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  86.   <h1>Source code for super_gradients.training.utils.detection_utils</h1><div class="highlight"><pre>
    87. 

  87. <span></span><span class="kn">import</span> <span class="nn">math</span>
    88. 

  88. <span class="kn">import</span> <span class="nn">os</span>
    89. 

  89. <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>
    90. 

  90. <span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
    91. 

  91. <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span>
    92. 

  92. 

  93. <span class="kn">import</span> <span class="nn">cv2</span>
    94. 

  94. <span class="kn">from</span> <span class="nn">deprecated</span> <span class="kn">import</span> <span class="n">deprecated</span>
    95. 

  95. <span class="kn">from</span> <span class="nn">scipy.cluster.vq</span> <span class="kn">import</span> <span class="n">kmeans</span>
    96. 

  96. <span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
    97. 

  97. <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
    98. 

  98. <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
    99. 

  99. 

  100. <span class="kn">import</span> <span class="nn">torch</span>
    101. 

  101. <span class="kn">import</span> <span class="nn">torchvision</span>
    102. 

  102. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
    103. 

  103. <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
    104. 

  104. <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
    105. 

  105. <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>
    106. 

  106. <span class="kn">from</span> <span class="nn">omegaconf</span> <span class="kn">import</span> <span class="n">ListConfig</span>
    107. 

  107. 

  108. 

  109. <div class="viewcode-block" id="base_detection_collate_fn"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.base_detection_collate_fn">[docs]</a><span class="k">def</span> <span class="nf">base_detection_collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
    110. 

  110.     <span class="sd">&quot;&quot;&quot;</span>
    111. 

  111. <span class="sd">    Batch Processing helper function for detection training/testing.</span>
    112. 

  112. <span class="sd">    stacks the lists of images and targets into tensors and adds the image index to each target (so the targets could</span>
    113. 

  113. <span class="sd">    later be associated to the correct images)</span>
    114. 

  114. <span class="sd">         :param batch:   Input batch from the Dataset __get_item__ method</span>
    115. 

  115. <span class="sd">         :return:        batch with the transformed values</span>
    116. 

  116. <span class="sd">     &quot;&quot;&quot;</span>
    117. 

  117. 

  118.     <span class="n">images_batch</span><span class="p">,</span> <span class="n">labels_batch</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">batch</span><span class="p">))</span>
    119. 

  119.     <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labels_batch</span><span class="p">):</span>
    120. 

  120.         <span class="c1"># ADD TARGET IMAGE INDEX</span>
    121. 

  121.         <span class="n">labels</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
    122. 

  122. 

  123.     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">images_batch</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">labels_batch</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></div>
    124. 

  124. 

  125. 

  126. <div class="viewcode-block" id="convert_xyxy_bbox_to_xywh"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.convert_xyxy_bbox_to_xywh">[docs]</a><span class="k">def</span> <span class="nf">convert_xyxy_bbox_to_xywh</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">):</span>
    127. 

  127.     <span class="sd">&quot;&quot;&quot;</span>
    128. 

  128. <span class="sd">    convert_xyxy_bbox_to_xywh - Converts bounding box format from [x1, y1, x2, y2] to [x, y, w, h]</span>
    129. 

  129. <span class="sd">        :param input_bbox:  input bbox</span>
    130. 

  130. <span class="sd">        :return:            Converted bbox</span>
    131. 

  131. <span class="sd">    &quot;&quot;&quot;</span>
    132. 

  132.     <span class="n">converted_bbox</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">)</span>
    133. 

  133.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
    134. 

  134.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
    135. 

  135.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
    136. 

  136.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
    137. 

  137.     <span class="k">return</span> <span class="n">converted_bbox</span></div>
    138. 

  138. 

  139. 

  140. <div class="viewcode-block" id="convert_xywh_bbox_to_xyxy"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.convert_xywh_bbox_to_xyxy">[docs]</a><span class="k">def</span> <span class="nf">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
    141. 

