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- <section id="training-package">
- <h1>Training package<a class="headerlink" href="#training-package" title="Permalink to this headline"></a></h1>
- <table class="longtable docutils align-default">
- <colgroup>
- <col style="width: 10%" />
- <col style="width: 90%" />
- </colgroup>
- <tbody>
- </tbody>
- </table>
- <section id="module-super_gradients.training">
- <span id="super-gradients-training-module"></span><h2>super_gradients.training module<a class="headerlink" href="#module-super_gradients.training" title="Permalink to this headline"></a></h2>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.DataAugmentation">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">DataAugmentation</span></span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DataAugmentation" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DataAugmentation.to_tensor">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">to_tensor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.to_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DataAugmentation.to_tensor" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DataAugmentation.normalize">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DataAugmentation.normalize" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DataAugmentation.cutout">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">cutout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutout_inside</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">mask_color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">0,</span> <span class="pre">0)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.cutout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DataAugmentation.cutout" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">DetectionDataSet</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></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">416</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</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="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_loading_method</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">'default'</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</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="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">'.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_offset</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">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_inclusion_list</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">all_classes_list</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/detection_datasets/detection_dataset.html#DetectionDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet" 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.DetectionDataSet.mixup">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">im</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.mixup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.mixup" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.sample_post_process">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_post_process</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/detection_datasets/detection_dataset.html#DetectionDataSet.sample_post_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.sample_post_process" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>sample_post_process - Normalizes and orders the image to be 3 x img_size x img_size</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param image</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.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><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.sample_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.sample_loader" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>sample_loader - Loads a coco dataset image from path</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param sample_path</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.sample_transform">
- <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/detection_datasets/detection_dataset.html#DetectionDataSet.sample_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.sample_transform" 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>image</strong> – </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.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>, <em class="sig-param"><span class="n"><span class="pre">class_inclusion_list</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">all_classes_list</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/detection_datasets/detection_dataset.html#DetectionDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.target_loader" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>coco_target_loader</dt><dd><p>@param target_path: str, path to target.
- @param all_classes_list: list(str) containing all the class names or None when subclassing is disabled.
- @param class_inclusion_list: list(str) containing the subclass names or None when subclassing is disabled.</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.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>, <em class="sig-param"><span class="n"><span class="pre">ratio</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</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/detection_datasets/detection_dataset.html#DetectionDataSet.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.target_transform" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>target</strong> – </p></li>
- <li><p><strong>ratio</strong> – </p></li>
- <li><p><strong>w</strong> – </p></li>
- <li><p><strong>h</strong> – </p></li>
- <li><p><strong>pad</strong> – </p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.exif_size">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">exif_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.exif_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.exif_size" 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>img</strong> – </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.augment_hsv">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">augment_hsv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.augment_hsv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.augment_hsv" 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>img</strong> – <dl class="field-list simple">
- <dt class="field-odd">param hgain</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">param sgain</dt>
- <dd class="field-even"><p></p></dd>
- <dt class="field-odd">param vgain</dt>
- <dd class="field-odd"><p></p></dd>
- </dl>
- </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.letterbox">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">letterbox</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(416,</span> <span class="pre">416)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(128,</span> <span class="pre">128,</span> <span class="pre">128)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaleFill</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">scaleup</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span> → <span class="pre">tuple</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.letterbox"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.letterbox" title="Permalink to this definition"></a></dt>
- <dd><p>letterbox - Resizes image to a 32-pixel-multiple rectangle
- :param img:
- :param new_shape:
- :param color:
- :param auto:
- :param scaleFill:
- :param scaleup:
- :param interp:
- :return:</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.random_perspective">
- <span class="sig-name descname"><span class="pre">random_perspective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">translate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shear</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">border</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perspective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.random_perspective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.random_perspective" title="Permalink to this definition"></a></dt>
- <dd><p>random images and labels using a perspective transform</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.box_candidates">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">box_candidates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">box1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">box2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wh_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ar_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">area_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.box_candidates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.box_candidates" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>compute candidate boxes</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param box1</dt>
- <dd class="field-odd"><p>before augment</p>
- </dd>
- <dt class="field-even">param box2</dt>
- <dd class="field-even"><p>after augment</p>
- </dd>
- <dt class="field-odd">param wh_thr</dt>
- <dd class="field-odd"><p>wh_thr (pixels)</p>
- </dd>
- <dt class="field-even">param ar_thr</dt>
- <dd class="field-even"><p>aspect_ratio_thr</p>
- </dd>
- <dt class="field-odd">param area_thr</dt>
- <dd class="field-odd"><p>area_ratio</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>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionDataSet.load_mosaic">
- <span class="sig-name descname"><span class="pre">load_mosaic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.load_mosaic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionDataSet.load_mosaic" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>load_mosaic - Load images in mosaic format to improve noise handling while training</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param index</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.TestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">TestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trainset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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/dataset_interfaces/dataset_interface.html#TestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.TestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.TestDatasetInterface.get_data_loaders">
- <span class="sig-name descname"><span class="pre">get_data_loaders</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_batch_size</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">val_batch_size</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">distributed_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#TestDatasetInterface.get_data_loaders"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.TestDatasetInterface.get_data_loaders" title="Permalink to this definition"></a></dt>
- <dd><p>Get self.train_loader, self.test_loader, self.classes.</p>
- <p>If the data loaders haven’t been initialized yet, build them first.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><p><strong>kwargs</strong> – kwargs are passed to build_data_loaders.</p>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">SgModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">experiment_name:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">multi_gpu:</span> <span class="pre">Union[super_gradients.training.sg_model.sg_model.MultiGPUMode</span></em>, <em class="sig-param"><span class="pre">str]</span> <span class="pre">=</span> <span class="pre"><MultiGPUMode.AUTO:</span> <span class="pre">'AUTO'></span></em>, <em class="sig-param"><span class="pre">model_checkpoints_location:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'local'</span></em>, <em class="sig-param"><span class="pre">overwrite_local_checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></em>, <em class="sig-param"><span class="pre">ckpt_name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'ckpt_latest.pth'</span></em>, <em class="sig-param"><span class="pre">post_prediction_callback:</span> <span class="pre">Optional[super_gradients.training.utils.detection_utils.DetectionPostPredictionCallback]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">ckpt_root_dir=None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <p>SuperGradient Model - Base Class for Sg Models</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.train">
- <span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_epochs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_epoch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.train" title="Permalink to this definition"></a></dt>
- <dd><p>the main function used for the training, h.p. updating, logging etc.</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.predict">
- <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.predict" title="Permalink to this definition"></a></dt>
- <dd><p>returns the predictions and label of the current inputs</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">test(epoch</span> <span class="pre">:</span> <span class="pre">int,</span> <span class="pre">idx</span> <span class="pre">:</span> <span class="pre">int,</span> <span class="pre">save</span> <span class="pre">:</span> <span class="pre">bool):</span></span></dt>
- <dd><p>returns the test loss, accuracy and runtime</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.connect_dataset_interface">
- <span class="sig-name descname"><span class="pre">connect_dataset_interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_interface</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_loader_num_workers</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">8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.connect_dataset_interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.connect_dataset_interface" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>dataset_interface</strong> – DatasetInterface object</p></li>
- <li><p><strong>data_loader_num_workers</strong> – The number of threads to initialize the Data Loaders with
- The dataset to be connected</p></li>
- </ul>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.build_model">
- <span class="sig-name descname"><span class="pre">build_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">architecture:</span> <span class="pre">Union[str,</span> <span class="pre">torch.nn.modules.module.Module],</span> <span class="pre">arch_params={},</span> <span class="pre">load_checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">strict_load:</span> <span class="pre">super_gradients.training.sg_model.sg_model.StrictLoad</span> <span class="pre">=</span> <span class="pre"><StrictLoad.ON:</span> <span class="pre">True>,</span> <span class="pre">source_ckpt_folder_name:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">load_weights_only:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">load_backbone:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">external_checkpoint_path:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">load_ema_as_net:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.build_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.build_model" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>architecture</strong> – Defines the network’s architecture from models/ALL_ARCHITECTURES</p></li>
- <li><p><strong>arch_params</strong> – Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p></li>
- <li><p><strong>load_checkpoint</strong> – Load a pre-trained checkpoint</p></li>
- <li><p><strong>strict_load</strong> – See StrictLoad class documentation for details.</p></li>
- <li><p><strong>source_ckpt_folder_name</strong> – folder name to load the checkpoint from (self.experiment_name if none is given)</p></li>
- <li><p><strong>load_weights_only</strong> – loads only the weight from the checkpoint and zeroize the training params</p></li>
- <li><p><strong>load_backbone</strong> – loads the provided checkpoint to self.net.backbone instead of self.net</p></li>
- <li><p><strong>external_checkpoint_path</strong> – The path to the external checkpoint to be loaded. Can be absolute or relative
- (ie: path/to/checkpoint.pth). If provided, will automatically attempt to
- load the checkpoint even if the load_checkpoint flag is not provided.</p></li>
- </ul>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.backward_step">
- <span class="sig-name descname"><span class="pre">backward_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.PhaseContext" title="super_gradients.training.utils.callbacks.PhaseContext"><span class="pre">super_gradients.training.utils.callbacks.PhaseContext</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.backward_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.backward_step" title="Permalink to this definition"></a></dt>
- <dd><p>Run backprop on the loss and perform a step
- :param loss: The value computed by the loss function
- :param optimizer: An object that can perform a gradient step and zeroize model gradient
- :param epoch: number of epoch the training is on
- :param batch_idx: number of iteration inside the current epoch
- :param context: current phase context
- :return:</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.save_checkpoint">
- <span class="sig-name descname"><span class="pre">save_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</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">epoch</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">int</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="n"><span class="pre">validation_results_tuple</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">tuple</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="n"><span class="pre">context</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><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.PhaseContext" title="super_gradients.training.utils.callbacks.PhaseContext"><span class="pre">super_gradients.training.utils.callbacks.PhaseContext</span></a><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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.save_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.save_checkpoint" title="Permalink to this definition"></a></dt>
- <dd><p>Save the current state dict as latest (always), best (if metric was improved), epoch# (if determined in training
- params)</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="id0">
- <span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training_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">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition"></a></dt>
- <dd><p>train - Trains the Model</p>
- <dl>
- <dt>IMPORTANT NOTE: Additional batch parameters can be added as a third item (optional) if a tuple is returned by</dt><dd><p>the data loaders, as dictionary. The phase context will hold the additional items, under an attribute with
- the same name as the key in this dictionary. Then such items can be accessed through phase callbacks.</p>
- <blockquote>
- <div><dl class="field-list">
- <dt class="field-odd">param training_params</dt>
- <dd class="field-odd"><ul>
- <li><p><cite>max_epochs</cite> : int</p>
- <blockquote>
- <div><p>Number of epochs to run training.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_updates</cite> : list(int)</p>
- <blockquote>
- <div><p>List of fixed epoch numbers to perform learning rate updates when <cite>lr_mode=’step’</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_decay_factor</cite> : float</p>
- <blockquote>
- <div><p>Decay factor to apply to the learning rate at each update when <cite>lr_mode=’step’</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_mode</cite> : str</p>
- <blockquote>
- <div><p>Learning rate scheduling policy, one of [‘step’,’poly’,’cosine’,’function’]. ‘step’ refers to
- constant updates at epoch numbers passed through <cite>lr_updates</cite>. ‘cosine’ refers to Cosine Anealing
- policy as mentioned in <a class="reference external" href="https://arxiv.org/abs/1608.03983">https://arxiv.org/abs/1608.03983</a>. ‘poly’ refers to polynomial decrease i.e
- in each epoch iteration <cite>self.lr = self.initial_lr * pow((1.0 - (current_iter / max_iter)),
- 0.9)</cite> ‘function’ refers to user defined learning rate scheduling function, that is passed through
- <cite>lr_schedule_function</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_schedule_function</cite> : Union[callable,None]</p>
- <blockquote>
- <div><p>Learning rate scheduling function to be used when <cite>lr_mode</cite> is ‘function’.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_warmup_epochs</cite> : int (default=0)</p>
- <blockquote>
- <div><p>Number of epochs for learning rate warm up - see <a class="reference external" href="https://arxiv.org/pdf/1706.02677.pdf">https://arxiv.org/pdf/1706.02677.pdf</a> (Section 2.2).</p>
- </div></blockquote>
- </li>
- <li><dl class="simple">
- <dt><cite>cosine_final_lr_ratio</cite><span class="classifier">float (default=0.01)</span></dt><dd><dl class="simple">
- <dt>Final learning rate ratio (only relevant when <a href="#id1"><span class="problematic" id="id2">`</span></a>lr_mode`=’cosine’). The cosine starts from initial_lr and reaches</dt><dd><p>initial_lr * cosine_final_lr_ratio in last epoch</p>
- </dd>
- </dl>
- </dd>
- </dl>
- </li>
- <li><p><cite>inital_lr</cite> : float</p>
- <blockquote>
- <div><p>Initial learning rate.</p>
- </div></blockquote>
- </li>
- <li><p><cite>loss</cite> : Union[nn.module, str]</p>
- <blockquote>
- <div><p>Loss function for training.
