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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="torch-nn-quantized">
<h1>torch.nn.quantized<a class="headerlink" href="#torch-nn-quantized" title="Permalink to this headline">¶</a></h1>
<p>This module implements the quantized versions of the nn modules and functionals.</p>
<div class="section" id="module-torch.nn.quantized.functional">
<span id="functional-interface"></span><h2>Functional interface<a class="headerlink" href="#module-torch.nn.quantized.functional" title="Permalink to this headline">¶</a></h2>
<p>Functional interface (quantized).</p>
<dl class="function">
<dt id="torch.nn.quantized.functional.linear">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">linear</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">scale=None</em>, <em class="sig-param">zero_point=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.linear" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a linear transformation to the incoming quantized data:
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.924661em;vertical-align:-0.08333em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413309999999999em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">b</span></span></span></span>
</span>.
See <a class="reference internal" href="#torch.nn.quantized.Linear" title="torch.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Current implementation packs weights on every call, which has penalty on performance.
If you want to avoid the overhead, use <a class="reference internal" href="#torch.nn.quantized.Linear" title="torch.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a>.</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>) – Quantized input of type <cite>torch.quint8</cite></p></li>
<li><p><strong>weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Quantized weight of type <cite>torch.qint8</cite></p></li>
<li><p><strong>bias</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – None or fp32 bias of type <cite>torch.float</cite></p></li>
<li><p><strong>scale</strong> (<em>double</em>) – output scale. If None, derived from the input scale</p></li>
<li><p><strong>zero_point</strong> (<em>long</em>) – output zero point. If None, derived from the input zero_point</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><ul class="simple">
<li><p>Input: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mo>∗</mo><mo separator="true">,</mo><mi>i</mi><mi>n</mi><mi mathvariant="normal">_</mi><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, *, in\_features)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal">n</span><span class="mord" style="margin-right:0.02778em;">_</span><span class="mord mathnormal" style="margin-right:0.10764em;">f</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">u</span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mord mathnormal">e</span><span class="mord mathnormal">s</span><span class="mclose">)</span></span></span></span>
</span> where <cite>*</cite> means any number of
additional dimensions</p></li>
<li><p>Weight: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>o</mi><mi>u</mi><mi>t</mi><mi mathvariant="normal">_</mi><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mi>s</mi><mo separator="true">,</mo><mi>i</mi><mi>n</mi><mi mathvariant="normal">_</mi><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(out\_features, in\_features)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord mathnormal">o</span><span class="mord mathnormal">u</span><span class="mord mathnormal">t</span><span class="mord" style="margin-right:0.02778em;">_</span><span class="mord mathnormal" style="margin-right:0.10764em;">f</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">u</span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mord mathnormal">e</span><span class="mord mathnormal">s</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal">n</span><span class="mord" style="margin-right:0.02778em;">_</span><span class="mord mathnormal" style="margin-right:0.10764em;">f</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">u</span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mord mathnormal">e</span><span class="mord mathnormal">s</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p>Bias: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>o</mi><mi>u</mi><mi>t</mi><mi mathvariant="normal">_</mi><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(out\_features)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord mathnormal">o</span><span class="mord mathnormal">u</span><span class="mord mathnormal">t</span><span class="mord" style="margin-right:0.02778em;">_</span><span class="mord mathnormal" style="margin-right:0.10764em;">f</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">u</span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mord mathnormal">e</span><span class="mord mathnormal">s</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p>Output: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mo>∗</mo><mo separator="true">,</mo><mi>o</mi><mi>u</mi><mi>t</mi><mi mathvariant="normal">_</mi><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, *, out\_features)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">o</span><span class="mord mathnormal">u</span><span class="mord mathnormal">t</span><span class="mord" style="margin-right:0.02778em;">_</span><span class="mord mathnormal" style="margin-right:0.10764em;">f</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">u</span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mord mathnormal">e</span><span class="mord mathnormal">s</span><span class="mclose">)</span></span></span></span>
</span></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.conv1d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">conv1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">padding_mode='zeros'</em>, <em class="sig-param">scale=1.0</em>, <em class="sig-param">zero_point=0</em>, <em class="sig-param">dtype=torch.quint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#conv1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.conv1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D convolution over a quantized 1D input composed of several input
planes.</p>
<p>See <a class="reference internal" href="#torch.nn.quantized.Conv1d" title="torch.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – quantized input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – quantized filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – <strong>non-quantized</strong> bias tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. The tensor type must be <cite>torch.float</cite>.</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <cite>(sW,)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a tuple <cite>(padW,)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dW,)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
<li><p><strong>padding_mode</strong> – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”</p></li>
<li><p><strong>scale</strong> – quantization scale for the output. Default: 1.0</p></li>
<li><p><strong>zero_point</strong> – quantization zero_point for the output. Default: 0</p></li>
<li><p><strong>dtype</strong> – quantization data type to use. Default: <code class="docutils literal notranslate"><span class="pre">torch.quint8</span></code></p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.nn.quantized</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">qF</span>
