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  1. <!DOCTYPE html>
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    69. 

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

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

  71. <span class="kn">import</span> <span class="nn">os</span>
    72. 

  72. <span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
    73. 

  73. <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Value</span><span class="p">,</span> <span class="n">Lock</span>
    74. 

  74. <span class="kn">import</span> <span class="nn">random</span>
    75. 

  75. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
    76. 

  76. <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
    77. 

  77. <span class="kn">import</span> <span class="nn">torchvision</span>
    78. 

  78. <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
    79. 

  79. <span class="kn">import</span> <span class="nn">torch</span>
    80. 

  80. 

  81. <span class="kn">from</span> <span class="nn">super_gradients.common.sg_loggers.abstract_sg_logger</span> <span class="kn">import</span> <span class="n">AbstractSGLogger</span>
    82. 

  82. <span class="kn">from</span> <span class="nn">super_gradients.training.datasets.detection_datasets.detection_dataset</span> <span class="kn">import</span> <span class="n">DetectionDataSet</span>
    83. 

  83. 

  84. <span class="kn">from</span> <span class="nn">super_gradients.common.abstractions.abstract_logger</span> <span class="kn">import</span> <span class="n">get_logger</span>
    85. 

  85. <span class="kn">from</span> <span class="nn">deprecated</span> <span class="kn">import</span> <span class="n">deprecated</span>
    86. 

  86. <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Rectangle</span>
    87. 

  87. <span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
    88. 

  88. <span class="kn">from</span> <span class="nn">torchvision.datasets</span> <span class="kn">import</span> <span class="n">ImageFolder</span>
    89. 

  89. <span class="kn">from</span> <span class="nn">super_gradients.training.datasets.auto_augment</span> <span class="kn">import</span> <span class="n">rand_augment_transform</span>
    90. 

  90. <span class="kn">from</span> <span class="nn">torchvision.transforms</span> <span class="kn">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">InterpolationMode</span><span class="p">,</span> <span class="n">RandomResizedCrop</span>
    91. 

  91. <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
    92. 

  92. 

  93. <span class="kn">from</span> <span class="nn">super_gradients.training.utils.utils</span> <span class="kn">import</span> <span class="n">AverageMeter</span>
    94. 

  94. <span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">DetectionVisualization</span>
    95. 

  95. 

  96. <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
    97. 

  97. 

  98. 

  99. <div class="viewcode-block" id="get_mean_and_std_torch"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.get_mean_and_std_torch">[docs]</a><span class="k">def</span> <span class="nf">get_mean_and_std_torch</span><span class="p">(</span><span class="n">data_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dataloader</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">RandomResizeSize</span><span class="o">=</span><span class="mi">224</span><span class="p">):</span>
    100. 

  100.     <span class="sd">&quot;&quot;&quot;</span>
    101. 

  101. <span class="sd">    A function for getting the mean and std of large datasets using pytorch dataloader and gpu functionality.</span>
    102. 

  102. 

  103. <span class="sd">    :param data_dir: String, path to none-library dataset folder. For example &quot;/data/Imagenette&quot; or &quot;/data/TinyImagenet&quot;</span>
    104. 

  104. <span class="sd">    :param dataloader: a torch DataLoader, as it would feed the data into the trainer (including transforms etc).</span>
    105. 

  105. <span class="sd">    :param RandomResizeSize: Int, the size of the RandomResizeCrop as it appears in the DataInterface (for example, for Imagenet,</span>
    106. 

  106. <span class="sd">    this value should be 224).</span>
    107. 

  107. <span class="sd">    :return: 2 lists,mean and std, each one of len 3 (1 for each channel)</span>
    108. 

  108. <span class="sd">    &quot;&quot;&quot;</span>
    109. 

  109.     <span class="k">assert</span> <span class="n">data_dir</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">dataloader</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;Please provide either path to data folder or DataLoader, not both.&#39;</span>
    110. 

  110. 

  111.     <span class="k">if</span> <span class="n">dataloader</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
    112. 

  112.         <span class="n">traindir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">data_dir</span><span class="p">),</span> <span class="s1">&#39;train&#39;</span><span class="p">)</span>
    113. 

  113.         <span class="n">trainset</span> <span class="o">=</span> <span class="n">ImageFolder</span><span class="p">(</span><span class="n">traindir</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="n">RandomResizeSize</span><span class="p">),</span>
    114. 

  114.                                                              <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
    115. 

  115.                                                              <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()]))</span>
    116. 

  116.         <span class="n">dataloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span>
    117. 

  117. 

  118.     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Calculating on </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">targets</span><span class="p">)</span><span class="si">}</span><span class="s1"> Training Samples&#39;</span><span class="p">)</span>
    119. 

  119. 

  120.     <span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda:0&#39;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
    121. 

  121.     <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
    122. 

  122.     <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
    123. 

  123.         <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    124. 

  124.         <span class="k">if</span> <span class="n">batch_idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    125. 

  125.             <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">inputs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
    126. 

  126.             <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Min: </span><span class="si">{</span><span class="n">inputs</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="si">}</span><span class="s1">, Max: </span><span class="si">{</span><span class="n">inputs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    127. 

  127.             <span class="n">chsum</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    128. 

  128.         <span class="k">else</span><span class="p">:</span>
    129. 

  129.             <span class="n">chsum</span> <span class="o">+=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    130. 

  130.     <span class="n">mean</span> <span class="o">=</span> <span class="n">chsum</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span> <span class="o">/</span> <span class="n">h</span> <span class="o">/</span> <span class="n">w</span>
    131. 

  131.     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;mean: </span><span class="si">{</span><span class="n">mean</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    132. 

  132. 

  133.     <span class="n">chsum</span> <span class="o">=</span> <span class="kc">None</span>
    134. 

  134.     <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
    135. 

  135.         <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    136. 

  136.         <span class="k">if</span> <span class="n">batch_idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    137. 

  137.             <span class="n">chsum</span> <span class="o">=</span> <span class="p">(</span><span class="n">inputs</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    138. 

  138.         <span class="k">else</span><span class="p">:</span>
    139. 