- One of SuperGradient’s built in options:</p>
- <blockquote>
- <div><p>“cross_entropy”: LabelSmoothingCrossEntropyLoss,
- “mse”: MSELoss,
- “r_squared_loss”: RSquaredLoss,
- “detection_loss”: YoLoV3DetectionLoss,
- “shelfnet_ohem_loss”: ShelfNetOHEMLoss,
- “shelfnet_se_loss”: ShelfNetSemanticEncodingLoss,
- “yolo_v5_loss”: YoLoV5DetectionLoss,
- “ssd_loss”: SSDLoss,</p>
- </div></blockquote>
- <p>or user defined nn.module loss function.</p>
- <p>IMPORTANT: forward(…) should return a (loss, loss_items) tuple where loss is the tensor used
- for backprop (i.e what your original loss function returns), and loss_items should be a tensor of
- shape (n_items), of values computed during the forward pass which we desire to log over the
- entire epoch. For example- the loss itself should always be logged. Another example is a scenario
- where the computed loss is the sum of a few components we would like to log- these entries in
- loss_items).</p>
- <p>When training, set the loss_logging_items_names parameter in train_params to be a list of
- strings, of length n_items who’s ith element is the name of the ith entry in loss_items. Then
- each item will be logged, rendered on tensorboard and “watched” (i.e saving model checkpoints
- according to it).</p>
- <p>Since running logs will save the loss_items in some internal state, it is recommended that
- loss_items are detached from their computational graph for memory efficiency.</p>
- </div></blockquote>
- </li>
- <li><p><cite>optimizer</cite> : Union[str, torch.optim.Optimizer]</p>
- <blockquote>
- <div><p>Optimization algorithm. One of [‘Adam’,’SGD’,’RMSProp’] corresponding to the torch.optim
- optimzers implementations, or any object that implements torch.optim.Optimizer.</p>
- </div></blockquote>
- </li>
- <li><p><cite>criterion_params</cite> : dict</p>
- <blockquote>
- <div><p>Loss function parameters.</p>
- </div></blockquote>
- </li>
- <li><dl>
- <dt><cite>optimizer_params</cite><span class="classifier">dict</span></dt><dd><p>When <cite>optimizer</cite> is one of [‘Adam’,’SGD’,’RMSProp’], it will be initialized with optimizer_params.</p>
- <p>(see <a class="reference external" href="https://pytorch.org/docs/stable/optim.html">https://pytorch.org/docs/stable/optim.html</a> for the full list of
- parameters for each optimizer).</p>
- </dd>
- </dl>
- </li>
- <li><p><cite>train_metrics_list</cite> : list(torchmetrics.Metric)</p>
- <blockquote>
- <div><p>Metrics to log during training. For more information on torchmetrics see
- <a class="reference external" href="https://torchmetrics.rtfd.io/en/latest/">https://torchmetrics.rtfd.io/en/latest/</a>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>valid_metrics_list</cite> : list(torchmetrics.Metric)</p>
- <blockquote>
- <div><p>Metrics to log during validation/testing. For more information on torchmetrics see
- <a class="reference external" href="https://torchmetrics.rtfd.io/en/latest/">https://torchmetrics.rtfd.io/en/latest/</a>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>loss_logging_items_names</cite> : list(str)</p>
- <blockquote>
- <div><p>The list of names/titles for the outputs returned from the loss functions forward pass (reminder-
- the loss function should return the tuple (loss, loss_items)). These names will be used for
- logging their values.</p>
- </div></blockquote>
- </li>
- <li><p><cite>metric_to_watch</cite> : str (default=”Accuracy”)</p>
- <blockquote>
- <div><p>will be the metric which the model checkpoint will be saved according to, and can be set to any
- of the following:</p>
- <blockquote>
- <div><p>a metric name (str) of one of the metric objects from the valid_metrics_list</p>
- <p>a “metric_name” if some metric in valid_metrics_list has an attribute component_names which
- is a list referring to the names of each entry in the output metric (torch tensor of size n)</p>
- <p>one of “loss_logging_items_names” i.e which will correspond to an item returned during the
- loss function’s forward pass.</p>
- </div></blockquote>
- <p>At the end of each epoch, if a new best metric_to_watch value is achieved, the models checkpoint
- is saved in YOUR_PYTHON_PATH/checkpoints/ckpt_best.pth</p>
- </div></blockquote>
- </li>
- <li><p><cite>greater_metric_to_watch_is_better</cite> : bool</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When choosing a model’s checkpoint to be saved, the best achieved model is the one that maximizes the</dt><dd><p>metric_to_watch when this parameter is set to True, and a one that minimizes it otherwise.</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>ema</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to use Model Exponential Moving Average (see
- <a class="reference external" href="https://github.com/rwightman/pytorch-image-models">https://github.com/rwightman/pytorch-image-models</a> ema implementation)</p>
- </div></blockquote>
- </li>
- <li><p><cite>batch_accumulate</cite> : int (default=1)</p>
- <blockquote>
- <div><p>Number of batches to accumulate before every backward pass.</p>
- </div></blockquote>
- </li>
- <li><p><cite>ema_params</cite> : dict</p>
- <blockquote>
- <div><p>Parameters for the ema model.</p>
- </div></blockquote>
- </li>
- <li><p><cite>zero_weight_decay_on_bias_and_bn</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to apply weight decay on batch normalization parameters or not (ignored when the passed
- optimizer has already been initialized).</p>
- </div></blockquote>
- </li>
- <li><p><cite>load_opt_params</cite> : bool (default=True)</p>
- <blockquote>
- <div><p>Whether to load the optimizers parameters as well when loading a model’s checkpoint.</p>
- </div></blockquote>
- </li>
- <li><p><cite>run_validation_freq</cite> : int (default=1)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>The frequency in which validation is performed during training (i.e the validation is ran every</dt><dd><p><cite>run_validation_freq</cite> epochs.</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>save_model</cite> : bool (default=True)</p>
- <blockquote>
- <div><p>Whether to save the model checkpoints.</p>
- </div></blockquote>
- </li>
- <li><p><cite>silent_mode</cite> : bool</p>
- <blockquote>
- <div><p>Silents the print outs.</p>
- </div></blockquote>
- </li>
- <li><p><cite>mixed_precision</cite> : bool</p>
- <blockquote>
- <div><p>Whether to use mixed precision or not.</p>
- </div></blockquote>
- </li>
- <li><p><cite>save_ckpt_epoch_list</cite> : list(int) (default=[])</p>
- <blockquote>
- <div><p>List of fixed epoch indices the user wishes to save checkpoints in.</p>
- </div></blockquote>
- </li>
- <li><p><cite>average_best_models</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>If set, a snapshot dictionary file and the average model will be saved / updated at every epoch
- and evaluated only when training is completed. The snapshot file will only be deleted upon
- completing the training. The snapshot dict will be managed on cpu.</p>
- </div></blockquote>
- </li>
- <li><p><cite>precise_bn</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to use precise_bn calculation during the training.</p>
- </div></blockquote>
- </li>
- <li><p><cite>precise_bn_batch_size</cite> : int (default=None)</p>
- <blockquote>
- <div><p>The effective batch size we want to calculate the batchnorm on. For example, if we are training a model
- on 8 gpus, with a batch of 128 on each gpu, a good rule of thumb would be to give it 8192
- (ie: effective_batch_size * num_gpus = batch_per_gpu * num_gpus * num_gpus).
- If precise_bn_batch_size is not provided in the training_params, the latter heuristic will be taken.</p>
- </div></blockquote>
- </li>
- <li><p><cite>seed</cite> : int (default=42)</p>
- <blockquote>
- <div><p>Random seed to be set for torch, numpy, and random. When using DDP each process will have it’s seed
- set to seed + rank.</p>
- </div></blockquote>
- </li>
- <li><p><cite>log_installed_packages</cite> : bool (default=False)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When set, the list of all installed packages (and their versions) will be written to the tensorboard</dt><dd><p>and logfile (useful when trying to reproduce results).</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>dataset_statistics</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Enable a statistic analysis of the dataset. If set to True the dataset will be analyzed and a report
- will be added to the tensorboard along with some sample images from the dataset. Currently only
- detection datasets are supported for analysis.</p>
- </div></blockquote>
- </li>
- <li><p><cite>save_full_train_log</cite> : bool (default=False)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When set, a full log (of all super_gradients modules, including uncaught exceptions from any other</dt><dd><p>module) of the training will be saved in the checkpoint directory under full_train_log.log</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>sg_logger</cite> : Union[AbstractSGLogger, str] (defauls=base_sg_logger)</p>
- <blockquote>
- <div><p>Define the SGLogger object for this training process. The SGLogger handles all disk writes, logs, TensorBoard, remote logging
- and remote storage. By overriding the default base_sg_logger, you can change the storage location, support external monitoring and logging
- or support remote storage.</p>
- </div></blockquote>
- </li>
- <li><p><cite>sg_logger_params</cite> : dict</p>
- <p>SGLogger parameters</p>
- </li>
- </ul>
- </dd>
- </dl>
- </div></blockquote>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="id3">
- <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</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">half</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">normalize</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">verbose</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">move_outputs_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id3" title="Permalink to this definition"></a></dt>
- <dd><p>A fast predictor for a batch of inputs
- :param inputs: torch.tensor or numpy.array</p>
- <blockquote>
- <div><p>a batch of inputs</p>
- </div></blockquote>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>targets</strong> – torch.tensor()
- corresponding labels - if non are given - accuracy will not be computed</p></li>
- <li><p><strong>verbose</strong> – bool
- print the results to screen</p></li>
- <li><p><strong>normalize</strong> – bool
- If true, normalizes the tensor according to the dataloader’s normalization values</p></li>
- <li><p><strong>half</strong> – Performs half precision evaluation</p></li>
- <li><p><strong>move_outputs_to_cpu</strong> – Moves the results from the GPU to the CPU</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>outputs, acc, net_time, gross_time
- networks predictions, accuracy calculation, forward pass net time, function gross time</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.compute_model_runtime">
- <span class="sig-name descname"><span class="pre">compute_model_runtime</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dims</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">tuple</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="n"><span class="pre">batch_sizes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">tuple</span><span class="p"><span class="pre">,</span> </span><span class="pre">list</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</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">(1,</span> <span class="pre">8,</span> <span class="pre">16,</span> <span class="pre">32,</span> <span class="pre">64)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</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">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.compute_model_runtime"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.compute_model_runtime" title="Permalink to this definition"></a></dt>
- <dd><p>Compute the “atomic” inference time and throughput.
