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  <h1>Source code for super_gradients.training.datasets.auto_augment</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot; RandAugment</span>
<span class="sd">RandAugment is a variant of AutoAugment which randomly selects transformations</span>
<span class="sd"> from AutoAugment to be applied on an image.</span>

<span class="sd">RandomAugmentation Implementation adapted from:</span>
<span class="sd">    https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/auto_augment.py</span>

<span class="sd">Papers:</span>
<span class="sd">    RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span><span class="p">,</span> <span class="n">ImageOps</span><span class="p">,</span> <span class="n">ImageEnhance</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>


<span class="n">_FILL</span> <span class="o">=</span> <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>

<span class="c1"># to unify the calls of the different augmentations in terms of params, all augmentations are set to work with a single</span>
<span class="c1"># magnitude params, normalized according to _MAX_MAGNITUDE</span>
<span class="n">_MAX_MAGNITUDE</span> <span class="o">=</span> <span class="mf">10.</span>

<span class="n">_HPARAMS_DEFAULT</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
    <span class="n">translate_const</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span>
    <span class="n">img_mean</span><span class="o">=</span><span class="n">_FILL</span><span class="p">,</span>
<span class="p">)</span>

<span class="c1"># Define the interpolation types</span>
<span class="n">_RANDOM_INTERPOLATION</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">BILINEAR</span>


<span class="k">def</span> <span class="nf">_interpolation</span><span class="p">(</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Performs Bi-Linear interpolation</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">interpolation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">BILINEAR</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">interpolation</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">interpolation</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">interpolation</span>


<div class="viewcode-block" id="shear_x"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.shear_x">[docs]</a><span class="k">def</span> <span class="nf">shear_x</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="shear_y"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.shear_y">[docs]</a><span class="k">def</span> <span class="nf">shear_y</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="translate_x_rel"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.translate_x_rel">[docs]</a><span class="k">def</span> <span class="nf">translate_x_rel</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">pct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">pixels</span> <span class="o">=</span> <span class="n">pct</span> <span class="o">*</span> <span class="n">img</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="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">pixels</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="translate_y_rel"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.translate_y_rel">[docs]</a><span class="k">def</span> <span class="nf">translate_y_rel</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">pct</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">pixels</span> <span class="o">=</span> <span class="n">pct</span> <span class="o">*</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">pixels</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="translate_x_abs"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.translate_x_abs">[docs]</a><span class="k">def</span> <span class="nf">translate_x_abs</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">pixels</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">pixels</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="translate_y_abs"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.translate_y_abs">[docs]</a><span class="k">def</span> <span class="nf">translate_y_abs</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">pixels</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">AFFINE</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">pixels</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="rotate"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.rotate">[docs]</a><span class="k">def</span> <span class="nf">rotate</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">degrees</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">degrees</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="auto_contrast"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.auto_contrast">[docs]</a><span class="k">def</span> <span class="nf">auto_contrast</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageOps</span><span class="o">.</span><span class="n">autocontrast</span><span class="p">(</span><span class="n">img</span><span class="p">)</span></div>


<div class="viewcode-block" id="invert"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.invert">[docs]</a><span class="k">def</span> <span class="nf">invert</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageOps</span><span class="o">.</span><span class="n">invert</span><span class="p">(</span><span class="n">img</span><span class="p">)</span></div>


<div class="viewcode-block" id="equalize"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.equalize">[docs]</a><span class="k">def</span> <span class="nf">equalize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageOps</span><span class="o">.</span><span class="n">equalize</span><span class="p">(</span><span class="n">img</span><span class="p">)</span></div>


<div class="viewcode-block" id="solarize"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.solarize">[docs]</a><span class="k">def</span> <span class="nf">solarize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageOps</span><span class="o">.</span><span class="n">solarize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span></div>


<div class="viewcode-block" id="solarize_add"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.solarize_add">[docs]</a><span class="k">def</span> <span class="nf">solarize_add</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">add</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="n">lut</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">256</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">thresh</span><span class="p">:</span>
            <span class="n">lut</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="n">add</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">lut</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">img</span><span class="o">.</span><span class="n">mode</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;L&quot;</span><span class="p">,</span> <span class="s2">&quot;RGB&quot;</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">img</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;RGB&quot;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">lut</span><span class="p">)</span> <span class="o">==</span> <span class="mi">256</span><span class="p">:</span>
            <span class="n">lut</span> <span class="o">=</span> <span class="n">lut</span> <span class="o">+</span> <span class="n">lut</span> <span class="o">+</span> <span class="n">lut</span>
        <span class="k">return</span> <span class="n">img</span><span class="o">.</span><span class="n">point</span><span class="p">(</span><span class="n">lut</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">img</span></div>