  139.             <span class="n">chsum</span> <span class="o">+=</span> <span class="p">(</span><span class="n">inputs</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    140. 

  140.     <span class="n">std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">chsum</span> <span class="o">/</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span> <span class="o">*</span> <span class="n">h</span> <span class="o">*</span> <span class="n">w</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
    141. 

  141.     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;std: </span><span class="si">{</span><span class="n">std</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    142. 

  142.     <span class="k">return</span> <span class="n">mean</span><span class="o">.</span><span class="n">view</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">std</span><span class="o">.</span><span class="n">view</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
    143. 

  143. 

  144. 

  145. <div class="viewcode-block" id="get_mean_and_std"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.get_mean_and_std">[docs]</a><span class="nd">@deprecated</span><span class="p">(</span><span class="n">reason</span><span class="o">=</span><span class="s1">&#39;Use get_mean_and_std_torch() instead. It is faster and more accurate&#39;</span><span class="p">)</span>
    146. 

  146. <span class="k">def</span> <span class="nf">get_mean_and_std</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
    147. 

  147.     <span class="sd">&#39;&#39;&#39;Compute the mean and std value of dataset.&#39;&#39;&#39;</span>
    148. 

  148.     <span class="n">dataloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    149. 

  149.     <span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
    150. 

  150.     <span class="n">std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
    151. 

  151.     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;==&gt; Computing mean and std..&#39;</span><span class="p">)</span>
    152. 

  152.     <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span>
    153. 

  153.     <span class="k">for</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span> <span class="ow">in</span> <span class="n">dataloader</span><span class="p">:</span>
    154. 

  154.         <span class="k">if</span> <span class="n">j</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    155. 

  155.             <span class="nb">print</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
    156. 

  156.         <span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
    157. 

  157.         <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">3</span><span class="p">):</span>
    158. 

  158.             <span class="n">mean</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">inputs</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
    159. 

  159.             <span class="n">std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">inputs</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
    160. 

  160.     <span class="n">mean</span><span class="o">.</span><span class="n">div_</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
    161. 

  161.     <span class="n">std</span><span class="o">.</span><span class="n">div_</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
    162. 

  162.     <span class="k">return</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span></div>
    163. 

  163. 

  164. 

  165. <div class="viewcode-block" id="AbstractCollateFunction"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.AbstractCollateFunction">[docs]</a><span class="k">class</span> <span class="nc">AbstractCollateFunction</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
    166. 

  166.     <span class="sd">&quot;&quot;&quot;</span>
    167. 

  167. <span class="sd">    A collate function (for torch DataLoader)</span>
    168. 

  168. <span class="sd">    &quot;&quot;&quot;</span>
    169. 

  169. 

  170.     <span class="nd">@abstractmethod</span>
    171. 

  171.     <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">batch</span><span class="p">):</span>
    172. 

  172.         <span class="k">pass</span></div>
    173. 

  173. 

  174. 

  175. <div class="viewcode-block" id="ComposedCollateFunction"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.ComposedCollateFunction">[docs]</a><span class="k">class</span> <span class="nc">ComposedCollateFunction</span><span class="p">(</span><span class="n">AbstractCollateFunction</span><span class="p">):</span>
    176. 

  176.     <span class="sd">&quot;&quot;&quot;</span>
    177. 

  177. <span class="sd">    A function (for torch DataLoader) which executes a sequence of sub collate functions</span>
    178. 

  178. <span class="sd">    &quot;&quot;&quot;</span>
    179. 

  179. 

  180.     <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">functions</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
    181. 

  181.         <span class="bp">self</span><span class="o">.</span><span class="n">functions</span> <span class="o">=</span> <span class="n">functions</span>
    182. 

  182. 

  183.     <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">batch</span><span class="p">):</span>
    184. 

  184.         <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">:</span>
    185. 

  185.             <span class="n">batch</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
    186. 

  186.         <span class="k">return</span> <span class="n">batch</span></div>
    187. 

  187. 

  188. 

  189. <div class="viewcode-block" id="AtomicInteger"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.AtomicInteger">[docs]</a><span class="k">class</span> <span class="nc">AtomicInteger</span><span class="p">:</span>
    190. 

  190.     <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">value</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
    191. 

  191.         <span class="bp">self</span><span class="o">.</span><span class="n">_value</span> <span class="o">=</span> <span class="n">Value</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
    192. 

  192. 

  193.     <span class="k">def</span> <span class="fm">__set__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instance</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
    194. 

  194.         <span class="bp">self</span><span class="o">.</span><span class="n">_value</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">value</span>
    195. 

  195. 

  196.     <span class="k">def</span> <span class="fm">__get__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instance</span><span class="p">,</span> <span class="n">owner</span><span class="p">):</span>
    197. 

  197.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value</span><span class="o">.</span><span class="n">value</span></div>
    198. 

  198. 

  199. 

  200. <div class="viewcode-block" id="MultiScaleCollateFunction"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.MultiScaleCollateFunction">[docs]</a><span class="k">class</span> <span class="nc">MultiScaleCollateFunction</span><span class="p">(</span><span class="n">AbstractCollateFunction</span><span class="p">):</span>
    201. 

  201.     <span class="sd">&quot;&quot;&quot;</span>
    202. 

  202. <span class="sd">    a collate function to implement multi-scale data augmentation</span>
    203. 

  203. <span class="sd">    according to https://arxiv.org/pdf/1612.08242.pdf</span>
    204. 

  204. <span class="sd">    &quot;&quot;&quot;</span>
    205. 

  205.     <span class="n">_counter</span> <span class="o">=</span> <span class="n">AtomicInteger</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    206. 

  206.     <span class="n">_current_size</span> <span class="o">=</span> <span class="n">AtomicInteger</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    207. 

  207.     <span class="n">_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</span>
    208. 

  208. 

  209.     <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">target_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">min_image_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_image_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    210. 

  210.                  <span class="n">image_size_steps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
    211. 

  211.                  <span class="n">change_frequency</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">):</span>
    212. 

  212.         <span class="sd">&quot;&quot;&quot;</span>
    213. 

  213. <span class="sd">        set parameters for the multi-scale collate function</span>
    214. 