- Atomic refers to calculating the forward pass independently, discarding effects such as data augmentation,
- data upload to device, multi-gpu distribution etc.
- :param input_dims: tuple</p>
- <blockquote>
- <div><p>shape of a basic input to the network (without the first index) e.g. (3, 224, 224)
- if None uses an input from the test loader</p>
- </div></blockquote>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>batch_sizes</strong> – int or list
- Batch sizes for latency calculation</p></li>
- <li><p><strong>verbose</strong> – bool
- Prints results to screen</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>log: dict
- Latency and throughput for each tested batch size</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.get_arch_params">
- <span class="sig-name descname"><span class="pre">get_arch_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_arch_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.get_arch_params" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.get_structure">
- <span class="sig-name descname"><span class="pre">get_structure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.get_structure" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.get_architecture">
- <span class="sig-name descname"><span class="pre">get_architecture</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_architecture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.get_architecture" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.set_experiment_name">
- <span class="sig-name descname"><span class="pre">set_experiment_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiment_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.set_experiment_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.set_experiment_name" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.re_build_model">
- <span class="sig-name descname"><span class="pre">re_build_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arch_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.re_build_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.re_build_model" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>arch_params<span class="classifier">dict</span></dt><dd><p>Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.update_architecture">
- <span class="sig-name descname"><span class="pre">update_architecture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">structure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.update_architecture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.update_architecture" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>architecture<span class="classifier">str</span></dt><dd><p>Defines the network’s architecture according to the options in models/all_architectures</p>
- </dd>
- <dt>load_checkpoint<span class="classifier">bool</span></dt><dd><p>Loads a checkpoint according to experiment_name</p>
- </dd>
- <dt>arch_params<span class="classifier">dict</span></dt><dd><p>Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.get_module">
- <span class="sig-name descname"><span class="pre">get_module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.get_module" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.set_module">
- <span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.set_module" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.test">
- <span class="sig-name descname"><span class="pre">test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_loader</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">torch.utils.data.dataloader.DataLoader</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="n"><span class="pre">loss</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">torch.nn.modules.loss._Loss</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="n"><span class="pre">silent_mode</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">test_metrics_list</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">loss_logging_items_names</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">metrics_progress_verbose</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">test_phase_callbacks</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">use_ema_net</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> → <span class="pre">tuple</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.test" title="Permalink to this definition"></a></dt>
- <dd><p>Evaluates the model on given dataloader and metrics.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>test_loader</strong> – dataloader to perform test on.</p></li>
- <li><p><strong>test_metrics_list</strong> – (list(torchmetrics.Metric)) metrics list for evaluation.</p></li>
- <li><p><strong>silent_mode</strong> – (bool) controls verbosity</p></li>
- <li><p><strong>metrics_progress_verbose</strong> – (bool) controls the verbosity of metrics progress (default=False). Slows down the program.</p></li>
- </ul>
- </dd>
- </dl>
- <dl class="simple">
- <dt>:param use_ema_net (bool) whether to perform test on self.ema_model.ema (when self.ema_model.ema exists,</dt><dd><p>otherwise self.net will be tested) (default=True)</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p>results tuple (tuple) containing the loss items and metric values.</p>
- </dd>
- </dl>
- <dl class="simple">
- <dt>All of the above args will override SgModel’s corresponding attribute when not equal to None. Then evaluation</dt><dd><p>is ran on self.test_loader with self.test_metrics.</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.SgModel.evaluate">
- <span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.utils.data.dataloader.DataLoader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchmetrics.collections.MetricCollection</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluation_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.sg_model.html#super_gradients.training.sg_model.sg_model.EvaluationType" title="super_gradients.training.sg_model.sg_model.EvaluationType"><span class="pre">super_gradients.training.sg_model.sg_model.EvaluationType</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</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">int</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="n"><span class="pre">silent_mode</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">metrics_progress_verbose</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.evaluate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SgModel.evaluate" title="Permalink to this definition"></a></dt>
- <dd><p>Evaluates the model on given dataloader and metrics.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>data_loader</strong> – dataloader to perform evaluataion on</p></li>
- <li><p><strong>metrics</strong> – (MetricCollection) metrics for evaluation</p></li>
- <li><p><strong>evaluation_type</strong> – (EvaluationType) controls which phase callbacks will be used (for example, on batch end,
- when evaluation_type=EvaluationType.VALIDATION the Phase.VALIDATION_BATCH_END callbacks will be triggered)</p></li>
- <li><p><strong>epoch</strong> – (int) epoch idx</p></li>
- <li><p><strong>silent_mode</strong> – (bool) controls verbosity</p></li>
- <li><p><strong>metrics_progress_verbose</strong> – (bool) controls the verbosity of metrics progress (default=False).
- Slows down the program significantly.</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>results tuple (tuple) containing the loss items and metric values.</p>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.MultiGPUMode">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">MultiGPUMode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#MultiGPUMode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.MultiGPUMode" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">OFF</span>                       <span class="pre">-</span> <span class="pre">Single</span> <span class="pre">GPU</span> <span class="pre">Mode</span> <span class="pre">/</span> <span class="pre">CPU</span> <span class="pre">Mode</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">DATA_PARALLEL</span>             <span class="pre">-</span> <span class="pre">Multiple</span> <span class="pre">GPUs,</span> <span class="pre">Synchronous</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">DISTRIBUTED_DATA_PARALLEL</span> <span class="pre">-</span> <span class="pre">Multiple</span> <span class="pre">GPUs,</span> <span class="pre">Asynchronous</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.MultiGPUMode.OFF">
- <span class="sig-name descname"><span class="pre">OFF</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'Off'</span></em><a class="headerlink" href="#super_gradients.training.MultiGPUMode.OFF" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.MultiGPUMode.DATA_PARALLEL">
- <span class="sig-name descname"><span class="pre">DATA_PARALLEL</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'DP'</span></em><a class="headerlink" href="#super_gradients.training.MultiGPUMode.DATA_PARALLEL" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.MultiGPUMode.DISTRIBUTED_DATA_PARALLEL">
- <span class="sig-name descname"><span class="pre">DISTRIBUTED_DATA_PARALLEL</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'DDP'</span></em><a class="headerlink" href="#super_gradients.training.MultiGPUMode.DISTRIBUTED_DATA_PARALLEL" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.MultiGPUMode.AUTO">
- <span class="sig-name descname"><span class="pre">AUTO</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'AUTO'</span></em><a class="headerlink" href="#super_gradients.training.MultiGPUMode.AUTO" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.SegmentationTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">SegmentationTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</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="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#SegmentationTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.SegmentationTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.DetectionTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">DetectionTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">320</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DetectionTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.DetectionTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.ClassificationTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">ClassificationTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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/dataset_interfaces/dataset_interface.html#ClassificationTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.ClassificationTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.StrictLoad">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.</span></span><span class="sig-name descname"><span class="pre">StrictLoad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#StrictLoad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.StrictLoad" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
- <dl>
- <dt>Wrapper for adding more functionality to torch’s strict_load parameter in load_state_dict().</dt><dd><dl>
- <dt>Attributes:</dt><dd><p>OFF - Native torch “strict_load = off” behaviour. See nn.Module.load_state_dict() documentation for more details.
- ON - Native torch “strict_load = on” behaviour. See nn.Module.load_state_dict() documentation for more details.
- NO_KEY_MATCHING - Allows the usage of SuperGradient’s adapt_checkpoint function, which loads a checkpoint by matching each</p>
- <blockquote>
- <div><p>layer’s shapes (and bypasses the strict matching of the names of each layer (ie: disregards the state_dict key matching)).</p>
- </div></blockquote>
- </dd>
- </dl>
- </dd>
- </dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.StrictLoad.OFF">
- <span class="sig-name descname"><span class="pre">OFF</span></span><em class="property"> <span class="pre">=</span> <span class="pre">False</span></em><a class="headerlink" href="#super_gradients.training.StrictLoad.OFF" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.StrictLoad.ON">
- <span class="sig-name descname"><span class="pre">ON</span></span><em class="property"> <span class="pre">=</span> <span class="pre">True</span></em><a class="headerlink" href="#super_gradients.training.StrictLoad.ON" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.StrictLoad.NO_KEY_MATCHING">
- <span class="sig-name descname"><span class="pre">NO_KEY_MATCHING</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'no_key_matching'</span></em><a class="headerlink" href="#super_gradients.training.StrictLoad.NO_KEY_MATCHING" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- </section>
- <section id="super-gradients-training-datasets-module">
- <h2>super_gradients.training.datasets module<a class="headerlink" href="#super-gradients-training-datasets-module" title="Permalink to this headline"></a></h2>
- <span class="target" id="module-super_gradients.training.datasets"></span><dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DataAugmentation">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">DataAugmentation</span></span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DataAugmentation" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DataAugmentation.to_tensor">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">to_tensor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.to_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DataAugmentation.to_tensor" 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.DataAugmentation.normalize">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DataAugmentation.normalize" 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.DataAugmentation.cutout">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">cutout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutout_inside</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">mask_color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">0,</span> <span class="pre">0)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/data_augmentation.html#DataAugmentation.cutout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DataAugmentation.cutout" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.ListDataset">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">ListDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">root</span></em>, <em class="sig-param"><span class="pre">file</span></em>, <em class="sig-param"><span class="pre">sample_loader:</span> <span class="pre">Callable</span> <span class="pre">=</span> <span class="pre"><function</span> <span class="pre">default_loader></span></em>, <em class="sig-param"><span class="pre">target_loader:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">collate_fn:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">sample_extensions:</span> <span class="pre">tuple</span> <span class="pre">=</span> <span class="pre">('.jpg'</span></em>, <em class="sig-param"><span class="pre">'.jpeg'</span></em>, <em class="sig-param"><span class="pre">'.png'</span></em>, <em class="sig-param"><span class="pre">'.ppm'</span></em>, <em class="sig-param"><span class="pre">'.bmp'</span></em>, <em class="sig-param"><span class="pre">'.pgm'</span></em>, <em class="sig-param"><span class="pre">'.tif'</span></em>, <em class="sig-param"><span class="pre">'.tiff'</span></em>, <em class="sig-param"><span class="pre">'.webp')</span></em>, <em class="sig-param"><span class="pre">sample_transform:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">target_transform:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">target_extension='.npy'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/sg_dataset.html#ListDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.ListDataset" 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>
- <dt>ListDataset - A PyTorch Vision Data Set extension that receives a file with FULL PATH to each of the samples.</dt><dd><p>Then, the assumption is that for every sample, there is a * matching target * in the same
- path but with a different extension, i.e:</p>
- <blockquote>
- <div><dl class="simple">
- <dt>for the samples paths: (That appear in the list file)</dt><dd><p>/root/dataset/class_x/sample1.png
- /root/dataset/class_y/sample123.png</p>
- </dd>
- <dt>the matching labels paths: (That DO NOT appear in the list file)</dt><dd><p>/root/dataset/class_x/sample1.ext
- /root/dataset/class_y/sample123.ext</p>
- </dd>
- </dl>
- </div></blockquote>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DirectoryDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">DirectoryDataSet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">root:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">samples_sub_directory:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">targets_sub_directory:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">target_extension:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">sample_loader:</span> <span class="pre">Callable</span> <span class="pre">=</span> <span class="pre"><function</span> <span class="pre">default_loader></span></em>, <em class="sig-param"><span class="pre">target_loader:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">collate_fn:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">sample_extensions:</span> <span class="pre">tuple</span> <span class="pre">=</span> <span class="pre">('.jpg'</span></em>, <em class="sig-param"><span class="pre">'.jpeg'</span></em>, <em class="sig-param"><span class="pre">'.png'</span></em>, <em class="sig-param"><span class="pre">'.ppm'</span></em>, <em class="sig-param"><span class="pre">'.bmp'</span></em>, <em class="sig-param"><span class="pre">'.pgm'</span></em>, <em class="sig-param"><span class="pre">'.tif'</span></em>, <em class="sig-param"><span class="pre">'.tiff'</span></em>, <em class="sig-param"><span class="pre">'.webp')</span></em>, <em class="sig-param"><span class="pre">sample_transform:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">target_transform:</span> <span class="pre">Optional[Callable]</span> <span class="pre">=</span> <span class="pre">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/sg_dataset.html#DirectoryDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DirectoryDataSet" 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="simple">
- <dt>DirectoryDataSet - A PyTorch Vision Data Set extension that receives a root Dir and two separate sub directories:</dt><dd><ul class="simple">
- <li><p>Sub-Directory for Samples</p></li>
- <li><p>Sub-Directory for Targets</p></li>
- </ul>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">DetectionDataSet</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></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">416</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</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="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_loading_method</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">'default'</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</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="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">'.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_offset</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">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_inclusion_list</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">all_classes_list</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/detection_datasets/detection_dataset.html#DetectionDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet" 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.DetectionDataSet.mixup">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">im</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.mixup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.mixup" 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.DetectionDataSet.sample_post_process">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">sample_post_process</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/detection_datasets/detection_dataset.html#DetectionDataSet.sample_post_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.sample_post_process" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>sample_post_process - Normalizes and orders the image to be 3 x img_size x img_size</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param image</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.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><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.sample_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.sample_loader" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>sample_loader - Loads a coco dataset image from path</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param sample_path</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.sample_transform">
- <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/detection_datasets/detection_dataset.html#DetectionDataSet.sample_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.sample_transform" 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>image</strong> – </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.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>, <em class="sig-param"><span class="n"><span class="pre">class_inclusion_list</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">all_classes_list</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/detection_datasets/detection_dataset.html#DetectionDataSet.target_loader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.target_loader" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>coco_target_loader</dt><dd><p>@param target_path: str, path to target.