  141.     <span class="sd">&quot;&quot;&quot;</span>
    142. 

  142. <span class="sd">    Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2]</span>
    143. 

  143. <span class="sd">        :param input_bbox:  input bbox either 2-dimensional (for all boxes of a single image) or 3-dimensional (for</span>
    144. 

  144. <span class="sd">                            boxes of a batch of images)</span>
    145. 

  145. <span class="sd">        :return:            Converted bbox in same dimensions as the original</span>
    146. 

  146. <span class="sd">    &quot;&quot;&quot;</span>
    147. 

  147.     <span class="n">need_squeeze</span> <span class="o">=</span> <span class="kc">False</span>
    148. 

  148.     <span class="c1"># the input is always processed as a batch. in case it not a batch, it is unsqueezed, process and than squeeze back.</span>
    149. 

  149.     <span class="k">if</span> <span class="n">input_bbox</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
    150. 

  150.         <span class="n">need_squeeze</span> <span class="o">=</span> <span class="kc">True</span>
    151. 

  151.         <span class="n">input_bbox</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    152. 

  152. 

  153.     <span class="n">converted_bbox</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">input_bbox</span><span class="p">)</span>
    154. 

  154.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    155. 

  155.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    156. 

  156.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    157. 

  157.     <span class="n">converted_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">input_bbox</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    158. 

  158. 

  159.     <span class="c1"># squeeze back if needed</span>
    160. 

  160.     <span class="k">if</span> <span class="n">need_squeeze</span><span class="p">:</span>
    161. 

  161.         <span class="n">converted_bbox</span> <span class="o">=</span> <span class="n">converted_bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    162. 

  162. 

  163.     <span class="k">return</span> <span class="n">converted_bbox</span></div>
    164. 

  164. 

  165. 

  166. <div class="viewcode-block" id="calculate_wh_iou"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.calculate_wh_iou">[docs]</a><span class="k">def</span> <span class="nf">calculate_wh_iou</span><span class="p">(</span><span class="n">box1</span><span class="p">,</span> <span class="n">box2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
    167. 

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

  168. <span class="sd">    calculate_wh_iou - Gets the Intersection over Union of the w,h values of the bboxes</span>
    169. 

  169. <span class="sd">        :param box1:</span>
    170. 

  170. <span class="sd">        :param box2:</span>
    171. 

  171. <span class="sd">        :return: IOU</span>
    172. 

  172. <span class="sd">    &quot;&quot;&quot;</span>
    173. 

  173.     <span class="c1"># RETURNS THE IOU OF WH1 TO WH2. WH1 IS 2, WH2 IS NX2</span>
    174. 

  174.     <span class="n">box2</span> <span class="o">=</span> <span class="n">box2</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
    175. 

  175. 

  176.     <span class="c1"># W, H = BOX1</span>
    177. 

  177.     <span class="n">w1</span><span class="p">,</span> <span class="n">h1</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    178. 

  178.     <span class="n">w2</span><span class="p">,</span> <span class="n">h2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">box2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    179. 

  179. 

  180.     <span class="c1"># INTERSECTION AREA</span>
    181. 

  181.     <span class="n">intersection_area</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">h1</span><span class="p">,</span> <span class="n">h2</span><span class="p">)</span>
    182. 

  182. 

  183.     <span class="c1"># UNION AREA</span>
    184. 

  184.     <span class="n">union_area</span> <span class="o">=</span> <span class="p">(</span><span class="n">w1</span> <span class="o">*</span> <span class="n">h1</span> <span class="o">+</span> <span class="mf">1e-16</span><span class="p">)</span> <span class="o">+</span> <span class="n">w2</span> <span class="o">*</span> <span class="n">h2</span> <span class="o">-</span> <span class="n">intersection_area</span>
    185. 

  185. 

  186.     <span class="k">return</span> <span class="n">intersection_area</span> <span class="o">/</span> <span class="n">union_area</span></div>
    187. 

