auto-generating sphinx docs

Author: pytorchbot

docs/stable/_modules/torch/functional.html

@@ -472,12 +472,12 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
 <span class="sd">        tensor (Tensor): A tensor to check</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        Tensor: A ``torch.ByteTensor`` containing a 1 at each location of finite elements and 0 otherwise</span>
+<span class="sd">        Tensor: ``A torch.Tensor with dtype torch.bool`` containing a True at each location of finite elements and False otherwise</span>
 
 <span class="sd">    Example::</span>
 
 <span class="sd">        &gt;&gt;&gt; torch.isfinite(torch.tensor([1, float(&#39;inf&#39;), 2, float(&#39;-inf&#39;), float(&#39;nan&#39;)]))</span>
-<span class="sd">        tensor([ 1,  0,  1,  0,  0], dtype=torch.uint8)</span>
+<span class="sd">        tensor([True,  False,  True,  False,  False])</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;The argument is not a tensor: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">repr</span><span class="p">(</span><span class="n">tensor</span><span class="p">)))</span>
@@ -487,7 +487,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="c1"># have a similar concept. It&#39;s safe to assume any created LongTensor doesn&#39;t</span>
     <span class="c1"># overflow and it&#39;s finite.</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">tensor</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">():</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
     <span class="k">return</span> <span class="p">(</span><span class="n">tensor</span> <span class="o">==</span> <span class="n">tensor</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">!=</span> <span class="n">inf</span><span class="p">)</span></div>
 
 
@@ -498,17 +498,17 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
 <span class="sd">        tensor (Tensor): A tensor to check</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        Tensor: A ``torch.ByteTensor`` containing a 1 at each location of `+/-INF` elements and 0 otherwise</span>
+<span class="sd">        Tensor: ``A torch.Tensor with dtype torch.bool`` containing a True at each location of `+/-INF` elements and False otherwise</span>
 
 <span class="sd">    Example::</span>
 
 <span class="sd">        &gt;&gt;&gt; torch.isinf(torch.tensor([1, float(&#39;inf&#39;), 2, float(&#39;-inf&#39;), float(&#39;nan&#39;)]))</span>
-<span class="sd">        tensor([ 0,  1,  0,  1,  0], dtype=torch.uint8)</span>
+<span class="sd">        tensor([False,  True,  False,  True,  False])</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;The argument is not a tensor: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">repr</span><span class="p">(</span><span class="n">tensor</span><span class="p">)))</span>
     <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int16</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">]:</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">==</span> <span class="n">inf</span></div>
 
 

docs/stable/_modules/torchvision/models/detection/mask_rcnn.html

@@ -294,7 +294,7 @@ <h1>Source code for torchvision.models.detection.mask_rcnn</h1><div class="highl
 <span class="sd">        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values</span>
 <span class="sd">          between 0 and H and 0 and W</span>
 <span class="sd">        - labels (Int64Tensor[N]): the class label for each ground-truth box</span>
-<span class="sd">        - masks (UInt8Tensor[N, 1, H, W]): the segmentation binary masks for each instance</span>
+<span class="sd">        - masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance</span>
 
 <span class="sd">    The model returns a Dict[Tensor] during training, containing the classification and regression</span>
 <span class="sd">    losses for both the RPN and the R-CNN, and the mask loss.</span>
@@ -541,7 +541,7 @@ <h1>Source code for torchvision.models.detection.mask_rcnn</h1><div class="highl
 <span class="sd">        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values</span>
 <span class="sd">          between ``0`` and ``H`` and ``0`` and ``W``</span>
 <span class="sd">        - labels (``Int64Tensor[N]``): the class label for each ground-truth box</span>
-<span class="sd">        - masks (``UInt8Tensor[N, 1, H, W]``): the segmentation binary masks for each instance</span>
+<span class="sd">        - masks (``UInt8Tensor[N, H, W]``): the segmentation binary masks for each instance</span>
 
 <span class="sd">    The model returns a ``Dict[Tensor]`` during training, containing the classification and regression</span>
 <span class="sd">    losses for both the RPN and the R-CNN, and the mask loss.</span>

docs/stable/objects.inv

@@ -5311,15 +5311,15 @@ <h3>Comparison Ops<a class="headerlink" href="#comparison-ops" title="Permalink
 <dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – A tensor to check</p>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p>A <code class="docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code> containing a 1 at each location of finite elements and 0 otherwise</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">torch.Tensor</span> <span class="pre">with</span> <span class="pre">dtype</span> <span class="pre">torch.bool</span></code> containing a True at each location of finite elements and False otherwise</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
 </dd>
 </dl>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;-inf&#39;</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">)]))</span>
-<span class="go">tensor([ 1,  0,  1,  0,  0], dtype=torch.uint8)</span>
+<span class="go">tensor([True,  False,  True,  False,  False])</span>
 </pre></div>
 </div>
 </dd></dl>
@@ -5333,15 +5333,15 @@ <h3>Comparison Ops<a class="headerlink" href="#comparison-ops" title="Permalink
 <dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – A tensor to check</p>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p>A <code class="docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code> containing a 1 at each location of <cite>+/-INF</cite> elements and 0 otherwise</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">torch.Tensor</span> <span class="pre">with</span> <span class="pre">dtype</span> <span class="pre">torch.bool</span></code> containing a True at each location of <cite>+/-INF</cite> elements and False otherwise</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
 </dd>
 </dl>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;-inf&#39;</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">)]))</span>
-<span class="go">tensor([ 0,  1,  0,  1,  0], dtype=torch.uint8)</span>
+<span class="go">tensor([False,  True,  False,  True,  False])</span>
 </pre></div>
 </div>
 </dd></dl>

docs/stable/torch.html

@@ -1394,7 +1394,7 @@ <h3>Mask R-CNN<a class="headerlink" href="#mask-r-cnn" title="Permalink to this
 <li><p>boxes (<code class="docutils literal notranslate"><span class="pre">FloatTensor[N,</span> <span class="pre">4]</span></code>): the ground-truth boxes in <code class="docutils literal notranslate"><span class="pre">[x1,</span> <span class="pre">y1,</span> <span class="pre">x2,</span> <span class="pre">y2]</span></code> format, with values
 between <code class="docutils literal notranslate"><span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">H</span></code> and <code class="docutils literal notranslate"><span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">W</span></code></p></li>
 <li><p>labels (<code class="docutils literal notranslate"><span class="pre">Int64Tensor[N]</span></code>): the class label for each ground-truth box</p></li>
-<li><p>masks (<code class="docutils literal notranslate"><span class="pre">UInt8Tensor[N,</span> <span class="pre">1,</span> <span class="pre">H,</span> <span class="pre">W]</span></code>): the segmentation binary masks for each instance</p></li>
+<li><p>masks (<code class="docutils literal notranslate"><span class="pre">UInt8Tensor[N,</span> <span class="pre">H,</span> <span class="pre">W]</span></code>): the segmentation binary masks for each instance</p></li>
 </ul>
 </div></blockquote>
 <p>The model returns a <code class="docutils literal notranslate"><span class="pre">Dict[Tensor]</span></code> during training, containing the classification and regression