Deci-AI
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super-gradients
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            <!DOCTYPE html>
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  <title>super_gradients.training.models.mobilenetv3 &mdash; SuperGradients 1.0 documentation</title>
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  <h1>Source code for super_gradients.training.models.mobilenetv3</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Creates a MobileNetV3 Model as defined in:</span>
<span class="sd">Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).</span>
<span class="sd">Searching for MobileNetV3</span>
<span class="sd">arXiv preprint arXiv:1905.02244.</span>
<span class="sd">&quot;&quot;&quot;</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">math</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.models.sg_module</span> <span class="kn">import</span> <span class="n">SgModule</span>


<span class="k">def</span> <span class="nf">_make_divisible</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">min_value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This function is taken from the original tf repo.</span>
<span class="sd">    It ensures that all layers have a channel number that is divisible by 8</span>
<span class="sd">    It can be seen here:</span>
<span class="sd">    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">min_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">min_value</span> <span class="o">=</span> <span class="n">divisor</span>
    <span class="n">new_v</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">min_value</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="n">divisor</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">divisor</span> <span class="o">*</span> <span class="n">divisor</span><span class="p">)</span>
    <span class="c1"># Make sure that round down does not go down by more than 10%.</span>
    <span class="k">if</span> <span class="n">new_v</span> <span class="o">&lt;</span> <span class="mf">0.9</span> <span class="o">*</span> <span class="n">v</span><span class="p">:</span>
        <span class="n">new_v</span> <span class="o">+=</span> <span class="n">divisor</span>
    <span class="k">return</span> <span class="n">new_v</span>


<div class="viewcode-block" id="h_sigmoid"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.h_sigmoid">[docs]</a><span class="k">class</span> <span class="nc">h_sigmoid</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">h_sigmoid</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU6</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>

<div class="viewcode-block" id="h_sigmoid.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.h_sigmoid.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</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="o">/</span> <span class="mi">6</span></div></div>


<div class="viewcode-block" id="h_swish"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.h_swish">[docs]</a><span class="k">class</span> <span class="nc">h_swish</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">h_swish</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sigmoid</span> <span class="o">=</span> <span class="n">h_sigmoid</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>

<div class="viewcode-block" id="h_swish.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.h_swish.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</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="bp">self</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="SELayer"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.SELayer">[docs]</a><span class="k">class</span> <span class="nc">SELayer</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">SELayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">channel</span><span class="p">,</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">channel</span> <span class="o">//</span> <span class="n">reduction</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">_make_divisible</span><span class="p">(</span><span class="n">channel</span> <span class="o">//</span> <span class="n">reduction</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">channel</span><span class="p">),</span>
            <span class="n">h_sigmoid</span><span class="p">()</span>
        <span class="p">)</span>

<div class="viewcode-block" id="SELayer.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.SELayer.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avg_pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span></div></div>


<div class="viewcode-block" id="conv_3x3_bn"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.conv_3x3_bn">[docs]</a><span class="k">def</span> <span class="nf">conv_3x3_bn</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">stride</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
        <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
        <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">oup</span><span class="p">),</span>
        <span class="n">h_swish</span><span class="p">()</span>
    <span class="p">)</span></div>


<div class="viewcode-block" id="conv_1x1_bn"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.conv_1x1_bn">[docs]</a><span class="k">def</span> <span class="nf">conv_1x1_bn</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
        <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
        <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">oup</span><span class="p">),</span>
        <span class="n">h_swish</span><span class="p">()</span>
    <span class="p">)</span></div>


<div class="viewcode-block" id="InvertedResidual"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.InvertedResidual">[docs]</a><span class="k">class</span> <span class="nc">InvertedResidual</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inp</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">use_se</span><span class="p">,</span> <span class="n">use_hs</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">InvertedResidual</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">assert</span> <span class="n">stride</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">identity</span> <span class="o">=</span> <span class="n">stride</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">oup</span>

        <span class="k">if</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">hidden_dim</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
                <span class="c1"># dw</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
                          <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">),</span>
                <span class="n">h_swish</span><span class="p">()</span> <span class="k">if</span> <span class="n">use_hs</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
                <span class="c1"># Squeeze-and-Excite</span>
                <span class="n">SELayer</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">)</span> <span class="k">if</span> <span class="n">use_se</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span>
                <span class="c1"># pw-linear</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">oup</span><span class="p">),</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
                <span class="c1"># pw</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">),</span>
                <span class="n">h_swish</span><span class="p">()</span> <span class="k">if</span> <span class="n">use_hs</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
                <span class="c1"># dw</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">,</span>
                          <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">),</span>
                <span class="c1"># Squeeze-and-Excite</span>
                <span class="n">SELayer</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">)</span> <span class="k">if</span> <span class="n">use_se</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span>
                <span class="n">h_swish</span><span class="p">()</span> <span class="k">if</span> <span class="n">use_hs</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
                <span class="c1"># pw-linear</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">oup</span><span class="p">),</span>
            <span class="p">)</span>

<div class="viewcode-block" id="InvertedResidual.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.InvertedResidual.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">identity</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>