  187. 

  188. 

  189. <span class="k">def</span> <span class="nf">_iou</span><span class="p">(</span><span class="n">CIoU</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">DIoU</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">GIoU</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
    190. 

  190.     <span class="sd">&quot;&quot;&quot;</span>
    191. 

  191. <span class="sd">    Internal function for the use of calculate_bbox_iou_matrix and calculate_bbox_iou_elementwise functions</span>
    192. 

  192. <span class="sd">    DO NOT CALL THIS FUNCTIONS DIRECTLY - use one of the functions mentioned above</span>
    193. 

  193. <span class="sd">    &quot;&quot;&quot;</span>
    194. 

  194.     <span class="c1"># Intersection area</span>
    195. 

  195.     <span class="n">intersection_area</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_x2</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_x1</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">))</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> \
    196. 

  196.                         <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_y1</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">))</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    197. 

  197.     <span class="c1"># Union Area</span>
    198. 

  198.     <span class="n">w1</span><span class="p">,</span> <span class="n">h1</span> <span class="o">=</span> <span class="n">b1_x2</span> <span class="o">-</span> <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">-</span> <span class="n">b1_y1</span>
    199. 

  199.     <span class="n">w2</span><span class="p">,</span> <span class="n">h2</span> <span class="o">=</span> <span class="n">b2_x2</span> <span class="o">-</span> <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">-</span> <span class="n">b2_y1</span>
    200. 

  200.     <span class="n">union_area</span> <span class="o">=</span> <span class="n">w1</span> <span class="o">*</span> <span class="n">h1</span> <span class="o">+</span> <span class="n">w2</span> <span class="o">*</span> <span class="n">h2</span> <span class="o">-</span> <span class="n">intersection_area</span> <span class="o">+</span> <span class="n">eps</span>
    201. 

  201.     <span class="n">iou</span> <span class="o">=</span> <span class="n">intersection_area</span> <span class="o">/</span> <span class="n">union_area</span>  <span class="c1"># iou</span>
    202. 

  202.     <span class="k">if</span> <span class="n">GIoU</span> <span class="ow">or</span> <span class="n">DIoU</span> <span class="ow">or</span> <span class="n">CIoU</span><span class="p">:</span>
    203. 

  203.         <span class="n">cw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_x2</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_x1</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">)</span>  <span class="c1"># convex (smallest enclosing box) width</span>
    204. 

  204.         <span class="n">ch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_y1</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">)</span>  <span class="c1"># convex height</span>
    205. 

  205.         <span class="c1"># Generalized IoU https://arxiv.org/pdf/1902.09630.pdf</span>
    206. 

  206.         <span class="k">if</span> <span class="n">GIoU</span><span class="p">:</span>
    207. 

  207.             <span class="n">c_area</span> <span class="o">=</span> <span class="n">cw</span> <span class="o">*</span> <span class="n">ch</span> <span class="o">+</span> <span class="n">eps</span>  <span class="c1"># convex area</span>
    208. 

  208.             <span class="n">iou</span> <span class="o">-=</span> <span class="p">(</span><span class="n">c_area</span> <span class="o">-</span> <span class="n">union_area</span><span class="p">)</span> <span class="o">/</span> <span class="n">c_area</span>  <span class="c1"># GIoU</span>
    209. 

  209.         <span class="c1"># Distance or Complete IoU https://arxiv.org/abs/1911.08287v1</span>
    210. 

  210.         <span class="k">if</span> <span class="n">DIoU</span> <span class="ow">or</span> <span class="n">CIoU</span><span class="p">:</span>
    211. 

  211.             <span class="c1"># convex diagonal squared</span>
    212. 

  212.             <span class="n">c2</span> <span class="o">=</span> <span class="n">cw</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">ch</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">eps</span>
    213. 

  213.             <span class="c1"># centerpoint distance squared</span>
    214. 