  214. <span class="sd">        the possible image sizes are in range [min_image_size, max_image_size] in steps of image_size_steps</span>
    215. 

  215. <span class="sd">        a new size will be randomly selected every change_frequency calls to the collate_fn()</span>
    216. 

  216. <span class="sd">            :param target_size: scales will be [0.66 * target_size, 1.5 * target_size]</span>
    217. 

  217. <span class="sd">            :param min_image_size: the minimum size to scale down to (in pixels)</span>
    218. 

  218. <span class="sd">            :param max_image_size: the maximum size to scale up to (in pixels)</span>
    219. 

  219. <span class="sd">            :param image_size_steps: typically, the stride of the net, which defines the possible image</span>
    220. 

  220. <span class="sd">                    size multiplications</span>
    221. 

  221. <span class="sd">            :param change_frequency:</span>
    222. 

  222. <span class="sd">        &quot;&quot;&quot;</span>
    223. 

  223.         <span class="k">assert</span> <span class="n">target_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="n">max_image_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">min_image_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">),</span> \
    224. 

  224.             <span class="s1">&#39;either target_size or min_image_size and max_image_size has to be set&#39;</span>
    225. 

  225.         <span class="k">assert</span> <span class="n">target_size</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_image_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;target_size and max_image_size cannot be both defined&#39;</span>
    226. 

  226. 

  227.         <span class="k">if</span> <span class="n">target_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
    228. 

  228.             <span class="n">min_image_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.66</span> <span class="o">*</span> <span class="n">target_size</span> <span class="o">-</span> <span class="p">((</span><span class="mf">0.66</span> <span class="o">*</span> <span class="n">target_size</span><span class="p">)</span> <span class="o">%</span> <span class="n">image_size_steps</span><span class="p">)</span> <span class="o">+</span> <span class="n">image_size_steps</span><span class="p">)</span>
    229. 

  229.             <span class="n">max_image_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">target_size</span> <span class="o">-</span> <span class="p">((</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">target_size</span><span class="p">)</span> <span class="o">%</span> <span class="n">image_size_steps</span><span class="p">))</span>
    230. 

  230. 

  231.         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using multi-scale </span><span class="si">%g</span><span class="s1"> - </span><span class="si">%g</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">min_image_size</span><span class="p">,</span> <span class="n">max_image_size</span><span class="p">))</span>
    232. 

  232. 

  233.         <span class="bp">self</span><span class="o">.</span><span class="n">sizes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">min_image_size</span><span class="p">,</span> <span class="n">max_image_size</span> <span class="o">+</span> <span class="n">image_size_steps</span><span class="p">,</span> <span class="n">image_size_steps</span><span class="p">)</span>
    234. 

  234.         <span class="bp">self</span><span class="o">.</span><span class="n">image_size_steps</span> <span class="o">=</span> <span class="n">image_size_steps</span>
    235. 

  235.         <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span> <span class="o">=</span> <span class="n">change_frequency</span>
    236. 

  236.         <span class="bp">self</span><span class="o">.</span><span class="n">_current_size</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">sizes</span><span class="p">)</span>
    237. 

  237. 

  238.     <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">batch</span><span class="p">):</span>
    239. 

  239. 

  240.         <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
    241. 

  241. 

  242.             <span class="c1"># Important: this implementation was tailored for a specific input. it assumes the batch is a tuple where</span>
    243. 

  243.             <span class="c1"># the images are the first item</span>
    244. 

  244.             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">),</span> <span class="s1">&#39;this collate function expects the input to be a tuple (images, labels)&#39;</span>
    245. 

  245.             <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    246. 

  246.             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    247. 

  247.                 <span class="bp">self</span><span class="o">.</span><span class="n">_current_size</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">sizes</span><span class="p">)</span>
    248. 

  248.             <span class="bp">self</span><span class="o">.</span><span class="n">_counter</span> <span class="o">+=</span> <span class="mi">1</span>
    249. 

  249. 

  250.             <span class="k">assert</span> <span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_size_steps</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_size_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> \
    251. 

  251.                 <span class="s1">&#39;images sized not divisible by </span><span class="si">%d</span><span class="s1">. (resize images before calling multi_scale)&#39;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_size_steps</span>
    252. 

  252. 

  253.             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_current_size</span> <span class="o">!=</span> <span class="nb">max</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]):</span>
    254. 

  254.                 <span class="n">ratio</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_current_size</span><span class="p">)</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
    255. 

  255.                 <span class="n">new_size</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)))</span>
    256. 

  256.                 <span class="n">images</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">new_size</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;bilinear&#39;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    257. 

  257. 

  258.             <span class="k">return</span> <span class="n">images</span><span class="p">,</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span></div>
    259. 

  259. 

  260. 

  261. <span class="n">_pil_interpolation_to_str</span> <span class="o">=</span> <span class="p">{</span>
    262. 

  262.     <span class="n">Image</span><span class="o">.</span><span class="n">NEAREST</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.NEAREST&#39;</span><span class="p">,</span>
    263. 

  263.     <span class="n">Image</span><span class="o">.</span><span class="n">BILINEAR</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.BILINEAR&#39;</span><span class="p">,</span>
    264. 

  264.     <span class="n">Image</span><span class="o">.</span><span class="n">BICUBIC</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.BICUBIC&#39;</span><span class="p">,</span>
    265. 

  265.     <span class="n">Image</span><span class="o">.</span><span class="n">LANCZOS</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.LANCZOS&#39;</span><span class="p">,</span>
    266. 

  266.     <span class="n">Image</span><span class="o">.</span><span class="n">HAMMING</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.HAMMING&#39;</span><span class="p">,</span>
    267. 

  267.     <span class="n">Image</span><span class="o">.</span><span class="n">BOX</span><span class="p">:</span> <span class="s1">&#39;PIL.Image.BOX&#39;</span><span class="p">,</span>
    268. 

  268. <span class="p">}</span>
    269. 

  269. 

  270. 

  271. <span class="k">def</span> <span class="nf">_pil_interp</span><span class="p">(</span><span class="n">method</span><span class="p">):</span>
    272. 

