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<p class="caption"><span class="caption-text">Notes</span></p>
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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-qat">
<h1>torch.nn.intrinsic.qat<a class="headerlink" href="#torch-nn-intrinsic-qat" title="Permalink to this headline">¶</a></h1>
<p>This module implements the versions of those fused operations needed for
quantization aware training.</p>
<span class="target" id="module-torch.nn.intrinsic.qat"></span><div class="section" id="convbn2d">
<h2>ConvBn2d<a class="headerlink" href="#convbn2d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.intrinsic.qat.ConvBn2d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.qat.</code><code class="sig-name descname">ConvBn2d</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=None</em>, <em class="sig-param">padding_mode='zeros'</em>, <em class="sig-param">eps=1e-05</em>, <em class="sig-param">momentum=0.1</em>, <em class="sig-param">freeze_bn=False</em>, <em class="sig-param">qconfig=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/intrinsic/qat/modules/conv_fused.html#ConvBn2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.qat.ConvBn2d" title="Permalink to this definition">¶</a></dt>
<dd><p>A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
attached with FakeQuantize modules for weight,
used in quantization aware training.</p>
<p>We combined the interface of <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">torch.nn.Conv2d</span></code></a> and
<a class="reference internal" href="generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm2d</span></code></a>.</p>
<p>Similar to <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">torch.nn.Conv2d</span></code></a>, with FakeQuantize modules initialized
to default.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>~ConvBn2d.freeze_bn</strong> – </p></li>
<li><p><strong>~ConvBn2d.weight_fake_quant</strong> – fake quant module for weight</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="convbnrelu2d">
<h2>ConvBnReLU2d<a class="headerlink" href="#convbnrelu2d" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.nn.intrinsic.qat.ConvBnReLU2d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.qat.</code><code class="sig-name descname">ConvBnReLU2d</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=None</em>, <em class="sig-param">padding_mode='zeros'</em>, <em class="sig-param">eps=1e-05</em>, <em class="sig-param">momentum=0.1</em>, <em class="sig-param">freeze_bn=False</em>, <em class="sig-param">qconfig=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.qat.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
<dd><p>A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
attached with FakeQuantize modules for weight,
used in quantization aware training.</p>
<p>We combined the interface of <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">torch.nn.Conv2d</span></code></a> and
<a class="reference internal" href="generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm2d</span></code></a> and <a class="reference internal" href="generated/torch.nn.ReLU.html#torch.nn.ReLU" title="torch.nn.ReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ReLU</span></code></a>.</p>
<p>Similar to <cite>torch.nn.Conv2d</cite>, with FakeQuantize modules initialized to
default.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>~ConvBnReLU2d.weight_fake_quant</strong> – fake quant module for weight</p>
</dd>
</dl>
</dd></dl>
</div>
<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.qat.ConvReLU2d">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.qat.</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>, <em class="sig-param">qconfig=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.qat.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
<dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
FakeQuantize modules for weight for
quantization aware training.</p>
<p>We combined the interface of <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> and
<a class="reference internal" href="generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>~ConvReLU2d.weight_fake_quant</strong> – fake quant module for weight</p>
</dd>
</dl>
</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.qat.LinearReLU">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.intrinsic.qat.</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">qconfig=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/intrinsic/qat/modules/linear_relu.html#LinearReLU"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.intrinsic.qat.LinearReLU" title="Permalink to this definition">¶</a></dt>
<dd><p>A LinearReLU module fused from Linear and ReLU modules, attached with
FakeQuantize modules for weight, used in
quantization aware training.</p>
<p>We adopt the same interface as <a class="reference internal" href="generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Linear</span></code></a>.</p>
<p>Similar to <cite>torch.nn.intrinsic.LinearReLU</cite>, with FakeQuantize modules initialized to
default.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>~LinearReLU.weight</strong> – fake quant module for weight</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">qat</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>
</div>
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