auto-generating sphinx docs

main/_sources/auto_examples/others/plot_optical_flow.rst.txt

@@ -352,7 +352,7 @@ ffmpeg -f image2 -framerate 30 -i predicted_flow_%d.jpg -loop -1 flow.gif
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.613 seconds)
+   **Total running time of the script:** (0 minutes 9.430 seconds)
 
 
 .. _sphx_glr_download_auto_examples_others_plot_optical_flow.py:

main/_sources/auto_examples/others/plot_repurposing_annotations.rst.txt

@@ -417,7 +417,7 @@ Here is an example where we re-purpose the dataset from the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.291 seconds)
+   **Total running time of the script:** (0 minutes 2.369 seconds)
 
 
 .. _sphx_glr_download_auto_examples_others_plot_repurposing_annotations.py:

main/_sources/auto_examples/others/plot_scripted_tensor_transforms.rst.txt

@@ -239,7 +239,7 @@ Since the model is scripted, it can be easily dumped on disk and re-used
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.565 seconds)
+   **Total running time of the script:** (0 minutes 1.575 seconds)
 
 
 .. _sphx_glr_download_auto_examples_others_plot_scripted_tensor_transforms.py:

main/_sources/auto_examples/others/plot_video_api.rst.txt

@@ -549,7 +549,7 @@ We can generate a dataloader and test the dataset.
 
  .. code-block:: none
 
-    {'video': ['./dataset/2/SOX5yA1l24A.mp4', './dataset/2/v_SoccerJuggling_g23_c01.avi', './dataset/2/SOX5yA1l24A.mp4', './dataset/1/WUzgd7C1pWA.mp4', './dataset/2/v_SoccerJuggling_g23_c01.avi'], 'start': [5.31105498128678, 2.0789014223402416, 9.49854975604133, 6.075792979599469, 4.502403489105758], 'end': [5.839167, 2.6026, 10.01, 6.606599999999999, 5.005], 'tensorsize': [torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112])]}
+    {'video': ['./dataset/2/v_SoccerJuggling_g24_c01.avi', './dataset/2/v_SoccerJuggling_g23_c01.avi', './dataset/1/WUzgd7C1pWA.mp4', './dataset/2/v_SoccerJuggling_g23_c01.avi', './dataset/1/WUzgd7C1pWA.mp4'], 'start': [2.0935120401385636, 0.4507920953003993, 1.475039877877628, 5.620562628198752, 3.11041692018258], 'end': [2.6026, 0.967633, 2.002, 6.139467, 3.636967], 'tensorsize': [torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112])]}
 
 
 
@@ -607,7 +607,7 @@ Cleanup the video and dataset:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.367 seconds)
+   **Total running time of the script:** (0 minutes 3.952 seconds)
 
 
 .. _sphx_glr_download_auto_examples_others_plot_video_api.py:

main/_sources/auto_examples/others/plot_visualization_utils.rst.txt

@@ -1162,7 +1162,7 @@ which we used in the first case, does so too.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 13.952 seconds)
+   **Total running time of the script:** (0 minutes 14.052 seconds)
 
 
 .. _sphx_glr_download_auto_examples_others_plot_visualization_utils.py:

main/_sources/auto_examples/others/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:31.790** total execution time for 5 files **from auto_examples/others**:
+**00:31.379** total execution time for 5 files **from auto_examples/others**:
 
 .. container::
 
@@ -33,17 +33,17 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_others_plot_visualization_utils.py` (``plot_visualization_utils.py``)
-     - 00:13.952
+     - 00:14.052
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_optical_flow.py` (``plot_optical_flow.py``)
-     - 00:09.613
+     - 00:09.430
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_video_api.py` (``plot_video_api.py``)
-     - 00:04.367
+     - 00:03.952
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_repurposing_annotations.py` (``plot_repurposing_annotations.py``)
-     - 00:02.291
+     - 00:02.369
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_scripted_tensor_transforms.py` (``plot_scripted_tensor_transforms.py``)
-     - 00:01.565
+     - 00:01.575
      - 0.0

main/_sources/auto_examples/transforms/plot_cutmix_mixup.rst.txt

@@ -263,7 +263,7 @@ by passing a callable to the ``labels_getter`` parameter. For example:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.178 seconds)
+   **Total running time of the script:** (0 minutes 0.163 seconds)
 
 
 .. _sphx_glr_download_auto_examples_transforms_plot_cutmix_mixup.py:

main/_sources/auto_examples/transforms/plot_transforms_e2e.rst.txt

@@ -336,7 +336,7 @@ v2). So don't be afraid to simplify and only keep what you need.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.882 seconds)
+   **Total running time of the script:** (0 minutes 4.836 seconds)
 
