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<h1>Source code for torchreid.losses.cross_entropy_loss</h1><div class="highlight"><pre>
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<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
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<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
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<span class="kn">import</span> <span class="nn">torch</span>
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<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
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<div class="viewcode-block" id="CrossEntropyLoss"><a class="viewcode-back" href="../../../pkg/losses.html#torchreid.losses.cross_entropy_loss.CrossEntropyLoss">[docs]</a><span class="k">class</span> <span class="nc">CrossEntropyLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""Cross entropy loss with label smoothing regularizer.</span>
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<span class="sd"> </span>
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<span class="sd"> Reference:</span>
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<span class="sd"> Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.</span>
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<span class="sd"> With label smoothing, the label :math:`y` for a class is computed by</span>
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<span class="sd"> </span>
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<span class="sd"> .. math::</span>
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<span class="sd"> \begin{equation}</span>
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<span class="sd"> (1 - \epsilon) \times y + \frac{\epsilon}{K},</span>
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<span class="sd"> \end{equation}</span>
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<span class="sd"> where :math:`K` denotes the number of classes and :math:`\epsilon` is a weight. When</span>
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<span class="sd"> :math:`\epsilon = 0`, the loss function reduces to the normal cross entropy.</span>
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<span class="sd"> </span>
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<span class="sd"> Args:</span>
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<span class="sd"> num_classes (int): number of classes.</span>
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<span class="sd"> epsilon (float, optional): weight. Default is 0.1.</span>
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<span class="sd"> use_gpu (bool, optional): whether to use gpu devices. Default is True.</span>
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<span class="sd"> label_smooth (bool, optional): whether to apply label smoothing. Default is True.</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">use_gpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label_smooth</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">CrossEntropyLoss</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>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> <span class="k">if</span> <span class="n">label_smooth</span> <span class="k">else</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span> <span class="o">=</span> <span class="n">use_gpu</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">logsoftmax</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class="viewcode-block" id="CrossEntropyLoss.forward"><a class="viewcode-back" href="../../../pkg/losses.html#torchreid.losses.cross_entropy_loss.CrossEntropyLoss.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">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Args:</span>
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<span class="sd"> inputs (torch.Tensor): prediction matrix (before softmax) with</span>
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<span class="sd"> shape (batch_size, num_classes).</span>
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<span class="sd"> targets (torch.LongTensor): ground truth labels with shape (batch_size).</span>
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<span class="sd"> Each position contains the label index.</span>
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<span class="sd"> """</span>
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<span class="n">log_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">logsoftmax</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
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<span class="n">targets</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="n">log_probs</span><span class="o">.</span><span class="n">size</span><span class="p">())</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">targets</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="mi">1</span><span class="p">)</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span> <span class="n">targets</span> <span class="o">=</span> <span class="n">targets</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<span class="n">targets</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">epsilon</span><span class="p">)</span> <span class="o">*</span> <span class="n">targets</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span>
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<span class="k">return</span> <span class="p">(</span><span class="o">-</span> <span class="n">targets</span> <span class="o">*</span> <span class="n">log_probs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div></div>
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