  214.             <span class="n">rho2</span> <span class="o">=</span> <span class="p">((</span><span class="n">b2_x1</span> <span class="o">+</span> <span class="n">b2_x2</span> <span class="o">-</span> <span class="n">b1_x1</span> <span class="o">-</span> <span class="n">b1_x2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">b2_y1</span> <span class="o">+</span> <span class="n">b2_y2</span> <span class="o">-</span> <span class="n">b1_y1</span> <span class="o">-</span> <span class="n">b1_y2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">4</span>
    215. 

  215.             <span class="k">if</span> <span class="n">DIoU</span><span class="p">:</span>
    216. 

  216.                 <span class="n">iou</span> <span class="o">-=</span> <span class="n">rho2</span> <span class="o">/</span> <span class="n">c2</span>  <span class="c1"># DIoU</span>
    217. 

  217.             <span class="k">elif</span> <span class="n">CIoU</span><span class="p">:</span>  <span class="c1"># https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47</span>
    218. 

  218.                 <span class="n">v</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">atan</span><span class="p">(</span><span class="n">w2</span> <span class="o">/</span> <span class="n">h2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">atan</span><span class="p">(</span><span class="n">w1</span> <span class="o">/</span> <span class="n">h1</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
    219. 

  219.                 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
    220. 

  220.                     <span class="n">alpha</span> <span class="o">=</span> <span class="n">v</span> <span class="o">/</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span> <span class="o">-</span> <span class="n">iou</span> <span class="o">+</span> <span class="n">v</span><span class="p">)</span>
    221. 

  221.                 <span class="n">iou</span> <span class="o">-=</span> <span class="p">(</span><span class="n">rho2</span> <span class="o">/</span> <span class="n">c2</span> <span class="o">+</span> <span class="n">v</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">)</span>  <span class="c1"># CIoU</span>
    222. 

  222.     <span class="k">return</span> <span class="n">iou</span>
    223. 

  223. 

  224. 

  225. <div class="viewcode-block" id="calculate_bbox_iou_matrix"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.calculate_bbox_iou_matrix">[docs]</a><span class="k">def</span> <span class="nf">calculate_bbox_iou_matrix</span><span class="p">(</span><span class="n">box1</span><span class="p">,</span> <span class="n">box2</span><span class="p">,</span> <span class="n">x1y1x2y2</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">GIoU</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DIoU</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">CIoU</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-9</span><span class="p">):</span>
    226. 

  226.     <span class="sd">&quot;&quot;&quot;</span>
    227. 

  227. <span class="sd">    calculate iou matrix containing the iou of every couple iuo(i,j) where i is in box1 and j is in box2</span>
    228. 

  228. <span class="sd">        :param box1: a 2D tensor of boxes (shape N x 4)</span>
    229. 

  229. <span class="sd">        :param box2: a 2D tensor of boxes (shape M x 4)</span>
    230. 

  230. <span class="sd">        :param x1y1x2y2: boxes format is x1y1x2y2 (True) or xywh where xy is the center (False)</span>
    231. 

  231. <span class="sd">        :return: a 2D iou matrix (shape NxM)</span>
    232. 

  232. <span class="sd">    &quot;&quot;&quot;</span>
    233. 

  233.     <span class="k">if</span> <span class="n">box1</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
    234. 

  234.         <span class="n">box1</span> <span class="o">=</span> <span class="n">box1</span><span class="o">.</span><span class="n">T</span>
    235. 

  235. 

  236.     <span class="c1"># Get the coordinates of bounding boxes</span>
    237. 

  237.     <span class="k">if</span> <span class="n">x1y1x2y2</span><span class="p">:</span>  <span class="c1"># x1, y1, x2, y2 = box1</span>
    238. 

  238.         <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    239. 

  239.         <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
    240. 

  240.     <span class="k">else</span><span class="p">:</span>  <span class="c1"># x, y, w, h = box1</span>
    241. 