<div class="viewcode-block" id="posterize"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.posterize">[docs]</a><span class="k">def</span> <span class="nf">posterize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">bits_to_keep</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">bits_to_keep</span> <span class="o">&gt;=</span> <span class="mi">8</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">img</span>
    <span class="k">return</span> <span class="n">ImageOps</span><span class="o">.</span><span class="n">posterize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">bits_to_keep</span><span class="p">)</span></div>


<div class="viewcode-block" id="contrast"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.contrast">[docs]</a><span class="k">def</span> <span class="nf">contrast</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageEnhance</span><span class="o">.</span><span class="n">Contrast</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span></div>


<div class="viewcode-block" id="color"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.color">[docs]</a><span class="k">def</span> <span class="nf">color</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageEnhance</span><span class="o">.</span><span class="n">Color</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span></div>


<div class="viewcode-block" id="brightness"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.brightness">[docs]</a><span class="k">def</span> <span class="nf">brightness</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageEnhance</span><span class="o">.</span><span class="n">Brightness</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span></div>


<div class="viewcode-block" id="sharpness"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.sharpness">[docs]</a><span class="k">def</span> <span class="nf">sharpness</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ImageEnhance</span><span class="o">.</span><span class="n">Sharpness</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span></div>


<span class="k">def</span> <span class="nf">_randomly_negate</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;With 50% prob, negate the value&quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="o">-</span><span class="n">v</span> <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="k">else</span> <span class="n">v</span>


<span class="k">def</span> <span class="nf">_rotate_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [-30, 30]</span>
    <span class="n">level</span> <span class="o">=</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mf">30.</span>
    <span class="n">level</span> <span class="o">=</span> <span class="n">_randomly_negate</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">level</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_enhance_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [0.1, 1.9]</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.8</span> <span class="o">+</span> <span class="mf">0.1</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_enhance_increasing_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [0.1, 1.9]</span>
    <span class="n">level</span> <span class="o">=</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mf">.9</span>
    <span class="n">level</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">_randomly_negate</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">level</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_shear_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [-0.3, 0.3]</span>
    <span class="n">level</span> <span class="o">=</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.3</span>
    <span class="n">level</span> <span class="o">=</span> <span class="n">_randomly_negate</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">level</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_translate_abs_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">hparams</span><span class="p">):</span>
    <span class="n">translate_const</span> <span class="o">=</span> <span class="n">hparams</span><span class="p">[</span><span class="s1">&#39;translate_const&#39;</span><span class="p">]</span>
    <span class="n">level</span> <span class="o">=</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">translate_const</span><span class="p">)</span>
    <span class="n">level</span> <span class="o">=</span> <span class="n">_randomly_negate</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">level</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_translate_rel_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">hparams</span><span class="p">):</span>
    <span class="c1"># default range [-0.45, 0.45]</span>
    <span class="n">translate_pct</span> <span class="o">=</span> <span class="n">hparams</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;translate_pct&#39;</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">)</span>
    <span class="n">level</span> <span class="o">=</span> <span class="p">(</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="n">translate_pct</span>
    <span class="n">level</span> <span class="o">=</span> <span class="n">_randomly_negate</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">level</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_posterize_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># As per Tensorflow TPU EfficientNet impl</span>
    <span class="c1"># range [0, 4], &#39;keep 0 up to 4 MSB of original image&#39;</span>
    <span class="c1"># intensity/severity of augmentation decreases with level</span>
    <span class="k">return</span> <span class="nb">int</span><span class="p">((</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mi">4</span><span class="p">),</span>


<span class="k">def</span> <span class="nf">_posterize_increasing_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">hparams</span><span class="p">):</span>
    <span class="c1"># As per Tensorflow models research and UDA impl</span>
    <span class="c1"># range [4, 0], &#39;keep 4 down to 0 MSB of original image&#39;,</span>
    <span class="c1"># intensity/severity of augmentation increases with level</span>
    <span class="k">return</span> <span class="mi">4</span> <span class="o">-</span> <span class="n">_posterize_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">hparams</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>


<span class="k">def</span> <span class="nf">_posterize_original_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># As per original AutoAugment paper description</span>
    <span class="c1"># range [4, 8], &#39;keep 4 up to 8 MSB of image&#39;</span>
    <span class="c1"># intensity/severity of augmentation decreases with level</span>
    <span class="k">return</span> <span class="nb">int</span><span class="p">((</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span><span class="p">,</span>


