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<h1>Source code for torchreid.data.sampler</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">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">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">import</span> <span class="nn">random</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.utils.data.sampler</span> <span class="k">import</span> <span class="n">Sampler</span><span class="p">,</span> <span class="n">RandomSampler</span>
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<div class="viewcode-block" id="RandomIdentitySampler"><a class="viewcode-back" href="../../../pkg/data.html#torchreid.data.sampler.RandomIdentitySampler">[docs]</a><span class="k">class</span> <span class="nc">RandomIdentitySampler</span><span class="p">(</span><span class="n">Sampler</span><span class="p">):</span>
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<span class="sd">"""Randomly samples N identities each with K instances.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> data_source (list): contains tuples of (img_path(s), pid, camid).</span>
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<span class="sd"> batch_size (int): batch size.</span>
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<span class="sd"> num_instances (int): number of instances per identity in a batch.</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">data_source</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">num_instances</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">data_source</span> <span class="o">=</span> <span class="n">data_source</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span> <span class="o">=</span> <span class="n">num_instances</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_pids_per_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">index_dic</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">index</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">pid</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">index_dic</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">index</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">pids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index_dic</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
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<span class="c1"># estimate number of examples in an epoch</span>
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<span class="c1"># TODO: improve precision</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">for</span> <span class="n">pid</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">pids</span><span class="p">:</span>
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<span class="n">idxs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_dic</span><span class="p">[</span><span class="n">pid</span><span class="p">]</span>
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<span class="n">num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">idxs</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">num</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span><span class="p">:</span>
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<span class="n">num</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">+=</span> <span class="n">num</span> <span class="o">-</span> <span class="n">num</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span>
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<span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="n">batch_idxs_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">pid</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">pids</span><span class="p">:</span>
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<span class="n">idxs</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index_dic</span><span class="p">[</span><span class="n">pid</span><span class="p">])</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">idxs</span><span class="p">)</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span><span class="p">:</span>
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<span class="n">idxs</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> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">idxs</span><span class="p">)</span>
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<span class="n">batch_idxs</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">:</span>
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<span class="n">batch_idxs</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="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_idxs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_instances</span><span class="p">:</span>
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<span class="n">batch_idxs_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">batch_idxs</span><span class="p">)</span>
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<span class="n">batch_idxs</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">avai_pids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pids</span><span class="p">)</span>
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<span class="n">final_idxs</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">avai_pids</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_pids_per_batch</span><span class="p">:</span>
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<span class="n">selected_pids</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">avai_pids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_pids_per_batch</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">pid</span> <span class="ow">in</span> <span class="n">selected_pids</span><span class="p">:</span>
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<span class="n">batch_idxs</span> <span class="o">=</span> <span class="n">batch_idxs_dict</span><span class="p">[</span><span class="n">pid</span><span class="p">]</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">final_idxs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">batch_idxs</span><span class="p">)</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_idxs_dict</span><span class="p">[</span><span class="n">pid</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">avai_pids</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">pid</span><span class="p">)</span>
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<span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="n">final_idxs</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span></div>
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<div class="viewcode-block" id="build_train_sampler"><a class="viewcode-back" href="../../../pkg/data.html#torchreid.data.sampler.build_train_sampler">[docs]</a><span class="k">def</span> <span class="nf">build_train_sampler</span><span class="p">(</span><span class="n">data_source</span><span class="p">,</span> <span class="n">train_sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">num_instances</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Builds a training sampler.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> data_source (list): contains tuples of (img_path(s), pid, camid).</span>
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<span class="sd"> train_sampler (str): sampler name (default: ``RandomSampler``).</span>
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<span class="sd"> batch_size (int, optional): batch size. Default is 32.</span>
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<span class="sd"> num_instances (int, optional): number of instances per identity in a</span>
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<span class="sd"> batch (for ``RandomIdentitySampler``). Default is 4.</span>
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<span class="sd"> """</span>
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<span class="k">if</span> <span class="n">train_sampler</span> <span class="o">==</span> <span class="s1">'RandomIdentitySampler'</span><span class="p">:</span>
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<span class="n">sampler</span> <span class="o">=</span> <span class="n">RandomIdentitySampler</span><span class="p">(</span><span class="n">data_source</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">num_instances</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">sampler</span> <span class="o">=</span> <span class="n">RandomSampler</span><span class="p">(</span><span class="n">data_source</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">sampler</span></div>
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