Deci-AI
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  <section id="super-gradients-training-datasets-segmentation-datasets-package">
<h1>super_gradients.training.datasets.segmentation_datasets package<a class="headerlink" href="#super-gradients-training-datasets-segmentation-datasets-package" title="Permalink to this headline"></a></h1>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation">
<span id="super-gradients-training-datasets-segmentation-datasets-cityscape-segmentation-module"></span><h2>super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation module<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.</span></span><span class="sig-name descname"><span class="pre">CityscapesDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_dir</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_file</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_csv_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>CityscapesDataset - Segmentation Data Set Class for Cityscapes Segmentation Data Set,
main resolution of dataset: (2048 x 1024).
Not all the original labels are used for training and evaluation, according to cityscape paper:
“Classes that are too rare are excluded from our benchmark, leaving 19 classes for evaluation”.
For more details about the dataset labels format see:
<a class="reference external" href="https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py">https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py</a></p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.target_loader">
<span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">label_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>Override target_loader function, load the labels mask image.</dt><dd><dl class="field-list simple">
<dt class="field-odd">param label_path</dt>
<dd class="field-odd"><p>Path to the label image.</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The mask image created from the array, with converted class labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.get_train_ids_color_palette">
<span class="sig-name descname"><span class="pre">get_train_ids_color_palette</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.get_train_ids_color_palette"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.get_train_ids_color_palette" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.target_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset.target_transform" title="Permalink to this definition"></a></dt>
<dd><p>target_transform - Transforms the sample image
This function overrides the original function from SegmentationDataSet and changes target pixels with value
255 to value = CITYSCAPES_IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not
support such a high ignore label value (crashed on OOM)</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param target</dt>
<dd class="field-odd"><p>The target mask to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed target mask</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

</dd></dl>

</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets.coco_segmentation">
<span id="super-gradients-training-datasets-segmentation-datasets-coco-segmentation-module"></span><h2>super_gradients.training.datasets.segmentation_datasets.coco_segmentation module<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets.coco_segmentation" title="Permalink to this headline"></a></h2>
<dl class="py exception">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.coco_segmentation.EmptyCoCoClassesSelectionException">
<em class="property"><span class="pre">exception</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.coco_segmentation.</span></span><span class="sig-name descname"><span class="pre">EmptyCoCoClassesSelectionException</span></span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/coco_segmentation.html#EmptyCoCoClassesSelectionException"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.coco_segmentation.EmptyCoCoClassesSelectionException" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Exception</span></code></p>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.coco_segmentation.CoCoSegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.coco_segmentation.</span></span><span class="sig-name descname"><span class="pre">CoCoSegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_classes_inclusion_tuples_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">list</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/coco_segmentation.html#CoCoSegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.coco_segmentation.CoCoSegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>CoCoSegmentationDataSet - Segmentation Data Set Class for COCO 2017 Segmentation Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.coco_segmentation.CoCoSegmentationDataSet.target_loader">
<span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_metadata_tuple</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/coco_segmentation.html#CoCoSegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.coco_segmentation.CoCoSegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mask_metadata_tuple</strong> – A tuple of (coco_image_id, original_image_height, original_image_width)</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The mask image created from the array</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation">
<span id="super-gradients-training-datasets-segmentation-datasets-pascal-aug-segmentation-module"></span><h2>super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation module<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation.PascalAUG2012SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation.</span></span><span class="sig-name descname"><span class="pre">PascalAUG2012SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_aug_segmentation.html#PascalAUG2012SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation.PascalAUG2012SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>PascalAUG2012SegmentationDataSet - Segmentation Data Set Class for Pascal AUG 2012 Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation.PascalAUG2012SegmentationDataSet.target_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_aug_segmentation.html#PascalAUG2012SegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation.PascalAUG2012SegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>target_path</strong> – The path to the target data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The loaded target</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation">
<span id="super-gradients-training-datasets-segmentation-datasets-pascal-voc-segmentation-module"></span><h2>super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation module<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.PascalVOC2012SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.</span></span><span class="sig-name descname"><span class="pre">PascalVOC2012SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_voc_segmentation.html#PascalVOC2012SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.PascalVOC2012SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>PascalVOC2012SegmentationDataSet - Segmentation Data Set Class for Pascal VOC 2012 Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.PascalVOC2012SegmentationDataSet.decode_segmentation_mask">
<span class="sig-name descname"><span class="pre">decode_segmentation_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">label_mask</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_voc_segmentation.html#PascalVOC2012SegmentationDataSet.decode_segmentation_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.PascalVOC2012SegmentationDataSet.decode_segmentation_mask" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>decode_segmentation_mask - Decodes the colors for the Segmentation Mask</dt><dd><dl class="field-list simple">
<dt class="field-odd">param</dt>
<dd class="field-odd"><p>label_mask:  an (M,N) array of integer values denoting
the class label at each spatial location.</p>
</dd>
</dl>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets.segmentation_dataset">
<span id="super-gradients-training-datasets-segmentation-datasets-segmentation-dataset-module"></span><h2>super_gradients.training.datasets.segmentation_datasets.segmentation_dataset module<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets.segmentation_dataset" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.</span></span><span class="sig-name descname"><span class="pre">SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_file</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_sub_directory</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_sub_directory</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">608</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">augment</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_hyper_params</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">dict</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_labels</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_images</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extension</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'.png'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_mask_transforms</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchvision.transforms.transforms.Compose</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_mask_transforms_aug</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchvision.transforms.transforms.Compose</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.sample_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.sample_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.sample_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>sample_loader - Loads a dataset image from path using PIL</dt><dd><dl class="field-list simple">
<dt class="field-odd">param sample_path</dt>
<dd class="field-odd"><p>The path to the sample image</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The loaded Image</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.sample_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.sample_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.sample_transform" title="Permalink to this definition"></a></dt>
<dd><p>sample_transform - Transforms the sample image</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param image</dt>
<dd class="field-odd"><p>The input image to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed image</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.target_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>target_path</strong> – The path to the sample image</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The loaded Image</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.target_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet.target_transform" title="Permalink to this definition"></a></dt>
<dd><p>target_transform - Transforms the sample image</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param target</dt>
<dd class="field-odd"><p>The target mask to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed target mask</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