<span class="k">def</span> <span class="nf">_solarize_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [0, 256]</span>
    <span class="c1"># intensity/severity of augmentation decreases with level</span>
    <span class="k">return</span> <span class="nb">int</span><span class="p">((</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mi">256</span><span class="p">),</span>


<span class="k">def</span> <span class="nf">_solarize_increasing_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [0, 256]</span>
    <span class="c1"># intensity/severity of augmentation increases with level</span>
    <span class="k">return</span> <span class="mi">256</span> <span class="o">-</span> <span class="n">_solarize_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>


<span class="k">def</span> <span class="nf">_solarize_add_level_to_arg</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">_hparams</span><span class="p">):</span>
    <span class="c1"># range [0, 110]</span>
    <span class="k">return</span> <span class="nb">int</span><span class="p">((</span><span class="n">level</span> <span class="o">/</span> <span class="n">_MAX_MAGNITUDE</span><span class="p">)</span> <span class="o">*</span> <span class="mi">110</span><span class="p">),</span>


<span class="n">LEVEL_TO_ARG</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;AutoContrast&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
    <span class="s1">&#39;Equalize&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
    <span class="s1">&#39;Invert&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
    <span class="s1">&#39;Rotate&#39;</span><span class="p">:</span> <span class="n">_rotate_level_to_arg</span><span class="p">,</span>
    <span class="c1"># There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers</span>
    <span class="s1">&#39;Posterize&#39;</span><span class="p">:</span> <span class="n">_posterize_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;PosterizeIncreasing&#39;</span><span class="p">:</span> <span class="n">_posterize_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;PosterizeOriginal&#39;</span><span class="p">:</span> <span class="n">_posterize_original_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;Solarize&#39;</span><span class="p">:</span> <span class="n">_solarize_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeIncreasing&#39;</span><span class="p">:</span> <span class="n">_solarize_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeAdd&#39;</span><span class="p">:</span> <span class="n">_solarize_add_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;Color&#39;</span><span class="p">:</span> <span class="n">_enhance_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;ColorIncreasing&#39;</span><span class="p">:</span> <span class="n">_enhance_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;Contrast&#39;</span><span class="p">:</span> <span class="n">_enhance_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;ContrastIncreasing&#39;</span><span class="p">:</span> <span class="n">_enhance_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;Brightness&#39;</span><span class="p">:</span> <span class="n">_enhance_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;BrightnessIncreasing&#39;</span><span class="p">:</span> <span class="n">_enhance_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;Sharpness&#39;</span><span class="p">:</span> <span class="n">_enhance_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;SharpnessIncreasing&#39;</span><span class="p">:</span> <span class="n">_enhance_increasing_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;ShearX&#39;</span><span class="p">:</span> <span class="n">_shear_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;ShearY&#39;</span><span class="p">:</span> <span class="n">_shear_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;TranslateX&#39;</span><span class="p">:</span> <span class="n">_translate_abs_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;TranslateY&#39;</span><span class="p">:</span> <span class="n">_translate_abs_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;TranslateXRel&#39;</span><span class="p">:</span> <span class="n">_translate_rel_level_to_arg</span><span class="p">,</span>
    <span class="s1">&#39;TranslateYRel&#39;</span><span class="p">:</span> <span class="n">_translate_rel_level_to_arg</span><span class="p">,</span>
<span class="p">}</span>


