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<h1>Source code for torchreid.metrics.rank</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">copy</span>
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<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span>
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<span class="kn">import</span> <span class="nn">sys</span>
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<span class="kn">import</span> <span class="nn">warnings</span>
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<span class="k">try</span><span class="p">:</span>
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<span class="kn">from</span> <span class="nn">torchreid.metrics.rank_cylib.rank_cy</span> <span class="k">import</span> <span class="n">evaluate_cy</span>
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<span class="n">IS_CYTHON_AVAI</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
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<span class="n">IS_CYTHON_AVAI</span> <span class="o">=</span> <span class="kc">False</span>
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<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
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<span class="s1">'Cython evaluation (very fast so highly recommended) is '</span>
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<span class="s1">'unavailable, now use python evaluation.'</span>
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<span class="p">)</span>
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<span class="k">def</span> <span class="nf">eval_cuhk03</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">):</span>
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<span class="sd">"""Evaluation with cuhk03 metric</span>
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<span class="sd"> Key: one image for each gallery identity is randomly sampled for each query identity.</span>
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<span class="sd"> Random sampling is performed num_repeats times.</span>
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<span class="sd"> """</span>
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<span class="n">num_repeats</span> <span class="o">=</span> <span class="mi">10</span>
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<span class="n">num_q</span><span class="p">,</span> <span class="n">num_g</span> <span class="o">=</span> <span class="n">distmat</span><span class="o">.</span><span class="n">shape</span>
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<span class="k">if</span> <span class="n">num_g</span> <span class="o"><</span> <span class="n">max_rank</span><span class="p">:</span>
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<span class="n">max_rank</span> <span class="o">=</span> <span class="n">num_g</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Note: number of gallery samples is quite small, got </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_g</span><span class="p">))</span>
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<span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">matches</span> <span class="o">=</span> <span class="p">(</span><span class="n">g_pids</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_pids</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
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<span class="c1"># compute cmc curve for each query</span>
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<span class="n">all_cmc</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">all_AP</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">num_valid_q</span> <span class="o">=</span> <span class="mf">0.</span> <span class="c1"># number of valid query</span>
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<span class="k">for</span> <span class="n">q_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_q</span><span class="p">):</span>
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<span class="c1"># get query pid and camid</span>
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<span class="n">q_pid</span> <span class="o">=</span> <span class="n">q_pids</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="n">q_camid</span> <span class="o">=</span> <span class="n">q_camids</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="c1"># remove gallery samples that have the same pid and camid with query</span>
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<span class="n">order</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="n">remove</span> <span class="o">=</span> <span class="p">(</span><span class="n">g_pids</span><span class="p">[</span><span class="n">order</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_pid</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">g_camids</span><span class="p">[</span><span class="n">order</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_camid</span><span class="p">)</span>
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<span class="n">keep</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">invert</span><span class="p">(</span><span class="n">remove</span><span class="p">)</span>
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<span class="c1"># compute cmc curve</span>
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<span class="n">raw_cmc</span> <span class="o">=</span> <span class="n">matches</span><span class="p">[</span><span class="n">q_idx</span><span class="p">][</span><span class="n">keep</span><span class="p">]</span> <span class="c1"># binary vector, positions with value 1 are correct matches</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">raw_cmc</span><span class="p">):</span>
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<span class="c1"># this condition is true when query identity does not appear in gallery</span>
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<span class="k">continue</span>
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<span class="n">kept_g_pids</span> <span class="o">=</span> <span class="n">g_pids</span><span class="p">[</span><span class="n">order</span><span class="p">][</span><span class="n">keep</span><span class="p">]</span>
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<span class="n">g_pids_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">pid</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kept_g_pids</span><span class="p">):</span>
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<span class="n">g_pids_dict</span><span class="p">[</span><span class="n">pid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
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<span class="n">cmc</span> <span class="o">=</span> <span class="mf">0.</span>
