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<h1>Source code for torchreid.metrics.distance</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">print_function</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">torch</span>
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<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
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<div class="viewcode-block" id="compute_distance_matrix"><a class="viewcode-back" href="../../../pkg/metrics.html#torchreid.metrics.distance.compute_distance_matrix">[docs]</a><span class="k">def</span> <span class="nf">compute_distance_matrix</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">):</span>
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<span class="sd">"""A wrapper function for computing distance matrix.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> input1 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> input2 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> metric (str, optional): "euclidean" or "cosine".</span>
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<span class="sd"> Default is "euclidean".</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> torch.Tensor: distance matrix.</span>
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<span class="sd"> Examples::</span>
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<span class="sd"> >>> from torchreid import metrics</span>
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<span class="sd"> >>> input1 = torch.rand(10, 2048)</span>
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<span class="sd"> >>> input2 = torch.rand(100, 2048)</span>
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<span class="sd"> >>> distmat = metrics.compute_distance_matrix(input1, input2)</span>
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<span class="sd"> >>> distmat.size() # (10, 100)</span>
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<span class="sd"> """</span>
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<span class="c1"># check input</span>
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<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
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<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input2</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
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<span class="k">assert</span> <span class="n">input1</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'Expected 2-D tensor, but got </span><span class="si">{}</span><span class="s1">-D'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">input1</span><span class="o">.</span><span class="n">dim</span><span class="p">())</span>
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<span class="k">assert</span> <span class="n">input2</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'Expected 2-D tensor, but got </span><span class="si">{}</span><span class="s1">-D'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">input2</span><span class="o">.</span><span class="n">dim</span><span class="p">())</span>
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<span class="k">assert</span> <span class="n">input1</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">input2</span><span class="o">.</span><span class="n">size</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="n">metric</span> <span class="o">==</span> <span class="s1">'euclidean'</span><span class="p">:</span>
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<span class="n">distmat</span> <span class="o">=</span> <span class="n">euclidean_squared_distance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span>
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<span class="k">elif</span> <span class="n">metric</span> <span class="o">==</span> <span class="s1">'cosine'</span><span class="p">:</span>
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<span class="n">distmat</span> <span class="o">=</span> <span class="n">cosine_distance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
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<span class="s1">'Unknown distance metric: </span><span class="si">{}</span><span class="s1">. '</span>
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<span class="s1">'Please choose either "euclidean" or "cosine"'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span>
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<span class="p">)</span>
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<span class="k">return</span> <span class="n">distmat</span></div>
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<div class="viewcode-block" id="euclidean_squared_distance"><a class="viewcode-back" href="../../../pkg/metrics.html#torchreid.metrics.distance.euclidean_squared_distance">[docs]</a><span class="k">def</span> <span class="nf">euclidean_squared_distance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">):</span>
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<span class="sd">"""Computes euclidean squared distance.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> input1 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> input2 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> torch.Tensor: distance matrix.</span>
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<span class="sd"> """</span>
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<span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">input1</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">input2</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">distmat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="mi">2</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">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="o">+</span> \
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<span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">input2</span><span class="p">,</span> <span class="mi">2</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">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
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<span class="n">distmat</span><span class="o">.</span><span class="n">addmm_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
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<span class="k">return</span> <span class="n">distmat</span></div>
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<div class="viewcode-block" id="cosine_distance"><a class="viewcode-back" href="../../../pkg/metrics.html#torchreid.metrics.distance.cosine_distance">[docs]</a><span class="k">def</span> <span class="nf">cosine_distance</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">):</span>
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<span class="sd">"""Computes cosine distance.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> input1 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> input2 (torch.Tensor): 2-D feature matrix.</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> torch.Tensor: distance matrix.</span>
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<span class="sd"> """</span>
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<span class="n">input1_normed</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</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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<span class="n">input2_normed</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">input2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</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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<span class="n">distmat</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">input1_normed</span><span class="p">,</span> <span class="n">input2_normed</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
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<span class="k">return</span> <span class="n">distmat</span></div>
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