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<h1>Source code for pathflowai.sampler</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">sampler.py</span>
<span class="sd">=======================</span>
<span class="sd">Balanced sampling based on one of the columns of the patch information.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.utils.data</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<div class="viewcode-block" id="ImbalancedDatasetSampler"><a class="viewcode-back" href="../../index.html#pathflowai.sampler.ImbalancedDatasetSampler">[docs]</a><span class="k">class</span> <span class="nc">ImbalancedDatasetSampler</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">Sampler</span><span class="p">):</span>
<span class="sd">"""Samples elements randomly from a given list of indices for imbalanced dataset</span>
<span class="sd"> https://raw.githubusercontent.com/ufoym/imbalanced-dataset-sampler/master/sampler.py</span>
<span class="sd"> Arguments:</span>
<span class="sd"> indices (list, optional): a list of indices</span>
<span class="sd"> num_samples (int, optional): number of samples to draw</span>
<span class="sd"> """</span>
<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">dataset</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># if indices is not provided,</span>
<span class="c1"># all elements in the dataset will be considered</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span> \
<span class="k">if</span> <span class="n">indices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">indices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_targets</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">)</span>
<span class="c1"># if num_samples is not provided,</span>
<span class="c1"># draw `len(indices)` samples in each iteration</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span> \
<span class="k">if</span> <span class="n">num_samples</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">num_samples</span>
<span class="c1"># distribution of classes in the dataset</span>
<span class="n">label_to_count</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_label</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
<span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">label_to_count</span><span class="p">:</span>
<span class="n">label_to_count</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">label_to_count</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># weight for each sample</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">label_to_count</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_label</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">idx</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">DoubleTensor</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_get_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
<span class="n">dataset_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dataset_type</span> <span class="ow">is</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">:</span>
<span class="k">return</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">dataset_type</span> <span class="ow">is</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">:</span>
<span class="k">return</span> <span class="n">dataset</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">y</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">patch_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_targets</span><span class="o">></span><span class="mi">1</span><span class="p">:</span>
<span class="n">y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
<span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1">#print(y)</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_samples</span><span class="p">,</span> <span class="n">replacement</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_samples</span></div>
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