 
 .. _sphx_glr_download_auto_examples_transforms_plot_transforms_e2e.py:

main/_sources/auto_examples/transforms/plot_transforms_getting_started.rst.txt

@@ -400,7 +400,7 @@ Either way, the logic will depend on your specific dataset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.815 seconds)
+   **Total running time of the script:** (0 minutes 0.836 seconds)
 
 
 .. _sphx_glr_download_auto_examples_transforms_plot_transforms_getting_started.py:

main/_sources/auto_examples/transforms/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:14.748** total execution time for 7 files **from auto_examples/transforms**:
+**00:14.709** total execution time for 7 files **from auto_examples/transforms**:
 
 .. container::
 
@@ -36,13 +36,13 @@ Computation times
      - 00:08.854
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py` (``plot_transforms_e2e.py``)
-     - 00:04.882
+     - 00:04.836
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` (``plot_transforms_getting_started.py``)
-     - 00:00.815
+     - 00:00.836
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` (``plot_cutmix_mixup.py``)
-     - 00:00.178
+     - 00:00.163
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_tv_tensors.py` (``plot_tv_tensors.py``)
      - 00:00.009

main/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:46.538** total execution time for 12 files **from all galleries**:
+**00:46.088** total execution time for 12 files **from all galleries**:
 
 .. container::
 
@@ -33,31 +33,31 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_others_plot_visualization_utils.py` (``../../gallery/others/plot_visualization_utils.py``)
-     - 00:13.952
+     - 00:14.052
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_optical_flow.py` (``../../gallery/others/plot_optical_flow.py``)
-     - 00:09.613
+     - 00:09.430
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_transforms_illustrations.py` (``../../gallery/transforms/plot_transforms_illustrations.py``)
      - 00:08.854
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py` (``../../gallery/transforms/plot_transforms_e2e.py``)
-     - 00:04.882
+     - 00:04.836
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_video_api.py` (``../../gallery/others/plot_video_api.py``)
-     - 00:04.367
+     - 00:03.952
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_repurposing_annotations.py` (``../../gallery/others/plot_repurposing_annotations.py``)
-     - 00:02.291
+     - 00:02.369
      - 0.0
    * - :ref:`sphx_glr_auto_examples_others_plot_scripted_tensor_transforms.py` (``../../gallery/others/plot_scripted_tensor_transforms.py``)
-     - 00:01.565
+     - 00:01.575
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` (``../../gallery/transforms/plot_transforms_getting_started.py``)
-     - 00:00.815
+     - 00:00.836
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` (``../../gallery/transforms/plot_cutmix_mixup.py``)
-     - 00:00.178
+     - 00:00.163
      - 0.0
    * - :ref:`sphx_glr_auto_examples_transforms_plot_tv_tensors.py` (``../../gallery/transforms/plot_tv_tensors.py``)
      - 00:00.009

main/auto_examples/others/plot_optical_flow.html

@@ -560,7 +560,7 @@ <h2>Bonus: Creating GIFs of predicted flows<a class="headerlink" href="#bonus-cr
 <p>Once the .jpg flow images are saved, you can convert them into a video or a
 GIF using ffmpeg with e.g.:</p>
 <p>ffmpeg -f image2 -framerate 30 -i predicted_flow_%d.jpg -loop -1 flow.gif</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.613 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.430 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-others-plot-optical-flow-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/8c66a0169c2c41937d8db4238842fe5b/plot_optical_flow.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_optical_flow.ipynb</span></code></a></p>

main/auto_examples/others/plot_repurposing_annotations.html

@@ -601,7 +601,7 @@ <h2>Converting Segmentation Dataset to Detection Dataset<a class="headerlink" hr
         <span class="k">return</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.291 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.369 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-others-plot-repurposing-annotations-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/2753c5fd8349a28b023c919d8ddf3573/plot_repurposing_annotations.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_repurposing_annotations.ipynb</span></code></a></p>

main/auto_examples/others/plot_scripted_tensor_transforms.html

@@ -486,7 +486,7 @@
 <span class="k">assert</span> <span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">res_scripted_dumped</span></a> <span class="o">==</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">res_scripted</span></a><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.565 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.575 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-others-plot-scripted-tensor-transforms-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/4333051365715be174a783856419631f/plot_scripted_tensor_transforms.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_scripted_tensor_transforms.ipynb</span></code></a></p>

