Generate Python docs from pytorch/pytorch@0df2e86

Author: pytorchbot

docs/master/_images/RReLU.png

@@ -410,7 +410,7 @@ <h1>Source code for torch.nn.modules.distance</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
 
 
-<div class="viewcode-block" id="PairwiseDistance"><a class="viewcode-back" href="../../../../generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance">[docs]</a><span class="k">class</span> <span class="nc">PairwiseDistance</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
+<span class="k">class</span> <span class="nc">PairwiseDistance</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:</span>
 
@@ -446,10 +446,10 @@ <h1>Source code for torch.nn.modules.distance</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">keepdim</span> <span class="o">=</span> <span class="n">keepdim</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x1</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">x2</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">pairwise_distance</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">keepdim</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">pairwise_distance</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">keepdim</span><span class="p">)</span>
 
 
-<span class="k">class</span> <span class="nc">CosineSimilarity</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="CosineSimilarity"><a class="viewcode-back" href="../../../../generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity">[docs]</a><span class="k">class</span> <span class="nc">CosineSimilarity</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along `dim`.</span>
 
 <span class="sd">    .. math ::</span>
@@ -480,7 +480,7 @@ <h1>Source code for torch.nn.modules.distance</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x1</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">x2</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">cosine_similarity</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">cosine_similarity</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span></div>
 </pre></div>
 
              </article>

docs/master/_modules/torch/cuda/random.html

@@ -478,7 +478,7 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_apply_instance_norm</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 
 
-<span class="k">class</span> <span class="nc">InstanceNorm1d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
+<div class="viewcode-block" id="InstanceNorm1d"><a class="viewcode-back" href="../../../../generated/torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm1d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input</span>
 <span class="sd">    as described in the paper</span>
 <span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization</span>
@@ -551,10 +551,10 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 2D or 3D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
 
 
-<div class="viewcode-block" id="LazyInstanceNorm1d"><a class="viewcode-back" href="../../../../generated/torch.nn.LazyInstanceNorm1d.html#torch.nn.LazyInstanceNorm1d">[docs]</a><span class="k">class</span> <span class="nc">LazyInstanceNorm1d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
+<span class="k">class</span> <span class="nc">LazyInstanceNorm1d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A :class:`torch.nn.InstanceNorm1d` module with lazy initialization of</span>
 <span class="sd">    the ``num_features`` argument of the :class:`InstanceNorm1d` that is inferred</span>
 <span class="sd">    from the ``input.size(1)``.</span>
@@ -590,10 +590,10 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 2D or 3D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
 
 
-<span class="k">class</span> <span class="nc">InstanceNorm2d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
+<div class="viewcode-block" id="InstanceNorm2d"><a class="viewcode-back" href="../../../../generated/torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm2d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs</span>
 <span class="sd">    with additional channel dimension) as described in the paper</span>
 <span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization</span>
@@ -667,10 +667,10 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 3D or 4D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
 
 
-<div class="viewcode-block" id="LazyInstanceNorm2d"><a class="viewcode-back" href="../../../../generated/torch.nn.LazyInstanceNorm2d.html#torch.nn.LazyInstanceNorm2d">[docs]</a><span class="k">class</span> <span class="nc">LazyInstanceNorm2d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
+<span class="k">class</span> <span class="nc">LazyInstanceNorm2d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A :class:`torch.nn.InstanceNorm2d` module with lazy initialization of</span>
 <span class="sd">    the ``num_features`` argument of the :class:`InstanceNorm2d` that is inferred</span>
 <span class="sd">    from the ``input.size(1)``.</span>
@@ -706,10 +706,10 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 3D or 4D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
 
 
-<span class="k">class</span> <span class="nc">InstanceNorm3d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
+<div class="viewcode-block" id="InstanceNorm3d"><a class="viewcode-back" href="../../../../generated/torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm3d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs</span>
 <span class="sd">    with additional channel dimension) as described in the paper</span>
 <span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization</span>
@@ -783,10 +783,10 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 4D or 5D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
 
 
-<div class="viewcode-block" id="LazyInstanceNorm3d"><a class="viewcode-back" href="../../../../generated/torch.nn.LazyInstanceNorm3d.html#torch.nn.LazyInstanceNorm3d">[docs]</a><span class="k">class</span> <span class="nc">LazyInstanceNorm3d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
+<span class="k">class</span> <span class="nc">LazyInstanceNorm3d</span><span class="p">(</span><span class="n">_LazyNormBase</span><span class="p">,</span> <span class="n">_InstanceNorm</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A :class:`torch.nn.InstanceNorm3d` module with lazy initialization of</span>
 <span class="sd">    the ``num_features`` argument of the :class:`InstanceNorm3d` that is inferred</span>
 <span class="sd">    from the ``input.size(1)``.</span>
@@ -822,7 +822,7 @@ <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pr
     <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 4D or 5D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
-                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
 </pre></div>
 
              </article>