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<div class="section" id="torch-storage">
<h1>torch.Storage<a class="headerlink" href="#torch-storage" title="Permalink to this headline">¶</a></h1>
<p>A <code class="xref py py-class docutils literal"><span class="pre">torch.Storage</span></code> is a contiguous, one-dimensional array of a single
data type.</p>
<p>Every <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> has a corresponding storage of the same data type.</p>
<dl class="class">
<dt id="torch.FloatStorage">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">FloatStorage</code><a class="reference internal" href="_modules/torch.html#FloatStorage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.FloatStorage" title="Permalink to this definition">¶</a></dt>
<dd><dl class="method">
<dt id="torch.FloatStorage.byte">
<code class="descname">byte</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.byte" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to byte type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.char">
<code class="descname">char</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.char" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to char type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.clone">
<code class="descname">clone</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.clone" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of this storage</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.copy_">
<code class="descname">copy_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.copy_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.cpu">
<code class="descname">cpu</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.cpu" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a CPU copy of this storage if it’s not already on the CPU</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.cuda">
<code class="descname">cuda</code><span class="sig-paren">(</span><em>device=None</em>, <em>non_blocking=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.cuda" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of this object in CUDA memory.</p>
<p>If this object is already in CUDA memory and on the correct device, then
no copy is performed and the original object is returned.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The destination GPU id. Defaults to the current device.</li>
<li><strong>non_blocking</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If <code class="docutils literal"><span class="pre">True</span></code> and the source is in pinned memory,
the copy will be asynchronous with respect to the host. Otherwise,
the argument has no effect.</li>
<li><strong>**kwargs</strong> – For compatibility, may contain the key <code class="docutils literal"><span class="pre">async</span></code> in place of
the <code class="docutils literal"><span class="pre">non_blocking</span></code> argument.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.data_ptr">
<code class="descname">data_ptr</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.data_ptr" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.double">
<code class="descname">double</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.double" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to double type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.element_size">
<code class="descname">element_size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.element_size" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.fill_">
<code class="descname">fill_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.fill_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.float">
<code class="descname">float</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.float" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to float type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.from_buffer">
<code class="descname">from_buffer</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.from_buffer" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.from_file">
<code class="descname">from_file</code><span class="sig-paren">(</span><em>filename</em>, <em>shared=False</em>, <em>size=0</em><span class="sig-paren">)</span> → Storage<a class="headerlink" href="#torch.FloatStorage.from_file" title="Permalink to this definition">¶</a></dt>
<dd><p>If <cite>shared</cite> is <cite>True</cite>, then memory is shared between all processes.
All changes are written to the file. If <cite>shared</cite> is <cite>False</cite>, then the changes on
the storage do not affect the file.</p>
<p><cite>size</cite> is the number of elements in the storage. If <cite>shared</cite> is <cite>False</cite>,
then the file must contain at least <cite>size * sizeof(Type)</cite> bytes
(<cite>Type</cite> is the type of storage). If <cite>shared</cite> is <cite>True</cite> the file will be
created if needed.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – file name to map</li>
<li><strong>shared</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – whether to share memory</li>
<li><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – number of elements in the storage</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.half">
<code class="descname">half</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.half" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to half type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.int">
<code class="descname">int</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.int" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to int type</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.FloatStorage.is_cuda">
<code class="descname">is_cuda</code><em class="property"> = False</em><a class="headerlink" href="#torch.FloatStorage.is_cuda" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.is_pinned">
<code class="descname">is_pinned</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.is_shared">
<code class="descname">is_shared</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.is_shared" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="torch.FloatStorage.is_sparse">
<code class="descname">is_sparse</code><em class="property"> = False</em><a class="headerlink" href="#torch.FloatStorage.is_sparse" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.long">
<code class="descname">long</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.long" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to long type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.new">
<code class="descname">new</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.new" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.pin_memory">
<code class="descname">pin_memory</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
<dd><p>Copies the storage to pinned memory, if it’s not already pinned.</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.resize_">
<code class="descname">resize_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.resize_" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.share_memory_">
<code class="descname">share_memory_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
<dd><p>Moves the storage to shared memory.</p>
<p>This is a no-op for storages already in shared memory and for CUDA
storages, which do not need to be moved for sharing across processes.
Storages in shared memory cannot be resized.</p>
<p>Returns: self</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.short">
<code class="descname">short</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.short" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this storage to short type</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.size">
<code class="descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.size" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.tolist">
<code class="descname">tolist</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.tolist" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a list containing the elements of this storage</p>
</dd></dl>
<dl class="method">
<dt id="torch.FloatStorage.type">
<code class="descname">type</code><span class="sig-paren">(</span><em>dtype=None</em>, <em>non_blocking=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.FloatStorage.type" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the type if <cite>dtype</cite> is not provided, else casts this object to
the specified type.</p>
<p>If this is already of the correct type, no copy is performed and the
original object is returned.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>dtype</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.7)"><em>type</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/string.html#module-string" title="(in Python v3.7)"><em>string</em></a>) – The desired type</li>
<li><strong>non_blocking</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If <code class="docutils literal"><span class="pre">True</span></code>, and the source is in pinned memory
and destination is on the GPU or vice versa, the copy is performed
asynchronously with respect to the host. Otherwise, the argument
has no effect.</li>
<li><strong>**kwargs</strong> – For compatibility, may contain the key <code class="docutils literal"><span class="pre">async</span></code> in place of
the <code class="docutils literal"><span class="pre">non_blocking</span></code> argument. The <code class="docutils literal"><span class="pre">async</span></code> arg is deprecated.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
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