<span class="gp">>>> </span><span class="n">filters</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">33</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</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">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bias</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">33</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">float</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="n">dtype_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span>
<span class="gp">>>> </span><span class="n">dtype_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">q_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_filters</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">q_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_inputs</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">qF</span><span class="o">.</span><span class="n">conv1d</span><span class="p">(</span><span class="n">q_inputs</span><span class="p">,</span> <span class="n">q_filters</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="n">zero_point</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.conv2d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">conv2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">padding_mode='zeros'</em>, <em class="sig-param">scale=1.0</em>, <em class="sig-param">zero_point=0</em>, <em class="sig-param">dtype=torch.quint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#conv2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D convolution over a quantized 2D input composed of several input
planes.</p>
<p>See <a class="reference internal" href="#torch.nn.quantized.Conv2d" title="torch.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – quantized input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – quantized filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – <strong>non-quantized</strong> bias tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. The tensor type must be <cite>torch.float</cite>.</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <cite>(sH, sW)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a tuple <cite>(padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dH, dW)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
<li><p><strong>padding_mode</strong> – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”</p></li>
<li><p><strong>scale</strong> – quantization scale for the output. Default: 1.0</p></li>
<li><p><strong>zero_point</strong> – quantization zero_point for the output. Default: 0</p></li>
<li><p><strong>dtype</strong> – quantization data type to use. Default: <code class="docutils literal notranslate"><span class="pre">torch.quint8</span></code></p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.nn.quantized</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">qF</span>
<span class="gp">>>> </span><span class="n">filters</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">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bias</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">8</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">float</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="n">dtype_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span>
<span class="gp">>>> </span><span class="n">dtype_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">q_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_filters</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">q_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_inputs</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">qF</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">q_inputs</span><span class="p">,</span> <span class="n">q_filters</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="n">zero_point</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.conv3d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">conv3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">padding_mode='zeros'</em>, <em class="sig-param">scale=1.0</em>, <em class="sig-param">zero_point=0</em>, <em class="sig-param">dtype=torch.quint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#conv3d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.conv3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D convolution over a quantized 3D input composed of several input
planes.</p>
<p>See <a class="reference internal" href="#torch.nn.quantized.Conv3d" title="torch.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – quantized input tensor of shape
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>D</mi><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iD , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – quantized filters of shape
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>D</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – <strong>non-quantized</strong> bias tensor of shape
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. The tensor type must be <cite>torch.float</cite>.</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <cite>(sD, sH, sW)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a tuple <cite>(padD, padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dD, dH, dW)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be
divisible by the number of groups. Default: 1</p></li>
<li><p><strong>padding_mode</strong> – the padding mode to use. Only “zeros” is supported for
quantized convolution at the moment. Default: “zeros”</p></li>
<li><p><strong>scale</strong> – quantization scale for the output. Default: 1.0</p></li>
<li><p><strong>zero_point</strong> – quantization zero_point for the output. Default: 0</p></li>
<li><p><strong>dtype</strong> – quantization data type to use. Default: <code class="docutils literal notranslate"><span class="pre">torch.quint8</span></code></p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.nn.quantized</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">qF</span>
<span class="gp">>>> </span><span class="n">filters</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">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bias</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">8</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">float</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="n">dtype_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quint8</span>
<span class="gp">>>> </span><span class="n">dtype_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qint8</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">q_filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_filters</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">q_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="p">,</span> <span class="n">dtype_inputs</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">qF</span><span class="o">.</span><span class="n">conv3d</span><span class="p">(</span><span class="n">q_inputs</span><span class="p">,</span> <span class="n">q_filters</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="n">zero_point</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.max_pool2d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">max_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">ceil_mode=False</em>, <em class="sig-param">return_indices=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#max_pool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D max pooling over a quantized input signal composed of
