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  <h1>Source code for super_gradients.training.utils.export_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>


<div class="viewcode-block" id="ExportableHardswish"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.export_utils.ExportableHardswish">[docs]</a><span class="k">class</span> <span class="nc">ExportableHardswish</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Export-friendly version of nn.Hardswish()</span>
<span class="sd">    &#39;&#39;&#39;</span>

<div class="viewcode-block" id="ExportableHardswish.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.export_utils.ExportableHardswish.forward">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">F</span><span class="o">.</span><span class="n">hardtanh</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">6.</span>  <span class="c1"># for torchscript, CoreML and ONNX</span></div></div>


<div class="viewcode-block" id="ExportableSiLU"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.export_utils.ExportableSiLU">[docs]</a><span class="k">class</span> <span class="nc">ExportableSiLU</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Export-friendly version of nn.SiLU()</span>
<span class="sd">    From https://github.com/ultralytics/yolov5</span>
<span class="sd">    &quot;&quot;&quot;</span>
<div class="viewcode-block" id="ExportableSiLU.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.export_utils.ExportableSiLU.forward">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>


<div class="viewcode-block" id="fuse_conv_bn"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.export_utils.fuse_conv_bn">[docs]</a><span class="k">def</span> <span class="nf">fuse_conv_bn</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">replace_bn_with_identity</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Fuses consecutive nn.Conv2d and nn.BatchNorm2d layers recursively inplace in all of the model</span>
<span class="sd">    :param replace_bn_with_identity: if set to true, bn will be replaced with identity. otherwise, bn will be removed</span>
<span class="sd">    :param model: the target model</span>
<span class="sd">    :return: the number of fuses executed</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">children</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">())</span>
    <span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">children</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">children</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">children</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">children</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">fuse_conv_bn_eval</span><span class="p">(</span><span class="n">children</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">children</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
            <span class="k">if</span> <span class="n">replace_bn_with_identity</span><span class="p">:</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">children</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">())</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="nb">delattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">children</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
            <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">for</span> <span class="n">child_name</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">children</span><span class="p">:</span>
        <span class="n">counter</span> <span class="o">+=</span> <span class="n">fuse_conv_bn</span><span class="p">(</span><span class="n">child</span><span class="p">,</span> <span class="n">replace_bn_with_identity</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">counter</span></div>
</pre></div>

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