  272.     <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;bicubic&#39;</span><span class="p">:</span>
    273. 

  273.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BICUBIC</span>
    274. 

  274.     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;lanczos&#39;</span><span class="p">:</span>
    275. 

  275.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">LANCZOS</span>
    276. 

  276.     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;hamming&#39;</span><span class="p">:</span>
    277. 

  277.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">HAMMING</span>
    278. 

  278.     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;nearest&#39;</span><span class="p">:</span>
    279. 

  279.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">NEAREST</span>
    280. 

  280.     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;bilinear&#39;</span><span class="p">:</span>
    281. 

  281.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BILINEAR</span>
    282. 

  282.     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;box&#39;</span><span class="p">:</span>
    283. 

  283.         <span class="k">return</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BOX</span>
    284. 

  284.     <span class="k">else</span><span class="p">:</span>
    285. 

  285.         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;interpolation type must be one of [&#39;bilinear&#39;, &#39;bicubic&#39;, &#39;lanczos&#39;, &#39;hamming&#39;, &quot;</span>
    286. 

  286.                          <span class="s2">&quot;&#39;nearest&#39;, &#39;box&#39;] for explicit interpolation type, or &#39;random&#39; for random&quot;</span><span class="p">)</span>
    287. 

  287. 

  288. 

  289. <span class="n">_RANDOM_INTERPOLATION</span> <span class="o">=</span> <span class="p">(</span><span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BILINEAR</span><span class="p">,</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BICUBIC</span><span class="p">)</span>
    290. 

  290. 

  291. 

  292. <div class="viewcode-block" id="RandomResizedCropAndInterpolation"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.RandomResizedCropAndInterpolation">[docs]</a><span class="k">class</span> <span class="nc">RandomResizedCropAndInterpolation</span><span class="p">(</span><span class="n">RandomResizedCrop</span><span class="p">):</span>
    293. 

  293.     <span class="sd">&quot;&quot;&quot;</span>
    294. 

  294. <span class="sd">    Crop the given PIL Image to random size and aspect ratio with explicitly chosen or random interpolation.</span>
    295. 

  295. 

  296. <span class="sd">    A crop of random size (default: of 0.08 to 1.0) of the original size and a random</span>
    297. 

  297. <span class="sd">    aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop</span>
    298. 

  298. <span class="sd">    is finally resized to given size.</span>
    299. 

  299. <span class="sd">    This is popularly used to train the Inception networks.</span>
    300. 

  300. 

  301. <span class="sd">    Args:</span>
    302. 

  302. <span class="sd">        size: expected output size of each edge</span>
    303. 

  303. <span class="sd">        scale: range of size of the origin size cropped</span>
    304. 

  304. <span class="sd">        ratio: range of aspect ratio of the origin aspect ratio cropped</span>
    305. 

  305. <span class="sd">        interpolation: Default: PIL.Image.BILINEAR</span>
    306. 

  306. <span class="sd">    &quot;&quot;&quot;</span>
    307. 

  307. 

  308.     <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">size</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="p">(</span><span class="mf">0.08</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">ratio</span><span class="o">=</span><span class="p">(</span><span class="mf">3.</span> <span class="o">/</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">),</span>
    309. 

  309.                  <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">):</span>
    310. 

  310.         <span class="nb">super</span><span class="p">(</span><span class="n">RandomResizedCropAndInterpolation</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><span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> <span class="n">ratio</span><span class="o">=</span><span class="n">ratio</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="n">interpolation</span><span class="p">)</span>
    311. 

  311.         <span class="k">if</span> <span class="n">interpolation</span> <span class="o">==</span> <span class="s1">&#39;random&#39;</span><span class="p">:</span>
    312. 

  312.             <span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span> <span class="o">=</span> <span class="n">_RANDOM_INTERPOLATION</span>
    313. 

  313.         <span class="k">elif</span> <span class="n">interpolation</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
    314. 

  314.             <span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span> <span class="o">=</span> <span class="n">InterpolationMode</span><span class="o">.</span><span class="n">BILINEAR</span>
    315. 

  315.         <span class="k">else</span><span class="p">:</span>
    316. 

  316.             <span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span> <span class="o">=</span> <span class="n">_pil_interp</span><span class="p">(</span><span class="n">interpolation</span><span class="p">)</span>
    317. 

  317. 

  318. <div class="viewcode-block" id="RandomResizedCropAndInterpolation.forward"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.RandomResizedCropAndInterpolation.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">img</span><span class="p">):</span>
    319. 

  319.         <span class="sd">&quot;&quot;&quot;</span>
    320. 

  320. <span class="sd">        Args:</span>
    321. 

  321. <span class="sd">            img (PIL Image): Image to be cropped and resized.</span>
    322. 

  322. 

  323. <span class="sd">        Returns:</span>
    324. 

  324. <span class="sd">            PIL Image: Randomly cropped and resized image.</span>
    325. 

  325. <span class="sd">        &quot;&quot;&quot;</span>
    326. 

  326.         <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ratio</span><span class="p">)</span>
    327. 

  327.         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
    328. 

  328.             <span class="n">interpolation</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">interpolation</span><span class="p">)</span>
    329. 

  329.         <span class="k">else</span><span class="p">:</span>
    330. 

  330.             <span class="n">interpolation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span>
    331. 

  331.         <span class="k">return</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">resized_crop</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">interpolation</span><span class="p">)</span></div>
    332. 

  332. 

  333.     <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
    334. 

  334.         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
    335. 

  335.             <span class="n">interpolate_str</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">_pil_interpolation_to_str</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span><span class="p">])</span>
    336. 

  336.         <span class="k">else</span><span class="p">:</span>
    337. 

  337.             <span class="n">interpolate_str</span> <span class="o">=</span> <span class="n">_pil_interpolation_to_str</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">interpolation</span><span class="p">]</span>
    338. 

  338.         <span class="n">format_string</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39;(size=</span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
    339. 

  339.         <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;, scale=</span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">))</span>
    340. 

  340.         <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;, ratio=</span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ratio</span><span class="p">))</span>
    341. 

