Update Sphinx documentation

Author: actions-user

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

@@ -782,13 +782,13 @@ <h1>Source code for perceptionmetrics.models.tf_segmentation</h1><div class="hig
             <span class="k">if</span> <span class="n">has_gpu</span><span class="p">:</span>
                 <span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">set_synchronous_execution</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 
-            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">dummy_input</span><span class="p">)</span>
 
             <span class="k">if</span> <span class="n">has_gpu</span><span class="p">:</span>
                 <span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">set_synchronous_execution</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 
-            <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
+            <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
 
         <span class="c1"># Retrieve computational cost information</span>
         <span class="n">result</span> <span class="o">=</span> <span class="p">{</span>

docs/py_docs/build/html/_modules/perceptionmetrics/models/tf_segmentation.html

@@ -354,17 +354,20 @@ <h1>Source code for perceptionmetrics.models.torch_detection</h1><div class="hig
                 <span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">dummy_tuple</span><span class="p">)</span>
 
     <span class="c1"># Measure inference time</span>
+    <span class="n">use_cuda</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span>
     <span class="n">inference_times</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">runs</span><span class="p">):</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-        <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</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>
             <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;inference&quot;</span><span class="p">):</span>
                 <span class="n">model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="o">*</span><span class="n">dummy_tuple</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">dummy_tuple</span><span class="p">)</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-        <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+        <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>
 
     <span class="c1"># Get number of parameters</span>
     <span class="n">n_params</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>

docs/py_docs/build/html/_modules/perceptionmetrics/models/torch_detection.html

@@ -798,18 +798,21 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
             <span class="n">size_mb</span> <span class="o">=</span> <span class="kc">None</span>
 
         <span class="c1"># Measure inference time with GPU synchronization</span>
+        <span class="n">use_cuda</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span>
         <span class="n">dummy_tuple</span> <span class="o">=</span> <span class="n">dummy_input</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dummy_input</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="n">dummy_input</span><span class="p">,)</span>
 
         <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">warm_up_runs</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">dummy_tuple</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 
         <span class="n">inference_times</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">runs</span><span class="p">):</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">dummy_tuple</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-            <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+            <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
             <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
 
         <span class="n">result</span> <span class="o">=</span> <span class="p">{</span>
@@ -1130,6 +1133,7 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
             <span class="n">size_mb</span> <span class="o">=</span> <span class="kc">None</span>
 
         <span class="c1"># Measure inference time with GPU synchronization</span>
+        <span class="n">use_cuda</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span>
         <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">warm_up_runs</span><span class="p">):</span>
             <span class="k">if</span> <span class="s2">&quot;o3d&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_format</span><span class="p">:</span>  <span class="c1"># reset random sampling for Open3D-ML models</span>
                 <span class="n">subsampled_points</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">sampler</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">sample</span>
@@ -1142,11 +1146,13 @@ <h1>Source code for perceptionmetrics.models.torch_segmentation</h1><div class="
             <span class="k">if</span> <span class="s2">&quot;o3d&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_format</span><span class="p">:</span>  <span class="c1"># reset random sampling for Open3D-ML models</span>
                 <span class="n">subsampled_points</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">sampler</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">sample</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_reset_sampler</span><span class="p">(</span><span class="n">sampler</span><span class="p">,</span> <span class="n">subsampled_points</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+            <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_cfg</span><span class="p">)</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
-            <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+            <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
             <span class="n">inference_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
 
         <span class="n">result</span> <span class="o">=</span> <span class="p">{</span>