several quantized input planes.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters are propagated to the output.</p>
</div>
<p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code> for details.</p>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.adaptive_avg_pool2d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">adaptive_avg_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">output_size</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#adaptive_avg_pool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D adaptive average pooling over a quantized input signal composed
of several quantized input planes.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool2d</span></code> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>output_size</strong> – the target output size (single integer or
double-integer tuple)</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.avg_pool2d">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">avg_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">ceil_mode=False</em>, <em class="sig-param">count_include_pad=True</em>, <em class="sig-param">divisor_override=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#avg_pool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies 2D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.77777em;vertical-align:-0.08333em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span>
</span> regions by step size
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.76666em;vertical-align:-0.08333em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span>
</span> steps. The number of output features is equal to the number of
input planes.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">AvgPool2d</span></code> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – quantized input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">i</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>kernel_size</strong> – size of the pooling region. Can be a single number or a
tuple <cite>(kH, kW)</cite></p></li>
<li><p><strong>stride</strong> – stride of the pooling operation. Can be a single number or a
tuple <cite>(sH, sW)</cite>. Default: <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code></p></li>
<li><p><strong>padding</strong> – implicit zero paddings on both sides of the input. Can be a
single number or a tuple <cite>(padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>ceil_mode</strong> – when True, will use <cite>ceil</cite> instead of <cite>floor</cite> in the formula
to compute the output shape. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
<li><p><strong>count_include_pad</strong> – when True, will include the zero-padding in the
averaging calculation. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>divisor_override</strong> – if specified, it will be used as divisor, otherwise
size of the pooling region will be used. Default: None</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.interpolate">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">interpolate</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">size=None</em>, <em class="sig-param">scale_factor=None</em>, <em class="sig-param">mode='nearest'</em>, <em class="sig-param">align_corners=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#interpolate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.interpolate" title="Permalink to this definition">¶</a></dt>
<dd><p>Down/up samples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
<code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
<p>See <a class="reference internal" href="nn.functional.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.interpolate()</span></code></a> for implementation details.</p>
<p>The input dimensions are interpreted in the form:
<cite>mini-batch x channels x [optional depth] x [optional height] x width</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only 2D/3D input is supported for quantized inputs</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only the following modes are supported for the quantized inputs:</p>
<ul class="simple">
<li><p><cite>bilinear</cite></p></li>
<li><p><cite>nearest</cite></p></li>
</ul>
</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>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em>) – output spatial size.</p></li>
<li><p><strong>scale_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>]</em>) – multiplier for spatial size. Has to match input size if it is a tuple.</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – algorithm used for upsampling:
<code class="docutils literal notranslate"><span class="pre">'nearest'</span></code> | <code class="docutils literal notranslate"><span class="pre">'bilinear'</span></code></p></li>
<li><p><strong>align_corners</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – Geometrically, we consider the pixels of the
input and output as squares rather than points.
If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, the input and output tensors are aligned by the
center points of their corner pixels, preserving the values at the corner pixels.
If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the input and output tensors are aligned by the corner
points of their corner pixels, and the interpolation uses edge value padding
for out-of-boundary values, making this operation <em>independent</em> of input size
when <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code> is kept the same. This only has an effect when <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>
is <code class="docutils literal notranslate"><span class="pre">'bilinear'</span></code>.
Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.hardswish">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">hardswish</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">scale</em>, <em class="sig-param">zero_point</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#hardswish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.hardswish" title="Permalink to this definition">¶</a></dt>
<dd><p>This is the quantized version of <a class="reference internal" href="nn.functional.html#torch.nn.functional.hardswish" title="torch.nn.functional.hardswish"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardswish()</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – quantized input</p></li>
<li><p><strong>scale</strong> – quantization scale of the output tensor</p></li>
<li><p><strong>zero_point</strong> – quantization zero point of the output tensor</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.upsample">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">upsample</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">size=None</em>, <em class="sig-param">scale_factor=None</em>, <em class="sig-param">mode='nearest'</em>, <em class="sig-param">align_corners=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#upsample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.upsample" title="Permalink to this definition">¶</a></dt>
<dd><p>Upsamples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
<code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This function is deprecated in favor of
<a class="reference internal" href="#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.quantized.functional.interpolate()</span></code></a>.
