Update documentation

Author: actions-user

master/python/models/model.html

@@ -507,9 +507,11 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>model</strong> (<em>str</em>) – model name from OpenVINO Model Zoo, path to model, OVMS URL</p></li>
-<li><p><strong>configuration</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">dict</span></code>, optional) – dictionary of model config with model properties, for example confidence_threshold, labels</p></li>
+<li><p><strong>configuration</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">dict</span></code>, optional) – dictionary of model config with model properties, for example
+confidence_threshold, labels</p></li>
 <li><p><strong>model_type</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – name of model wrapper to create (e.g. “ssd”)</p></li>
-<li><p><strong>preload</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">bool</span></code>, optional) – whether to call load_model(). Can be set to false to reshape model before loading</p></li>
+<li><p><strong>preload</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">bool</span></code>, optional) – whether to call load_model(). Can be set to false to reshape model before
+loading.</p></li>
 <li><p><strong>core</strong> (<em>optional</em>) – openvino.Core instance, passed to OpenvinoAdapter</p></li>
 <li><p><strong>weights_path</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – path to .bin file with model weights</p></li>
 <li><p><strong>adaptor_parameters</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">dict</span></code>, optional) – parameters of ModelAdaptor</p></li>
@@ -519,7 +521,8 @@
 <li><p><strong>max_num_requests</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">int</span></code>, optional) – number of infer requests for asynchronous inference</p></li>
 <li><p><strong>precision</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – inference precision (e.g. “FP16”)</p></li>
 <li><p><strong>download_dir</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – directory where to store downloaded models</p></li>
-<li><p><strong>cache_dir</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – directory where to store compiled models to reduce the load time before the inference</p></li>
+<li><p><strong>cache_dir</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, optional) – directory where to store compiled models to reduce the load time before
+the inference.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>

master/python/models/visual_prompting.html

@@ -403,7 +403,8 @@
 <li><p><strong>encoder_model</strong> (<a class="reference internal" href="sam_models.html#model_api.models.sam_models.SAMImageEncoder" title="model_api.models.sam_models.SAMImageEncoder"><em>SAMImageEncoder</em></a>) – initialized decoder wrapper</p></li>
 <li><p><strong>decoder_model</strong> (<a class="reference internal" href="sam_models.html#model_api.models.sam_models.SAMDecoder" title="model_api.models.sam_models.SAMDecoder"><em>SAMDecoder</em></a>) – initialized encoder wrapper</p></li>
 <li><p><strong>reference_features</strong> (<a class="reference internal" href="#model_api.models.visual_prompting.VisualPromptingFeatures" title="model_api.models.visual_prompting.VisualPromptingFeatures"><em>VisualPromptingFeatures</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Previously generated reference features.
-Once the features are passed, one can skip learn() method, and start predicting masks right away. Defaults to None.</p></li>
+Once the features are passed, one can skip learn() method, and start predicting masks right away.
+Defaults to None.</p></li>
 <li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em>) – Threshold to match vs reference features on infer(). Greater value means a</p></li>
 <li><p><strong>0.65.</strong> (<em>stricter matching. Defaults to</em>)</p></li>
 </ul>
@@ -441,18 +442,22 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>image</strong> (<em>np.ndarray</em>) – HWC-shaped image</p></li>
-<li><p><strong>reference_features</strong> (<a class="reference internal" href="#model_api.models.visual_prompting.VisualPromptingFeatures" title="model_api.models.visual_prompting.VisualPromptingFeatures"><em>VisualPromptingFeatures</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Reference features object obtained during previous learn() calls.</p></li>
-<li><p><strong>passed</strong> (<em>If not</em>)</p></li>
-<li><p><strong>used</strong> (<em>object internal state is</em>)</p></li>
-<li><p><strong>None.</strong> (<em>which reflects the last learn</em><em>(</em><em>) </em><em>call. Defaults to</em>)</p></li>
-<li><p><strong>apply_masks_refinement</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flag controlling additional refinement stage on inference. Once enabled, decoder will</p></li>
-<li><p><strong>True.</strong> (<em>be launched 2 extra times to refine the masks obtained with the first decoder call. Defaults to</em>)</p></li>
+<li><p><strong>reference_features</strong> (<a class="reference internal" href="#model_api.models.visual_prompting.VisualPromptingFeatures" title="model_api.models.visual_prompting.VisualPromptingFeatures"><em>VisualPromptingFeatures</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Reference features object obtained during
+previous learn() calls. If not passed, object internal state is used, which reflects the last learn()
+call. Defaults to None.</p></li>
+<li><p><strong>apply_masks_refinement</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flag controlling additional refinement stage on inference.</p></li>
+<li><p><strong>enabled</strong> (<em>Once</em>)</p></li>
+<li><p><strong>decoder</strong> (<em>decoder will be launched 2 extra times to refine the masks obtained with the first</em>)</p></li>
+<li><p><strong>True.</strong> (<em>call. Defaults to</em>)</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Mapping label -&gt; predicted mask. Each mask object contains a list of binary masks, and a list of
-related prompts. Each binary mask corresponds to one prompt point. Class mask can be obtained by applying OR operation to all
-mask corresponding to one label.</p>
+<dd class="field-even"><p><dl class="simple">
+<dt>Mapping label -&gt; predicted mask. Each mask object contains a list of binary masks,</dt><dd><p>and a list of related prompts. Each binary mask corresponds to one prompt point. Class mask can be
+obtained by applying OR operation to all mask corresponding to one label.</p>
+</dd>
+</dl>
+</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="utils.html#model_api.models.utils.ZSLVisualPromptingResult" title="model_api.models.utils.ZSLVisualPromptingResult">ZSLVisualPromptingResult</a></p>
@@ -482,8 +487,12 @@
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>return values are the updated VPT reference features and reference masks.
-The shape of the reference mask is N_labels x H x W, where H and W are the same as in the input image.</p>
+<dd class="field-even"><p><dl class="simple">
+<dt>return values are the updated VPT reference features and</dt><dd><p>reference masks.</p>
+</dd>
+</dl>
+<p>The shape of the reference mask is N_labels x H x W, where H and W are the same as in the input image.</p>
+</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>tuple[<a class="reference internal" href="#model_api.models.visual_prompting.VisualPromptingFeatures" title="model_api.models.visual_prompting.VisualPromptingFeatures">VisualPromptingFeatures</a>, np.ndarray]</p>