  241.         <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_x2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    242. 

  242.         <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    243. 

  243.         <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_x2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    244. 

  244.         <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    245. 

  245. 

  246.     <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">b1_x1</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">b1_y1</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">b1_x2</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">b1_y2</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    247. 

  247. 

  248.     <span class="k">return</span> <span class="n">_iou</span><span class="p">(</span><span class="n">CIoU</span><span class="p">,</span> <span class="n">DIoU</span><span class="p">,</span> <span class="n">GIoU</span><span class="p">,</span> <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span></div>
    249. 

  249. 

  250. 

  251. <div class="viewcode-block" id="calculate_bbox_iou_elementwise"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.calculate_bbox_iou_elementwise">[docs]</a><span class="k">def</span> <span class="nf">calculate_bbox_iou_elementwise</span><span class="p">(</span><span class="n">box1</span><span class="p">,</span> <span class="n">box2</span><span class="p">,</span> <span class="n">x1y1x2y2</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">GIoU</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DIoU</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">CIoU</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-9</span><span class="p">):</span>
    252. 

  252.     <span class="sd">&quot;&quot;&quot;</span>
    253. 

  253. <span class="sd">    calculate elementwise iou of two bbox tensors</span>
    254. 

  254. <span class="sd">        :param box1: a 2D tensor of boxes (shape N x 4)</span>
    255. 

  255. <span class="sd">        :param box2: a 2D tensor of boxes (shape N x 4)</span>
    256. 

  256. <span class="sd">        :param x1y1x2y2: boxes format is x1y1x2y2 (True) or xywh where xy is the center (False)</span>
    257. 

  257. <span class="sd">        :return: a 1D iou tensor (shape N)</span>
    258. 

  258. <span class="sd">    &quot;&quot;&quot;</span>
    259. 

  259.     <span class="c1"># Returns the IoU of box1 to box2. box1 is 4, box2 is nx4</span>
    260. 

  260.     <span class="n">box2</span> <span class="o">=</span> <span class="n">box2</span><span class="o">.</span><span class="n">T</span>
    261. 

  261. 

  262.     <span class="c1"># Get the coordinates of bounding boxes</span>
    263. 

  263.     <span class="k">if</span> <span class="n">x1y1x2y2</span><span class="p">:</span>  <span class="c1"># x1, y1, x2, y2 = box1</span>
    264. 

  264.         <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    265. 

  265.         <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">box2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">box2</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">box2</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    266. 

  266.     <span class="k">else</span><span class="p">:</span>  <span class="c1"># x, y, w, h = box1</span>
    267. 

  267.         <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_x2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">box1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    268. 

  268.         <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">box1</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    269. 

  269.         <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_x2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">box2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">box2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    270. 

  270.         <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">=</span> <span class="n">box2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">box2</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">box2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">box2</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    271. 

  271. 

  272.     <span class="k">return</span> <span class="n">_iou</span><span class="p">(</span><span class="n">CIoU</span><span class="p">,</span> <span class="n">DIoU</span><span class="p">,</span> <span class="n">GIoU</span><span class="p">,</span> <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y1</span><span class="p">,</span> <span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span></div>
    273. 

  273. 

  274. 

  275. <div class="viewcode-block" id="calc_bbox_iou_matrix"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.calc_bbox_iou_matrix">[docs]</a><span class="k">def</span> <span class="nf">calc_bbox_iou_matrix</span><span class="p">(</span><span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
    276. 

  276.     <span class="sd">&quot;&quot;&quot;</span>
    277. 

  277. <span class="sd">    calculate iou for every pair of boxes in the boxes vector</span>
    278. 

  278. <span class="sd">    :param pred: a 3-dimensional tensor containing all boxes for a batch of images [N, num_boxes, 4], where</span>
    279. 

  279. <span class="sd">                 each box format is [x1,y1,x2,y2]</span>
    280. 