This is equivalent with <code class="docutils literal notranslate"><span class="pre">nn.quantized.functional.interpolate(...)</span></code>.</p>
</div>
<p>See <a class="reference internal" href="nn.functional.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.interpolate()</span></code></a> for implementation details.</p>
<p>The input dimensions are interpreted in the form:
<cite>mini-batch x channels x [optional depth] x [optional height] x width</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only 2D input is supported for quantized inputs</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only the following modes are supported for the quantized inputs:</p>
<ul class="simple">
<li><p><cite>bilinear</cite></p></li>
<li><p><cite>nearest</cite></p></li>
</ul>
</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>) – quantized input tensor</p></li>
<li><p><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em>) – output spatial size.</p></li>
<li><p><strong>scale_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>]</em>) – multiplier for spatial size. Has to be an integer.</p></li>
<li><p><strong>mode</strong> (<em>string</em>) – algorithm used for upsampling:
<code class="docutils literal notranslate"><span class="pre">'nearest'</span></code> | <code class="docutils literal notranslate"><span class="pre">'bilinear'</span></code></p></li>
<li><p><strong>align_corners</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – Geometrically, we consider the pixels of the
input and output as squares rather than points.
If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, the input and output tensors are aligned by the
center points of their corner pixels, preserving the values at the corner pixels.
If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the input and output tensors are aligned by the corner
points of their corner pixels, and the interpolation uses edge value padding
for out-of-boundary values, making this operation <em>independent</em> of input size
when <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code> is kept the same. This only has an effect when <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>
is <code class="docutils literal notranslate"><span class="pre">'bilinear'</span></code>.
Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
</ul>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>With <code class="docutils literal notranslate"><span class="pre">align_corners</span> <span class="pre">=</span> <span class="pre">True</span></code>, the linearly interpolating modes
(<cite>bilinear</cite>) don’t proportionally align the
output and input pixels, and thus the output values can depend on the
input size. This was the default behavior for these modes up to version
0.3.1. Since then, the default behavior is <code class="docutils literal notranslate"><span class="pre">align_corners</span> <span class="pre">=</span> <span class="pre">False</span></code>.
See <a class="reference internal" href="generated/torch.nn.Upsample.html#torch.nn.Upsample" title="torch.nn.Upsample"><code class="xref py py-class docutils literal notranslate"><span class="pre">Upsample</span></code></a> for concrete examples on how this
affects the outputs.</p>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.upsample_bilinear">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">upsample_bilinear</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">size=None</em>, <em class="sig-param">scale_factor=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#upsample_bilinear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
<dd><p>Upsamples the input, using bilinear upsampling.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This function is deprecated in favor of
<a class="reference internal" href="#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.quantized.functional.interpolate()</span></code></a>.
This is equivalent with
<code class="docutils literal notranslate"><span class="pre">nn.quantized.functional.interpolate(...,</span> <span class="pre">mode='bilinear',</span> <span class="pre">align_corners=True)</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only 2D inputs are supported</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>) – quantized input</p></li>
<li><p><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em>) – output spatial size.</p></li>
<li><p><strong>scale_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em>) – multiplier for spatial size</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.nn.quantized.functional.upsample_nearest">
<code class="sig-prename descclassname">torch.nn.quantized.functional.</code><code class="sig-name descname">upsample_nearest</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">size=None</em>, <em class="sig-param">scale_factor=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/functional.html#upsample_nearest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
<dd><p>Upsamples the input, using nearest neighbours’ pixel values.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This function is deprecated in favor of
<a class="reference internal" href="#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.quantized.functional.interpolate()</span></code></a>.
This is equivalent with <code class="docutils literal notranslate"><span class="pre">nn.quantized.functional.interpolate(...,</span> <span class="pre">mode='nearest')</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The input quantization parameters propagate to the output.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only 2D inputs are supported</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>) – quantized input</p></li>
<li><p><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em>) – output spatial
size.</p></li>
<li><p><strong>scale_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – multiplier for spatial size. Has to be an integer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<span class="target" id="module-torch.nn.quantized"></span></div>
<div class="section" id="relu6">
<h2>ReLU6<a class="headerlink" href="#relu6" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.ReLU6">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.</code><code class="sig-name descname">ReLU6</code><span class="sig-paren">(</span><em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/activation.html#ReLU6"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.ReLU6" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the element-wise function:</p>
<p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><msub><mi>x</mi><mn>0</mn></msub><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(x_0, x), q(6))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">)</span><span class="mclose">)</span></span></span></span>
</span>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>0</mn></msub></mrow><annotation encoding="application/x-tex">x_0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.58056em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span>
</span> is the
zero_point, and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">q(6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">)</span></span></span></span>
</span> is the quantized representation of number 6.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>inplace</strong> – can optionally do the operation in-place. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><ul class="simple">
<li><p>Input: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span>
</span> where <cite>*</cite> means, any number of additional
dimensions</p></li>
<li><p>Output: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span>
