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  <h1>Source code for super_gradients.training.metrics.segmentation_metrics</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchmetrics</span>
<span class="kn">from</span> <span class="nn">torchmetrics</span> <span class="kn">import</span> <span class="n">Metric</span>


<div class="viewcode-block" id="batch_pix_accuracy"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.batch_pix_accuracy">[docs]</a><span class="k">def</span> <span class="nf">batch_pix_accuracy</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Batch Pixel Accuracy</span>
<span class="sd">    Args:</span>
<span class="sd">        predict: input 4D tensor</span>
<span class="sd">        target: label 3D tensor</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">_</span><span class="p">,</span> <span class="n">predict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">predict</span> <span class="o">=</span> <span class="n">predict</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">pixel_labeled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">target</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">pixel_correct</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">predict</span> <span class="o">==</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">target</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">))</span>
    <span class="k">assert</span> <span class="n">pixel_correct</span> <span class="o">&lt;=</span> <span class="n">pixel_labeled</span><span class="p">,</span> \
        <span class="s2">&quot;Correct area should be smaller than Labeled&quot;</span>
    <span class="k">return</span> <span class="n">pixel_correct</span><span class="p">,</span> <span class="n">pixel_labeled</span></div>


<div class="viewcode-block" id="batch_intersection_union"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.batch_intersection_union">[docs]</a><span class="k">def</span> <span class="nf">batch_intersection_union</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">nclass</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Batch Intersection of Union</span>
<span class="sd">    Args:</span>
<span class="sd">        predict: input 4D tensor</span>
<span class="sd">        target: label 3D tensor</span>
<span class="sd">        nclass: number of categories (int)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">_</span><span class="p">,</span> <span class="n">predict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">mini</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">maxi</span> <span class="o">=</span> <span class="n">nclass</span>
    <span class="n">nbins</span> <span class="o">=</span> <span class="n">nclass</span>
    <span class="n">predict</span> <span class="o">=</span> <span class="n">predict</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>

    <span class="n">predict</span> <span class="o">=</span> <span class="n">predict</span> <span class="o">*</span> <span class="p">(</span><span class="n">target</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">predict</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
    <span class="n">intersection</span> <span class="o">=</span> <span class="n">predict</span> <span class="o">*</span> <span class="p">(</span><span class="n">predict</span> <span class="o">==</span> <span class="n">target</span><span class="p">)</span>
    <span class="c1"># areas of intersection and union</span>
    <span class="n">area_inter</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">intersection</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">mini</span><span class="p">,</span> <span class="n">maxi</span><span class="p">))</span>
    <span class="n">area_pred</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">mini</span><span class="p">,</span> <span class="n">maxi</span><span class="p">))</span>
    <span class="n">area_lab</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="n">mini</span><span class="p">,</span> <span class="n">maxi</span><span class="p">))</span>
    <span class="n">area_union</span> <span class="o">=</span> <span class="n">area_pred</span> <span class="o">+</span> <span class="n">area_lab</span> <span class="o">-</span> <span class="n">area_inter</span>
    <span class="k">assert</span> <span class="p">(</span><span class="n">area_inter</span> <span class="o">&lt;=</span> <span class="n">area_union</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(),</span> \
        <span class="s2">&quot;Intersection area should be smaller than Union area&quot;</span>
    <span class="k">return</span> <span class="n">area_inter</span><span class="p">,</span> <span class="n">area_union</span></div>


<span class="c1"># ref https://github.com/CSAILVision/sceneparsing/blob/master/evaluationCode/utils_eval.py</span>
<div class="viewcode-block" id="pixel_accuracy"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.pixel_accuracy">[docs]</a><span class="k">def</span> <span class="nf">pixel_accuracy</span><span class="p">(</span><span class="n">im_pred</span><span class="p">,</span> <span class="n">im_lab</span><span class="p">):</span>
    <span class="n">im_pred</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">im_pred</span><span class="p">)</span>
    <span class="n">im_lab</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">im_lab</span><span class="p">)</span>

    <span class="c1"># Remove classes from unlabeled pixels in gt image.</span>
    <span class="c1"># We should not penalize detections in unlabeled portions of the image.</span>
    <span class="n">pixel_labeled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">im_lab</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">pixel_correct</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">im_pred</span> <span class="o">==</span> <span class="n">im_lab</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">im_lab</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">))</span>
    <span class="c1"># pixel_accuracy = 1.0 * pixel_correct / pixel_labeled</span>
    <span class="k">return</span> <span class="n">pixel_correct</span><span class="p">,</span> <span class="n">pixel_labeled</span></div>