- @param all_classes_list: list(str) containing all the class names or None when subclassing is disabled.
- @param class_inclusion_list: list(str) containing the subclass names or None when subclassing is disabled.</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.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>, <em class="sig-param"><span class="n"><span class="pre">ratio</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</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/detection_datasets/detection_dataset.html#DetectionDataSet.target_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.target_transform" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>target</strong> – </p></li>
- <li><p><strong>ratio</strong> – </p></li>
- <li><p><strong>w</strong> – </p></li>
- <li><p><strong>h</strong> – </p></li>
- <li><p><strong>pad</strong> – </p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.exif_size">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">exif_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.exif_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.exif_size" 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>img</strong> – </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.augment_hsv">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">augment_hsv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vgain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.augment_hsv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.augment_hsv" 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>img</strong> – <dl class="field-list simple">
- <dt class="field-odd">param hgain</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">param sgain</dt>
- <dd class="field-even"><p></p></dd>
- <dt class="field-odd">param vgain</dt>
- <dd class="field-odd"><p></p></dd>
- </dl>
- </p>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.letterbox">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">letterbox</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(416,</span> <span class="pre">416)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(128,</span> <span class="pre">128,</span> <span class="pre">128)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaleFill</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">scaleup</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span> → <span class="pre">tuple</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.letterbox"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.letterbox" title="Permalink to this definition"></a></dt>
- <dd><p>letterbox - Resizes image to a 32-pixel-multiple rectangle
- :param img:
- :param new_shape:
- :param color:
- :param auto:
- :param scaleFill:
- :param scaleup:
- :param interp:
- :return:</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.random_perspective">
- <span class="sig-name descname"><span class="pre">random_perspective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">translate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shear</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">border</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perspective</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.random_perspective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.random_perspective" title="Permalink to this definition"></a></dt>
- <dd><p>random images and labels using a perspective transform</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.box_candidates">
- <em class="property"><span class="pre">static</span> </em><span class="sig-name descname"><span class="pre">box_candidates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">box1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">box2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wh_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ar_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">area_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.box_candidates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.box_candidates" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>compute candidate boxes</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param box1</dt>
- <dd class="field-odd"><p>before augment</p>
- </dd>
- <dt class="field-even">param box2</dt>
- <dd class="field-even"><p>after augment</p>
- </dd>
- <dt class="field-odd">param wh_thr</dt>
- <dd class="field-odd"><p>wh_thr (pixels)</p>
- </dd>
- <dt class="field-even">param ar_thr</dt>
- <dd class="field-even"><p>aspect_ratio_thr</p>
- </dd>
- <dt class="field-odd">param area_thr</dt>
- <dd class="field-odd"><p>area_ratio</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>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionDataSet.load_mosaic">
- <span class="sig-name descname"><span class="pre">load_mosaic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/detection_datasets/detection_dataset.html#DetectionDataSet.load_mosaic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionDataSet.load_mosaic" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>load_mosaic - Load images in mosaic format to improve noise handling while training</dt><dd><dl class="field-list simple">
- <dt class="field-odd">param index</dt>
- <dd class="field-odd"><p></p></dd>
- <dt class="field-even">return</dt>
- <dd class="field-even"><p></p></dd>
- </dl>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.COCODetectionDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">COCODetectionDataSet</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/detection_datasets/coco_detection.html#COCODetectionDataSet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.COCODetectionDataSet" 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>COCODetectionDataSet - Detection Data Set Class COCO Data Set</p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.SegmentationDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.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.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.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> → <span class="pre"><module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/home/avi/git/super-gradients/venv/lib/python3.9/site-packages/PIL/Image.py’></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.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.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.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.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> → <span class="pre"><module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/home/avi/git/super-gradients/venv/lib/python3.9/site-packages/PIL/Image.py’></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.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.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.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.PascalVOC2012SegmentationDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.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.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.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.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.PascalAUG2012SegmentationDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.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.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.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> → <span class="pre"><module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/home/avi/git/super-gradients/venv/lib/python3.9/site-packages/PIL/Image.py’></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.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.CoCoSegmentationDataSet">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.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.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.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> → <span class="pre"><module</span> <span class="pre">‘PIL.Image’</span> <span class="pre">from</span> <span class="pre">‘/home/avi/git/super-gradients/venv/lib/python3.9/site-packages/PIL/Image.py’></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.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.TestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">TestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trainset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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/dataset_interfaces/dataset_interface.html#TestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.TestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.TestDatasetInterface.get_data_loaders">
- <span class="sig-name descname"><span class="pre">get_data_loaders</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_batch_size</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">val_batch_size</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">distributed_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#TestDatasetInterface.get_data_loaders"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.TestDatasetInterface.get_data_loaders" title="Permalink to this definition"></a></dt>
- <dd><p>Get self.train_loader, self.test_loader, self.classes.</p>
- <p>If the data loaders haven’t been initialized yet, build them first.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><p><strong>kwargs</strong> – kwargs are passed to build_data_loaders.</p>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">DatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_loader</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">val_loader</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">test_loader</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">classes</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/dataset_interfaces/dataset_interface.html#DatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <p>DatasetInterface - This class manages all of the “communiation” the Model has with the Data Sets</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DatasetInterface.download_from_cloud">
- <span class="sig-name descname"><span class="pre">download_from_cloud</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.download_from_cloud"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.download_from_cloud" 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.DatasetInterface.build_data_loaders">
- <span class="sig-name descname"><span class="pre">build_data_loaders</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_batch_size</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">val_batch_size</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">test_batch_size</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">distributed_sampler</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.build_data_loaders"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.build_data_loaders" title="Permalink to this definition"></a></dt>
- <dd><p>define train, val (and optionally test) loaders. The method deals separately with distributed training and standard
- (non distributed, or parallel training). In the case of distributed training we need to rely on distributed
- samplers.
- :param batch_size_factor: int - factor to multiply the batch size (usually for multi gpu)
- :param num_workers: int - number of workers (parallel processes) for dataloaders
- :param train_batch_size: int - batch size for train loader, if None will be taken from dataset_params
- :param val_batch_size: int - batch size for val loader, if None will be taken from dataset_params
- :param distributed_sampler: boolean flag for distributed training mode
- :return: train_loader, val_loader, classes: list of classes</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DatasetInterface.get_data_loaders">
- <span class="sig-name descname"><span class="pre">get_data_loaders</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">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.get_data_loaders"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.get_data_loaders" title="Permalink to this definition"></a></dt>
- <dd><p>Get self.train_loader, self.test_loader, self.classes.</p>
- <p>If the data loaders haven’t been initialized yet, build them first.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><p><strong>kwargs</strong> – kwargs are passed to build_data_loaders.</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DatasetInterface.get_val_sample">
- <span class="sig-name descname"><span class="pre">get_val_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.get_val_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.get_val_sample" 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.DatasetInterface.get_dataset_params">
- <span class="sig-name descname"><span class="pre">get_dataset_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.get_dataset_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.get_dataset_params" 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.DatasetInterface.print_dataset_details">
- <span class="sig-name descname"><span class="pre">print_dataset_details</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DatasetInterface.print_dataset_details"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DatasetInterface.print_dataset_details" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.Cifar10DatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">Cifar10DatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#Cifar10DatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.Cifar10DatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.LibraryDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.LibraryDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.LibraryDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.CoCoSegmentationDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">CoCoSegmentationDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</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">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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#CoCoSegmentationDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.CoCoSegmentationDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.CoCoDetectionDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">CoCoDetectionDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</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="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="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">train_list_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train2017.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_list_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'val2017.txt'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#CoCoDetectionDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.CoCoDetectionDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDataSetInterfaceBase</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.CoCo2014DetectionDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">CoCo2014DetectionDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</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="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="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">train_list_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train2014.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_list_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'val2014.txt'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#CoCo2014DetectionDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.CoCo2014DetectionDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDetectionDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDetectionDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDetectionDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.PascalVOC2012SegmentationDataSetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">PascalVOC2012SegmentationDataSetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</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="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="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#PascalVOC2012SegmentationDataSetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.PascalVOC2012SegmentationDataSetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.PascalAUG2012SegmentationDataSetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">PascalAUG2012SegmentationDataSetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</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="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="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#PascalAUG2012SegmentationDataSetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.PascalAUG2012SegmentationDataSetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.TestYoloDetectionDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">TestYoloDetectionDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(3,</span> <span class="pre">32,</span> <span class="pre">32)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#TestYoloDetectionDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.TestYoloDetectionDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- <p>note: the output size is (batch_size, 6) in the test while in real training
