#609 Ci fix

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  <h1>Source code for super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">scipy.io</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">ConcatDataset</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.datasets.segmentation_datasets.segmentation_dataset</span> <span class="kn">import</span> <span class="n">SegmentationDataSet</span>
<span class="kn">from</span> <span class="nn">super_gradients.common.abstractions.abstract_logger</span> <span class="kn">import</span> <span class="n">get_logger</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">PASCAL_VOC_2012_CLASSES</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s2">&quot;background&quot;</span><span class="p">,</span>
    <span class="s2">&quot;aeroplane&quot;</span><span class="p">,</span>
    <span class="s2">&quot;bicycle&quot;</span><span class="p">,</span>
    <span class="s2">&quot;bird&quot;</span><span class="p">,</span>
    <span class="s2">&quot;boat&quot;</span><span class="p">,</span>
    <span class="s2">&quot;bottle&quot;</span><span class="p">,</span>
    <span class="s2">&quot;bus&quot;</span><span class="p">,</span>
    <span class="s2">&quot;car&quot;</span><span class="p">,</span>
    <span class="s2">&quot;cat&quot;</span><span class="p">,</span>
    <span class="s2">&quot;chair&quot;</span><span class="p">,</span>
    <span class="s2">&quot;cow&quot;</span><span class="p">,</span>
    <span class="s2">&quot;diningtable&quot;</span><span class="p">,</span>
    <span class="s2">&quot;dog&quot;</span><span class="p">,</span>
    <span class="s2">&quot;horse&quot;</span><span class="p">,</span>
    <span class="s2">&quot;motorbike&quot;</span><span class="p">,</span>
    <span class="s2">&quot;person&quot;</span><span class="p">,</span>
    <span class="s2">&quot;potted-plant&quot;</span><span class="p">,</span>
    <span class="s2">&quot;sheep&quot;</span><span class="p">,</span>
    <span class="s2">&quot;sofa&quot;</span><span class="p">,</span>
    <span class="s2">&quot;train&quot;</span><span class="p">,</span>
    <span class="s2">&quot;tv/monitor&quot;</span><span class="p">,</span>
<span class="p">]</span>
<div class="viewcode-block" id="PascalVOC2012SegmentationDataSet"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalVOC2012SegmentationDataSet">[docs]</a><span class="k">class</span> <span class="nc">PascalVOC2012SegmentationDataSet</span><span class="p">(</span><span class="n">SegmentationDataSet</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    PascalVOC2012SegmentationDataSet - Segmentation Data Set Class for Pascal VOC 2012 Data Set</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">IGNORE_LABEL</span> <span class="o">=</span> <span class="mi">21</span>
    <span class="n">_ORIGINAL_IGNORE_LABEL</span> <span class="o">=</span> <span class="mi">255</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_suffix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_suffix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sample_suffix</span> <span class="o">=</span> <span class="s2">&quot;.jpg&quot;</span> <span class="k">if</span> <span class="n">sample_suffix</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">sample_suffix</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_suffix</span> <span class="o">=</span> <span class="s2">&quot;.png&quot;</span> <span class="k">if</span> <span class="n">target_suffix</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">target_suffix</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="n">PASCAL_VOC_2012_CLASSES</span>
<div class="viewcode-block" id="PascalVOC2012SegmentationDataSet.target_transform"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalVOC2012SegmentationDataSet.target_transform">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">target_transform</span><span class="p">(</span><span class="n">target</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        target_transform - Transforms the label mask</span>
<span class="sd">        This function overrides the original function from SegmentationDataSet and changes target pixels with value</span>
<span class="sd">        255 to value = IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not</span>
<span class="sd">        support such a high ignore label value (crashed on OOM)</span>
<span class="sd">            :param target: The target mask to transform</span>
<span class="sd">            :return:       The transformed target mask</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">SegmentationDataSet</span><span class="o">.</span><span class="n">target_transform</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
        <span class="n">out</span><span class="p">[</span><span class="n">out</span> <span class="o">==</span> <span class="n">PascalVOC2012SegmentationDataSet</span><span class="o">.</span><span class="n">_ORIGINAL_IGNORE_LABEL</span><span class="p">]</span> <span class="o">=</span> <span class="n">PascalVOC2012SegmentationDataSet</span><span class="o">.</span><span class="n">IGNORE_LABEL</span>
        <span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="PascalVOC2012SegmentationDataSet.decode_segmentation_mask"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalVOC2012SegmentationDataSet.decode_segmentation_mask">[docs]</a>    <span class="k">def</span> <span class="nf">decode_segmentation_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label_mask</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        decode_segmentation_mask - Decodes the colors for the Segmentation Mask</span>
