remove private path info from module name

Author: mattip

docs/0.2.0/torch.html

@@ -1842,7 +1842,7 @@ <h2>Random sampling<a class="headerlink" href="#random-sampling" title="Permalin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1864,7 +1864,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p>torch.load can dynamically remap storages to be loaded on a different device
 using the map_location argument. If it&#8217;s a callable, it will be called with

docs/0.3.0/torch.html

@@ -1992,7 +1992,7 @@ <h3>In-place random sampling<a class="headerlink" href="#in-place-random-samplin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2014,7 +2014,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p>torch.load uses Python&#8217;s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
@@ -6861,4 +6861,4 @@ <h3>BLAS and LAPACK Operations<a class="headerlink" href="#blas-and-lapack-opera
 
 
 </body>
-</html>
+</html>

docs/0.3.1/torch.html

@@ -1957,7 +1957,7 @@ <h3>In-place random sampling<a class="headerlink" href="#in-place-random-samplin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1979,7 +1979,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p>torch.load uses Python&#8217;s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
@@ -6895,4 +6895,4 @@ <h3>BLAS and LAPACK Operations<a class="headerlink" href="#blas-and-lapack-opera
 
 
 </body>
-</html>
+</html>

docs/0.4.0/torch.html

@@ -2460,7 +2460,7 @@ <h3>Indexing, Slicing, Joining, Mutating Ops<a class="headerlink" href="#indexin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2498,7 +2498,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/soumith/anaconda3/lib/python3.6/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-meth docutils literal"><span class="pre">torch.load()</span></code></a> uses Python&#8217;s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
@@ -7880,4 +7880,4 @@ <h3>BLAS and LAPACK Operations<a class="headerlink" href="#blas-and-lapack-opera
 
 
 </body>
-</html>
+</html>

docs/0.4.1/torch.html

@@ -2698,7 +2698,7 @@ <h3>Indexing, Slicing, Joining, Mutating Ops<a class="headerlink" href="#indexin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/rzou/dev/pytorch-4.0.1-docs-env/lib/python3.7/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2736,7 +2736,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/private/home/rzou/dev/pytorch-4.0.1-docs-env/lib/python3.7/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses Python’s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
@@ -8613,4 +8613,4 @@ <h3>BLAS and LAPACK Operations<a class="headerlink" href="#blas-and-lapack-opera
 
 
 </body>
-</html>
+</html>

docs/1.0.0/torch.html

@@ -2288,7 +2288,7 @@ <h3>Indexing, Slicing, Joining, Mutating Ops<a class="headerlink" href="#indexin
 <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="torch.save">
-<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=&lt;module 'pickle' from '/scratch/rzou/pt/release-env/lib/python3.7/pickle.py'&gt;</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">save</code><span class="sig-paren">(</span><em>obj</em>, <em>f</em>, <em>pickle_module=pickle</em>, <em>pickle_protocol=2</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="notes/serialization.html#recommend-saving-models"><span class="std std-ref">Recommended approach for saving a model</span></a></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -2326,7 +2326,7 @@ <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to
 
 <dl class="function">
 <dt id="torch.load">
-<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=&lt;module 'pickle' from '/scratch/rzou/pt/release-env/lib/python3.7/pickle.py'&gt;</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">torch.</code><code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>map_location=None</em>, <em>pickle_module=pickle</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses Python’s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
@@ -9069,4 +9069,4 @@ <h2>Resources</h2>
     })
   </script>
 </body>
-</html>
+</html>