span><span class="nn">data</span><span class="o">-></span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s">"\n"</span> <span class="p">}</span>
<span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">25</span><span class="n">x10</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
<span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">25</span><span class="n">x10</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
<span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">25</span><span class="n">x10</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
<span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">25</span><span class="n">x10</span> <span class="nv">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
</pre></div>
</div>
<p>If <code class="docutils literal"><span class="pre">$nd_iter->reset()</span></code> is called, then reads the data again from beginning.</p>
<p>In addition, an iterator provides information about the batch, including the
shapes and name.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="nv">$nd_iter</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="o">=></span><span class="p">{</span><span class="n">data</span> <span class="o">=></span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span><span class="mi">10</span><span class="p">])},</span> <span class="n">label</span><span class="o">=></span><span class="p">{</span><span class="n">softmax_label</span> <span class="o">=></span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">([</span><span class="mi">100</span><span class="p">])},</span> <span class="n">batch_size</span><span class="o">=></span><span class="mi">25</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="k">print</span><span class="p">(</span><span class="nv">$nd_iter</span><span class="o">-></span><span class="nn">provide_data</span><span class="o">-></span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s">"\n"</span><span class="p">)</span>
<span class="n">DataDesc</span><span class="p">[</span><span class="n">data</span><span class="p">,</span><span class="mi">25</span><span class="n">x10</span><span class="p">,</span><span class="n">float32</span><span class="p">,</span><span class="n">NCHW</span><span class="p">]</span>
<span class="n">pdl</span><span class="o">></span> <span class="k">print</span><span class="p">(</span><span class="nv">$nd_iter</span><span class="o">-></span><span class="nn">provide_label</span><span class="o">-></span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s">"\n"</span><span class="p">)</span>
<span class="n">DataDesc</span><span class="p">[</span><span class="n">softmax_label</span><span class="p">,</span><span class="mi">25</span><span class="p">,</span><span class="n">float32</span><span class="p">,</span><span class="n">NCHW</span><span class="p">]</span>
</pre></div>
</div>
<p>So this iterator can be used to train a symbol whose input data variable has
name <code class="docutils literal"><span class="pre">data</span></code> and input label variable has name <code class="docutils literal"><span class="pre">softmax_label</span></code>.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="nv">$data</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">sym</span><span class="o">-></span><span class="n">Variable</span><span class="p">(</span><span class="s">'data'</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$label</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">sym</span><span class="o">-></span><span class="n">Variable</span><span class="p">(</span><span class="s">'softmax_label'</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$fullc</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">sym</span><span class="o">-></span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=></span><span class="nv">$data</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=></span><span class="mi">1</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$loss</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">sym</span><span class="o">-></span><span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=></span><span class="nv">$data</span><span class="p">,</span> <span class="n">label</span><span class="o">=></span><span class="nv">$label</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$mod</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">mod</span><span class="o">-></span><span class="n">Module</span><span class="p">(</span><span class="nv">$loss</span><span class="p">)</span>
<span class="n">pdl</span><span class="o">></span> <span class="k">print</span><span class="p">(</span><span class="nv">$mod</span><span class="o">-></span><span class="nn">data_names</span><span class="o">-></span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">data</span>
<span class="n">pdl</span><span class="o">></span> <span class="k">print</span><span class="p">(</span><span class="nv">$mod</span><span class="o">-></span><span class="nn">label_names</span><span class="o">-></span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">softmax_label</span>
<span class="n">pdl</span><span class="o">></span> <span class="nv">$mod</span><span class="o">-></span><span class="nb">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="o">=></span><span class="nv">$nd_iter</span><span class="o">-></span><span class="n">provide_data</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=></span><span class="nv">$nd_iter</span><span class="o">-></span><span class="n">provide_label</span><span class="p">)</span>
</pre></div>
</div>
<p>Then we can call <code class="docutils literal"><span class="pre">$mod->fit($nd_iter,</span> <span class="pre">num_epoch=>2)</span></code> to train <code class="docutils literal"><span class="pre">loss</span></code> by 2 epochs.</p>
</div>
<div class="section" id="predefined-data-iterators">
<span id="predefined-data-iterators"></span><h2>Predefined Data iterators<a class="headerlink" href="#predefined-data-iterators" title="Permalink to this headline"></a></h2>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">NDArrayIter</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">CSVIter</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">ImageRecordIter</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">ImageRecordUInt8Iter</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">io</span><span class="o">-></span><span class="n">MNISTIter</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">recordio</span><span class="o">-></span><span class="n">MXRecordIO</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">recordio</span><span class="o">-></span><span class="n">MXIndexedRecordIO</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">ImageIter</span>
</pre></div>
</div>
</div>
<div class="section" id="helper-classes-and-functions">
<span id="helper-classes-and-functions"></span><h2>Helper classes and functions<a class="headerlink" href="#helper-classes-and-functions" title="Permalink to this headline"></a></h2>
<p>Data structures and other iterators provided in the <code class="docutils literal"><span class="pre">AI::MXNet::IO</span></code> package.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="nn">AI::MXNet::</span><span class="n">DataDesc</span>
<span class="nn">AI::MXNet::</span><span class="n">DataBatch</span>
<span class="nn">AI::MXNet::</span><span class="n">DataIter</span>
<span class="nn">AI::MXNet::</span><span class="n">ResizeIter</span>
<span class="nn">AI::MXNet::</span><span class="n">MXDataIter</span>
</pre></div>
</div>
<p>A list of image modification functions provided by <code class="docutils literal"><span class="pre">AI::MXNet::Image</span></code>.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">imdecode</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">scale_down</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">resize_short</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">fixed_crop</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">random_crop</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">center_crop</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">color_normalize</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">random_size_crop</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">ResizeAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">RandomCropAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">RandomSizedCropAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">CenterCropAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">RandomOrderAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">ColorJitterAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">LightingAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">ColorNormalizeAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">HorizontalFlipAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">CastAug</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">image</span><span class="o">-></span><span class="n">CreateAugmenter</span>
</pre></div>
</div>
<p>Functions to read and write RecordIO files.</p>
<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="nn">mx</span><span class="o">-></span><span class="nn">recordio</span><span class="o">-></span><span class="nb">pack</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">recordio</span><span class="o">-></span><span class="nb">unpack</span>
<span class="nn">mx</span><span class="o">-></span><span class="nn">recordio</span><span class="o">-></span><span class="n">unpack_img</span>
</pre></div>
</div>
</div>
<div class="section" id="develop-a-new-iterator">
<span id="develop-a-new-iterator"></span><h2>Develop a new iterator<a class="headerlink" href="#develop-a-new-iterator" title="Permalink to this headline"></a></h2>
<p>Writing a new data iterator in Perl is straightforward. Most MXNet
training/inference programs accept an object with <code class="docutils literal"><span class="pre">provide_data</span></code>
and <code class="docutils literal"><span class="pre">provide_label</span></code> properties.
Please refer to AI-MXNet/examples for the examples of custom iterators.</p>
</div>
</div>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Data Loading API</a><ul>
<li><a class="reference internal" href="#overview">Overview</a></li>
<li><a class="reference internal" href="#predefined-data-iterators">Predefined Data iterators</a></li>
<li><a class="reference internal" href="#helper-classes-and-functions">Helper classes and functions</a></li>
<li><a class="reference internal" href="#develop-a-new-iterator">Develop a new iterator</a></li>
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