main/auto_examples/others/plot_video_api.html

@@ -858,7 +858,7 @@ <h2>3. Building an example randomly sampled dataset (can be applied to training
 <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;video&#39;: [&#39;./dataset/2/SOX5yA1l24A.mp4&#39;, &#39;./dataset/2/v_SoccerJuggling_g23_c01.avi&#39;, &#39;./dataset/2/SOX5yA1l24A.mp4&#39;, &#39;./dataset/1/WUzgd7C1pWA.mp4&#39;, &#39;./dataset/2/v_SoccerJuggling_g23_c01.avi&#39;], &#39;start&#39;: [5.31105498128678, 2.0789014223402416, 9.49854975604133, 6.075792979599469, 4.502403489105758], &#39;end&#39;: [5.839167, 2.6026, 10.01, 6.606599999999999, 5.005], &#39;tensorsize&#39;: [torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112])]}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;video&#39;: [&#39;./dataset/2/v_SoccerJuggling_g24_c01.avi&#39;, &#39;./dataset/2/v_SoccerJuggling_g23_c01.avi&#39;, &#39;./dataset/1/WUzgd7C1pWA.mp4&#39;, &#39;./dataset/2/v_SoccerJuggling_g23_c01.avi&#39;, &#39;./dataset/1/WUzgd7C1pWA.mp4&#39;], &#39;start&#39;: [2.0935120401385636, 0.4507920953003993, 1.475039877877628, 5.620562628198752, 3.11041692018258], &#39;end&#39;: [2.6026, 0.967633, 2.002, 6.139467, 3.636967], &#39;tensorsize&#39;: [torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112])]}
 </pre></div>
 </div>
 </section>
@@ -881,7 +881,7 @@ <h2>4. Data Visualization<a class="headerlink" href="#data-visualization" title=
 <a href="https://docs.python.org/3/library/shutil.html#shutil.rmtree" title="shutil.rmtree" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span></a><span class="p">(</span><span class="s2">&quot;./dataset&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.367 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.952 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-others-plot-video-api-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/4b3151295a1f6b75ffd88837fb31f719/plot_video_api.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_video_api.ipynb</span></code></a></p>

main/auto_examples/others/plot_visualization_utils.html

@@ -1004,7 +1004,7 @@ <h2>Visualizing segmentation masks<a class="headerlink" href="#visualizing-segme
 Most torch keypoint-prediction models return the visibility for every prediction, ready for you to use it.
 The <a class="reference internal" href="../../models/generated/torchvision.models.detection.keypointrcnn_resnet50_fpn.html#torchvision.models.detection.keypointrcnn_resnet50_fpn" title="torchvision.models.detection.keypointrcnn_resnet50_fpn"><code class="xref py py-func docutils literal notranslate"><span class="pre">keypointrcnn_resnet50_fpn()</span></code></a> model,
 which we used in the first case, does so too.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 13.952 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 14.052 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-others-plot-visualization-utils-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/95e7320166df5d4ddcbdd5ea64a5c98b/plot_visualization_utils.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_visualization_utils.ipynb</span></code></a></p>

main/auto_examples/others/sg_execution_times.html

@@ -357,7 +357,7 @@
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-others-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">¶</a></h1>
-<p><strong>00:31.790</strong> total execution time for 5 files <strong>from auto_examples/others</strong>:</p>
+<p><strong>00:31.379</strong> total execution time for 5 files <strong>from auto_examples/others</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -379,23 +379,23 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_visualization_utils.html#sphx-glr-auto-examples-others-plot-visualization-utils-py"><span class="std std-ref">Visualization utilities</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_visualization_utils.py</span></code>)</p></td>
-<td><p>00:13.952</p></td>
+<td><p>00:14.052</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_optical_flow.html#sphx-glr-auto-examples-others-plot-optical-flow-py"><span class="std std-ref">Optical Flow: Predicting movement with the RAFT model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_optical_flow.py</span></code>)</p></td>
-<td><p>00:09.613</p></td>
+<td><p>00:09.430</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_video_api.html#sphx-glr-auto-examples-others-plot-video-api-py"><span class="std std-ref">Video API</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_video_api.py</span></code>)</p></td>
-<td><p>00:04.367</p></td>
+<td><p>00:03.952</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_repurposing_annotations.html#sphx-glr-auto-examples-others-plot-repurposing-annotations-py"><span class="std std-ref">Repurposing masks into bounding boxes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_repurposing_annotations.py</span></code>)</p></td>
-<td><p>00:02.291</p></td>
+<td><p>00:02.369</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_scripted_tensor_transforms.html#sphx-glr-auto-examples-others-plot-scripted-tensor-transforms-py"><span class="std std-ref">Torchscript support</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_scripted_tensor_transforms.py</span></code>)</p></td>
-<td><p>00:01.565</p></td>
+<td><p>00:01.575</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>

main/auto_examples/transforms/plot_cutmix_mixup.html

@@ -512,7 +512,7 @@ <h2>Non-standard input format<a class="headerlink" href="#non-standard-input-for
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>out[&#39;imgs&#39;].shape = torch.Size([4, 3, 224, 224]), out[&#39;target&#39;][&#39;classes&#39;].shape = torch.Size([4, 100])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.178 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.163 seconds)</p>
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