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    69. 

  69.   <h1>Source code for super_gradients.training.losses.focal_loss</h1><div class="highlight"><pre>
    70. 

  70. <span></span><span class="kn">import</span> <span class="nn">torch</span>
    71. 

  71. <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
    72. 

  72. <span class="kn">from</span> <span class="nn">torch.nn.modules.loss</span> <span class="kn">import</span> <span class="n">_Loss</span>
    73. 

  73. 

  74. 

  75. <div class="viewcode-block" id="FocalLoss"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.focal_loss.FocalLoss">[docs]</a><span class="k">class</span> <span class="nc">FocalLoss</span><span class="p">(</span><span class="n">_Loss</span><span class="p">):</span>
    76. 

  76.     <span class="sd">&quot;&quot;&quot;Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)&quot;&quot;&quot;</span>
    77. 

  77. 

  78.     <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">loss_fcn</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">):</span>
    79. 

  79.         <span class="nb">super</span><span class="p">(</span><span class="n">FocalLoss</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>
    80. 

  80.         <span class="bp">self</span><span class="o">.</span><span class="n">loss_fcn</span> <span class="o">=</span> <span class="n">loss_fcn</span>  <span class="c1"># must be nn.BCEWithLogitsLoss()</span>
    81. 

  81.         <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
    82. 

  82.         <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
    83. 

  83.         <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">loss_fcn</span><span class="o">.</span><span class="n">reduction</span>
    84. 

  84.         <span class="bp">self</span><span class="o">.</span><span class="n">loss_fcn</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="s1">&#39;none&#39;</span>  <span class="c1"># required to apply FocalLoss to each element</span>
    85. 

  85. 

  86. <div class="viewcode-block" id="FocalLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.focal_loss.FocalLoss.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">pred</span><span class="p">,</span> <span class="n">true</span><span class="p">):</span>
    87. 

  87.         <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_fcn</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">true</span><span class="p">)</span>
    88. 

  88. 

  89.         <span class="n">pred_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span>  <span class="c1"># prob from logits</span>
    90. 

  90.         <span class="n">p_t</span> <span class="o">=</span> <span class="n">true</span> <span class="o">*</span> <span class="n">pred_prob</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">true</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">pred_prob</span><span class="p">)</span>
    91. 

  91.         <span class="n">alpha_factor</span> <span class="o">=</span> <span class="n">true</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="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">true</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
    92. 

  92.         <span class="n">modulating_factor</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">p_t</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span>
    93. 

  93.         <span class="n">loss</span> <span class="o">*=</span> <span class="n">alpha_factor</span> <span class="o">*</span> <span class="n">modulating_factor</span>
    94. 

  94. 

  95.         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
    96. 

  96.             <span class="k">return</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
    97. 

  97.         <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s1">&#39;sum&#39;</span><span class="p">:</span>
    98. 

  98.             <span class="k">return</span> <span class="n">loss</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    99. 

  99.         <span class="k">else</span><span class="p">:</span>  <span class="c1"># &#39;none&#39;</span>
    100. 

  100.             <span class="k">return</span> <span class="n">loss</span></div></div>
    101. 

  101. </pre></div>
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