Update Sphinx documentation

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docs/py_docs/build/html/.doctrees/api/perceptionmetrics.models.doctree

@@ -989,6 +989,8 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
         <span class="n">translation_direction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;dataset_to_model&quot;</span><span class="p">,</span>
         <span class="n">predictions_outdir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">results_per_sample</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">progress_callback</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">metrics_callback</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform evaluation for a LiDAR segmentation dataset</span>
 
@@ -1004,6 +1006,10 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
 <span class="sd">        :type predictions_outdir: Optional[str], optional</span>
 <span class="sd">        :param results_per_sample: Whether to store results per sample or not, defaults to False. If True, predictions_outdir must be provided.</span>
 <span class="sd">        :type results_per_sample: bool, optional</span>
+<span class="sd">        :param progress_callback: Optional callback called as progress_callback(processed, total), defaults to None</span>
+<span class="sd">        :type progress_callback: Optional[Callable], optional</span>
+<span class="sd">        :param metrics_callback: Optional callback called as metrics_callback(metrics_df, processed, total), defaults to None</span>
+<span class="sd">        :type metrics_callback: Optional[Callable], optional</span>
 <span class="sd">        :return: DataFrame containing evaluation results</span>
 <span class="sd">        :rtype: pd.DataFrame</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -1053,6 +1059,9 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
 
         <span class="c1"># Init metrics</span>
         <span class="n">metrics_factory</span> <span class="o">=</span> <span class="n">um</span><span class="o">.</span><span class="n">SegmentationMetricsFactory</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)</span>
+        <span class="n">total_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+        <span class="n">processed_samples</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">evaluation_step</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_cfg</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;evaluation_step&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
         <span class="c1"># Evaluation loop</span>
         <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
@@ -1107,6 +1116,26 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
                             <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictions_outdir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">sample_name</span><span class="si">}</span><span class="s2">.bin&quot;</span><span class="p">)</span>
                         <span class="p">)</span>
 
+                <span class="n">processed_samples</span> <span class="o">+=</span> <span class="mi">1</span>
+
+                <span class="k">if</span> <span class="n">progress_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">progress_callback</span><span class="p">(</span><span class="n">processed_samples</span><span class="p">,</span> <span class="n">total_samples</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="p">(</span>
+                    <span class="n">metrics_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                    <span class="ow">and</span> <span class="n">evaluation_step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                    <span class="ow">and</span> <span class="n">evaluation_step</span> <span class="o">&gt;</span> <span class="mi">0</span>
+                    <span class="ow">and</span> <span class="p">(</span>
+                        <span class="n">processed_samples</span> <span class="o">%</span> <span class="n">evaluation_step</span> <span class="o">==</span> <span class="mi">0</span>
+                        <span class="ow">or</span> <span class="n">processed_samples</span> <span class="o">==</span> <span class="n">total_samples</span>
+                    <span class="p">)</span>
+                <span class="p">):</span>
+                    <span class="n">metrics_callback</span><span class="p">(</span>
+                        <span class="n">um</span><span class="o">.</span><span class="n">get_metrics_dataframe</span><span class="p">(</span><span class="n">metrics_factory</span><span class="p">,</span> <span class="n">eval_ontology</span><span class="p">),</span>
+                        <span class="n">processed_samples</span><span class="p">,</span>
+                        <span class="n">total_samples</span><span class="p">,</span>
+                    <span class="p">)</span>
+
         <span class="k">return</span> <span class="n">um</span><span class="o">.</span><span class="n">get_metrics_dataframe</span><span class="p">(</span><span class="n">metrics_factory</span><span class="p">,</span> <span class="n">eval_ontology</span><span class="p">)</span></div>
 
 

docs/py_docs/build/html/.doctrees/environment.pickle

@@ -1421,7 +1421,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="perceptionmetrics.models.torch_segmentation.TorchLiDARSegmentationModel.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ontology_translation</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">translation_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dataset_to_model'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_outdir</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">results_per_sample</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/perceptionmetrics/models/torch_segmentation.html#TorchLiDARSegmentationModel.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#perceptionmetrics.models.torch_segmentation.TorchLiDARSegmentationModel.eval" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'test'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ontology_translation</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">translation_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dataset_to_model'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_outdir</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">results_per_sample</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">progress_callback</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_callback</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/perceptionmetrics/models/torch_segmentation.html#TorchLiDARSegmentationModel.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#perceptionmetrics.models.torch_segmentation.TorchLiDARSegmentationModel.eval" title="Link to this definition">¶</a></dt>
 <dd><p>Perform evaluation for a LiDAR segmentation dataset</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1432,6 +1432,8 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 <li><p><strong>translation_direction</strong> (<em>str</em><em>, </em><em>optional</em>) – Direction of the ontology translation, either ‘dataset_to_model’ or ‘model_to_dataset’, defaults to “dataset_to_model”</p></li>
 <li><p><strong>predictions_outdir</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em><em>, </em><em>optional</em>) – Directory to save predictions per sample, defaults to None. If None, predictions are not saved.</p></li>
 <li><p><strong>results_per_sample</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to store results per sample or not, defaults to False. If True, predictions_outdir must be provided.</p></li>
+<li><p><strong>progress_callback</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – Optional callback called as progress_callback(processed, total), defaults to None</p></li>
+<li><p><strong>metrics_callback</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – Optional callback called as metrics_callback(metrics_df, processed, total), defaults to None</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>