- the size of axis 0 can vary (the number of bounding boxes)</p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.DetectionTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">DetectionTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">320</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#DetectionTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.DetectionTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.ClassificationTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">ClassificationTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</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/dataset_interfaces/dataset_interface.html#ClassificationTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.ClassificationTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.SegmentationTestDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">SegmentationTestDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</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="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#SegmentationTestDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.SegmentationTestDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.TestDatasetInterface</span></code></a></p>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.datasets.ImageNetDatasetInterface">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.datasets.</span></span><span class="sig-name descname"><span class="pre">ImageNetDatasetInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'/data/Imagenet'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/datasets/dataset_interfaces/dataset_interface.html#ImageNetDatasetInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.datasets.ImageNetDatasetInterface" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></code></a></p>
- </dd></dl>
- </section>
- <section id="super-gradients-training-exceptions-module">
- <h2>super_gradients.training.exceptions module<a class="headerlink" href="#super-gradients-training-exceptions-module" title="Permalink to this headline"></a></h2>
- <span class="target" id="module-super_gradients.training.exceptions"></span></section>
- <section id="module-super_gradients.training.legacy">
- <span id="super-gradients-training-legacy-module"></span><h2>super_gradients.training.legacy module<a class="headerlink" href="#module-super_gradients.training.legacy" title="Permalink to this headline"></a></h2>
- </section>
- <section id="module-super_gradients.training.losses">
- <span id="super-gradients-training-losses-models-module"></span><h2>super_gradients.training.losses_models module<a class="headerlink" href="#module-super_gradients.training.losses" title="Permalink to this headline"></a></h2>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.FocalLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">FocalLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_fcn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.nn.modules.loss.BCEWithLogitsLoss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/focal_loss.html#FocalLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.FocalLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code></p>
- <p>Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)</p>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.FocalLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.FocalLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.FocalLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/focal_loss.html#FocalLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.FocalLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.LabelSmoothingCrossEntropyLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">LabelSmoothingCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</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">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_eps</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">smooth_dist</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">from_logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/label_smoothing_cross_entropy_loss.html#LabelSmoothingCrossEntropyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.LabelSmoothingCrossEntropyLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss.CrossEntropyLoss</span></code></p>
- <p>CrossEntropyLoss - with ability to recieve distrbution as targets, and optional label smoothing</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_dist</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/losses/label_smoothing_cross_entropy_loss.html#LabelSmoothingCrossEntropyLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.ignore_index">
- <span class="sig-name descname"><span class="pre">ignore_index</span></span><em class="property"><span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.ignore_index" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.label_smoothing">
- <span class="sig-name descname"><span class="pre">label_smoothing</span></span><em class="property"><span class="pre">:</span> <span class="pre">float</span></em><a class="headerlink" href="#super_gradients.training.losses.LabelSmoothingCrossEntropyLoss.label_smoothing" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetOHEMLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">ShelfNetOHEMLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mining_percent</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_lb</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">255</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/shelfnet_ohem_loss.html#ShelfNetOHEMLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.ShelfNetOHEMLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <a class="reference internal" href="super_gradients.training.losses.html#super_gradients.training.losses.ohem_ce_loss.OhemCELoss" title="super_gradients.training.losses.ohem_ce_loss.OhemCELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.losses.ohem_ce_loss.OhemCELoss</span></code></a></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetOHEMLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/shelfnet_ohem_loss.html#ShelfNetOHEMLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.ShelfNetOHEMLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetOHEMLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.ShelfNetOHEMLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetSemanticEncodingLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">ShelfNetSemanticEncodingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">se_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nclass</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</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">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/shelfnet_semantic_encoding_loss.html#ShelfNetSemanticEncodingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.ShelfNetSemanticEncodingLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss.CrossEntropyLoss</span></code></p>
- <p>2D Cross Entropy Loss with Auxilary Loss</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetSemanticEncodingLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/shelfnet_semantic_encoding_loss.html#ShelfNetSemanticEncodingLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.ShelfNetSemanticEncodingLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetSemanticEncodingLoss.ignore_index">
- <span class="sig-name descname"><span class="pre">ignore_index</span></span><em class="property"><span class="pre">:</span> <span class="pre">int</span></em><a class="headerlink" href="#super_gradients.training.losses.ShelfNetSemanticEncodingLoss.ignore_index" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.ShelfNetSemanticEncodingLoss.label_smoothing">
- <span class="sig-name descname"><span class="pre">label_smoothing</span></span><em class="property"><span class="pre">:</span> <span class="pre">float</span></em><a class="headerlink" href="#super_gradients.training.losses.ShelfNetSemanticEncodingLoss.label_smoothing" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV3DetectionLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">YoLoV3DetectionLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.nn.modules.module.Module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cls_pw</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj_pw</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">giou</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">3.54</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">64.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cls</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">37.4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v3_loss.html#YoLoV3DetectionLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV3DetectionLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code></p>
- <p>YoLoV3DetectionLoss - Loss Class for Object Detection</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV3DetectionLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v3_loss.html#YoLoV3DetectionLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV3DetectionLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV3DetectionLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.YoLoV3DetectionLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV5DetectionLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">YoLoV5DetectionLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchors</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.Anchors" title="super_gradients.training.utils.detection_utils.Anchors"><span class="pre">super_gradients.training.utils.detection_utils.Anchors</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">cls_pos_weight</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></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">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj_pos_weight</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj_loss_gain</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">box_loss_gain</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cls_loss_gain</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_loss_gamma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cls_objectness_weights</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">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></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="n"><span class="pre">anchor_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v5_loss.html#YoLoV5DetectionLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV5DetectionLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code></p>
- <p>Calculate YOLO V5 loss:
- L = L_objectivness + L_boxes + L_classification</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV5DetectionLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v5_loss.html#YoLoV5DetectionLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV5DetectionLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV5DetectionLoss.build_targets">
- <span class="sig-name descname"><span class="pre">build_targets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> → <span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v5_loss.html#YoLoV5DetectionLoss.build_targets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV5DetectionLoss.build_targets" title="Permalink to this definition"></a></dt>
- <dd><dl>
- <dt>Assign targets to anchors to use in L_boxes & L_classification calculation:</dt><dd><ul class="simple">
- <li><p>each target can be assigned to a few anchors,</p></li>
- </ul>
- <p>all anchors that are within [1/self.anchor_threshold, self.anchor_threshold] times target size range
- * each anchor can be assigned to a few targets</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>predictions</strong> – Yolo predictions</p></li>
- <li><p><strong>targets</strong> – ground truth targets</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p><p>each of 4 outputs contains one element for each Yolo output,
- correspondences are raveled over the whole batch and all anchors:</p>
- <blockquote>
- <div><ul class="simple">
- <li><p>classes of the targets;</p></li>
- <li><p>boxes of the targets;</p></li>
- <li><p>image id in a batch, anchor id, grid y, grid x coordinates;</p></li>
- <li><p>anchor sizes.</p></li>
- </ul>
- </div></blockquote>
- <p>All the above can be indexed in parallel to get the selected correspondences</p>
- </p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV5DetectionLoss.compute_loss">
- <span class="sig-name descname"><span class="pre">compute_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">giou_loss_ratio</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span> → <span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><a class="reference internal" href="_modules/super_gradients/training/losses/yolo_v5_loss.html#YoLoV5DetectionLoss.compute_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.YoLoV5DetectionLoss.compute_loss" title="Permalink to this definition"></a></dt>
- <dd><p>L = L_objectivness + L_boxes + L_classification
- where:</p>
- <blockquote>
- <div><ul class="simple">
- <li><p>L_boxes and L_classification are calculated only between anchors and targets that suit them;</p></li>
- <li><p>L_objectivness is calculated on all anchors.</p></li>
- </ul>
- </div></blockquote>
- <dl>
- <dt>L_classification:</dt><dd><p>for anchors that have suitable ground truths in their grid locations add BCEs
- to force max probability for each GT class in a multi-label way
- Coef: self.cls_loss_gain</p>
- </dd>
- <dt>L_boxes:</dt><dd><p>for anchors that have suitable ground truths in their grid locations
- add (1 - IoU), IoU between a predicted box and each GT box, force maximum IoU
- Coef: self.box_loss_gain</p>
- </dd>
- <dt>L_objectness:</dt><dd><p>for each anchor add BCE to force a prediction of (1 - giou_loss_ratio) + giou_loss_ratio * IoU,
- IoU between a predicted box and random GT in it
- Coef: self.obj_loss_gain, loss from each YOLO grid is additionally multiplied by balance = [4.0, 1.0, 0.4]</p>
- <blockquote>
- <div><p>to balance different contributions coming from different numbers of grid cells</p>
- </div></blockquote>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>predictions</strong> – output from all Yolo levels, each of shape
- [Batch x Num_Anchors x GridSizeY x GridSizeX x (4 + 1 + Num_classes)]</p></li>
- <li><p><strong>targets</strong> – [Num_targets x (4 + 2)], values on dim 1 are: image id in a batch, class, box x y w h</p></li>
- <li><p><strong>giou_loss_ratio</strong> – a coef in L_objectness defining what should be predicted as objecness