</dd></dl>

</section>
<section id="module-super_gradients.training.datasets.segmentation_datasets">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-super_gradients.training.datasets.segmentation_datasets" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_file</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_sub_directory</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_sub_directory</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">608</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">augment</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_hyper_params</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">dict</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_labels</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_images</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extension</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'.png'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_mask_transforms</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchvision.transforms.transforms.Compose</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_mask_transforms_aug</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchvision.transforms.transforms.Compose</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.sample_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.sample_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.sample_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>sample_loader - Loads a dataset image from path using PIL</dt><dd><dl class="field-list simple">
<dt class="field-odd">param sample_path</dt>
<dd class="field-odd"><p>The path to the sample image</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The loaded Image</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.sample_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.sample_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.sample_transform" title="Permalink to this definition"></a></dt>
<dd><p>sample_transform - Transforms the sample image</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param image</dt>
<dd class="field-odd"><p>The input image to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed image</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.target_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>target_path</strong> – The path to the sample image</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The loaded Image</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.target_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/segmentation_dataset.html#SegmentationDataSet.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SegmentationDataSet.target_transform" title="Permalink to this definition"></a></dt>
<dd><p>target_transform - Transforms the sample image</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param target</dt>
<dd class="field-odd"><p>The target mask to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed target mask</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CoCoSegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">CoCoSegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_classes_inclusion_tuples_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">list</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/coco_segmentation.html#CoCoSegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CoCoSegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>CoCoSegmentationDataSet - Segmentation Data Set Class for COCO 2017 Segmentation Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CoCoSegmentationDataSet.target_loader">
<span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_metadata_tuple</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/coco_segmentation.html#CoCoSegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CoCoSegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mask_metadata_tuple</strong> – A tuple of (coco_image_id, original_image_height, original_image_width)</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The mask image created from the array</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.PascalAUG2012SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">PascalAUG2012SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_aug_segmentation.html#PascalAUG2012SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.PascalAUG2012SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>PascalAUG2012SegmentationDataSet - Segmentation Data Set Class for Pascal AUG 2012 Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.PascalAUG2012SegmentationDataSet.target_loader">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_aug_segmentation.html#PascalAUG2012SegmentationDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.PascalAUG2012SegmentationDataSet.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>target_path</strong> – The path to the target data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The loaded target</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.PascalVOC2012SegmentationDataSet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">PascalVOC2012SegmentationDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_voc_segmentation.html#PascalVOC2012SegmentationDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.PascalVOC2012SegmentationDataSet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>PascalVOC2012SegmentationDataSet - Segmentation Data Set Class for Pascal VOC 2012 Data Set</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.PascalVOC2012SegmentationDataSet.decode_segmentation_mask">