  280. <span class="sd">    :return: a 3-dimensional matrix where M_i_j_k is the iou of box j and box k of the i&#39;th image in the batch</span>
    281. 

  281. <span class="sd">    &quot;&quot;&quot;</span>
    282. 

  282.     <span class="n">box</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span>  <span class="c1">#</span>
    283. 

  283.     <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y1</span> <span class="o">=</span> <span class="n">box</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">box</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    284. 

  284.     <span class="n">b1_x2</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">=</span> <span class="n">box</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">box</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    285. 

  285. 

  286.     <span class="n">b2_x1</span> <span class="o">=</span> <span class="n">b1_x1</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    287. 

  287.     <span class="n">b2_x2</span> <span class="o">=</span> <span class="n">b1_x2</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    288. 

  288.     <span class="n">b2_y1</span> <span class="o">=</span> <span class="n">b1_y1</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    289. 

  289.     <span class="n">b2_y2</span> <span class="o">=</span> <span class="n">b1_y2</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    290. 

  290.     <span class="n">intersection_area</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_x2</span><span class="p">,</span> <span class="n">b2_x2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_x1</span><span class="p">,</span> <span class="n">b2_x1</span><span class="p">))</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> \
    291. 

  291.                         <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">b1_y2</span><span class="p">,</span> <span class="n">b2_y2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">b1_y1</span><span class="p">,</span> <span class="n">b2_y1</span><span class="p">))</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    292. 

  292.     <span class="c1"># Union Area</span>
    293. 

  293.     <span class="n">w1</span><span class="p">,</span> <span class="n">h1</span> <span class="o">=</span> <span class="n">b1_x2</span> <span class="o">-</span> <span class="n">b1_x1</span><span class="p">,</span> <span class="n">b1_y2</span> <span class="o">-</span> <span class="n">b1_y1</span>
    294. 

  294.     <span class="n">w2</span><span class="p">,</span> <span class="n">h2</span> <span class="o">=</span> <span class="n">b2_x2</span> <span class="o">-</span> <span class="n">b2_x1</span><span class="p">,</span> <span class="n">b2_y2</span> <span class="o">-</span> <span class="n">b2_y1</span>
    295. 

  295.     <span class="n">union_area</span> <span class="o">=</span> <span class="p">(</span><span class="n">w1</span> <span class="o">*</span> <span class="n">h1</span> <span class="o">+</span> <span class="mf">1e-16</span><span class="p">)</span> <span class="o">+</span> <span class="n">w2</span> <span class="o">*</span> <span class="n">h2</span> <span class="o">-</span> <span class="n">intersection_area</span>
    296. 

  296.     <span class="n">ious</span> <span class="o">=</span> <span class="n">intersection_area</span> <span class="o">/</span> <span class="n">union_area</span>
    297. 

  297.     <span class="k">return</span> <span class="n">ious</span></div>
    298. 

  298. 

  299. 

  300. <div class="viewcode-block" id="build_detection_targets"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.build_detection_targets">[docs]</a><span class="k">def</span> <span class="nf">build_detection_targets</span><span class="p">(</span><span class="n">detection_net</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">targets</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
    301. 

  301.     <span class="sd">&quot;&quot;&quot;</span>
    302. 

  302. <span class="sd">    build_detection_targets - Builds the outputs of the Detection NN</span>
    303. 

  303. <span class="sd">                              This function filters all of the targets that don&#39;t have a sufficient iou coverage</span>
    304. 

  304. <span class="sd">                              of the Model&#39;s pre-trained k-means anchors</span>
    305. 

  305. <span class="sd">                              The iou_threshold is a parameter of the NN Model</span>
    306. 

  306. <span class="sd">        :param detection_net:   The nn.Module of the Detection Algorithm</span>
    307. 

  307. <span class="sd">        :param targets:         targets (labels)</span>
    308. 

  308. <span class="sd">        :return:</span>
    309. 

  309. <span class="sd">    &quot;&quot;&quot;</span>
    310. 