  341.         <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;, interpolation=</span><span class="si">{0}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">interpolate_str</span><span class="p">)</span>
    342. 

  342.         <span class="k">return</span> <span class="n">format_string</span></div>
    343. 

  343. 

  344. 

  345. <span class="n">STAT_LOGGER_FONT_SIZE</span> <span class="o">=</span> <span class="mi">15</span>
    346. 

  346. 

  347. 

  348. <div class="viewcode-block" id="DatasetStatisticsTensorboardLogger"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.DatasetStatisticsTensorboardLogger">[docs]</a><span class="k">class</span> <span class="nc">DatasetStatisticsTensorboardLogger</span><span class="p">:</span>
    349. 

  349. 

  350.     <span class="n">logger</span> <span class="o">=</span> <span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
    351. 

  351.     <span class="n">DEFAULT_SUMMARY_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
    352. 

  352.         <span class="s1">&#39;sample_images&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span>  <span class="c1"># by default, 32 images will be sampled from each dataset</span>
    353. 

  353.         <span class="s1">&#39;plot_class_distribution&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
    354. 

  354.         <span class="s1">&#39;plot_box_size_distribution&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
    355. 

  355.         <span class="s1">&#39;plot_anchors_coverage&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
    356. 

  356.         <span class="s1">&#39;max_batches&#39;</span><span class="p">:</span> <span class="mi">30</span>
    357. 

  357.     <span class="p">}</span>
    358. 

  358. 

  359.     <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">sg_logger</span><span class="p">:</span> <span class="n">AbstractSGLogger</span><span class="p">,</span> <span class="n">summary_params</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="n">DEFAULT_SUMMARY_PARAMS</span><span class="p">):</span>
    360. 

  360.         <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span> <span class="o">=</span> <span class="n">sg_logger</span>
    361. 

  361.         <span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">DatasetStatisticsTensorboardLogger</span><span class="o">.</span><span class="n">DEFAULT_SUMMARY_PARAMS</span><span class="p">,</span> <span class="o">**</span><span class="n">summary_params</span><span class="p">}</span>
    362. 

  362. 

  363. <div class="viewcode-block" id="DatasetStatisticsTensorboardLogger.analyze"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.DatasetStatisticsTensorboardLogger.analyze">[docs]</a>    <span class="k">def</span> <span class="nf">analyze</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">,</span> <span class="n">dataset_params</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">anchors</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    364. 

  364.         <span class="sd">&quot;&quot;&quot;</span>
    365. 

  365. <span class="sd">        :param data_loader: the dataset data loader</span>
    366. 

  366. <span class="sd">        :param dataset_params: the dataset parameters</span>
    367. 

  367. <span class="sd">        :param title: the title for this dataset (i.e. Coco 2017 test set)</span>
    368. 

  368. <span class="sd">        :param anchors: the list of anchors used by the model. applicable only for detection datasets</span>
    369. 

  369. <span class="sd">        &quot;&quot;&quot;</span>
    370. 

  370.         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">,</span> <span class="n">DetectionDataSet</span><span class="p">):</span>
    371. 

  371.             <span class="bp">self</span><span class="o">.</span><span class="n">_analyze_detection</span><span class="p">(</span><span class="n">data_loader</span><span class="o">=</span><span class="n">data_loader</span><span class="p">,</span> <span class="n">dataset_params</span><span class="o">=</span><span class="n">dataset_params</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">anchors</span><span class="o">=</span><span class="n">anchors</span><span class="p">)</span>
    372. 

  372.         <span class="k">else</span><span class="p">:</span>
    373. 

  373.             <span class="n">DatasetStatisticsTensorboardLogger</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">&#39;only DetectionDataSet are currently supported&#39;</span><span class="p">)</span></div>
    374. 

  374. 

  375.     <span class="k">def</span> <span class="nf">_analyze_detection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">dataset_params</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">anchors</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    376. 

  376.         <span class="sd">&quot;&quot;&quot;</span>
    377. 

  377. <span class="sd">        Analyze a detection dataset</span>
    378. 

  378. 

  379. <span class="sd">        :param data_loader: the dataset data loader</span>
    380. 

  380. <span class="sd">        :param dataset_params: the dataset parameters</span>
    381. 

  381. <span class="sd">        :param title: the title for this dataset (i.e. Coco 2017 test set)</span>
    382. 

  382. <span class="sd">        :param anchors: the list of anchors used by the model. if not provided, anchors coverage will not be analyzed</span>
    383. 

  383. <span class="sd">        &quot;&quot;&quot;</span>
    384. 

  384.         <span class="k">try</span><span class="p">:</span>
    385. 

  385.             <span class="n">color_mean</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
    386. 

  386.             <span class="n">color_std</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
    387. 

  387.             <span class="n">all_labels</span> <span class="o">=</span> <span class="p">[]</span>
    388. 

  388. 

  389.             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">data_loader</span><span class="p">)):</span>
    390. 

  390. 

  391.                 <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span><span class="p">[</span><span class="s1">&#39;max_batches&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
    392. 

  392.                     <span class="k">break</span>
    393. 

  393. 

  394.                 <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    395. 

  395.                     <span class="k">if</span> <span class="n">images</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">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span><span class="p">[</span><span class="s1">&#39;sample_images&#39;</span><span class="p">]:</span>
    396. 

  396.                         <span class="n">samples</span> <span class="o">=</span> <span class="n">images</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span><span class="p">[</span><span class="s1">&#39;sample_images&#39;</span><span class="p">]]</span>
    397. 

  397.                     <span class="k">else</span><span class="p">:</span>
    398. 

  398.                         <span class="n">samples</span> <span class="o">=</span> <span class="n">images</span>
    399. 

  399. 

  400.                     <span class="n">pred</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">))]</span>
    401. 

  401.                     <span class="n">class_names</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">all_classes_list</span>
    402. 

  402.                     <span class="n">result_images</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">visualize_batch</span><span class="p">(</span><span class="n">image_tensor</span><span class="o">=</span><span class="n">samples</span><span class="p">,</span> <span class="n">pred_boxes</span><span class="o">=</span><span class="n">pred</span><span class="p">,</span>
    403. 