<span class="sig-name descname"><span class="pre">decode_segmentation_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">label_mask</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/pascal_voc_segmentation.html#PascalVOC2012SegmentationDataSet.decode_segmentation_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.PascalVOC2012SegmentationDataSet.decode_segmentation_mask" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>decode_segmentation_mask - Decodes the colors for the Segmentation Mask</dt><dd><dl class="field-list simple">
<dt class="field-odd">param</dt>
<dd class="field-odd"><p>label_mask:  an (M,N) array of integer values denoting
the class label at each spatial location.</p>
</dd>
</dl>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CityscapesDataset">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">CityscapesDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_dir</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_file</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_csv_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CityscapesDataset" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>CityscapesDataset - Segmentation Data Set Class for Cityscapes Segmentation Data Set,
main resolution of dataset: (2048 x 1024).
Not all the original labels are used for training and evaluation, according to cityscape paper:
“Classes that are too rare are excluded from our benchmark, leaving 19 classes for evaluation”.
For more details about the dataset labels format see:
<a class="reference external" href="https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py">https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py</a></p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.target_loader">
<span class="sig-name descname"><span class="pre">target_loader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">label_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">&lt;module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/Users/shaniperl/opt/anaconda3/lib/python3.9/site-packages/PIL/Image.py’&gt;</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.target_loader" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>Override target_loader function, load the labels mask image.</dt><dd><dl class="field-list simple">
<dt class="field-odd">param label_path</dt>
<dd class="field-odd"><p>Path to the label image.</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The mask image created from the array, with converted class labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.get_train_ids_color_palette">
<span class="sig-name descname"><span class="pre">get_train_ids_color_palette</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.get_train_ids_color_palette"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.get_train_ids_color_palette" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.target_transform">
<em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">target_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/cityscape_segmentation.html#CityscapesDataset.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.CityscapesDataset.target_transform" title="Permalink to this definition"></a></dt>
<dd><p>target_transform - Transforms the sample image
This function overrides the original function from SegmentationDataSet and changes target pixels with value
255 to value = CITYSCAPES_IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not
support such a high ignore label value (crashed on OOM)</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">param target</dt>
<dd class="field-odd"><p>The target mask to transform</p>
</dd>
<dt class="field-even">return</dt>
<dd class="field-even"><p>The transformed target mask</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SuperviselyPersonsDataset">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.segmentation_datasets.</span></span><span class="sig-name descname"><span class="pre">SuperviselyPersonsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_dir</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_file</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/segmentation_datasets/supervisely_persons_segmentation.html#SuperviselyPersonsDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SuperviselyPersonsDataset" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Generic</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.utils.data.dataset.T_co</span></code>]</p>
<p>SuperviselyPersonsDataset - Segmentation Data Set Class for Supervisely Persons Segmentation Data Set,
main resolution of dataset: (600 x 800).
This dataset is a subset of the original dataset (see below) and contains filtered samples
For more details about the ORIGINAL dataset see: <a class="reference external" href="https://app.supervise.ly/ecosystem/projects/persons">https://app.supervise.ly/ecosystem/projects/persons</a>
For more details about the FILTERED dataset see:
<a class="reference external" href="https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.3/contrib/PP-HumanSeg">https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.3/contrib/PP-HumanSeg</a></p>
<dl class="py attribute">
<dt class="sig sig-object py" id="super_gradients.training.datasets.segmentation_datasets.SuperviselyPersonsDataset.CLASS_LABELS">
<span class="sig-name descname"><span class="pre">CLASS_LABELS</span></span><em class="property"> <span class="pre">=</span> <span class="pre">{0:</span> <span class="pre">'background',</span> <span class="pre">1:</span> <span class="pre">'person'}</span></em><a class="headerlink" href="#super_gradients.training.datasets.segmentation_datasets.SuperviselyPersonsDataset.CLASS_LABELS" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

</dd></dl>

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