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Deci-AI
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  69.   <h1>Source code for super_gradients.training.losses.r_squared_loss</h1><div class="highlight"><pre>
    70. 

  70. <span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">absolute_import</span>
    71. 

  71. 

  72. <span class="kn">import</span> <span class="nn">torch</span>
    73. 

  73. <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
    74. 

  74. <span class="kn">from</span> <span class="nn">torch.nn.modules.loss</span> <span class="kn">import</span> <span class="n">_Loss</span>
    75. 

  75. 

  76. <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>
    77. 

  77. 

  78. 

  79. <div class="viewcode-block" id="RSquaredLoss"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.r_squared_loss.RSquaredLoss">[docs]</a><span class="k">class</span> <span class="nc">RSquaredLoss</span><span class="p">(</span><span class="n">_Loss</span><span class="p">):</span>
    80. 

  80. 

  81. <div class="viewcode-block" id="RSquaredLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.r_squared_loss.RSquaredLoss.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">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
    82. 

  82.         <span class="c1"># FIXME - THIS NEEDS TO BE CHANGED SUCH THAT THIS CLASS INHERETS FROM _Loss (TAKE A LOOK AT YoLoV3DetectionLoss)</span>
    83. 

  83.         <span class="sd">&quot;&quot;&quot;Computes the R-squared for the output and target values</span>
    84. 

  84. <span class="sd">        :param output: Tensor / Numpy / List</span>
    85. 

  85. <span class="sd">            The prediction</span>
    86. 

  86. <span class="sd">        :param target: Tensor / Numpy / List</span>
    87. 

  87. <span class="sd">            The corresponding lables</span>
    88. 

  88. <span class="sd">        &quot;&quot;&quot;</span>
    89. 

  89.         <span class="c1"># Convert to tensor</span>
    90. 

  90.         <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>
    91. 

  91.         <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>
    92. 

  92. 

  93.         <span class="n">criterion_mse</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">()</span>
    94. 

  94.         <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">criterion_mse</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="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span></div></div>
    95. 

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