- in a call with a target: can be a value in [IoU, 1] range</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>loss, all losses separately in a detached tensor</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.YoLoV5DetectionLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.YoLoV5DetectionLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.RSquaredLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">RSquaredLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</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">reduce</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">reduction</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">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/r_squared_loss.html#RSquaredLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.RSquaredLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.RSquaredLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <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/losses/r_squared_loss.html#RSquaredLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.RSquaredLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Computes the R-squared for the output and target values
- :param output: Tensor / Numpy / List</p>
- <blockquote>
- <div><p>The prediction</p>
- </div></blockquote>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><p><strong>target</strong> – Tensor / Numpy / List
- The corresponding lables</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.RSquaredLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.RSquaredLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.losses.SSDLoss">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.losses.</span></span><span class="sig-name descname"><span class="pre">SSDLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dboxes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes" title="super_gradients.training.utils.ssd_utils.DefaultBoxes"><span class="pre">super_gradients.training.utils.ssd_utils.DefaultBoxes</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/ssd_loss.html#SSDLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.SSDLoss" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code></p>
- <p>Implements the loss as the sum of the followings:
- 1. Confidence Loss: All labels, with hard negative mining
- 2. Localization Loss: Only on positive labels</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.SSDLoss.match_dboxes">
- <span class="sig-name descname"><span class="pre">match_dboxes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/ssd_loss.html#SSDLoss.match_dboxes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.SSDLoss.match_dboxes" title="Permalink to this definition"></a></dt>
- <dd><p>convert ground truth boxes into a tensor with the same size as dboxes. each gt bbox is matched to every
- destination box which overlaps it over 0.5 (IoU). so some gt bboxes can be duplicated to a few destination boxes
- :param targets: a tensor containing the boxes for a single image. shape [num_boxes, 5] (x,y,w,h,label)
- :return: two tensors</p>
- <blockquote>
- <div><p>boxes - shape of dboxes [4, num_dboxes] (x,y,w,h)
- labels - sahpe [num_dboxes]</p>
- </div></blockquote>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.losses.SSDLoss.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/losses/ssd_loss.html#SSDLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.losses.SSDLoss.forward" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>Compute the loss</dt><dd><p>:param predictions - predictions tensor coming from the network. shape [N, num_classes+4, num_dboxes]
- were the first four items are (x,y,w,h) and the rest are class confidence
- :param targets - targets for the batch. [num targets, 6] (index in batch, label, x,y,w,h)</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.losses.SSDLoss.reduction">
- <span class="sig-name descname"><span class="pre">reduction</span></span><em class="property"><span class="pre">:</span> <span class="pre">str</span></em><a class="headerlink" href="#super_gradients.training.losses.SSDLoss.reduction" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- </section>
- <section id="module-super_gradients.training.metrics">
- <span id="super-gradients-training-metrics-module"></span><h2>super_gradients.training.metrics module<a class="headerlink" href="#module-super_gradients.training.metrics" title="Permalink to this headline"></a></h2>
- </section>
- <section id="module-super_gradients.training.models">
- <span id="super-gradients-training-models-module"></span><h2>super_gradients.training.models module<a class="headerlink" href="#module-super_gradients.training.models" title="Permalink to this headline"></a></h2>
- </section>
- <section id="module-super_gradients.training.sg_model">
- <span id="super-gradients-training-sg-model-module"></span><h2>super_gradients.training.sg_model module<a class="headerlink" href="#module-super_gradients.training.sg_model" title="Permalink to this headline"></a></h2>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.sg_model.</span></span><span class="sig-name descname"><span class="pre">SgModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">experiment_name:</span> <span class="pre">str</span></em>, <em class="sig-param"><span class="pre">device:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">multi_gpu:</span> <span class="pre">Union[super_gradients.training.sg_model.sg_model.MultiGPUMode</span></em>, <em class="sig-param"><span class="pre">str]</span> <span class="pre">=</span> <span class="pre"><MultiGPUMode.AUTO:</span> <span class="pre">'AUTO'></span></em>, <em class="sig-param"><span class="pre">model_checkpoints_location:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'local'</span></em>, <em class="sig-param"><span class="pre">overwrite_local_checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></em>, <em class="sig-param"><span class="pre">ckpt_name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'ckpt_latest.pth'</span></em>, <em class="sig-param"><span class="pre">post_prediction_callback:</span> <span class="pre">Optional[super_gradients.training.utils.detection_utils.DetectionPostPredictionCallback]</span> <span class="pre">=</span> <span class="pre">None</span></em>, <em class="sig-param"><span class="pre">ckpt_root_dir=None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <p>SuperGradient Model - Base Class for Sg Models</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.train">
- <span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_epochs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_epoch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.train" title="Permalink to this definition"></a></dt>
- <dd><p>the main function used for the training, h.p. updating, logging etc.</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.predict">
- <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.predict" title="Permalink to this definition"></a></dt>
- <dd><p>returns the predictions and label of the current inputs</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">test(epoch</span> <span class="pre">:</span> <span class="pre">int,</span> <span class="pre">idx</span> <span class="pre">:</span> <span class="pre">int,</span> <span class="pre">save</span> <span class="pre">:</span> <span class="pre">bool):</span></span></dt>
- <dd><p>returns the test loss, accuracy and runtime</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.connect_dataset_interface">
- <span class="sig-name descname"><span class="pre">connect_dataset_interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_interface</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.datasets.dataset_interfaces.html#super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface" title="super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface"><span class="pre">super_gradients.training.datasets.dataset_interfaces.dataset_interface.DatasetInterface</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_loader_num_workers</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">8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.connect_dataset_interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.connect_dataset_interface" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>dataset_interface</strong> – DatasetInterface object</p></li>
- <li><p><strong>data_loader_num_workers</strong> – The number of threads to initialize the Data Loaders with
- The dataset to be connected</p></li>
- </ul>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.build_model">
- <span class="sig-name descname"><span class="pre">build_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">architecture:</span> <span class="pre">Union[str,</span> <span class="pre">torch.nn.modules.module.Module],</span> <span class="pre">arch_params={},</span> <span class="pre">load_checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">strict_load:</span> <span class="pre">super_gradients.training.sg_model.sg_model.StrictLoad</span> <span class="pre">=</span> <span class="pre"><StrictLoad.ON:</span> <span class="pre">True>,</span> <span class="pre">source_ckpt_folder_name:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">load_weights_only:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">load_backbone:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">external_checkpoint_path:</span> <span class="pre">Optional[str]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">load_ema_as_net:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.build_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.build_model" title="Permalink to this definition"></a></dt>
- <dd><dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>architecture</strong> – Defines the network’s architecture from models/ALL_ARCHITECTURES</p></li>
- <li><p><strong>arch_params</strong> – Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p></li>
- <li><p><strong>load_checkpoint</strong> – Load a pre-trained checkpoint</p></li>
- <li><p><strong>strict_load</strong> – See StrictLoad class documentation for details.</p></li>
- <li><p><strong>source_ckpt_folder_name</strong> – folder name to load the checkpoint from (self.experiment_name if none is given)</p></li>
- <li><p><strong>load_weights_only</strong> – loads only the weight from the checkpoint and zeroize the training params</p></li>
- <li><p><strong>load_backbone</strong> – loads the provided checkpoint to self.net.backbone instead of self.net</p></li>
- <li><p><strong>external_checkpoint_path</strong> – The path to the external checkpoint to be loaded. Can be absolute or relative
- (ie: path/to/checkpoint.pth). If provided, will automatically attempt to
- load the checkpoint even if the load_checkpoint flag is not provided.</p></li>
- </ul>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.backward_step">
- <span class="sig-name descname"><span class="pre">backward_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.PhaseContext" title="super_gradients.training.utils.callbacks.PhaseContext"><span class="pre">super_gradients.training.utils.callbacks.PhaseContext</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.backward_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.backward_step" title="Permalink to this definition"></a></dt>
- <dd><p>Run backprop on the loss and perform a step
- :param loss: The value computed by the loss function
- :param optimizer: An object that can perform a gradient step and zeroize model gradient
- :param epoch: number of epoch the training is on
- :param batch_idx: number of iteration inside the current epoch
- :param context: current phase context
- :return:</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.save_checkpoint">
- <span class="sig-name descname"><span class="pre">save_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</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">epoch</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">int</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="n"><span class="pre">validation_results_tuple</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">tuple</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="n"><span class="pre">context</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><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.PhaseContext" title="super_gradients.training.utils.callbacks.PhaseContext"><span class="pre">super_gradients.training.utils.callbacks.PhaseContext</span></a><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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.save_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.save_checkpoint" title="Permalink to this definition"></a></dt>
- <dd><p>Save the current state dict as latest (always), best (if metric was improved), epoch# (if determined in training
- params)</p>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="id4">
- <span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training_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">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id4" title="Permalink to this definition"></a></dt>
- <dd><p>train - Trains the Model</p>
- <dl>
- <dt>IMPORTANT NOTE: Additional batch parameters can be added as a third item (optional) if a tuple is returned by</dt><dd><p>the data loaders, as dictionary. The phase context will hold the additional items, under an attribute with
- the same name as the key in this dictionary. Then such items can be accessed through phase callbacks.</p>
- <blockquote>
- <div><dl class="field-list">
- <dt class="field-odd">param training_params</dt>
- <dd class="field-odd"><ul>
- <li><p><cite>max_epochs</cite> : int</p>
- <blockquote>
- <div><p>Number of epochs to run training.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_updates</cite> : list(int)</p>
- <blockquote>
- <div><p>List of fixed epoch numbers to perform learning rate updates when <cite>lr_mode=’step’</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_decay_factor</cite> : float</p>
- <blockquote>
- <div><p>Decay factor to apply to the learning rate at each update when <cite>lr_mode=’step’</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_mode</cite> : str</p>
- <blockquote>
- <div><p>Learning rate scheduling policy, one of [‘step’,’poly’,’cosine’,’function’]. ‘step’ refers to
- constant updates at epoch numbers passed through <cite>lr_updates</cite>. ‘cosine’ refers to Cosine Anealing
- policy as mentioned in <a class="reference external" href="https://arxiv.org/abs/1608.03983">https://arxiv.org/abs/1608.03983</a>. ‘poly’ refers to polynomial decrease i.e
- in each epoch iteration <cite>self.lr = self.initial_lr * pow((1.0 - (current_iter / max_iter)),
- 0.9)</cite> ‘function’ refers to user defined learning rate scheduling function, that is passed through
- <cite>lr_schedule_function</cite>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_schedule_function</cite> : Union[callable,None]</p>
- <blockquote>
- <div><p>Learning rate scheduling function to be used when <cite>lr_mode</cite> is ‘function’.</p>
- </div></blockquote>
- </li>
- <li><p><cite>lr_warmup_epochs</cite> : int (default=0)</p>
- <blockquote>
- <div><p>Number of epochs for learning rate warm up - see <a class="reference external" href="https://arxiv.org/pdf/1706.02677.pdf">https://arxiv.org/pdf/1706.02677.pdf</a> (Section 2.2).</p>
- </div></blockquote>
- </li>
- <li><dl class="simple">
- <dt><cite>cosine_final_lr_ratio</cite><span class="classifier">float (default=0.01)</span></dt><dd><dl class="simple">
- <dt>Final learning rate ratio (only relevant when <a href="#id5"><span class="problematic" id="id6">`</span></a>lr_mode`=’cosine’). The cosine starts from initial_lr and reaches</dt><dd><p>initial_lr * cosine_final_lr_ratio in last epoch</p>
- </dd>
- </dl>
- </dd>
- </dl>
- </li>
- <li><p><cite>inital_lr</cite> : float</p>
- <blockquote>
- <div><p>Initial learning rate.</p>
- </div></blockquote>
- </li>
- <li><p><cite>loss</cite> : Union[nn.module, str]</p>
- <blockquote>
- <div><p>Loss function for training.