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<span class="k">for</span> <span class="n">repeat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_repeats</span><span class="p">):</span>
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<span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">raw_cmc</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">idxs</span> <span class="ow">in</span> <span class="n">g_pids_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
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<span class="c1"># randomly sample one image for each gallery person</span>
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<span class="n">rnd_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">idxs</span><span class="p">)</span>
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<span class="n">mask</span><span class="p">[</span><span class="n">rnd_idx</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="n">masked_raw_cmc</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
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<span class="n">_cmc</span> <span class="o">=</span> <span class="n">masked_raw_cmc</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
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<span class="n">_cmc</span><span class="p">[</span><span class="n">_cmc</span> <span class="o">></span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
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<span class="n">cmc</span> <span class="o">+=</span> <span class="n">_cmc</span><span class="p">[:</span><span class="n">max_rank</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">cmc</span> <span class="o">/=</span> <span class="n">num_repeats</span>
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<span class="n">all_cmc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cmc</span><span class="p">)</span>
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<span class="c1"># compute AP</span>
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<span class="n">num_rel</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mf">1.</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tmp_cmc</span><span class="p">)]</span>
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">tmp_cmc</span><span class="p">)</span> <span class="o">*</span> <span class="n">raw_cmc</span>
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<span class="n">AP</span> <span class="o">=</span> <span class="n">tmp_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">num_rel</span>
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<span class="n">all_AP</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AP</span><span class="p">)</span>
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<span class="n">num_valid_q</span> <span class="o">+=</span> <span class="mf">1.</span>
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<span class="k">assert</span> <span class="n">num_valid_q</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Error: all query identities do not appear in gallery'</span>
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<span class="n">all_cmc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">all_cmc</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">all_cmc</span> <span class="o">=</span> <span class="n">all_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_valid_q</span>
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<span class="n">mAP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">all_AP</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">all_cmc</span><span class="p">,</span> <span class="n">mAP</span>
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<span class="k">def</span> <span class="nf">eval_market1501</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">):</span>
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<span class="sd">"""Evaluation with market1501 metric</span>
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<span class="sd"> Key: for each query identity, its gallery images from the same camera view are discarded.</span>
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<span class="sd"> """</span>
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<span class="n">num_q</span><span class="p">,</span> <span class="n">num_g</span> <span class="o">=</span> <span class="n">distmat</span><span class="o">.</span><span class="n">shape</span>
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<span class="k">if</span> <span class="n">num_g</span> <span class="o"><</span> <span class="n">max_rank</span><span class="p">:</span>
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<span class="n">max_rank</span> <span class="o">=</span> <span class="n">num_g</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Note: number of gallery samples is quite small, got </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_g</span><span class="p">))</span>
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<span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">matches</span> <span class="o">=</span> <span class="p">(</span><span class="n">g_pids</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_pids</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
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<span class="c1"># compute cmc curve for each query</span>
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<span class="n">all_cmc</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">all_AP</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">num_valid_q</span> <span class="o">=</span> <span class="mf">0.</span> <span class="c1"># number of valid query</span>
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<span class="k">for</span> <span class="n">q_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_q</span><span class="p">):</span>
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<span class="c1"># get query pid and camid</span>
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<span class="n">q_pid</span> <span class="o">=</span> <span class="n">q_pids</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="n">q_camid</span> <span class="o">=</span> <span class="n">q_camids</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="c1"># remove gallery samples that have the same pid and camid with query</span>
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<span class="n">order</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">q_idx</span><span class="p">]</span>
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<span class="n">remove</span> <span class="o">=</span> <span class="p">(</span><span class="n">g_pids</span><span class="p">[</span><span class="n">order</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_pid</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">g_camids</span><span class="p">[</span><span class="n">order</span><span class="p">]</span> <span class="o">==</span> <span class="n">q_camid</span><span class="p">)</span>