<span class="n">NAME_TO_OP</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;AutoContrast&#39;</span><span class="p">:</span> <span class="n">auto_contrast</span><span class="p">,</span>
    <span class="s1">&#39;Equalize&#39;</span><span class="p">:</span> <span class="n">equalize</span><span class="p">,</span>
    <span class="s1">&#39;Invert&#39;</span><span class="p">:</span> <span class="n">invert</span><span class="p">,</span>
    <span class="s1">&#39;Rotate&#39;</span><span class="p">:</span> <span class="n">rotate</span><span class="p">,</span>
    <span class="s1">&#39;Posterize&#39;</span><span class="p">:</span> <span class="n">posterize</span><span class="p">,</span>
    <span class="s1">&#39;PosterizeIncreasing&#39;</span><span class="p">:</span> <span class="n">posterize</span><span class="p">,</span>
    <span class="s1">&#39;PosterizeOriginal&#39;</span><span class="p">:</span> <span class="n">posterize</span><span class="p">,</span>
    <span class="s1">&#39;Solarize&#39;</span><span class="p">:</span> <span class="n">solarize</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeIncreasing&#39;</span><span class="p">:</span> <span class="n">solarize</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeAdd&#39;</span><span class="p">:</span> <span class="n">solarize_add</span><span class="p">,</span>
    <span class="s1">&#39;Color&#39;</span><span class="p">:</span> <span class="n">color</span><span class="p">,</span>
    <span class="s1">&#39;ColorIncreasing&#39;</span><span class="p">:</span> <span class="n">color</span><span class="p">,</span>
    <span class="s1">&#39;Contrast&#39;</span><span class="p">:</span> <span class="n">contrast</span><span class="p">,</span>
    <span class="s1">&#39;ContrastIncreasing&#39;</span><span class="p">:</span> <span class="n">contrast</span><span class="p">,</span>
    <span class="s1">&#39;Brightness&#39;</span><span class="p">:</span> <span class="n">brightness</span><span class="p">,</span>
    <span class="s1">&#39;BrightnessIncreasing&#39;</span><span class="p">:</span> <span class="n">brightness</span><span class="p">,</span>
    <span class="s1">&#39;Sharpness&#39;</span><span class="p">:</span> <span class="n">sharpness</span><span class="p">,</span>
    <span class="s1">&#39;SharpnessIncreasing&#39;</span><span class="p">:</span> <span class="n">sharpness</span><span class="p">,</span>
    <span class="s1">&#39;ShearX&#39;</span><span class="p">:</span> <span class="n">shear_x</span><span class="p">,</span>
    <span class="s1">&#39;ShearY&#39;</span><span class="p">:</span> <span class="n">shear_y</span><span class="p">,</span>
    <span class="s1">&#39;TranslateX&#39;</span><span class="p">:</span> <span class="n">translate_x_abs</span><span class="p">,</span>
    <span class="s1">&#39;TranslateY&#39;</span><span class="p">:</span> <span class="n">translate_y_abs</span><span class="p">,</span>
    <span class="s1">&#39;TranslateXRel&#39;</span><span class="p">:</span> <span class="n">translate_x_rel</span><span class="p">,</span>
    <span class="s1">&#39;TranslateYRel&#39;</span><span class="p">:</span> <span class="n">translate_y_rel</span><span class="p">,</span>
<span class="p">}</span>


<div class="viewcode-block" id="AugmentOp"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.AugmentOp">[docs]</a><span class="k">class</span> <span class="nc">AugmentOp</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    single auto augment operations</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">name</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">magnitude</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">hparams</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">hparams</span> <span class="o">=</span> <span class="n">hparams</span> <span class="ow">or</span> <span class="n">_HPARAMS_DEFAULT</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">aug_fn</span> <span class="o">=</span> <span class="n">NAME_TO_OP</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">level_fn</span> <span class="o">=</span> <span class="n">LEVEL_TO_ARG</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">prob</span> <span class="o">=</span> <span class="n">prob</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">magnitude</span> <span class="o">=</span> <span class="n">magnitude</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">hparams</span> <span class="o">=</span> <span class="n">hparams</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">fillcolor</span><span class="o">=</span><span class="n">hparams</span><span class="p">[</span><span class="s1">&#39;img_mean&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;img_mean&#39;</span> <span class="ow">in</span> <span class="n">hparams</span> <span class="k">else</span> <span class="n">_FILL</span><span class="p">,</span>
            <span class="n">resample</span><span class="o">=</span><span class="n">hparams</span><span class="p">[</span><span class="s1">&#39;interpolation&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;interpolation&#39;</span> <span class="ow">in</span> <span class="n">hparams</span> <span class="k">else</span> <span class="n">_RANDOM_INTERPOLATION</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="c1"># If magnitude_std is &gt; 0, introduce some randomness</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">magnitude_std</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hparams</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;magnitude_std&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob</span> <span class="o">&lt;</span> <span class="mf">1.0</span> <span class="ow">and</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">img</span>
        <span class="n">magnitude</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnitude</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnitude_std</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnitude_std</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">):</span>
                <span class="n">magnitude</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">magnitude</span><span class="p">)</span>
            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnitude_std</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">magnitude</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">gauss</span><span class="p">(</span><span class="n">magnitude</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnitude_std</span><span class="p">)</span>
        <span class="n">magnitude</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">_MAX_MAGNITUDE</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">magnitude</span><span class="p">))</span>  <span class="c1"># clip to valid range</span>
        <span class="n">level_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">level_fn</span><span class="p">(</span><span class="n">magnitude</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hparams</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">level_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aug_fn</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="o">*</span><span class="n">level_args</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span></div>


