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  69.   <h1>Source code for super_gradients.training.utils.regularization_utils</h1><div class="highlight"><pre>
    70. 

  70. <span></span><span class="kn">import</span> <span class="nn">torch</span>
    71. 

  71. <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
    72. 

  72. 

  73. 

  74. <div class="viewcode-block" id="DropPath"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.regularization_utils.DropPath">[docs]</a><span class="k">class</span> <span class="nc">DropPath</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    75. 

  75.     <span class="sd">&quot;&quot;&quot;</span>
    76. 

  76. <span class="sd">    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).</span>
    77. 

  77. 

  78. <span class="sd">    Code taken from TIMM (https://github.com/rwightman/pytorch-image-models)</span>
    79. 

  79. <span class="sd">    Apache License 2.0</span>
    80. 

  80. <span class="sd">    &quot;&quot;&quot;</span>
    81. 

  81.     <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">drop_prob</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    82. 

  82.         <span class="nb">super</span><span class="p">(</span><span class="n">DropPath</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
    83. 

  83.         <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
    84. 

  84. 

  85. <div class="viewcode-block" id="DropPath.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.regularization_utils.DropPath.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">x</span><span class="p">):</span>
    86. 

  86.         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">==</span> <span class="mf">0.</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">:</span>
    87. 

  87.             <span class="k">return</span> <span class="n">x</span>
    88. 

  88. 

  89.         <span class="n">keep_prob</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span>
    90. 

  90.         <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># work with diff dim tensors, not just 2D ConvNets</span>
    91. 

  91.         <span class="n">random_tensor</span> <span class="o">=</span> <span class="n">keep_prob</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
    92. 

  92.         <span class="n">random_tensor</span><span class="o">.</span><span class="n">floor_</span><span class="p">()</span>  <span class="c1"># binarize</span>
    93. 

  93.         <span class="n">output</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">keep_prob</span><span class="p">)</span> <span class="o">*</span> <span class="n">random_tensor</span>
    94. 

  94.         <span class="k">return</span> <span class="n">output</span></div></div>
    95. 

  95. </pre></div>
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