  403.                                                                            <span class="n">target_boxes</span><span class="o">=</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">labels</span><span class="p">),</span>
    404. 

  404.                                                                            <span class="n">batch_name</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
    405. 

  405.                                                                            <span class="n">box_thickness</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
    406. 

  406.                                                                            <span class="n">gt_alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
    407. 

  407. 

  408.                     <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_images</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> sample images&#39;</span><span class="p">,</span> <span class="n">images</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">result_images</span><span class="p">)</span>
    409. 

  409.                                            <span class="o">.</span><span class="n">transpose</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])[:,</span> <span class="p">::</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:])</span>
    410. 

  410. 

  411.                 <span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
    412. 

  412.                 <span class="n">color_mean</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
    413. 

  413.                 <span class="n">color_std</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
    414. 

  414. 

  415.             <span class="n">all_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_labels</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="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
    416. 

  416. 

  417.             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span><span class="p">[</span><span class="s1">&#39;plot_class_distribution&#39;</span><span class="p">]:</span>
    418. 

  418.                 <span class="bp">self</span><span class="o">.</span><span class="n">_analyze_class_distribution</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">dataset_params</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
    419. 

  419. 

  420.             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary_params</span><span class="p">[</span><span class="s1">&#39;plot_box_size_distribution&#39;</span><span class="p">]:</span>
    421. 

  421.                 <span class="bp">self</span><span class="o">.</span><span class="n">_analyze_object_size_distribution</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
    422. 

  422. 

  423.             <span class="n">summary</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    424. 

  424.             <span class="n">summary</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;dataset size: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">data_loader</span><span class="p">)</span><span class="si">}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">&#39;</span>
    425. 

  425.             <span class="n">summary</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;color mean: </span><span class="si">{</span><span class="n">color_mean</span><span class="o">.</span><span class="n">average</span><span class="si">}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">&#39;</span>
    426. 

  426.             <span class="n">summary</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;color std: </span><span class="si">{</span><span class="n">color_std</span><span class="o">.</span><span class="n">average</span><span class="si">}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">&#39;</span>
    427. 

  427. 

  428.             <span class="k">if</span> <span class="n">anchors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
    429. 

  429.                 <span class="n">coverage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_analyze_anchors_coverage</span><span class="p">(</span><span class="n">anchors</span><span class="o">=</span><span class="n">anchors</span><span class="p">,</span> <span class="n">image_size</span><span class="o">=</span><span class="n">dataset_params</span><span class="o">.</span><span class="n">train_image_size</span><span class="p">,</span>
    430. 

  430.                                                           <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">all_labels</span><span class="p">)</span>
    431. 

  431.                 <span class="n">summary</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;anchors: </span><span class="si">{</span><span class="n">anchors</span><span class="si">}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">&#39;</span>
    432. 

  432.                 <span class="n">summary</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;anchors coverage: </span><span class="si">{</span><span class="n">coverage</span><span class="si">}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">&#39;</span>
    433. 

  433. 

  434.             <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> Statistics&#39;</span><span class="p">,</span> <span class="n">text_string</span><span class="o">=</span><span class="n">summary</span><span class="p">)</span>
    435. 

  435.             <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
    436. 

  436.         <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
    437. 

  437.             <span class="c1"># any exception is caught here. we dont want the DatasetStatisticsLogger to crash any training</span>
    438. 

  438.             <span class="n">DatasetStatisticsTensorboardLogger</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;dataset analysis failed: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    439. 

  439. 

  440.     <span class="k">def</span> <span class="nf">_analyze_class_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    441. 

  441.         <span class="n">hist</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">labels</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">num_classes</span><span class="p">)</span>
    442. 

  442. 

  443.         <span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
    444. 

  444. 

  445.         <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_classes</span><span class="p">),</span> <span class="n">hist</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#0504aa&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
    446. 

  446.         <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
    447. 

  447.         <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
    448. 

  448.         <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Value&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    449. 

  449.         <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Frequency&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    450. 

  450.         <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    451. 

  451.         <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    452. 

  452.         <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> class distribution&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    453. 

  453. 

  454.         <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_figure</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s2"> class distribution&quot;</span><span class="p">,</span> <span class="n">figure</span><span class="o">=</span><span class="n">f</span><span class="p">)</span>
    455. 

  455.         <span class="n">text_dist</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    456. 

  456.         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">hist</span><span class="p">):</span>
    457. 

  457.             <span class="n">text_dist</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">val</span><span class="si">}</span><span class="s1">, &#39;</span>
    458. 

  458. 

  459.         <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s2"> class distribution&quot;</span><span class="p">,</span> <span class="n">text_string</span><span class="o">=</span><span class="n">text_dist</span><span class="p">)</span>
    460. 

  460. 

  461.     <span class="k">def</span> <span class="nf">_analyze_object_size_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    462. 

  462.         <span class="sd">&quot;&quot;&quot;</span>
    463. 

  463. <span class="sd">        This function will add two plots to the tensorboard.</span>
    464. 

  464. <span class="sd">        one is a 2D histogram and the other is a scatter plot. in both cases the X axis is the object width and Y axis</span>
    465. 

  465. <span class="sd">        is the object width (both normalized by image size)</span>
    466. 

  466. <span class="sd">        :param labels: all the labels of the dataset of the shape [class_label, x_center, y_center, w, h]</span>
    467. 

  467. <span class="sd">        :param title: the dataset title</span>
    468. 

  468. <span class="sd">        &quot;&quot;&quot;</span>
    469. 

  469. 

  470.         <span class="c1"># histogram plot</span>
    471. 

  471.         <span class="n">hist</span><span class="p">,</span> <span class="n">xedges</span><span class="p">,</span> <span class="n">yedges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram2d</span><span class="p">(</span><span class="n">labels</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">],</span> <span class="mi">50</span><span class="p">)</span>  <span class="c1"># x and y are deliberately switched</span>
    472. 

  472. 

  473.         <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
    474. 

  474.         <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> boxes w/h distribution&#39;</span><span class="p">)</span>
    475. 

  475.         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
    476. 