<span class="n">_RAND_TRANSFORMS</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">&#39;AutoContrast&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Equalize&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Invert&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Rotate&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Posterize&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Solarize&#39;</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeAdd&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Color&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Contrast&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Brightness&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Sharpness&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShearX&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShearY&#39;</span><span class="p">,</span>
    <span class="s1">&#39;TranslateXRel&#39;</span><span class="p">,</span>
    <span class="s1">&#39;TranslateYRel&#39;</span><span class="p">,</span>
    <span class="c1"># &#39;Cutout&#39;  # NOTE I&#39;ve implement this as random erasing separately</span>
<span class="p">]</span>


<span class="n">_RAND_INCREASING_TRANSFORMS</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">&#39;AutoContrast&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Equalize&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Invert&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Rotate&#39;</span><span class="p">,</span>
    <span class="s1">&#39;PosterizeIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeAdd&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ColorIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ContrastIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;BrightnessIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;SharpnessIncreasing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShearX&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShearY&#39;</span><span class="p">,</span>
    <span class="s1">&#39;TranslateXRel&#39;</span><span class="p">,</span>
    <span class="s1">&#39;TranslateYRel&#39;</span><span class="p">,</span>
    <span class="c1"># &#39;Cutout&#39;  # NOTE I&#39;ve implement this as random erasing separately</span>
<span class="p">]</span>

<span class="c1"># These experimental weights are based loosely on the relative improvements mentioned in paper.</span>
<span class="c1"># They may not result in increased performance, but could likely be tuned to so.</span>
<span class="n">_RAND_CHOICE_WEIGHTS_0</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;Rotate&#39;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
    <span class="s1">&#39;ShearX&#39;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
    <span class="s1">&#39;ShearY&#39;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
    <span class="s1">&#39;TranslateXRel&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
    <span class="s1">&#39;TranslateYRel&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
    <span class="s1">&#39;Color&#39;</span><span class="p">:</span> <span class="mf">.025</span><span class="p">,</span>
    <span class="s1">&#39;Sharpness&#39;</span><span class="p">:</span> <span class="mf">0.025</span><span class="p">,</span>
    <span class="s1">&#39;AutoContrast&#39;</span><span class="p">:</span> <span class="mf">0.025</span><span class="p">,</span>
    <span class="s1">&#39;Solarize&#39;</span><span class="p">:</span> <span class="mf">.005</span><span class="p">,</span>
    <span class="s1">&#39;SolarizeAdd&#39;</span><span class="p">:</span> <span class="mf">.005</span><span class="p">,</span>
    <span class="s1">&#39;Contrast&#39;</span><span class="p">:</span> <span class="mf">.005</span><span class="p">,</span>
    <span class="s1">&#39;Brightness&#39;</span><span class="p">:</span> <span class="mf">.005</span><span class="p">,</span>
    <span class="s1">&#39;Equalize&#39;</span><span class="p">:</span> <span class="mf">.005</span><span class="p">,</span>
    <span class="s1">&#39;Posterize&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
    <span class="s1">&#39;Invert&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
<span class="p">}</span>


<span class="k">def</span> <span class="nf">_select_rand_weights</span><span class="p">(</span><span class="n">weight_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span> <span class="ow">or</span> <span class="n">_RAND_TRANSFORMS</span>
    <span class="k">assert</span> <span class="n">weight_idx</span> <span class="o">==</span> <span class="mi">0</span>  <span class="c1"># only one set of weights currently</span>
    <span class="n">rand_weights</span> <span class="o">=</span> <span class="n">_RAND_CHOICE_WEIGHTS_0</span>
    <span class="n">probs</span> <span class="o">=</span> <span class="p">[</span><span class="n">rand_weights</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">transforms</span><span class="p">]</span>
    <span class="n">probs</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">probs</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">probs</span>


<div class="viewcode-block" id="rand_augment_ops"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.rand_augment_ops">[docs]</a><span class="k">def</span> <span class="nf">rand_augment_ops</span><span class="p">(</span><span class="n">magnitude</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">hparams</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="n">hparams</span> <span class="o">=</span> <span class="n">hparams</span> <span class="ow">or</span> <span class="n">_HPARAMS_DEFAULT</span>
    <span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span> <span class="ow">or</span> <span class="n">_RAND_TRANSFORMS</span>
    <span class="k">return</span> <span class="p">[</span><span class="n">AugmentOp</span><span class="p">(</span>
        <span class="n">name</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">magnitude</span><span class="o">=</span><span class="n">magnitude</span><span class="p">,</span> <span class="n">hparams</span><span class="o">=</span><span class="n">hparams</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">transforms</span><span class="p">]</span></div>