<div class="viewcode-block" id="intersection_and_union"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.intersection_and_union">[docs]</a><span class="k">def</span> <span class="nf">intersection_and_union</span><span class="p">(</span><span class="n">im_pred</span><span class="p">,</span> <span class="n">im_lab</span><span class="p">,</span> <span class="n">num_class</span><span class="p">):</span>
    <span class="n">im_pred</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">im_pred</span><span class="p">)</span>
    <span class="n">im_lab</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">im_lab</span><span class="p">)</span>
    <span class="c1"># Remove classes from unlabeled pixels in gt image.</span>
    <span class="n">im_pred</span> <span class="o">=</span> <span class="n">im_pred</span> <span class="o">*</span> <span class="p">(</span><span class="n">im_lab</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
    <span class="c1"># Compute area intersection:</span>
    <span class="n">intersection</span> <span class="o">=</span> <span class="n">im_pred</span> <span class="o">*</span> <span class="p">(</span><span class="n">im_pred</span> <span class="o">==</span> <span class="n">im_lab</span><span class="p">)</span>
    <span class="n">area_inter</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">intersection</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
                                 <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
    <span class="c1"># Compute area union:</span>
    <span class="n">area_pred</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">im_pred</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
                                <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">area_lab</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">im_lab</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
                               <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_class</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">area_union</span> <span class="o">=</span> <span class="n">area_pred</span> <span class="o">+</span> <span class="n">area_lab</span> <span class="o">-</span> <span class="n">area_inter</span>
    <span class="k">return</span> <span class="n">area_inter</span><span class="p">,</span> <span class="n">area_union</span></div>


<div class="viewcode-block" id="PixelAccuracy"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.PixelAccuracy">[docs]</a><span class="k">class</span> <span class="nc">PixelAccuracy</span><span class="p">(</span><span class="n">Metric</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ignore_label</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">dist_sync_on_step</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dist_sync_on_step</span><span class="o">=</span><span class="n">dist_sync_on_step</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span> <span class="o">=</span> <span class="n">ignore_label</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_state</span><span class="p">(</span><span class="s2">&quot;total_correct&quot;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.</span><span class="p">),</span> <span class="n">dist_reduce_fx</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_state</span><span class="p">(</span><span class="s2">&quot;total_label&quot;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.</span><span class="p">),</span> <span class="n">dist_reduce_fx</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>

<div class="viewcode-block" id="PixelAccuracy.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.PixelAccuracy.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
            <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">predict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">labeled_mask</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span><span class="p">)</span>
        <span class="n">pixel_labeled</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">labeled_mask</span><span class="p">)</span>
        <span class="n">pixel_correct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">predict</span> <span class="o">==</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="n">labeled_mask</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">total_correct</span> <span class="o">+=</span> <span class="n">pixel_correct</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">total_label</span> <span class="o">+=</span> <span class="n">pixel_labeled</span></div>

<div class="viewcode-block" id="PixelAccuracy.compute"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.PixelAccuracy.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">_total_correct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_correct</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
        <span class="n">_total_label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_label</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
        <span class="n">pix_acc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">_total_correct</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">spacing</span><span class="p">(</span><span class="mi">1</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">float64</span><span class="p">)</span> <span class="o">+</span> <span class="n">_total_label</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">pix_acc</span></div></div>


<div class="viewcode-block" id="IoU"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.IoU">[docs]</a><span class="k">class</span> <span class="nc">IoU</span><span class="p">(</span><span class="n">torchmetrics</span><span class="o">.</span><span class="n">IoU</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__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">dist_sync_on_step</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">dist_sync_on_step</span><span class="o">=</span><span class="n">dist_sync_on_step</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="n">ignore_index</span><span class="p">)</span>

<div class="viewcode-block" id="IoU.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.segmentation_metrics.IoU.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="c1"># WHEN DEALING WITH MULTIPLE OUTPUTS- OUTPUTS[0] IS THE MAIN SEGMENTATION MAP</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
            <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="n">target</span><span class="p">)</span></div></div>
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