  476.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    477. 

  477.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    478. 

  478.         <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">hist</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower&#39;</span><span class="p">,</span>
    479. 

  479.                    <span class="n">extent</span><span class="o">=</span><span class="p">[</span><span class="n">xedges</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xedges</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">yedges</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">yedges</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])</span>
    480. 

  480. 

  481.         <span class="c1"># scatter plot</span>
    482. 

  482.         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">10000</span><span class="p">:</span>
    483. 

  483.             <span class="c1"># we randomly sample just 10000 objects so that the scatter plot will not get too dense</span>
    484. 

  484.             <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)]</span>
    485. 

  485.         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
    486. 

  486.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    487. 

  487.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    488. 

  488. 

  489.         <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">labels</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
    490. 

  490. 

  491.         <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_figure</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> boxes w/h distribution&#39;</span><span class="p">,</span> <span class="n">figure</span><span class="o">=</span><span class="n">fig</span><span class="p">)</span>
    492. 

  492. 

  493.     <span class="nd">@staticmethod</span>
    494. 

  494.     <span class="k">def</span> <span class="nf">_get_rect</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
    495. 

  495.         <span class="n">min_w</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="mf">4.0</span>
    496. 

  496.         <span class="n">min_h</span> <span class="o">=</span> <span class="n">h</span> <span class="o">/</span> <span class="mf">4.0</span>
    497. 

  497.         <span class="k">return</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">min_w</span><span class="p">,</span> <span class="n">min_h</span><span class="p">),</span> <span class="n">w</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">-</span> <span class="n">min_w</span><span class="p">,</span> <span class="n">h</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">-</span> <span class="n">min_h</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
    498. 

  498. 

  499.     <span class="nd">@staticmethod</span>
    500. 

  500.     <span class="k">def</span> <span class="nf">_get_score</span><span class="p">(</span><span class="n">anchors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">points</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">image_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
    501. 

  501.         <span class="sd">&quot;&quot;&quot;</span>
    502. 

  502. <span class="sd">        Calculate the ratio (and 1/ratio) between each anchor width and height and each point (representing a possible</span>
    503. 

  503. <span class="sd">        object width and height).</span>
    504. 

  504. <span class="sd">        i.e. for an anchor with w=10,h=20 the point w=11,h=25 will have the ratios 11/10=1.1 and 25/20=1.25</span>
    505. 

  505. <span class="sd">        or 10/11=0.91 and 20/25=0.8 respectively</span>
    506. 

  506. 

  507. <span class="sd">        :param anchors: array of anchors of the shape [2,N]</span>
    508. 

  508. <span class="sd">        :param points: array of points of the shape [2,M]</span>
    509. 

  509. <span class="sd">        :param image_size the size of the input image</span>
    510. 

  510. 

  511. <span class="sd">        :returns: an array of size [image_size - 1, image_size - 1] where each cell i,j represent the minimum ratio</span>
    512. 

  512. <span class="sd">        for that cell (point) from all anchors</span>
    513. 

  513. <span class="sd">        &quot;&quot;&quot;</span>
    514. 

  514. 

  515.         <span class="n">ratio</span> <span class="o">=</span> <span class="n">anchors</span><span class="p">[:,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">/</span> <span class="n">points</span><span class="p">[:,</span> <span class="p">]</span>
    516. 

  516.         <span class="n">inv_ratio</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">ratio</span>
    517. 

  517.         <span class="n">min_ratio</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">ratio</span><span class="p">,</span> <span class="n">inv_ratio</span><span class="p">)</span>
    518. 

  518.         <span class="n">min_ratio</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">min_ratio</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    519. 

  519.         <span class="n">to_closest_anchor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">min_ratio</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    520. 

  520.         <span class="n">to_closest_anchor</span><span class="p">[</span><span class="n">to_closest_anchor</span> <span class="o">&gt;</span> <span class="mf">0.75</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
    521. 

  521.         <span class="k">return</span> <span class="n">to_closest_anchor</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">image_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    522. 

  522. 

  523.     <span class="k">def</span> <span class="nf">_analyze_anchors_coverage</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">anchors</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">image_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">labels</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    524. 

  524.         <span class="sd">&quot;&quot;&quot;</span>
    525. 

  525. <span class="sd">        This function will add anchors coverage plots to the tensorboard.</span>
    526. 

  526. <span class="sd">        :param anchors: a list of anchors</span>
    527. 

  527. <span class="sd">        :param image_size: the input image size for this training</span>
    528. 

  528. <span class="sd">        :param labels: all the labels of the dataset of the shape [class_label, x_center, y_center, w, h]</span>
    529. 

  529. <span class="sd">        :param title: the dataset title</span>
    530. 

  530. <span class="sd">        &quot;&quot;&quot;</span>
    531. 

  531. 

  532.         <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
    533. 

  533.         <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> anchors coverage&#39;</span><span class="p">)</span>
    534. 

  534. 

  535.         <span class="c1"># box style plot</span>
    536. 

  536.         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
    537. 

  537.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    538. 

  538.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    539. 

  539.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">image_size</span><span class="p">])</span>
    540. 

  540.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">image_size</span><span class="p">])</span>
    541. 

  541. 

  542.         <span class="n">anchors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anchors</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="mi">2</span><span class="p">)</span>
    543. 

  543.         <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="nb">len</span><span class="p">(</span><span class="n">anchors</span><span class="p">)):</span>
    544. 

  544.             <span class="n">rect</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_rect</span><span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
    545. 

  545.             <span class="n">rect</span><span class="o">.</span><span class="n">set_alpha</span><span class="p">(</span><span class="mf">0.3</span><span class="p">)</span>
    546. 

  546.             <span class="n">rect</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">([</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="mf">0.3</span><span class="p">])</span>
    547. 

  547.             <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">rect</span><span class="p">)</span>
    548. 

  548. 

  549.         <span class="c1"># distance from anchor plot</span>
    550. 

  550.         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
    551. 

  551.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    552. 

  552.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    553. 

  553. 

  554.         <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">image_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    555. 

  555.         <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">image_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    556. 

  556.         <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    557. 