<div class="viewcode-block" id="MobileNetV3"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.MobileNetV3">[docs]</a><span class="k">class</span> <span class="nc">MobileNetV3</span><span class="p">(</span><span class="n">SgModule</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cfgs</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">MobileNetV3</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="c1"># setting of inverted residual blocks</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cfgs</span> <span class="o">=</span> <span class="n">cfgs</span>
        <span class="k">assert</span> <span class="n">mode</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;large&#39;</span><span class="p">,</span> <span class="s1">&#39;small&#39;</span><span class="p">]</span>

        <span class="c1"># building first layer</span>
        <span class="n">input_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="mi">16</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">conv_3x3_bn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">input_channel</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
        <span class="c1"># building inverted residual blocks</span>
        <span class="n">block</span> <span class="o">=</span> <span class="n">InvertedResidual</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">use_se</span><span class="p">,</span> <span class="n">use_hs</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cfgs</span><span class="p">:</span>
            <span class="n">output_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">c</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
            <span class="n">exp_size</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">input_channel</span> <span class="o">*</span> <span class="n">t</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
            <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="n">exp_size</span><span class="p">,</span> <span class="n">output_channel</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">use_se</span><span class="p">,</span> <span class="n">use_hs</span><span class="p">))</span>
            <span class="n">input_channel</span> <span class="o">=</span> <span class="n">output_channel</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
        <span class="c1"># building last several layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">conv_1x1_bn</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="n">exp_size</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
        <span class="n">output_channel</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;large&#39;</span><span class="p">:</span> <span class="mi">1280</span><span class="p">,</span> <span class="s1">&#39;small&#39;</span><span class="p">:</span> <span class="mi">1024</span><span class="p">}</span>
        <span class="n">output_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">output_channel</span><span class="p">[</span><span class="n">mode</span><span class="p">]</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span> <span class="k">if</span> <span class="n">width_mult</span> <span class="o">&gt;</span> <span class="mf">1.0</span> <span class="k">else</span> <span class="n">output_channel</span><span class="p">[</span>
            <span class="n">mode</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">exp_size</span><span class="p">,</span> <span class="n">output_channel</span><span class="p">),</span>
            <span class="n">h_swish</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">output_channel</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">),</span>
        <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_weights</span><span class="p">()</span>

<div class="viewcode-block" id="MobileNetV3.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.MobileNetV3.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span></div>

    <span class="k">def</span> <span class="nf">_initialize_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
                <span class="n">n</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">out_channels</span>
                <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.</span> <span class="o">/</span> <span class="n">n</span><span class="p">))</span>
                <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                    <span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
                <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
                <span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
                <span class="n">n</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
                <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
                <span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span></div>


<div class="viewcode-block" id="mobilenetv3_large"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.mobilenetv3_large">[docs]</a><span class="k">def</span> <span class="nf">mobilenetv3_large</span><span class="p">(</span><span class="n">arch_params</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Constructs a MobileNetV3-Large model</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">width_mult</span> <span class="o">=</span> <span class="n">arch_params</span><span class="o">.</span><span class="n">width_mult</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">arch_params</span><span class="p">,</span> <span class="s1">&#39;width_mult&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="mf">1.</span>
    <span class="n">cfgs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="c1"># k, t, c, SE, HS, s</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="mi">16</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</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">24</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">2.3</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">2.3</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">112</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">112</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
    <span class="p">]</span>
    <span class="k">return</span> <span class="n">MobileNetV3</span><span class="p">(</span><span class="n">cfgs</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="n">width_mult</span><span class="p">)</span></div>


<div class="viewcode-block" id="mobilenetv3_small"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.mobilenetv3_small">[docs]</a><span class="k">def</span> <span class="nf">mobilenetv3_small</span><span class="p">(</span><span class="n">arch_params</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Constructs a MobileNetV3-Small model</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">width_mult</span> <span class="o">=</span> <span class="n">arch_params</span><span class="o">.</span><span class="n">width_mult</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">arch_params</span><span class="p">,</span> <span class="s1">&#39;width_mult&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="mf">1.</span>
    <span class="n">cfgs</span> <span class="o">=</span> <span class="p">[</span>
        <span class="c1"># k, t, c, SE, HS, s</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="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">4.5</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">3.67</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
        <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
    <span class="p">]</span>

    <span class="k">return</span> <span class="n">MobileNetV3</span><span class="p">(</span><span class="n">cfgs</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="n">width_mult</span><span class="p">)</span></div>


<div class="viewcode-block" id="mobilenetv3_custom"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.mobilenetv3.mobilenetv3_custom">[docs]</a><span class="k">def</span> <span class="nf">mobilenetv3_custom</span><span class="p">(</span><span class="n">arch_params</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Constructs a MobileNetV3-Customized model</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">MobileNetV3</span><span class="p">(</span><span class="n">cfgs</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">structure</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span>
                       <span class="n">width_mult</span><span class="o">=</span><span class="n">arch_params</span><span class="o">.</span><span class="n">width_mult</span><span class="p">)</span></div>
</pre></div>

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