<span class="sd">            :param: label_mask:  an (M,N) array of integer values denoting</span>
<span class="sd">                                the class label at each spatial location.</span>
<span class="sd">        :return:</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">label_colours</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_pascal_labels</span><span class="p">()</span>
        <span class="n">r</span> <span class="o">=</span> <span class="n">label_mask</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">g</span> <span class="o">=</span> <span class="n">label_mask</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">b</span> <span class="o">=</span> <span class="n">label_mask</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">num_classes_to_plot</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">ll</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_classes_to_plot</span><span class="p">):</span>
            <span class="n">r</span><span class="p">[</span><span class="n">label_mask</span> <span class="o">==</span> <span class="n">ll</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_colours</span><span class="p">[</span><span class="n">ll</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
            <span class="n">g</span><span class="p">[</span><span class="n">label_mask</span> <span class="o">==</span> <span class="n">ll</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_colours</span><span class="p">[</span><span class="n">ll</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
            <span class="n">b</span><span class="p">[</span><span class="n">label_mask</span> <span class="o">==</span> <span class="n">ll</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_colours</span><span class="p">[</span><span class="n">ll</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
        <span class="n">rgb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">label_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label_mask</span><span class="o">.</span><span class="n">shape</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="n">rgb</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">r</span> <span class="o">/</span> <span class="mf">255.0</span>
        <span class="n">rgb</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">g</span> <span class="o">/</span> <span class="mf">255.0</span>
        <span class="n">rgb</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">b</span> <span class="o">/</span> <span class="mf">255.0</span>
        <span class="k">return</span> <span class="n">rgb</span></div>
    <span class="k">def</span> <span class="nf">_generate_samples_and_targets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        _generate_samples_and_targets</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># GENERATE SAMPLES AND TARGETS HERE SPECIFICALLY FOR PASCAL VOC 2012</span>
        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">root</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">sep</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">list_file_path</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">lines</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
                <span class="n">image_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="bp">self</span><span class="o">.</span><span class="n">root</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples_sub_directory</span><span class="p">,</span> <span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_suffix</span><span class="p">)</span>
                <span class="n">mask_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="bp">self</span><span class="o">.</span><span class="n">root</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">targets_sub_directory</span><span class="p">,</span> <span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_suffix</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">mask_path</span><span class="p">)</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">image_path</span><span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">samples_targets_tuples_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">image_path</span><span class="p">,</span> <span class="n">mask_path</span><span class="p">))</span>
        <span class="c1"># GENERATE SAMPLES AND TARGETS OF THE SEGMENTATION DATA SET CLASS</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_generate_samples_and_targets</span><span class="p">()</span>
    <span class="k">def</span> <span class="nf">_get_pascal_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Load the mapping that associates pascal classes with label colors</span>
<span class="sd">        Returns:</span>
<span class="sd">            np.ndarray with dimensions (21, 3)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
            <span class="p">[</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">192</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">192</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">192</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">192</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span>
            <span class="p">]</span>
        <span class="p">)</span></div>
