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<div class="section" id="torch-median">
<h1>torch.median<a class="headerlink" href="#torch-median" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.median">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">median</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</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.median" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the median of the values in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The median is not unique for <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensors with an even number
of elements. In this case the lower of the two medians is returned. To
compute the mean of both medians, use <a class="reference internal" href="torch.quantile.html#torch.quantile" title="torch.quantile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantile()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">q=0.5</span></code> instead.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This function produces deterministic (sub)gradients unlike <code class="docutils literal notranslate"><span class="pre">median(dim=0)</span></code></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 1.5219, -1.5212, 0.2202]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor(0.2202)</span>
</pre></div>
</div>
<dl class="py function">
<dt>
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">median</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span></dt>
<dd></dd></dl>
<p>Returns a namedtuple <code class="docutils literal notranslate"><span class="pre">(values,</span> <span class="pre">indices)</span></code> where <code class="docutils literal notranslate"><span class="pre">values</span></code> contains the median of each row of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>
in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>, and <code class="docutils literal notranslate"><span class="pre">indices</span></code> contains the index of the median values found in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>.</p>
<p>By default, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is the last dimension of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">keepdim</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the output tensors are of the same size
as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> except in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> where they are of size 1.
Otherwise, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is squeezed (see <a class="reference internal" href="torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.squeeze()</span></code></a>), resulting in
the outputs tensor having 1 fewer dimension than <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The median is not unique for <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensors with an even number
of elements in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>. In this case the lower of the
two medians is returned. To compute the mean of both medians in
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, use <a class="reference internal" href="torch.quantile.html#torch.quantile" title="torch.quantile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantile()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">q=0.5</span></code> instead.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><code class="docutils literal notranslate"><span class="pre">indices</span></code> does not necessarily contain the first occurrence of each
median value found, unless it is unique.
The exact implementation details are device-specific.
Do not expect the same result when run on CPU and GPU in general.
For the same reason do not expect the gradients to be deterministic.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – the dimension to reduce.</p></li>
<li><p><strong>keepdim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether the output tensor has <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> retained or not.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><p><strong>out</strong> (<em>(</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>)</em><em>, </em><em>optional</em>) – The first tensor will be populated with the median values and the second
tensor, which must have dtype long, with their indices in the dimension
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131],</span>
<span class="go"> [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270],</span>
<span class="go"> [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488],</span>
<span class="go"> [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="go">torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3]))</span>
</pre></div>
</div>
</dd></dl>
</div>
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