</span>, same shape as the input</p></li>
</ul>
</dd>
</dl>
<img alt="_images/ReLU61.png" src="_images/ReLU61.png" />
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">ReLU6</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">input</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="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</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">qint32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="elu">
<h2>ELU<a class="headerlink" href="#elu" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.ELU">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.</code><code class="sig-name descname">ELU</code><span class="sig-paren">(</span><em class="sig-param">scale</em>, <em class="sig-param">zero_point</em>, <em class="sig-param">alpha=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/activation.html#ELU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.ELU" title="Permalink to this definition">¶</a></dt>
<dd><p>This is the quantized equivalent of <a class="reference internal" href="generated/torch.nn.ELU.html#torch.nn.ELU" title="torch.nn.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>scale</strong> – quantization scale of the output tensor</p></li>
<li><p><strong>zero_point</strong> – quantization zero point of the output tensor</p></li>
<li><p><strong>alpha</strong> – the alpha constant</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="hardswish">
<h2>Hardswish<a class="headerlink" href="#hardswish" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.Hardswish">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.</code><code class="sig-name descname">Hardswish</code><span class="sig-paren">(</span><em class="sig-param">scale</em>, <em class="sig-param">zero_point</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/activation.html#Hardswish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.Hardswish" title="Permalink to this definition">¶</a></dt>
<dd><p>This is the quantized version of <a class="reference internal" href="generated/torch.nn.Hardswish.html#torch.nn.Hardswish" title="torch.nn.Hardswish"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardswish</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>scale</strong> – quantization scale of the output tensor</p></li>
<li><p><strong>zero_point</strong> – quantization zero point of the output tensor</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="conv1d">
<h2>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.Conv1d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.</code><code class="sig-name descname">Conv1d</code><span class="sig-paren">(</span><em class="sig-param">in_channels</em>, <em class="sig-param">out_channels</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">bias=True</em>, <em class="sig-param">padding_mode='zeros'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/conv.html#Conv1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.Conv1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D convolution over a quantized input signal composed of
several quantized input planes.</p>
<p>For details on input arguments, parameters, and implementation see
<a class="reference internal" href="generated/torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only <cite>zeros</cite> is supported for the <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_mode</span></code> argument.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only <cite>torch.quint8</cite> is supported for the input data type.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>~Conv1d.weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – packed tensor derived from the learnable weight
parameter.</p></li>
<li><p><strong>~Conv1d.scale</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – scalar for the output scale</p></li>
<li><p><strong>~Conv1d.zero_point</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – scalar for the output zero point</p></li>
</ul>
</dd>
</dl>
<p>See <a class="reference internal" href="generated/torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> for other attributes.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</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">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># quantize input to quint8</span>
<span class="gp">>>> </span><span class="n">q_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantize_per_tensor</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">zero_point</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="go"> dtype=torch.quint8)</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">q_input</span><span class="p">)</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.nn.quantized.Conv1d.from_float">
<em class="property">classmethod </em><code class="sig-name descname">from_float</code><span class="sig-paren">(</span><em class="sig-param">mod</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/conv.html#Conv1d.from_float"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.Conv1d.from_float" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a quantized module from a float module or qparams_dict.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mod</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>Module</em></a>) – a float module, either produced by torch.quantization
utilities or provided by the user</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="conv2d">
<h2>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.quantized.Conv2d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.quantized.</code><code class="sig-name descname">Conv2d</code><span class="sig-paren">(</span><em class="sig-param">in_channels</em>, <em class="sig-param">out_channels</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">bias=True</em>, <em class="sig-param">padding_mode='zeros'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/quantized/modules/conv.html#Conv2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.quantized.Conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D convolution over a quantized input signal composed of
several quantized input planes.</p>
<p>For details on input arguments, parameters, and implementation see
<a class="reference internal" href="generated/torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only <cite>zeros</cite> is supported for the <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_mode</span></code> argument.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Only <cite>torch.quint8</cite> is supported for the input data type.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>~Conv2d.weight</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – packed tensor derived from the learnable weight
parameter.</p></li>
<li><p><strong>~Conv2d.scale</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – scalar for the output scale</p></li>
<li><p><strong>~Conv2d.zero_point</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – scalar for the output zero point</p></li>
</ul>
</dd>
</dl>
<p>See <a class="reference internal" href="generated/torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> for other attributes.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># With square kernels and equal stride</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># non-square kernels and unequal stride and with padding</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="c1"># non-square kernels and unequal stride and with padding and dilation</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="nb">input</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">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>