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<div class="section" id="torch-tensor-to">
<h1>torch.Tensor.to<a class="headerlink" href="#torch-tensor-to" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt id="torch.Tensor.to">
<code class="sig-prename descclassname"><span class="pre">Tensor.</span></code><code class="sig-name descname"><span class="pre">to</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.Tensor.to" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Tensor dtype and/or device conversion. A <a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> and <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> are
inferred from the arguments of <code class="docutils literal notranslate"><span class="pre">self.to(*args,</span> <span class="pre">**kwargs)</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the <code class="docutils literal notranslate"><span class="pre">self</span></code> Tensor already
has the correct <a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> and <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, then <code class="docutils literal notranslate"><span class="pre">self</span></code> is returned.
Otherwise, the returned tensor is a copy of <code class="docutils literal notranslate"><span class="pre">self</span></code> with the desired
<a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> and <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>.</p>
</div>
<p>Here are the ways to call <code class="docutils literal notranslate"><span class="pre">to</span></code>:</p>
<dl class="py method">
<dt>
<code class="sig-name descname"><span class="pre">to</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.preserve_format</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></dt>
<dd><blockquote>
<div><p>Returns a Tensor with the specified <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code></p>
<dl class="simple">
<dt>Args:</dt><dd><p>memory_format (<a class="reference internal" href="../tensor_attributes.html#torch.torch.memory_format" title="torch.torch.memory_format"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code></a>, optional): the desired memory format of
returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.preserve_format</span></code>.</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>
<dl class="py method">
<dt>
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">to</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.preserve_format</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></dt>
<dd><blockquote>
<div><p>Returns a Tensor with the specified <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> and (optional)
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> it is inferred to be <code class="docutils literal notranslate"><span class="pre">self.dtype</span></code>.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code>, tries to convert asynchronously with respect to
the host if possible, e.g., converting a CPU Tensor with pinned memory to a
CUDA Tensor.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">copy</span></code> is set, a new Tensor is created even when the Tensor
already matches the desired conversion.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>memory_format (<a class="reference internal" href="../tensor_attributes.html#torch.torch.memory_format" title="torch.torch.memory_format"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code></a>, optional): the desired memory format of
returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.preserve_format</span></code>.</p>
</dd>
</dl>
</div></blockquote>
</dd></dl>
<dl class="py method">
<dt>
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">to</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></dt>
<dd><blockquote>
<div><p>Returns a Tensor with same <a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> and <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as
the Tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>. When <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code>, tries to convert
asynchronously with respect to the host if possible, e.g., converting a CPU
Tensor with pinned memory to a CUDA Tensor.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">copy</span></code> is set, a new Tensor is created even when the Tensor
already matches the desired conversion.</p>
</div></blockquote>
</dd></dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># Initially dtype=float32, device=cpu</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor([[-0.5044, 0.0005],</span>
<span class="go"> [ 0.3310, -0.0584]], dtype=torch.float64)</span>
<span class="gp">>>> </span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</span><span class="p">)</span>
<span class="go">tensor([[-0.5044, 0.0005],</span>
<span class="go"> [ 0.3310, -0.0584]], device='cuda:0')</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</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">float64</span><span class="p">)</span>
<span class="go">tensor([[-0.5044, 0.0005],</span>
<span class="go"> [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')</span>
<span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</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">float64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">tensor([[-0.5044, 0.0005],</span>
<span class="go"> [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')</span>
</pre></div>
</div>
</dd></dl>
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