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  <h1>Source code for super_gradients.training.utils.ssd_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>

<span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">non_max_suppression</span><span class="p">,</span> <span class="n">NMS_Type</span><span class="p">,</span> \
    <span class="n">matrix_non_max_suppression</span><span class="p">,</span> <span class="n">DetectionPostPredictionCallback</span>


<div class="viewcode-block" id="DefaultBoxes"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes">[docs]</a><span class="k">class</span> <span class="nc">DefaultBoxes</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Default Boxes, (aka: anchor boxes or priors boxes) used by SSD model</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">fig_size</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">scales</span><span class="p">,</span> <span class="n">aspect_ratios</span><span class="p">,</span> <span class="n">scale_xy</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">scale_wh</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">feat_size</span> <span class="o">=</span> <span class="n">feat_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fig_size</span> <span class="o">=</span> <span class="n">fig_size</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy_</span> <span class="o">=</span> <span class="n">scale_xy</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh_</span> <span class="o">=</span> <span class="n">scale_wh</span>

        <span class="c1"># According to https://github.com/weiliu89/caffe</span>
        <span class="c1"># Calculation method slightly different from paper</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">steps</span> <span class="o">=</span> <span class="n">steps</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scales</span> <span class="o">=</span> <span class="n">scales</span>

        <span class="n">fk</span> <span class="o">=</span> <span class="n">fig_size</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">aspect_ratios</span> <span class="o">=</span> <span class="n">aspect_ratios</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="c1"># size of feature and number of feature</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">sfeat</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">feat_size</span><span class="p">):</span>

            <span class="n">sk1</span> <span class="o">=</span> <span class="n">scales</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">/</span> <span class="n">fig_size</span>
            <span class="n">sk2</span> <span class="o">=</span> <span class="n">scales</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">fig_size</span>
            <span class="n">sk3</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">sk1</span> <span class="o">*</span> <span class="n">sk2</span><span class="p">)</span>
            <span class="n">all_sizes</span> <span class="o">=</span> <span class="p">[(</span><span class="n">sk1</span><span class="p">,</span> <span class="n">sk1</span><span class="p">),</span> <span class="p">(</span><span class="n">sk3</span><span class="p">,</span> <span class="n">sk3</span><span class="p">)]</span>

            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">aspect_ratios</span><span class="p">[</span><span class="n">idx</span><span class="p">]:</span>
                <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">sk1</span> <span class="o">*</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">alpha</span><span class="p">),</span> <span class="n">sk1</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span>
                <span class="n">all_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
                <span class="n">all_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
            <span class="k">for</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">all_sizes</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">sfeat</span><span class="p">),</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
                    <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">fk</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">fk</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="c1"># For IoU calculation</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</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">dboxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</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">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</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">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">scale_xy</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">scale_xy_</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">scale_wh</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">scale_wh_</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s2">&quot;xyxy&quot;</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">order</span> <span class="o">==</span> <span class="s2">&quot;xyxy&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span>
        <span class="k">if</span> <span class="n">order</span> <span class="o">==</span> <span class="s2">&quot;xywh&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span>

<div class="viewcode-block" id="DefaultBoxes.dboxes300_coco"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes.dboxes300_coco">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">dboxes300_coco</span><span class="p">():</span>
        <span class="n">figsize</span> <span class="o">=</span> <span class="mi">300</span>
        <span class="n">feat_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">38</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">10</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">1</span><span class="p">]</span>
        <span class="n">steps</span> <span class="o">=</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</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="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">]</span>
        <span class="c1"># use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py</span>
        <span class="n">scales</span> <span class="o">=</span> <span class="p">[</span><span class="mi">21</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="mi">153</span><span class="p">,</span> <span class="mi">207</span><span class="p">,</span> <span class="mi">261</span><span class="p">,</span> <span class="mi">315</span><span class="p">]</span>
        <span class="n">aspect_ratios</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">]]</span>
        <span class="k">return</span> <span class="n">DefaultBoxes</span><span class="p">(</span><span class="n">figsize</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">scales</span><span class="p">,</span> <span class="n">aspect_ratios</span><span class="p">)</span></div>

<div class="viewcode-block" id="DefaultBoxes.dboxes300_coco_from19"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes.dboxes300_coco_from19">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">dboxes300_coco_from19</span><span class="p">():</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This dbox configuration is a bit different from the original dboxes300_coco</span>
<span class="sd">        It is suitable for a network taking the first skip connection from a 19x19 layer (instead of 38x38 in the</span>
<span class="sd">        original paper).</span>
<span class="sd">        This offers less coverage for small objects but more aspect ratios options to larger objects (the original</span>
<span class="sd">        paper supports object starting from size 21 pixels, while this config support objects starting from 60 pixels)</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="c1"># https://github.com/qfgaohao/pytorch-ssd/blob/f61ab424d09bf3d4bb3925693579ac0a92541b0d/vision/ssd/config/mobilenetv1_ssd_config.py</span>
        <span class="n">figsize</span> <span class="o">=</span> <span class="mi">300</span>
        <span class="n">feat_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">19</span><span class="p">,</span> <span class="mi">10</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
        <span class="n">steps</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</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="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">]</span>
        <span class="n">scales</span> <span class="o">=</span> <span class="p">[</span><span class="mi">60</span><span class="p">,</span> <span class="mi">105</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">195</span><span class="p">,</span> <span class="mi">240</span><span class="p">,</span> <span class="mi">285</span><span class="p">,</span> <span class="mi">330</span><span class="p">]</span>
        <span class="n">aspect_ratios</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
        <span class="k">return</span> <span class="n">DefaultBoxes</span><span class="p">(</span><span class="n">figsize</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">scales</span><span class="p">,</span> <span class="n">aspect_ratios</span><span class="p">)</span></div>