<div class="viewcode-block" id="PascalAUG2012SegmentationDataSet"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalAUG2012SegmentationDataSet">[docs]</a><span class="k">class</span> <span class="nc">PascalAUG2012SegmentationDataSet</span><span class="p">(</span><span class="n">PascalVOC2012SegmentationDataSet</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    PascalAUG2012SegmentationDataSet - Segmentation Data Set Class for Pascal AUG 2012 Data Set</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sample_suffix</span> <span class="o">=</span> <span class="s2">&quot;.jpg&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_suffix</span> <span class="o">=</span> <span class="s2">&quot;.mat&quot;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">sample_suffix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_suffix</span><span class="p">,</span> <span class="n">target_suffix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_suffix</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="PascalAUG2012SegmentationDataSet.target_loader"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalAUG2012SegmentationDataSet.target_loader">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">target_loader</span><span class="p">(</span><span class="n">target_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Image</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        target_loader</span>
<span class="sd">            :param target_path: The path to the target data</span>
<span class="sd">            :return:            The loaded target</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">mat</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">loadmat</span><span class="p">(</span><span class="n">target_path</span><span class="p">,</span> <span class="n">mat_dtype</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">squeeze_me</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">struct_as_record</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">mask</span> <span class="o">=</span> <span class="n">mat</span><span class="p">[</span><span class="s2">&quot;GTcls&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">Segmentation</span>
        <span class="k">return</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="PascalVOCAndAUGUnifiedDataset"><a class="viewcode-back" href="../../../../../super_gradients.training.html#super_gradients.training.datasets.PascalVOCAndAUGUnifiedDataset">[docs]</a><span class="k">class</span> <span class="nc">PascalVOCAndAUGUnifiedDataset</span><span class="p">(</span><span class="n">ConcatDataset</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Pascal VOC + AUG train dataset, aka `SBD` dataset contributed in &quot;Semantic contours from inverse detectors&quot;.</span>
<span class="sd">    This is class implement the common usage of the SBD and PascalVOC datasets as a unified augmented trainset.</span>
<span class="sd">    The unified dataset includes a total of 10,582 samples and don&#39;t contains duplicate samples from the PascalVOC</span>
<span class="sd">    validation set.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;list_file&quot;</span><span class="p">),</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;samples_sub_directory&quot;</span><span class="p">),</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;targets_sub_directory&quot;</span><span class="p">)]):</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;[list_file, samples_sub_directory, targets_sub_directory] arguments passed will not be used&quot;</span>
                <span class="s2">&quot; when passed to `PascalVOCAndAUGUnifiedDataset`. Those values are predefined for initiating&quot;</span>
                <span class="s2">&quot; the Pascal VOC + AUG training set.&quot;</span>
            <span class="p">)</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
            <span class="n">datasets</span><span class="o">=</span><span class="p">[</span>
                <span class="n">PascalVOC2012SegmentationDataSet</span><span class="p">(</span>
                    <span class="n">list_file</span><span class="o">=</span><span class="s2">&quot;VOCdevkit/VOC2012/ImageSets/Segmentation/train.txt&quot;</span><span class="p">,</span>
                    <span class="n">samples_sub_directory</span><span class="o">=</span><span class="s2">&quot;VOCdevkit/VOC2012/JPEGImages&quot;</span><span class="p">,</span>
                    <span class="n">targets_sub_directory</span><span class="o">=</span><span class="s2">&quot;VOCdevkit/VOC2012/SegmentationClass&quot;</span><span class="p">,</span>
                    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
                <span class="p">),</span>
                <span class="n">PascalAUG2012SegmentationDataSet</span><span class="p">(</span>
                    <span class="n">list_file</span><span class="o">=</span><span class="s2">&quot;VOCaug/dataset/aug.txt&quot;</span><span class="p">,</span> <span class="n">samples_sub_directory</span><span class="o">=</span><span class="s2">&quot;VOCaug/dataset/img&quot;</span><span class="p">,</span> <span class="n">targets_sub_directory</span><span class="o">=</span><span class="s2">&quot;VOCaug/dataset/cls&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
                <span class="p">),</span>
            <span class="p">]</span>
        <span class="p">)</span></div>
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