- One of SuperGradient’s built in options:</p>
- <blockquote>
- <div><p>“cross_entropy”: LabelSmoothingCrossEntropyLoss,
- “mse”: MSELoss,
- “r_squared_loss”: RSquaredLoss,
- “detection_loss”: YoLoV3DetectionLoss,
- “shelfnet_ohem_loss”: ShelfNetOHEMLoss,
- “shelfnet_se_loss”: ShelfNetSemanticEncodingLoss,
- “yolo_v5_loss”: YoLoV5DetectionLoss,
- “ssd_loss”: SSDLoss,</p>
- </div></blockquote>
- <p>or user defined nn.module loss function.</p>
- <p>IMPORTANT: forward(…) should return a (loss, loss_items) tuple where loss is the tensor used
- for backprop (i.e what your original loss function returns), and loss_items should be a tensor of
- shape (n_items), of values computed during the forward pass which we desire to log over the
- entire epoch. For example- the loss itself should always be logged. Another example is a scenario
- where the computed loss is the sum of a few components we would like to log- these entries in
- loss_items).</p>
- <p>When training, set the loss_logging_items_names parameter in train_params to be a list of
- strings, of length n_items who’s ith element is the name of the ith entry in loss_items. Then
- each item will be logged, rendered on tensorboard and “watched” (i.e saving model checkpoints
- according to it).</p>
- <p>Since running logs will save the loss_items in some internal state, it is recommended that
- loss_items are detached from their computational graph for memory efficiency.</p>
- </div></blockquote>
- </li>
- <li><p><cite>optimizer</cite> : Union[str, torch.optim.Optimizer]</p>
- <blockquote>
- <div><p>Optimization algorithm. One of [‘Adam’,’SGD’,’RMSProp’] corresponding to the torch.optim
- optimzers implementations, or any object that implements torch.optim.Optimizer.</p>
- </div></blockquote>
- </li>
- <li><p><cite>criterion_params</cite> : dict</p>
- <blockquote>
- <div><p>Loss function parameters.</p>
- </div></blockquote>
- </li>
- <li><dl>
- <dt><cite>optimizer_params</cite><span class="classifier">dict</span></dt><dd><p>When <cite>optimizer</cite> is one of [‘Adam’,’SGD’,’RMSProp’], it will be initialized with optimizer_params.</p>
- <p>(see <a class="reference external" href="https://pytorch.org/docs/stable/optim.html">https://pytorch.org/docs/stable/optim.html</a> for the full list of
- parameters for each optimizer).</p>
- </dd>
- </dl>
- </li>
- <li><p><cite>train_metrics_list</cite> : list(torchmetrics.Metric)</p>
- <blockquote>
- <div><p>Metrics to log during training. For more information on torchmetrics see
- <a class="reference external" href="https://torchmetrics.rtfd.io/en/latest/">https://torchmetrics.rtfd.io/en/latest/</a>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>valid_metrics_list</cite> : list(torchmetrics.Metric)</p>
- <blockquote>
- <div><p>Metrics to log during validation/testing. For more information on torchmetrics see
- <a class="reference external" href="https://torchmetrics.rtfd.io/en/latest/">https://torchmetrics.rtfd.io/en/latest/</a>.</p>
- </div></blockquote>
- </li>
- <li><p><cite>loss_logging_items_names</cite> : list(str)</p>
- <blockquote>
- <div><p>The list of names/titles for the outputs returned from the loss functions forward pass (reminder-
- the loss function should return the tuple (loss, loss_items)). These names will be used for
- logging their values.</p>
- </div></blockquote>
- </li>
- <li><p><cite>metric_to_watch</cite> : str (default=”Accuracy”)</p>
- <blockquote>
- <div><p>will be the metric which the model checkpoint will be saved according to, and can be set to any
- of the following:</p>
- <blockquote>
- <div><p>a metric name (str) of one of the metric objects from the valid_metrics_list</p>
- <p>a “metric_name” if some metric in valid_metrics_list has an attribute component_names which
- is a list referring to the names of each entry in the output metric (torch tensor of size n)</p>
- <p>one of “loss_logging_items_names” i.e which will correspond to an item returned during the
- loss function’s forward pass.</p>
- </div></blockquote>
- <p>At the end of each epoch, if a new best metric_to_watch value is achieved, the models checkpoint
- is saved in YOUR_PYTHON_PATH/checkpoints/ckpt_best.pth</p>
- </div></blockquote>
- </li>
- <li><p><cite>greater_metric_to_watch_is_better</cite> : bool</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When choosing a model’s checkpoint to be saved, the best achieved model is the one that maximizes the</dt><dd><p>metric_to_watch when this parameter is set to True, and a one that minimizes it otherwise.</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>ema</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to use Model Exponential Moving Average (see
- <a class="reference external" href="https://github.com/rwightman/pytorch-image-models">https://github.com/rwightman/pytorch-image-models</a> ema implementation)</p>
- </div></blockquote>
- </li>
- <li><p><cite>batch_accumulate</cite> : int (default=1)</p>
- <blockquote>
- <div><p>Number of batches to accumulate before every backward pass.</p>
- </div></blockquote>
- </li>
- <li><p><cite>ema_params</cite> : dict</p>
- <blockquote>
- <div><p>Parameters for the ema model.</p>
- </div></blockquote>
- </li>
- <li><p><cite>zero_weight_decay_on_bias_and_bn</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to apply weight decay on batch normalization parameters or not (ignored when the passed
- optimizer has already been initialized).</p>
- </div></blockquote>
- </li>
- <li><p><cite>load_opt_params</cite> : bool (default=True)</p>
- <blockquote>
- <div><p>Whether to load the optimizers parameters as well when loading a model’s checkpoint.</p>
- </div></blockquote>
- </li>
- <li><p><cite>run_validation_freq</cite> : int (default=1)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>The frequency in which validation is performed during training (i.e the validation is ran every</dt><dd><p><cite>run_validation_freq</cite> epochs.</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>save_model</cite> : bool (default=True)</p>
- <blockquote>
- <div><p>Whether to save the model checkpoints.</p>
- </div></blockquote>
- </li>
- <li><p><cite>silent_mode</cite> : bool</p>
- <blockquote>
- <div><p>Silents the print outs.</p>
- </div></blockquote>
- </li>
- <li><p><cite>mixed_precision</cite> : bool</p>
- <blockquote>
- <div><p>Whether to use mixed precision or not.</p>
- </div></blockquote>
- </li>
- <li><p><cite>save_ckpt_epoch_list</cite> : list(int) (default=[])</p>
- <blockquote>
- <div><p>List of fixed epoch indices the user wishes to save checkpoints in.</p>
- </div></blockquote>
- </li>
- <li><p><cite>average_best_models</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>If set, a snapshot dictionary file and the average model will be saved / updated at every epoch
- and evaluated only when training is completed. The snapshot file will only be deleted upon
- completing the training. The snapshot dict will be managed on cpu.</p>
- </div></blockquote>
- </li>
- <li><p><cite>precise_bn</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Whether to use precise_bn calculation during the training.</p>
- </div></blockquote>
- </li>
- <li><p><cite>precise_bn_batch_size</cite> : int (default=None)</p>
- <blockquote>
- <div><p>The effective batch size we want to calculate the batchnorm on. For example, if we are training a model
- on 8 gpus, with a batch of 128 on each gpu, a good rule of thumb would be to give it 8192
- (ie: effective_batch_size * num_gpus = batch_per_gpu * num_gpus * num_gpus).
- If precise_bn_batch_size is not provided in the training_params, the latter heuristic will be taken.</p>
- </div></blockquote>
- </li>
- <li><p><cite>seed</cite> : int (default=42)</p>
- <blockquote>
- <div><p>Random seed to be set for torch, numpy, and random. When using DDP each process will have it’s seed
- set to seed + rank.</p>
- </div></blockquote>
- </li>
- <li><p><cite>log_installed_packages</cite> : bool (default=False)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When set, the list of all installed packages (and their versions) will be written to the tensorboard</dt><dd><p>and logfile (useful when trying to reproduce results).</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>dataset_statistics</cite> : bool (default=False)</p>
- <blockquote>
- <div><p>Enable a statistic analysis of the dataset. If set to True the dataset will be analyzed and a report
- will be added to the tensorboard along with some sample images from the dataset. Currently only
- detection datasets are supported for analysis.</p>
- </div></blockquote>
- </li>
- <li><p><cite>save_full_train_log</cite> : bool (default=False)</p>
- <blockquote>
- <div><dl class="simple">
- <dt>When set, a full log (of all super_gradients modules, including uncaught exceptions from any other</dt><dd><p>module) of the training will be saved in the checkpoint directory under full_train_log.log</p>
- </dd>
- </dl>
- </div></blockquote>
- </li>
- <li><p><cite>sg_logger</cite> : Union[AbstractSGLogger, str] (defauls=base_sg_logger)</p>
- <blockquote>
- <div><p>Define the SGLogger object for this training process. The SGLogger handles all disk writes, logs, TensorBoard, remote logging
- and remote storage. By overriding the default base_sg_logger, you can change the storage location, support external monitoring and logging
- or support remote storage.</p>
- </div></blockquote>
- </li>
- <li><p><cite>sg_logger_params</cite> : dict</p>
- <p>SGLogger parameters</p>
- </li>
- </ul>
- </dd>
- </dl>
- </div></blockquote>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="id7">
- <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</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">half</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">normalize</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">verbose</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">move_outputs_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id7" title="Permalink to this definition"></a></dt>
- <dd><p>A fast predictor for a batch of inputs
- :param inputs: torch.tensor or numpy.array</p>
- <blockquote>
- <div><p>a batch of inputs</p>
- </div></blockquote>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>targets</strong> – torch.tensor()
- corresponding labels - if non are given - accuracy will not be computed</p></li>
- <li><p><strong>verbose</strong> – bool
- print the results to screen</p></li>
- <li><p><strong>normalize</strong> – bool
- If true, normalizes the tensor according to the dataloader’s normalization values</p></li>
- <li><p><strong>half</strong> – Performs half precision evaluation</p></li>
- <li><p><strong>move_outputs_to_cpu</strong> – Moves the results from the GPU to the CPU</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>outputs, acc, net_time, gross_time
- networks predictions, accuracy calculation, forward pass net time, function gross time</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.compute_model_runtime">
- <span class="sig-name descname"><span class="pre">compute_model_runtime</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dims</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">tuple</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="n"><span class="pre">batch_sizes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">tuple</span><span class="p"><span class="pre">,</span> </span><span class="pre">list</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</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">(1,</span> <span class="pre">8,</span> <span class="pre">16,</span> <span class="pre">32,</span> <span class="pre">64)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</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">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.compute_model_runtime"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.compute_model_runtime" title="Permalink to this definition"></a></dt>
- <dd><p>Compute the “atomic” inference time and throughput.
- Atomic refers to calculating the forward pass independently, discarding effects such as data augmentation,
- data upload to device, multi-gpu distribution etc.
- :param input_dims: tuple</p>
- <blockquote>
- <div><p>shape of a basic input to the network (without the first index) e.g. (3, 224, 224)
- if None uses an input from the test loader</p>
- </div></blockquote>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>batch_sizes</strong> – int or list
- Batch sizes for latency calculation</p></li>
- <li><p><strong>verbose</strong> – bool
- Prints results to screen</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>log: dict
- Latency and throughput for each tested batch size</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.get_arch_params">
- <span class="sig-name descname"><span class="pre">get_arch_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_arch_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.get_arch_params" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.get_structure">
- <span class="sig-name descname"><span class="pre">get_structure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.get_structure" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.get_architecture">
- <span class="sig-name descname"><span class="pre">get_architecture</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_architecture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.get_architecture" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.set_experiment_name">
- <span class="sig-name descname"><span class="pre">set_experiment_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiment_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.set_experiment_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.set_experiment_name" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.re_build_model">
- <span class="sig-name descname"><span class="pre">re_build_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arch_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.re_build_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.re_build_model" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>arch_params<span class="classifier">dict</span></dt><dd><p>Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.update_architecture">
- <span class="sig-name descname"><span class="pre">update_architecture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">structure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.update_architecture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.update_architecture" title="Permalink to this definition"></a></dt>
- <dd><dl class="simple">
- <dt>architecture<span class="classifier">str</span></dt><dd><p>Defines the network’s architecture according to the options in models/all_architectures</p>
- </dd>
- <dt>load_checkpoint<span class="classifier">bool</span></dt><dd><p>Loads a checkpoint according to experiment_name</p>
- </dd>
- <dt>arch_params<span class="classifier">dict</span></dt><dd><p>Architecture H.P. e.g.: block, num_blocks, num_classes, etc.</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p></p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.get_module">
- <span class="sig-name descname"><span class="pre">get_module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.get_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.get_module" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.set_module">
- <span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.set_module" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.test">
- <span class="sig-name descname"><span class="pre">test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_loader</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">torch.utils.data.dataloader.DataLoader</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="n"><span class="pre">loss</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">torch.nn.modules.loss._Loss</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="n"><span class="pre">silent_mode</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">test_metrics_list</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">loss_logging_items_names</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">metrics_progress_verbose</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">test_phase_callbacks</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">use_ema_net</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> → <span class="pre">tuple</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.test" title="Permalink to this definition"></a></dt>
- <dd><p>Evaluates the model on given dataloader and metrics.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>test_loader</strong> – dataloader to perform test on.</p></li>
- <li><p><strong>test_metrics_list</strong> – (list(torchmetrics.Metric)) metrics list for evaluation.</p></li>
- <li><p><strong>silent_mode</strong> – (bool) controls verbosity</p></li>
- <li><p><strong>metrics_progress_verbose</strong> – (bool) controls the verbosity of metrics progress (default=False). Slows down the program.</p></li>
- </ul>
- </dd>
- </dl>
- <dl class="simple">
- <dt>:param use_ema_net (bool) whether to perform test on self.ema_model.ema (when self.ema_model.ema exists,</dt><dd><p>otherwise self.net will be tested) (default=True)</p>
- </dd>
- </dl>
- <dl class="field-list simple">
- <dt class="field-odd">Returns</dt>
- <dd class="field-odd"><p>results tuple (tuple) containing the loss items and metric values.</p>
- </dd>
- </dl>
- <dl class="simple">
- <dt>All of the above args will override SgModel’s corresponding attribute when not equal to None. Then evaluation</dt><dd><p>is ran on self.test_loader with self.test_metrics.</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.SgModel.evaluate">
- <span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_loader</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.utils.data.dataloader.DataLoader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torchmetrics.collections.MetricCollection</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluation_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.sg_model.html#super_gradients.training.sg_model.sg_model.EvaluationType" title="super_gradients.training.sg_model.sg_model.EvaluationType"><span class="pre">super_gradients.training.sg_model.sg_model.EvaluationType</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</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">int</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="n"><span class="pre">silent_mode</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">metrics_progress_verbose</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#SgModel.evaluate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.SgModel.evaluate" title="Permalink to this definition"></a></dt>
- <dd><p>Evaluates the model on given dataloader and metrics.</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>data_loader</strong> – dataloader to perform evaluataion on</p></li>
- <li><p><strong>metrics</strong> – (MetricCollection) metrics for evaluation</p></li>
- <li><p><strong>evaluation_type</strong> – (EvaluationType) controls which phase callbacks will be used (for example, on batch end,
- when evaluation_type=EvaluationType.VALIDATION the Phase.VALIDATION_BATCH_END callbacks will be triggered)</p></li>
- <li><p><strong>epoch</strong> – (int) epoch idx</p></li>
- <li><p><strong>silent_mode</strong> – (bool) controls verbosity</p></li>
- <li><p><strong>metrics_progress_verbose</strong> – (bool) controls the verbosity of metrics progress (default=False).
