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  <h1>Source code for super_gradients.training.models.ssd</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">from</span> <span class="nn">super_gradients.training.models</span> <span class="kn">import</span> <span class="n">MobileNet</span><span class="p">,</span> <span class="n">SgModule</span><span class="p">,</span> <span class="n">MobileNetV2</span><span class="p">,</span> <span class="n">InvertedResidual</span>

<span class="kn">from</span> <span class="nn">super_gradients.training.utils</span> <span class="kn">import</span> <span class="n">HpmStruct</span><span class="p">,</span> <span class="n">utils</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.module_utils</span> <span class="kn">import</span> <span class="n">MultiOutputModule</span>

<span class="n">DEFAULT_SSD_ARCH_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;num_defaults&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
    <span class="s2">&quot;additional_blocks_bottleneck_channels&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">]</span>
<span class="p">}</span>

<span class="n">DEFAULT_SSD_MOBILENET_V1_ARCH_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;out_channels&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span>
    <span class="s2">&quot;kernel_sizes&quot;</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">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="p">}</span>

<span class="n">DEFAULT_SSD_LITE_MOBILENET_V2_ARCH_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;out_channels&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">576</span><span class="p">,</span> <span class="mi">1280</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
    <span class="s2">&quot;expand_ratios&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">],</span>
    <span class="s2">&quot;num_defaults&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span>
    <span class="s2">&quot;lite&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
    <span class="s2">&quot;width_mult&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span>
    <span class="s2">&quot;output_paths&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">14</span><span class="p">,</span> <span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="mi">18</span><span class="p">]</span>
<span class="p">}</span>


<div class="viewcode-block" id="SeperableConv2d"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SeperableConv2d">[docs]</a><span class="k">def</span> <span class="nf">SeperableConv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</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="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.</span>
<span class="sd">    &quot;&quot;&quot;</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">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
                  <span class="n">groups</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</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">in_channels</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">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</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="p">)</span></div>


<div class="viewcode-block" id="SSD"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SSD">[docs]</a><span class="k">class</span> <span class="nc">SSD</span><span class="p">(</span><span class="n">SgModule</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    paper: https://arxiv.org/pdf/1512.02325.pdf</span>
<span class="sd">    based on code: https://github.com/NVIDIA/DeepLearningExamples</span>
<span class="sd">    &quot;&quot;&quot;</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">backbone</span><span class="p">,</span> <span class="n">arch_params</span><span class="p">):</span>
        <span class="nb">super</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">arch_params</span> <span class="o">=</span> <span class="n">HpmStruct</span><span class="p">(</span><span class="o">**</span><span class="n">DEFAULT_SSD_ARCH_PARAMS</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">override</span><span class="p">(</span><span class="o">**</span><span class="n">arch_params</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span>

        <span class="n">paths</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">get_param</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="p">,</span> <span class="s1">&#39;output_paths&#39;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">backbone</span> <span class="o">=</span> <span class="n">MultiOutputModule</span><span class="p">(</span><span class="n">backbone</span><span class="p">,</span> <span class="n">paths</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">backbone</span> <span class="o">=</span> <span class="n">backbone</span>

        <span class="n">lite</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">get_param</span><span class="p">(</span><span class="n">arch_params</span><span class="p">,</span> <span class="s1">&#39;lite&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
        <span class="c1"># NUMBER OF CLASSES + 1 NO_CLASS</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">num_classes</span>

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

        <span class="bp">self</span><span class="o">.</span><span class="n">_build_location_and_conf_branches</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">lite</span><span class="p">)</span>

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

    <span class="k">def</span> <span class="nf">_build_location_and_conf_branches</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">lite</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Add the sdd blocks after the backbone&quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_defaults</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">num_defaults</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conf</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">conv_to_use</span> <span class="o">=</span> <span class="n">SeperableConv2d</span> <span class="k">if</span> <span class="n">lite</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">nd</span><span class="p">,</span> <span class="n">oc</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_defaults</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)):</span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_defaults</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">conv_to_use</span><span class="p">(</span><span class="n">oc</span><span class="p">,</span> <span class="n">nd</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">conv_to_use</span><span class="p">(</span><span class="n">oc</span><span class="p">,</span> <span class="n">nd</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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="k">else</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">append</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">oc</span><span class="p">,</span> <span class="n">nd</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">append</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">oc</span><span class="p">,</span> <span class="n">nd</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conf</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_build_additional_blocks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">input_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">out_channels</span>
        <span class="n">kernel_sizes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">kernel_sizes</span>
        <span class="n">bottleneck_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">additional_blocks_bottleneck_channels</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
                <span class="nb">zip</span><span class="p">(</span><span class="n">input_size</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">input_size</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">bottleneck_channels</span><span class="p">,</span> <span class="n">kernel_sizes</span><span class="p">)):</span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
                <span class="n">middle_layer</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="n">channels</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</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">stride</span><span class="o">=</span><span class="mi">2</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="k">else</span><span class="p">:</span>
                <span class="n">middle_layer</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="n">channels</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</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">layer</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">Conv2d</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">channels</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="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">channels</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">middle_layer</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">output_size</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="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_init_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
                <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                    <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>