<div class="viewcode-block" id="DefaultBoxes.dboxes256_coco"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes.dboxes256_coco">[docs]</a>    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">dboxes256_coco</span><span class="p">():</span>
        <span class="n">figsize</span> <span class="o">=</span> <span class="mi">256</span>
        <span class="n">feat_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
        <span class="n">steps</span> <span class="o">=</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</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="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]</span>
        <span class="c1"># use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py</span>
        <span class="n">scales</span> <span class="o">=</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">38</span><span class="p">,</span> <span class="mi">84</span><span class="p">,</span> <span class="mi">131</span><span class="p">,</span> <span class="mi">1177</span><span class="p">,</span> <span class="mi">223</span><span class="p">,</span> <span class="mi">269</span><span class="p">]</span>
        <span class="n">aspect_ratios</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">]]</span>
        <span class="k">return</span> <span class="n">DefaultBoxes</span><span class="p">(</span><span class="n">figsize</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">scales</span><span class="p">,</span> <span class="n">aspect_ratios</span><span class="p">)</span></div></div>


<div class="viewcode-block" id="SSDPostPredictCallback"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.SSDPostPredictCallback">[docs]</a><span class="k">class</span> <span class="nc">SSDPostPredictCallback</span><span class="p">(</span><span class="n">DetectionPostPredictionCallback</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    post prediction callback module to convert and filter predictions coming from the SSD net to a format</span>
<span class="sd">    used by all other detection models</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">conf</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">iou</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.45</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_predictions</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">300</span><span class="p">,</span>
                 <span class="n">nms_type</span><span class="p">:</span> <span class="n">NMS_Type</span> <span class="o">=</span> <span class="n">NMS_Type</span><span class="o">.</span><span class="n">ITERATIVE</span><span class="p">,</span>
                 <span class="n">dboxes</span><span class="p">:</span> <span class="n">DefaultBoxes</span> <span class="o">=</span> <span class="n">DefaultBoxes</span><span class="o">.</span><span class="n">dboxes300_coco</span><span class="p">(),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        :param conf: confidence threshold</span>
<span class="sd">        :param iou: IoU threshold</span>
<span class="sd">        :param classes: (optional list) filter by class</span>
<span class="sd">        :param nms_type: the type of nms to use (iterative or matrix)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">SSDPostPredictCallback</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">conf</span> <span class="o">=</span> <span class="n">conf</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iou</span> <span class="o">=</span> <span class="n">iou</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nms_type</span> <span class="o">=</span> <span class="n">nms_type</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="n">classes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span> <span class="o">=</span> <span class="n">max_predictions</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xywh</span> <span class="o">=</span> <span class="n">dboxes</span><span class="p">(</span><span class="s1">&#39;xywh&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy</span> <span class="o">=</span> <span class="n">dboxes</span><span class="o">.</span><span class="n">scale_xy</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh</span> <span class="o">=</span> <span class="n">dboxes</span><span class="o">.</span><span class="n">scale_wh</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">img_size</span> <span class="o">=</span> <span class="n">dboxes</span><span class="o">.</span><span class="n">fig_size</span>

<div class="viewcode-block" id="SSDPostPredictCallback.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.SSDPostPredictCallback.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">device</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">bboxes_in</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">scores_in</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

        <span class="n">bboxes_in</span> <span class="o">=</span> <span class="n">bboxes_in</span><span class="o">.</span><span class="n">permute</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="mi">1</span><span class="p">)</span>
        <span class="n">scores_in</span> <span class="o">=</span> <span class="n">scores_in</span><span class="o">.</span><span class="n">permute</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="mi">1</span><span class="p">)</span>

        <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy</span>
        <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh</span>

        <span class="c1"># CONVERT RELATIVE LOCATIONS INTO ABSOLUTE LOCATION (OUTPUT LOCATIONS ARE RELATIVE TO THE DBOXES)</span>
        <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xywh</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xywh</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
        <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">:]</span> <span class="o">=</span> <span class="n">bboxes_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xywh</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span>

        <span class="n">scores_in</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">scores_in</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># TODO softmax without first item?</span>

        <span class="c1"># REPLACE THE CONFIDENCE OF CLASS NONE WITH OBJECT CONFIDENCE</span>
        <span class="c1"># SSD DOES NOT OUTPUT OBJECT CONFIDENCE, REQUIRED FOR THE NMS</span>
        <span class="n">scores_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">scores_in</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">bboxes_in</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">img_size</span>

        <span class="n">nms_input</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">bboxes_in</span><span class="p">,</span> <span class="n">scores_in</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nms_type</span> <span class="o">==</span> <span class="n">NMS_Type</span><span class="o">.</span><span class="n">ITERATIVE</span><span class="p">:</span>
            <span class="n">nms_res</span> <span class="o">=</span> <span class="n">non_max_suppression</span><span class="p">(</span><span class="n">nms_input</span><span class="p">,</span> <span class="n">conf_thres</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="n">iou_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">iou</span><span class="p">,</span>
                                          <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">nms_res</span> <span class="o">=</span> <span class="n">matrix_non_max_suppression</span><span class="p">(</span><span class="n">nms_input</span><span class="p">,</span> <span class="n">conf_thres</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="n">max_num_of_detections</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span><span class="p">)</span>

        <span class="c1"># NMS OUTPUT A 0-BASED CLASS LABEL, BUT SSD WORKS WITH 1-BASED CLASS LABEL</span>
        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">nms_res</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">t</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">t</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>

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

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