- Slows down the program significantly.</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>results tuple (tuple) containing the loss items and metric values.</p>
- </dd>
- </dl>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.MultiGPUMode">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.sg_model.</span></span><span class="sig-name descname"><span class="pre">MultiGPUMode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#MultiGPUMode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.MultiGPUMode" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">OFF</span>                       <span class="pre">-</span> <span class="pre">Single</span> <span class="pre">GPU</span> <span class="pre">Mode</span> <span class="pre">/</span> <span class="pre">CPU</span> <span class="pre">Mode</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">DATA_PARALLEL</span>             <span class="pre">-</span> <span class="pre">Multiple</span> <span class="pre">GPUs,</span> <span class="pre">Synchronous</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py">
- <span class="sig-name descname"><span class="pre">DISTRIBUTED_DATA_PARALLEL</span> <span class="pre">-</span> <span class="pre">Multiple</span> <span class="pre">GPUs,</span> <span class="pre">Asynchronous</span></span></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.MultiGPUMode.OFF">
- <span class="sig-name descname"><span class="pre">OFF</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'Off'</span></em><a class="headerlink" href="#super_gradients.training.sg_model.MultiGPUMode.OFF" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.MultiGPUMode.DATA_PARALLEL">
- <span class="sig-name descname"><span class="pre">DATA_PARALLEL</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'DP'</span></em><a class="headerlink" href="#super_gradients.training.sg_model.MultiGPUMode.DATA_PARALLEL" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.MultiGPUMode.DISTRIBUTED_DATA_PARALLEL">
- <span class="sig-name descname"><span class="pre">DISTRIBUTED_DATA_PARALLEL</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'DDP'</span></em><a class="headerlink" href="#super_gradients.training.sg_model.MultiGPUMode.DISTRIBUTED_DATA_PARALLEL" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.MultiGPUMode.AUTO">
- <span class="sig-name descname"><span class="pre">AUTO</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'AUTO'</span></em><a class="headerlink" href="#super_gradients.training.sg_model.MultiGPUMode.AUTO" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.StrictLoad">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.sg_model.</span></span><span class="sig-name descname"><span class="pre">StrictLoad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/sg_model/sg_model.html#StrictLoad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.sg_model.StrictLoad" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
- <dl>
- <dt>Wrapper for adding more functionality to torch’s strict_load parameter in load_state_dict().</dt><dd><dl>
- <dt>Attributes:</dt><dd><p>OFF - Native torch “strict_load = off” behaviour. See nn.Module.load_state_dict() documentation for more details.
- ON - Native torch “strict_load = on” behaviour. See nn.Module.load_state_dict() documentation for more details.
- NO_KEY_MATCHING - Allows the usage of SuperGradient’s adapt_checkpoint function, which loads a checkpoint by matching each</p>
- <blockquote>
- <div><p>layer’s shapes (and bypasses the strict matching of the names of each layer (ie: disregards the state_dict key matching)).</p>
- </div></blockquote>
- </dd>
- </dl>
- </dd>
- </dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.StrictLoad.OFF">
- <span class="sig-name descname"><span class="pre">OFF</span></span><em class="property"> <span class="pre">=</span> <span class="pre">False</span></em><a class="headerlink" href="#super_gradients.training.sg_model.StrictLoad.OFF" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.StrictLoad.ON">
- <span class="sig-name descname"><span class="pre">ON</span></span><em class="property"> <span class="pre">=</span> <span class="pre">True</span></em><a class="headerlink" href="#super_gradients.training.sg_model.StrictLoad.ON" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.sg_model.StrictLoad.NO_KEY_MATCHING">
- <span class="sig-name descname"><span class="pre">NO_KEY_MATCHING</span></span><em class="property"> <span class="pre">=</span> <span class="pre">'no_key_matching'</span></em><a class="headerlink" href="#super_gradients.training.sg_model.StrictLoad.NO_KEY_MATCHING" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- </section>
- <section id="module-super_gradients.training.utils">
- <span id="super-gradients-training-utils-module"></span><h2>super_gradients.training.utils module<a class="headerlink" href="#module-super_gradients.training.utils" title="Permalink to this headline"></a></h2>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.utils.Timer">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</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><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.Timer" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <p>A class to measure time handling both GPU & CPU processes
- Returns time in milliseconds</p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.Timer.start">
- <span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#Timer.start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.Timer.start" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.Timer.stop">
- <span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#Timer.stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.Timer.stop" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.utils.HpmStruct">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">HpmStruct</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">entries</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#HpmStruct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.HpmStruct" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.HpmStruct.set_schema">
- <span class="sig-name descname"><span class="pre">set_schema</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#HpmStruct.set_schema"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.HpmStruct.set_schema" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.HpmStruct.override">
- <span class="sig-name descname"><span class="pre">override</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">entries</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#HpmStruct.override"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.HpmStruct.override" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.HpmStruct.to_dict">
- <span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#HpmStruct.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.HpmStruct.to_dict" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.HpmStruct.validate">
- <span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#HpmStruct.validate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.HpmStruct.validate" title="Permalink to this definition"></a></dt>
- <dd><p>Validate the current dict values according to the provided schema
- :raises</p>
- <blockquote>
- <div><p><cite>AttributeError</cite> if schema was not set
- <cite>jsonschema.exceptions.ValidationError</cite> if the instance is invalid
- <cite>jsonschema.exceptions.SchemaError</cite> if the schema itselfis invalid</p>
- </div></blockquote>
- </dd></dl>
- </dd></dl>
- <dl class="py class">
- <dt class="sig sig-object py" id="super_gradients.training.utils.WrappedModel">
- <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">WrappedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#WrappedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.WrappedModel" title="Permalink to this definition"></a></dt>
- <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
- <dl class="py method">
- <dt class="sig sig-object py" id="super_gradients.training.utils.WrappedModel.forward">
- <span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#WrappedModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.WrappedModel.forward" title="Permalink to this definition"></a></dt>
- <dd><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div>
- </dd></dl>
- <dl class="py attribute">
- <dt class="sig sig-object py" id="super_gradients.training.utils.WrappedModel.training">
- <span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="pre">:</span> <span class="pre">bool</span></em><a class="headerlink" href="#super_gradients.training.utils.WrappedModel.training" title="Permalink to this definition"></a></dt>
- <dd></dd></dl>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.convert_to_tensor">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">convert_to_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#convert_to_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.convert_to_tensor" title="Permalink to this definition"></a></dt>
- <dd><p>Converts numpy arrays and lists to Torch tensors before calculation losses
- :param array: torch.tensor / Numpy array / List</p>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.get_param">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">get_param</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_val</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/utils/utils.html#get_param"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.get_param" title="Permalink to this definition"></a></dt>
- <dd><p>Retrieves a param from a parameter object/dict. If the parameter does not exist, will return default_val.
- In case the default_val is of type dictionary, and a value is found in the params - the function
- will return the default value dictionary with internal values overridden by the found value</p>
- <p>i.e.
- default_opt_params = {‘lr’:0.1, ‘momentum’:0.99, ‘alpha’:0.001}
- training_params = {‘optimizer_params’: {‘lr’:0.0001}, ‘batch’: 32 …. }
- get_param(training_params, name=’optimizer_params’, default_val=default_opt_params)
- will return {‘lr’:0.0001, ‘momentum’:0.99, ‘alpha’:0.001}</p>
- <dl class="field-list simple">
- <dt class="field-odd">Parameters</dt>
- <dd class="field-odd"><ul class="simple">
- <li><p><strong>params</strong> – an object (typically HpmStruct) or a dict holding the params</p></li>
- <li><p><strong>name</strong> – name of the searched parameter</p></li>
- <li><p><strong>default_val</strong> – assumed to be the same type as the value searched in the params</p></li>
- </ul>
- </dd>
- <dt class="field-even">Returns</dt>
- <dd class="field-even"><p>the found value, or default if not found</p>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.tensor_container_to_device">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">tensor_container_to_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">tuple</span><span class="p"><span class="pre">,</span> </span><span class="pre">list</span><span class="p"><span class="pre">,</span> </span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</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">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#tensor_container_to_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.tensor_container_to_device" title="Permalink to this definition"></a></dt>
- <dd><dl>
- <dt>recursively send compounded objects to device (sending all tensors to device and maintaining structure)</dt><dd><p>:param obj the object to send to device (list / tuple / tensor / dict)
- :param device: device to send the tensors to
- :param non_blocking: used for DistributedDataParallel
- :returns an object with the same structure (tensors, lists, tuples) with the device pointers (like</p>
- <blockquote>
- <div><p>the return value of Tensor.to(device)</p>
- </div></blockquote>
- </dd>
- </dl>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.adapt_state_dict_to_fit_model_layer_names">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">adapt_state_dict_to_fit_model_layer_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_ckpt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">list</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/checkpoint_utils.html#adapt_state_dict_to_fit_model_layer_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.adapt_state_dict_to_fit_model_layer_names" title="Permalink to this definition"></a></dt>
- <dd><p>Given a model state dict and source checkpoints, the method tries to correct the keys in the model_state_dict to fit
- the ckpt in order to properly load the weights into the model. If unsuccessful - returns None</p>
- <blockquote>
- <div><dl class="field-list simple">
- <dt class="field-odd">param model_state_dict</dt>
- <dd class="field-odd"><p>the model state_dict</p>
- </dd>
- <dt class="field-even">param source_ckpt</dt>
- <dd class="field-even"><p>checkpoint dict</p>
- </dd>
- </dl>
- <p>:exclude optional list for excluded layers
- :return: renamed checkpoint dict (if possible)</p>
- </div></blockquote>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.raise_informative_runtime_error">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">raise_informative_runtime_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exception_msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/checkpoint_utils.html#raise_informative_runtime_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.raise_informative_runtime_error" title="Permalink to this definition"></a></dt>
- <dd><p>Given a model state dict and source checkpoints, the method calls “adapt_state_dict_to_fit_model_layer_names”
- and enhances the exception_msg if loading the checkpoint_dict via the conversion method is possible</p>
- </dd></dl>
- <dl class="py function">
- <dt class="sig sig-object py" id="super_gradients.training.utils.random_seed">
- <span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.</span></span><span class="sig-name descname"><span class="pre">random_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_ddp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/utils.html#random_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.random_seed" title="Permalink to this definition"></a></dt>
- <dd><p>Sets random seed of numpy, torch and random.</p>
- <p>When using ddp a seed will be set for each process according to its local rank derived from the device number.
- :param is_ddp: bool, will set different random seed for each process when using ddp.
- :param device: ‘cuda’,’cpu’, ‘cuda:<device_number>’
- :param seed: int, random seed to be set</p>
- </dd></dl>
- </section>
- <section id="module-contents">
- <h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline"></a></h2>
- </section>
- </section>
- </div>
- </div>
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