<div class="viewcode-block" id="SSD.bbox_view"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SSD.bbox_view">[docs]</a>    <span class="k">def</span> <span class="nf">bbox_view</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">conf</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Shape the classifier to the view of bboxes &quot;&quot;&quot;</span>
        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">conf</span><span class="p">):</span>
            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">l</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">s</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="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">c</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">s</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="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span>

        <span class="n">locs</span><span class="p">,</span> <span class="n">confs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">ret</span><span class="p">))</span>
        <span class="n">locs</span><span class="p">,</span> <span class="n">confs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">locs</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">confs</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">locs</span><span class="p">,</span> <span class="n">confs</span></div>

<div class="viewcode-block" id="SSD.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SSD.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">backbone</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="c1"># IF THE BACKBONE IS A MultiOutputModule WE GET A LIST, OTHERWISE WE WRAP IT IN A LIST</span>
        <span class="n">detection_feed</span> <span class="o">=</span> <span class="n">x</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="k">else</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">detection_feed</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>

        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="p">:</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
            <span class="n">detection_feed</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="c1"># FEATURE MAPS: i.e. FOR 300X300 INPUT - 38X38X4, 19X19X6, 10X10X6, 5X5X6, 3X3X4, 1X1X4</span>
        <span class="n">locs</span><span class="p">,</span> <span class="n">confs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox_view</span><span class="p">(</span><span class="n">detection_feed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">)</span>

        <span class="c1"># FOR 300X300 INPUT - RETURN N_BATCH X 8732 X {N_LABELS, N_LOCS} RESULTS</span>
        <span class="k">return</span> <span class="n">locs</span><span class="p">,</span> <span class="n">confs</span></div></div>


<div class="viewcode-block" id="SSDMobileNetV1"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SSDMobileNetV1">[docs]</a><span class="k">class</span> <span class="nc">SSDMobileNetV1</span><span class="p">(</span><span class="n">SSD</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    paper: http://ceur-ws.org/Vol-2500/paper_5.pdf</span>
<span class="sd">    &quot;&quot;&quot;</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">arch_params</span><span class="p">:</span> <span class="n">HpmStruct</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span> <span class="o">=</span> <span class="n">HpmStruct</span><span class="p">(</span><span class="o">**</span><span class="n">DEFAULT_SSD_MOBILENET_V1_ARCH_PARAMS</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">override</span><span class="p">(</span><span class="o">**</span><span class="n">arch_params</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span>
        <span class="n">mobilenet_backbone</span> <span class="o">=</span> <span class="n">MobileNet</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backbone_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">up_to_layer</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">backbone</span><span class="o">=</span><span class="n">mobilenet_backbone</span><span class="p">,</span> <span class="n">arch_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="p">)</span></div>


<div class="viewcode-block" id="SSDLiteMobileNetV2"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.ssd.SSDLiteMobileNetV2">[docs]</a><span class="k">class</span> <span class="nc">SSDLiteMobileNetV2</span><span class="p">(</span><span class="n">SSD</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">arch_params</span><span class="p">:</span> <span class="n">HpmStruct</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span> <span class="o">=</span> <span class="n">HpmStruct</span><span class="p">(</span><span class="o">**</span><span class="n">DEFAULT_SSD_LITE_MOBILENET_V2_ARCH_PARAMS</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">override</span><span class="p">(</span><span class="o">**</span><span class="n">arch_params</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">out_channels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">out_channels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</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>
        <span class="n">mobilenetv2</span> <span class="o">=</span> <span class="n">MobileNetV2</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backbone_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="bp">self</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>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">backbone</span><span class="o">=</span><span class="n">mobilenetv2</span><span class="o">.</span><span class="n">features</span><span class="p">,</span> <span class="n">arch_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="p">)</span>

    <span class="c1"># OVERRIDE THE DEFAULT FUNCTION FROM SSD. ADD THE SDD BLOCKS AFTER THE BACKBONE.</span>
    <span class="k">def</span> <span class="nf">_build_additional_blocks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">out_channels</span>
        <span class="n">expand_ratios</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_params</span><span class="o">.</span><span class="n">expand_ratios</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">expand_ratio</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">channels</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">expand_ratios</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                <span class="n">InvertedResidual</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</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="n">expand_ratio</span><span class="o">=</span><span class="n">expand_ratio</span><span class="p">))</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">additional_blocks</span><span class="p">)</span></div>
</pre></div>

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