Deci-AI
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super-gradients
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  <h1>Source code for super_gradients.training.models.darknet53</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Darknet</span>

<span class="sd">credits: https://github.com/ultralytics</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</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>


<div class="viewcode-block" id="create_conv_module"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.create_conv_module">[docs]</a><span class="k">def</span> <span class="nf">create_conv_module</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">3</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="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="n">nn_sequential_module</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_sequential_module</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;Conv2d&#39;</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="p">,</span> <span class="n">out_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">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="kc">False</span><span class="p">))</span>
    <span class="n">nn_sequential_module</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;BatchNorm2d&#39;</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">out_channels</span><span class="p">))</span>
    <span class="n">nn_sequential_module</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;LeakyRelu&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">())</span>

    <span class="k">return</span> <span class="n">nn_sequential_module</span></div>


<span class="c1"># Residual block</span>
<div class="viewcode-block" id="DarkResidualBlock"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.DarkResidualBlock">[docs]</a><span class="k">class</span> <span class="nc">DarkResidualBlock</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">    DarkResidualBlock - The Darknet Residual Block</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">in_channels</span><span class="p">,</span> <span class="n">shortcut</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">DarkResidualBlock</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">shortcut</span> <span class="o">=</span> <span class="n">shortcut</span>
        <span class="n">reduced_channels</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">in_channels</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">layer1</span> <span class="o">=</span> <span class="n">create_conv_module</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">reduced_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="bp">self</span><span class="o">.</span><span class="n">layer2</span> <span class="o">=</span> <span class="n">create_conv_module</span><span class="p">(</span><span class="n">reduced_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">)</span>

<div class="viewcode-block" id="DarkResidualBlock.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.DarkResidualBlock.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">residual</span> <span class="o">=</span> <span class="n">x</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">+=</span> <span class="n">residual</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shortcut</span> <span class="k">else</span> <span class="n">out</span>
        <span class="k">return</span> <span class="n">out</span></div></div>


<div class="viewcode-block" id="Darknet53Base"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.Darknet53Base">[docs]</a><span class="k">class</span> <span class="nc">Darknet53Base</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="nb">super</span><span class="p">(</span><span class="n">Darknet53Base</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"># THE MODULES LIST IS APPROACHABLE FROM &quot;OUTSIDE THE CLASS - SO WE CAN CHANGE IT&#39;S STRUCTURE&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</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">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>  <span class="c1"># 0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</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="c1"># 1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">DarkResidualBlock</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">num_blocks</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>  <span class="c1"># 2</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</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="c1"># 3</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">DarkResidualBlock</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">num_blocks</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>  <span class="c1"># 4</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</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="c1"># 5</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">DarkResidualBlock</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">num_blocks</span><span class="o">=</span><span class="mi">8</span><span class="p">))</span>  <span class="c1"># 6</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</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="c1"># 7</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">DarkResidualBlock</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">num_blocks</span><span class="o">=</span><span class="mi">8</span><span class="p">))</span>  <span class="c1"># 8</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_conv_module</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="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>  <span class="c1"># 9</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">DarkResidualBlock</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span> <span class="n">num_blocks</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>  <span class="c1"># 10</span>

<div class="viewcode-block" id="Darknet53Base.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.Darknet53Base.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">out</span> <span class="o">=</span> <span class="n">x</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="p">):</span>
            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">out</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">out</span></div>

    <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">num_blocks</span><span class="p">):</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</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="mi">0</span><span class="p">,</span> <span class="n">num_blocks</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">in_channels</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="o">*</span><span class="n">layers</span><span class="p">)</span></div>


<div class="viewcode-block" id="Darknet53"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.Darknet53">[docs]</a><span class="k">class</span> <span class="nc">Darknet53</span><span class="p">(</span><span class="n">Darknet53Base</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="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">num_classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Darknet53</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"># IN ORDER TO ALLOW PASSING PARAMETERS WITH ARCH_PARAMS BUT NOT BREAK YOLOV3 INTEGRATION</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">backbone_mode</span> <span class="o">=</span> <span class="n">arch_params</span><span class="o">.</span><span class="n">backbone_mode</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;backbone_mode&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">backbone_mode</span>
        <span class="bp">self</span><span class="o">.</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="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;num_classes&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">num_classes</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">backbone_mode</span><span class="p">:</span>
            <span class="c1"># IF NOT USED AS A BACKEND BUT AS A CLASSIFIER WE ADD THE CLASSIFICATION LAYERS</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">nn_sequential_block</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_sequential_block</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;global_avg_pool&#39;</span><span class="p">,</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">nn_sequential_block</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;view&#39;</span><span class="p">,</span> <span class="n">ViewModule</span><span class="p">(</span><span class="mi">1024</span><span class="p">))</span>
                <span class="n">nn_sequential_block</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;fc&#39;</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="mi">1024</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="bp">self</span><span class="o">.</span><span class="n">modules_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn_sequential_block</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;num_classes must be specified to use Darknet53 as a classifier&#39;</span><span class="p">)</span>

<div class="viewcode-block" id="Darknet53.get_modules_list"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.Darknet53.get_modules_list">[docs]</a>    <span class="k">def</span> <span class="nf">get_modules_list</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules_list</span></div>

<div class="viewcode-block" id="Darknet53.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.Darknet53.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="sd">&quot;&quot;&quot;</span>
<span class="sd">        forward - Forward pass on the modules list</span>
<span class="sd">            :param x: The input data</span>
<span class="sd">            :return: forward pass for backbone pass or classification pass</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>


<span class="c1"># Residual block</span>
<div class="viewcode-block" id="ViewModule"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.ViewModule">[docs]</a><span class="k">class</span> <span class="nc">ViewModule</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">    Returns a reshaped version of the input, to be used in None-Backbone Mode</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">features</span><span class="o">=</span><span class="mi">1024</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ViewModule</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">features</span> <span class="o">=</span> <span class="n">features</span>

<div class="viewcode-block" id="ViewModule.forward"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.darknet53.ViewModule.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="n">view</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">features</span><span class="p">)</span></div></div>
</pre></div>

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