#561 Feature/sg 193 extend output formator

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  <h1>Source code for super_gradients.training.losses.kd_losses</h1><div class="highlight"><pre>
<span></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="p">,</span> <span class="n">KLDivLoss</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">KDklDivLoss</span><span class="p">(</span><span class="n">KLDivLoss</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; KL divergence wrapper for knowledge distillation&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="nb">super</span><span class="p">(</span><span class="n">KDklDivLoss</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="n">reduction</span><span class="o">=</span><span class="s1">&#39;batchmean&#39;</span><span class="p">)</span>
    <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">student_output</span><span class="p">,</span> <span class="n">teacher_output</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">KDklDivLoss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">student_output</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">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">teacher_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<div class="viewcode-block" id="KDLogitsLoss"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.KDLogitsLoss">[docs]</a><span class="k">class</span> <span class="nc">KDLogitsLoss</span><span class="p">(</span><span class="n">_Loss</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Knowledge distillation loss, wraps the task loss and distillation loss &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">task_loss_fn</span><span class="p">:</span> <span class="n">_Loss</span><span class="p">,</span> <span class="n">distillation_loss_fn</span><span class="p">:</span> <span class="n">_Loss</span> <span class="o">=</span> <span class="n">KDklDivLoss</span><span class="p">(),</span> <span class="n">distillation_loss_coeff</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">):</span>
        <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">        :param task_loss_fn: task loss. E.g., LabelSmoothingCrossEntropyLoss</span>
<span class="sd">        :param distillation_loss_fn: distillation loss. E.g., KLDivLoss</span>
<span class="sd">        :param distillation_loss_coeff:</span>
<span class="sd">        &#39;&#39;&#39;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">KDLogitsLoss</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">task_loss_fn</span> <span class="o">=</span> <span class="n">task_loss_fn</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">distillation_loss_fn</span> <span class="o">=</span> <span class="n">distillation_loss_fn</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">distillation_loss_coeff</span> <span class="o">=</span> <span class="n">distillation_loss_coeff</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">component_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Component names for logging during training.</span>
<span class="sd">        These correspond to 2nd item in the tuple returned in self.forward(...).</span>
<span class="sd">        See super_gradients.Trainer.train() docs for more info.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="s2">&quot;Task Loss&quot;</span><span class="p">,</span> <span class="s2">&quot;Distillation Loss&quot;</span><span class="p">]</span>
<div class="viewcode-block" id="KDLogitsLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.KDLogitsLoss.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">kd_module_output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
        <span class="n">task_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">task_loss_fn</span><span class="p">(</span><span class="n">kd_module_output</span><span class="o">.</span><span class="n">student_output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">task_loss</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>  <span class="c1"># SOME LOSS FUNCTIONS RETURNS LOSS AND LOG_ITEMS</span>
            <span class="n">task_loss</span> <span class="o">=</span> <span class="n">task_loss</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">distillation_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">distillation_loss_fn</span><span class="p">(</span><span class="n">kd_module_output</span><span class="o">.</span><span class="n">student_output</span><span class="p">,</span> <span class="n">kd_module_output</span><span class="o">.</span><span class="n">teacher_output</span><span class="p">)</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">task_loss</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">distillation_loss_coeff</span><span class="p">)</span> <span class="o">+</span> <span class="n">distillation_loss</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">distillation_loss_coeff</span>
        <span class="k">return</span> <span class="n">loss</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">loss</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">task_loss</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">distillation_loss</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>
</pre></div>
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Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#561 Feature/sg 193 extend output formator

Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git