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<span class="n">keep</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">invert</span><span class="p">(</span><span class="n">remove</span><span class="p">)</span>
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<span class="c1"># compute cmc curve</span>
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<span class="n">raw_cmc</span> <span class="o">=</span> <span class="n">matches</span><span class="p">[</span><span class="n">q_idx</span><span class="p">][</span><span class="n">keep</span><span class="p">]</span> <span class="c1"># binary vector, positions with value 1 are correct matches</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">raw_cmc</span><span class="p">):</span>
|
|
<span class="c1"># this condition is true when query identity does not appear in gallery</span>
|
|
<span class="k">continue</span>
|
|
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|
<span class="n">cmc</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
|
|
<span class="n">cmc</span><span class="p">[</span><span class="n">cmc</span> <span class="o">></span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
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<span class="n">all_cmc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cmc</span><span class="p">[:</span><span class="n">max_rank</span><span class="p">])</span>
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<span class="n">num_valid_q</span> <span class="o">+=</span> <span class="mf">1.</span>
|
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<span class="c1"># compute average precision</span>
|
|
<span class="c1"># reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision</span>
|
|
<span class="n">num_rel</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="n">raw_cmc</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
|
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mf">1.</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tmp_cmc</span><span class="p">)]</span>
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<span class="n">tmp_cmc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">tmp_cmc</span><span class="p">)</span> <span class="o">*</span> <span class="n">raw_cmc</span>
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<span class="n">AP</span> <span class="o">=</span> <span class="n">tmp_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">num_rel</span>
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<span class="n">all_AP</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AP</span><span class="p">)</span>
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<span class="k">assert</span> <span class="n">num_valid_q</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Error: all query identities do not appear in gallery'</span>
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<span class="n">all_cmc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">all_cmc</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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|
<span class="n">all_cmc</span> <span class="o">=</span> <span class="n">all_cmc</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_valid_q</span>
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|
<span class="n">mAP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">all_AP</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">all_cmc</span><span class="p">,</span> <span class="n">mAP</span>
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<span class="k">def</span> <span class="nf">evaluate_py</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">,</span> <span class="n">use_metric_cuhk03</span><span class="p">):</span>
|
|
<span class="k">if</span> <span class="n">use_metric_cuhk03</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="n">eval_cuhk03</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="n">eval_market1501</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">)</span>
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<div class="viewcode-block" id="evaluate_rank"><a class="viewcode-back" href="../../../pkg/metrics.html#torchreid.metrics.rank.evaluate_rank">[docs]</a><span class="k">def</span> <span class="nf">evaluate_rank</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
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<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_cython</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
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<span class="sd">"""Evaluates CMC rank.</span>
|
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|
<span class="sd"> Args:</span>
|
|
<span class="sd"> distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).</span>
|
|
<span class="sd"> q_pids (numpy.ndarray): 1-D array containing person identities</span>
|
|
<span class="sd"> of each query instance.</span>
|
|
<span class="sd"> g_pids (numpy.ndarray): 1-D array containing person identities</span>
|
|
<span class="sd"> of each gallery instance.</span>
|
|
<span class="sd"> q_camids (numpy.ndarray): 1-D array containing camera views under</span>
|
|
<span class="sd"> which each query instance is captured.</span>
|
|
<span class="sd"> g_camids (numpy.ndarray): 1-D array containing camera views under</span>
|
|
<span class="sd"> which each gallery instance is captured.</span>
|
|
<span class="sd"> max_rank (int, optional): maximum CMC rank to be computed. Default is 50.</span>
|
|
<span class="sd"> use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.</span>
|
|
<span class="sd"> Default is False. This should be enabled when using cuhk03 classic split.</span>
|
|
<span class="sd"> use_cython (bool, optional): use cython code for evaluation. Default is True.</span>
|
|
<span class="sd"> This is highly recommended as the cython code can speed up the cmc computation</span>
|
|
<span class="sd"> by more than 10x. This requires Cython to be installed.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">use_cython</span> <span class="ow">and</span> <span class="n">IS_CYTHON_AVAI</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="n">evaluate_cy</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">,</span> <span class="n">use_metric_cuhk03</span><span class="p">)</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="n">evaluate_py</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">q_camids</span><span class="p">,</span> <span class="n">g_camids</span><span class="p">,</span> <span class="n">max_rank</span><span class="p">,</span> <span class="n">use_metric_cuhk03</span><span class="p">)</span></div>
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</pre></div>
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