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  <h1>Source code for super_gradients.training.losses.ssd_loss</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</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">torch.nn.modules.loss</span> <span class="kn">import</span> <span class="n">_Loss</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">calculate_bbox_iou_matrix</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.ssd_utils</span> <span class="kn">import</span> <span class="n">DefaultBoxes</span>
<div class="viewcode-block" id="SSDLoss"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.ssd_loss.SSDLoss">[docs]</a><span class="k">class</span> <span class="nc">SSDLoss</span><span class="p">(</span><span class="n">_Loss</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Implements the loss as the sum of the followings:</span>
<span class="sd">        1. Confidence Loss: All labels, with hard negative mining</span>
<span class="sd">        2. Localization Loss: Only on positive labels</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">dboxes</span><span class="p">:</span> <span class="n">DefaultBoxes</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">SSDLoss</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">scale_xy</span> <span class="o">=</span> <span class="mf">1.0</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="mf">1.0</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">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sl1_loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">SmoothL1Loss</span><span class="p">(</span><span class="n">reduce</span><span class="o">=</span><span class="kc">False</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">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">dboxes</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="s2">&quot;xywh&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">con_loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">(</span><span class="n">reduce</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="k">def</span> <span class="nf">_norm_relative_bbox</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        convert bbox locations into relative locations (relative to the dboxes) and normalized by w,h</span>
<span class="sd">        :param loc a tensor of shape [batch, 4, num_boxes]</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">gxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy</span> <span class="o">*</span> <span class="p">(</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">,</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="p">:</span><span class="mi">2</span><span class="p">,</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">2</span><span class="p">:,</span> <span class="p">]</span>
        <span class="n">gwh</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh</span> <span class="o">*</span> <span class="p">(</span><span class="n">loc</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:,</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">2</span><span class="p">:,</span> <span class="p">:])</span><span class="o">.</span><span class="n">log</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">gxy</span><span class="p">,</span> <span class="n">gwh</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="o">.</span><span class="n">contiguous</span><span class="p">()</span>
<div class="viewcode-block" id="SSDLoss.match_dboxes"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.ssd_loss.SSDLoss.match_dboxes">[docs]</a>    <span class="k">def</span> <span class="nf">match_dboxes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        convert ground truth boxes into a tensor with the same size as dboxes. each gt bbox is matched to every</span>
<span class="sd">        destination box which overlaps it over 0.5 (IoU). so some gt bboxes can be duplicated to a few destination boxes</span>
<span class="sd">        :param targets: a tensor containing the boxes for a single image. shape [num_boxes, 5] (x,y,w,h,label)</span>
<span class="sd">        :return: two tensors</span>
<span class="sd">            boxes - shape of dboxes [4, num_dboxes] (x,y,w,h)</span>
<span class="sd">            labels - sahpe [num_dboxes]</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">target_locations</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">data</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
        <span class="n">target_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</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">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">to</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">device</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">boxes</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span>
            <span class="n">ious</span> <span class="o">=</span> <span class="n">calculate_bbox_iou_matrix</span><span class="p">(</span><span class="n">boxes</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">data</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">x1y1x2y2</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
            <span class="n">values</span><span class="p">,</span> <span class="n">indices</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">ious</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">mask</span> <span class="o">=</span> <span class="n">values</span> <span class="o">&gt;</span> <span class="mf">0.5</span>
            <span class="n">target_locations</span><span class="p">[:,</span> <span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span> <span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span>
            <span class="n">target_labels</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span> <span class="mi">1</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">target_locations</span><span class="p">,</span> <span class="n">target_labels</span></div>
<div class="viewcode-block" id="SSDLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.ssd_loss.SSDLoss.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">predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Compute the loss</span>
<span class="sd">            :param predictions - predictions tensor coming from the network. shape [N, num_classes+4, num_dboxes]</span>
<span class="sd">            were the first four items are (x,y,w,h) and the rest are class confidence</span>
<span class="sd">            :param targets - targets for the batch. [num targets, 6] (index in batch, label, x,y,w,h)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">batch_target_locations</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">batch_target_labels</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="p">(</span><span class="n">ploc</span><span class="p">,</span> <span class="n">plabel</span><span class="p">)</span> <span class="o">=</span> <span class="n">predictions</span>
        <span class="n">targets</span> <span class="o">=</span> <span class="n">targets</span><span class="o">.</span><span class="n">to</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">device</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="n">ploc</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
            <span class="n">target_locations</span><span class="p">,</span> <span class="n">target_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">match_dboxes</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">targets</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">])</span>
            <span class="n">batch_target_locations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target_locations</span><span class="p">)</span>
            <span class="n">batch_target_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target_labels</span><span class="p">)</span>
        <span class="n">batch_target_locations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batch_target_locations</span><span class="p">)</span>
        <span class="n">batch_target_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batch_target_labels</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
        <span class="n">mask</span> <span class="o">=</span> <span class="n">batch_target_labels</span> <span class="o">&gt;</span> <span class="mi">0</span>
        <span class="n">pos_num</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">sum</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="n">vec_gd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm_relative_bbox</span><span class="p">(</span><span class="n">batch_target_locations</span><span class="p">)</span>
        <span class="c1"># SUM ON FOUR COORDINATES, AND MASK</span>
        <span class="n">sl1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sl1_loss</span><span class="p">(</span><span class="n">ploc</span><span class="p">,</span> <span class="n">vec_gd</span><span class="p">)</span><span class="o">.</span><span class="n">sum</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="n">sl1</span> <span class="o">=</span> <span class="p">(</span><span class="n">mask</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">*</span> <span class="n">sl1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</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"># HARD NEGATIVE MINING</span>
        <span class="n">con</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">con_loss</span><span class="p">(</span><span class="n">plabel</span><span class="p">,</span> <span class="n">batch_target_labels</span><span class="p">)</span>
        <span class="c1"># POSITIVE MASK WILL NEVER SELECTED</span>
        <span class="n">con_neg</span> <span class="o">=</span> <span class="n">con</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
        <span class="n">con_neg</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">con_idx</span> <span class="o">=</span> <span class="n">con_neg</span><span class="o">.</span><span class="n">sort</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="n">descending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">con_rank</span> <span class="o">=</span> <span class="n">con_idx</span><span class="o">.</span><span class="n">sort</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"># NUMBER OF NEGATIVE THREE TIMES POSITIVE</span>
        <span class="n">neg_num</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">pos_num</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">mask</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">neg_mask</span> <span class="o">=</span> <span class="n">con_rank</span> <span class="o">&lt;</span> <span class="n">neg_num</span>
        <span class="n">closs</span> <span class="o">=</span> <span class="p">(</span><span class="n">con</span> <span class="o">*</span> <span class="p">(</span><span class="n">mask</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">+</span> <span class="n">neg_mask</span><span class="o">.</span><span class="n">float</span><span class="p">()))</span><span class="o">.</span><span class="n">sum</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"># AVOID NO OBJECT DETECTED</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sl1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">closs</span>
        <span class="n">num_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">pos_num</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">pos_num</span> <span class="o">=</span> <span class="n">pos_num</span><span class="o">.</span><span class="n">float</span><span class="p">()</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="mf">1e-6</span><span class="p">)</span>
        <span class="n">ret</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_loss</span> <span class="o">*</span> <span class="n">num_mask</span> <span class="o">/</span> <span class="n">pos_num</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">ret</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">sl1</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">closs</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">ret</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span></div></div>
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