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  <h1>Source code for super_gradients.training.metrics.classification_metrics</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">super_gradients.training.utils</span> <span class="kn">import</span> <span class="n">convert_to_tensor</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="accuracy"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.accuracy">[docs]</a><span class="k">def</span> <span class="nf">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,)):</span>
    <span class="sd">&quot;&quot;&quot;Computes the precision@k for the specified values of k</span>
<span class="sd">    :param output: Tensor / Numpy / List</span>
<span class="sd">        The prediction</span>
<span class="sd">    :param target: Tensor / Numpy / List</span>
<span class="sd">        The corresponding lables</span>
<span class="sd">    :param topk: tuple</span>
<span class="sd">        The type of accuracy to calculate, e.g. topk=(1,5) returns accuracy for top-1 and top-5&quot;&quot;&quot;</span>
    <span class="c1"># Convert to tensor</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
    <span class="n">target</span> <span class="o">=</span> <span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>

    <span class="c1"># Get the maximal value of the accuracy measurment and the batch size</span>
    <span class="n">maxk</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">topk</span><span class="p">)</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">target</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="c1"># Get the top k predictions</span>
    <span class="n">_</span><span class="p">,</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">maxk</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
    <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
    <span class="c1"># Count the number of correct predictions only for the highest k</span>
    <span class="n">correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span>

    <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">topk</span><span class="p">:</span>
        <span class="c1"># Count the number of correct prediction for the different K (the top predictions) values</span>
        <span class="n">correct_k</span> <span class="o">=</span> <span class="n">correct</span><span class="p">[:</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</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="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">correct_k</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mf">100.0</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
    <span class="k">return</span> <span class="n">res</span></div>


<div class="viewcode-block" id="Accuracy"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.Accuracy">[docs]</a><span class="k">class</span> <span class="nc">Accuracy</span><span class="p">(</span><span class="n">torchmetrics</span><span class="o">.</span><span class="n">Accuracy</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">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>

<div class="viewcode-block" id="Accuracy.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.Accuracy.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="n">target</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># supports smooth labels</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="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</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>


<div class="viewcode-block" id="Top5"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.Top5">[docs]</a><span class="k">class</span> <span class="nc">Top5</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">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">add_state</span><span class="p">(</span><span class="s2">&quot;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&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="mi">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="Top5.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.Top5.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="n">target</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># supports smooth labels</span>

        <span class="c1"># Get the maximal value of the accuracy measurment and the batch size</span>
        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">target</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="c1"># Get the top k predictions</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
        <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
        <span class="c1"># Count the number of correct predictions only for the highest 5</span>
        <span class="n">correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span>
        <span class="n">correct5</span> <span class="o">=</span> <span class="n">correct</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">correct</span> <span class="o">+=</span> <span class="n">correct5</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">+=</span> <span class="n">batch_size</span></div>

<div class="viewcode-block" id="Top5.compute"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.Top5.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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">correct</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">total</span></div></div>


<div class="viewcode-block" id="ToyTestClassificationMetric"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.ToyTestClassificationMetric">[docs]</a><span class="k">class</span> <span class="nc">ToyTestClassificationMetric</span><span class="p">(</span><span class="n">Metric</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Dummy classification Mettric object returning 0 always (for testing).</span>
<span class="sd">    &quot;&quot;&quot;</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">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>

<div class="viewcode-block" id="ToyTestClassificationMetric.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.ToyTestClassificationMetric.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="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">pass</span></div>

<div class="viewcode-block" id="ToyTestClassificationMetric.compute"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.ToyTestClassificationMetric.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="k">return</span> <span class="mi">0</span></div></div>
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