<div class="viewcode-block" id="RandAugment"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.RandAugment">[docs]</a><span class="k">class</span> <span class="nc">RandAugment</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Random auto augment class, will select auto augment transforms according to probability weights for each op</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">ops</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">choice_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ops</span> <span class="o">=</span> <span class="n">ops</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">choice_weights</span> <span class="o">=</span> <span class="n">choice_weights</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
        <span class="c1"># no replacement when using weighted choice</span>
        <span class="n">ops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">ops</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">choice_weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">choice_weights</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="n">ops</span><span class="p">:</span>
            <span class="n">img</span> <span class="o">=</span> <span class="n">op</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">img</span></div>


<div class="viewcode-block" id="rand_augment_transform"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.auto_augment.rand_augment_transform">[docs]</a><span class="k">def</span> <span class="nf">rand_augment_transform</span><span class="p">(</span><span class="n">config_str</span><span class="p">,</span> <span class="n">hparams</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Create a RandAugment transform</span>

<span class="sd">    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by</span>
<span class="sd">    dashes (&#39;-&#39;). The first section defines the specific variant of rand augment (currently only &#39;rand&#39;). The remaining</span>
<span class="sd">    sections, not order sepecific determine</span>
<span class="sd">        &#39;m&#39; - integer magnitude of rand augment</span>
<span class="sd">        &#39;n&#39; - integer num layers (number of transform ops selected per image)</span>
<span class="sd">        &#39;w&#39; - integer probabiliy weight index (index of a set of weights to influence choice of op)</span>
<span class="sd">        &#39;mstd&#39; -  float std deviation of magnitude noise applied</span>
<span class="sd">        &#39;inc&#39; - integer (bool), use augmentations that increase in severity with magnitude (default: 0)</span>
<span class="sd">    Ex &#39;rand-m9-n3-mstd0.5&#39; results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5</span>
<span class="sd">    &#39;rand-mstd1-w0&#39; results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2</span>

<span class="sd">    :param hparams: Other hparams (kwargs) for the RandAugmentation scheme</span>

<span class="sd">    :return: A PyTorch compatible Transform</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">magnitude</span> <span class="o">=</span> <span class="n">_MAX_MAGNITUDE</span>  <span class="c1"># default to _MAX_MAGNITUDE for magnitude (currently 10)</span>
    <span class="n">num_layers</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># default to 2 ops per image</span>
    <span class="n">weight_idx</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># default to no probability weights for op choice</span>
    <span class="n">transforms</span> <span class="o">=</span> <span class="n">_RAND_TRANSFORMS</span>
    <span class="n">config</span> <span class="o">=</span> <span class="n">config_str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">config</span><span class="p">:</span>
        <span class="n">cs</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;(\d.*)&#39;</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cs</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
            <span class="k">continue</span>
        <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">cs</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
        <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;mstd&#39;</span><span class="p">:</span>
            <span class="c1"># noise param injected via hparams for now</span>
            <span class="n">hparams</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s1">&#39;magnitude_std&#39;</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
        <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;inc&#39;</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">bool</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
                <span class="n">transforms</span> <span class="o">=</span> <span class="n">_RAND_INCREASING_TRANSFORMS</span>
        <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;m&#39;</span><span class="p">:</span>
            <span class="n">magnitude</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;n&#39;</span><span class="p">:</span>
            <span class="n">num_layers</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;w&#39;</span><span class="p">:</span>
            <span class="n">weight_idx</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">assert</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;Unknown RandAugment config section&#39;</span>
    <span class="n">ra_ops</span> <span class="o">=</span> <span class="n">rand_augment_ops</span><span class="p">(</span><span class="n">magnitude</span><span class="o">=</span><span class="n">magnitude</span><span class="p">,</span> <span class="n">hparams</span><span class="o">=</span><span class="n">hparams</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">)</span>
    <span class="n">choice_weights</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">weight_idx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_select_rand_weights</span><span class="p">(</span><span class="n">weight_idx</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">RandAugment</span><span class="p">(</span><span class="n">ra_ops</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">choice_weights</span><span class="o">=</span><span class="n">choice_weights</span><span class="p">)</span></div>
</pre></div>

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