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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-intrinsic-quantized">
<h1>torch.nn.intrinsic.quantized<a class="headerlink" href="#torch-nn-intrinsic-quantized" title="Permalink to this headline">¶</a></h1>
<p>This module implements the quantized implementations of fused operations like conv + relu.</p>
<span class="target" id="module-torch.nn.intrinsic.quantized"></span><div class="section" id="convrelu2d">
<h2>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.intrinsic.quantized.ConvReLU2d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.quantized.</code><code class="sig-name descname">ConvReLU2d</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/intrinsic/quantized/modules/conv_relu.html#ConvReLU2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.quantized.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
<dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU</p>
<p>We adopt the same interface as <a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Conv2d" title="torch.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.quantized.Conv2d</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>as torch.nn.quantized.Conv2d</strong> (<em>Same</em>) – </p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="convrelu3d">
<h2>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.intrinsic.quantized.ConvReLU3d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.quantized.</code><code class="sig-name descname">ConvReLU3d</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/intrinsic/quantized/modules/conv_relu.html#ConvReLU3d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.quantized.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
<dd><p>A ConvReLU3d module is a fused module of Conv3d and ReLU</p>
<p>We adopt the same interface as <a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Conv3d" title="torch.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.quantized.Conv3d</span></code></a>.</p>
<p>Attributes: Same as torch.nn.quantized.Conv3d</p>
</dd></dl>
</div>
<div class="section" id="linearrelu">
<h2>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.intrinsic.quantized.LinearReLU">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.quantized.</code><code class="sig-name descname">LinearReLU</code><span class="sig-paren">(</span><em class="sig-param">in_features</em>, <em class="sig-param">out_features</em>, <em class="sig-param">bias=True</em>, <em class="sig-param">dtype=torch.qint8</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/intrinsic/quantized/modules/linear_relu.html#LinearReLU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.quantized.LinearReLU" title="Permalink to this definition">¶</a></dt>
<dd><p>A LinearReLU module fused from Linear and ReLU modules</p>
<p>We adopt the same interface as <a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Linear" title="torch.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.quantized.Linear</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>as torch.nn.quantized.Linear</strong> (<em>Same</em>) – </p>
</dd>
</dl>
<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">intrinsic</span><span class="o">.</span><span class="n">LinearReLU</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</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">128</span><span class="p">,</span> <span class="mi">20</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>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="go">torch.Size([128, 30])</span>
</pre></div>
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