#561 Feature/sg 193 extend output formator

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  <h1>Source code for super_gradients.training.losses.shelfnet_ohem_loss</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.losses.ohem_ce_loss</span> <span class="kn">import</span> <span class="n">OhemCELoss</span>
<div class="viewcode-block" id="ShelfNetOHEMLoss"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.ShelfNetOHEMLoss">[docs]</a><span class="k">class</span> <span class="nc">ShelfNetOHEMLoss</span><span class="p">(</span><span class="n">OhemCELoss</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">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span> <span class="n">mining_percent</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-4</span><span class="p">,</span> <span class="n">ignore_lb</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">255</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This loss is an extension of the Ohem (Online Hard Example Mining Cross Entropy) Loss.</span>
<span class="sd">        :param threshold: threshold to th hard example mining algorithm</span>
<span class="sd">        :param mining_percent: minimum percentage of total pixels for the hard example mining algorithm</span>
<span class="sd">        (taking only the largest) losses.</span>
<span class="sd">        Default is 1e-4, according to legacy settings, number of 400 pixels for typical input of (512x512) and batch of</span>
<span class="sd">         16.</span>
<span class="sd">        :param ignore_lb: targets label to be ignored</span>
<span class="sd">        &quot;&quot;&quot;</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">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">mining_percent</span><span class="o">=</span><span class="n">mining_percent</span><span class="p">,</span> <span class="n">ignore_lb</span><span class="o">=</span><span class="n">ignore_lb</span><span class="p">)</span>
<div class="viewcode-block" id="ShelfNetOHEMLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.ShelfNetOHEMLoss.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions_list</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
        <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">predictions</span> <span class="ow">in</span> <span class="n">predictions_list</span><span class="p">:</span>
            <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">))</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
        <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">total_loss</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">total_loss</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">losses</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span></div>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">component_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Component names for logging during training.</span>
<span class="sd">        These correspond to 2nd item in the tuple returned in self.forward(...).</span>
<span class="sd">        See super_gradients.Trainer.train() docs for more info.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;Loss1/4&quot;</span><span class="p">,</span> <span class="s2">&quot;Loss1/8&quot;</span><span class="p">,</span> <span class="s2">&quot;Loss1/16&quot;</span><span class="p">,</span> <span class="s2">&quot;Loss&quot;</span><span class="p">]</span></div>
</pre></div>
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Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#561 Feature/sg 193 extend output formator

Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git