  557.         <span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">xx</span><span class="o">.</span><span class="n">reshape</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="n">yy</span><span class="o">.</span><span class="n">reshape</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>
    558. 

  558. 

  559.         <span class="n">color</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_score</span><span class="p">(</span><span class="n">anchors</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">image_size</span><span class="p">)</span>
    560. 

  560. 

  561.         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    562. 

  562.         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">STAT_LOGGER_FONT_SIZE</span><span class="p">)</span>
    563. 

  563.         <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">color</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower&#39;</span><span class="p">,</span>
    564. 

  564.                    <span class="n">extent</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">image_size</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">image_size</span><span class="p">])</span>
    565. 

  565. 

  566.         <span class="c1"># calculate the coverage for the dataset labels</span>
    567. 

  567.         <span class="n">cover_masks</span> <span class="o">=</span> <span class="p">[]</span>
    568. 

  568.         <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="nb">len</span><span class="p">(</span><span class="n">anchors</span><span class="p">)):</span>
    569. 

  569.             <span class="n">w_max</span> <span class="o">=</span> <span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_size</span><span class="p">)</span> <span class="o">*</span> <span class="mi">4</span>
    570. 

  570.             <span class="n">w_min</span> <span class="o">=</span> <span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_size</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.25</span>
    571. 

  571.             <span class="n">h_max</span> <span class="o">=</span> <span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_size</span><span class="p">)</span> <span class="o">*</span> <span class="mi">4</span>
    572. 

  572.             <span class="n">h_min</span> <span class="o">=</span> <span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_size</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.25</span>
    573. 

  573.             <span class="n">cover_masks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span>
    574. 

  574.                 <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">labels</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">w_max</span><span class="p">,</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">w_min</span><span class="p">),</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">h_max</span><span class="p">),</span>
    575. 

  575.                 <span class="n">labels</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">h_min</span><span class="p">))</span>
    576. 

  576.         <span class="n">cover_masks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">cover_masks</span><span class="p">)</span>
    577. 

  577.         <span class="n">coverage</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">cover_masks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
    578. 

  578. 

  579.         <span class="bp">self</span><span class="o">.</span><span class="n">sg_logger</span><span class="o">.</span><span class="n">add_figure</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s1"> anchors coverage&#39;</span><span class="p">,</span> <span class="n">figure</span><span class="o">=</span><span class="n">fig</span><span class="p">)</span>
    580. 

  580.         <span class="k">return</span> <span class="n">coverage</span></div>
    581. 

  581. 

  582. 

  583. <div class="viewcode-block" id="get_color_augmentation"><a class="viewcode-back" href="../../../../super_gradients.training.datasets.html#super_gradients.training.datasets.datasets_utils.get_color_augmentation">[docs]</a><span class="k">def</span> <span class="nf">get_color_augmentation</span><span class="p">(</span><span class="n">rand_augment_config_string</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">color_jitter</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">crop_size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">img_mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]):</span>
    584. 

  584.     <span class="sd">&quot;&quot;&quot;</span>
    585. 

  585. <span class="sd">    Returns color augmentation class. As these augmentation cannot work on top one another, only one is returned according to rand_augment_config_string</span>
    586. 

  586. <span class="sd">    :param rand_augment_config_string: string which defines the auto augment configurations. If none, color jitter will be returned. For possibile values see auto_augment.py</span>
    587. 

  587. <span class="sd">    :param color_jitter: tuple for color jitter value.</span>
    588. 

  588. <span class="sd">    :param crop_size: relevant only for auto augment</span>
    589. 

  589. <span class="sd">    :param img_mean: relevant only for auto augment</span>
    590. 

  590. <span class="sd">    :return: RandAugment transform or ColorJitter</span>
    591. 

  591. <span class="sd">    &quot;&quot;&quot;</span>
    592. 

  592.     <span class="k">if</span> <span class="n">rand_augment_config_string</span><span class="p">:</span>
    593. 

  593.         <span class="n">auto_augment_params</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="nb">int</span><span class="p">(</span><span class="n">crop_size</span> <span class="o">*</span> <span class="mf">0.45</span><span class="p">),</span>
    594. 

  594.                                    <span class="n">img_mean</span><span class="o">=</span><span class="nb">tuple</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="nb">round</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">x</span><span class="p">))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">img_mean</span><span class="p">]))</span>
    595. 

  595. 

  596.         <span class="n">color_augmentation</span> <span class="o">=</span> <span class="n">rand_augment_transform</span><span class="p">(</span><span class="n">rand_augment_config_string</span><span class="p">,</span> <span class="n">auto_augment_params</span><span class="p">)</span>
    597. 

  597. 

  598.     <span class="k">else</span><span class="p">:</span>  <span class="c1"># RandAugment includes colorjitter like augmentations, both cannot be applied together.</span>
    599. 

  599.         <span class="n">color_augmentation</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">ColorJitter</span><span class="p">(</span><span class="o">*</span><span class="n">color_jitter</span><span class="p">)</span>
    600. 

  600.     <span class="k">return</span> <span class="n">color_augmentation</span></div>
    601. 

  601. </pre></div>
    602. 

  602. 

  603.            </div>
    604. 

  604.           </div>
    605. 

  605.           <footer>
    606. 

  606. 

  607.   <hr/>
    608. 

  608. 

  609.   <div role="contentinfo">
    610. 

  610.     <p>&#169; Copyright 2021, SuperGradients team.</p>
    611. 

  611.   </div>
    612. 

  612. 

  613.   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
    614. 

  614.     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
    615. 

  615.     provided by <a href="https://readthedocs.org">Read the Docs</a>.
    616. 

  616.    
    617. 

  617. 

  618. </footer>
    619. 

  619.         </div>
    620. 

  620.       </div>
    621. 

  621.     </section>
    622. 

  622.   </div>
    623. 

  623.   <script>
    624. 

  624.       jQuery(function () {
    625. 

  625.           SphinxRtdTheme.Navigation.enable(true);
    626. 

  626.       });
    627. 

  627.   </script> 
    628. 

  628. 

  629. </body>
    630. 

  630. </html>
    631.
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