  310.     <span class="c1"># TARGETS = [image, class, x, y, w, h]</span>
    311. 

  311.     <span class="n">targets_num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
    312. 

  312.     <span class="n">target_classes</span><span class="p">,</span> <span class="n">target_bbox</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">anchor_vector_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
    313. 

  313.     <span class="n">reject</span><span class="p">,</span> <span class="n">use_all_anchors</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span>
    314. 

  314. 

  315.     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">detection_net</span><span class="o">.</span><span class="n">yolo_layers_indices</span><span class="p">:</span>
    316. 

  316.         <span class="n">yolo_layer_module</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">detection_net</span><span class="o">.</span><span class="n">module_list</span><span class="p">)[</span><span class="n">i</span><span class="p">]</span>
    317. 

  317. 

  318.         <span class="c1"># GET NUMBER OF GRID POINTS AND ANCHOR VEC FOR THIS YOLO LAYER</span>
    319. 

  319.         <span class="n">grid_points_num</span><span class="p">,</span> <span class="n">anchor_vec</span> <span class="o">=</span> <span class="n">yolo_layer_module</span><span class="o">.</span><span class="n">grid_size</span><span class="p">,</span> <span class="n">yolo_layer_module</span><span class="o">.</span><span class="n">anchor_vec</span>
    320. 

  320. 

  321.         <span class="c1"># IOU OF TARGETS-ANCHORS</span>
    322. 

  322.         <span class="n">iou_targets</span><span class="p">,</span> <span class="n">anchors</span> <span class="o">=</span> <span class="n">targets</span><span class="p">,</span> <span class="p">[]</span>
    323. 

  323.         <span class="n">gwh</span> <span class="o">=</span> <span class="n">iou_targets</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">*</span> <span class="n">grid_points_num</span>
    324. 

  324.         <span class="k">if</span> <span class="n">targets_num</span><span class="p">:</span>
    325. 

  325.             <span class="n">iou</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">calculate_wh_iou</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">gwh</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">anchor_vec</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
    326. 

  326. 

  327.             <span class="k">if</span> <span class="n">use_all_anchors</span><span class="p">:</span>
    328. 

  328.                 <span class="n">anchors_num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">anchor_vec</span><span class="p">)</span>
    329. 

  329.                 <span class="n">anchors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">anchors_num</span><span class="p">)</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="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">targets_num</span><span class="p">])</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>
    330. 

  330.                 <span class="n">iou_targets</span> <span class="o">=</span> <span class="n">targets</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="n">anchors_num</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
    331. 

  331.                 <span class="n">gwh</span> <span class="o">=</span> <span class="n">gwh</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="n">anchors_num</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
    332. 

  332.             <span class="k">else</span><span class="p">:</span>
    333. 

  333.                 <span class="c1"># USE ONLY THE BEST ANCHOR</span>
    334. 

  334.                 <span class="n">iou</span><span class="p">,</span> <span class="n">anchors</span> <span class="o">=</span> <span class="n">iou</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># best iou and anchor</span>
    335. 

  335. 

  336.             <span class="c1"># REJECT ANCHORS BELOW IOU_THRES (OPTIONAL, INCREASES P, LOWERS R)</span>
    337. 

  337.             <span class="k">if</span> <span class="n">reject</span><span class="p">:</span>
    338. 

  338.                 <span class="c1"># IOU THRESHOLD HYPERPARAMETER</span>
    339. 

  339.                 <span class="n">j</span> <span class="o">=</span> <span class="n">iou</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">&gt;</span> <span class="n">detection_net</span><span class="o">.</span><span class="n">iou_t</span>
    340. 

  340.                 <span class="n">iou_targets</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">gwh</span> <span class="o">=</span> <span class="n">iou_targets</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">anchors</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">gwh</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
    341. 

  341. 

  342.         <span class="c1"># INDICES</span>
    343. 

  343.         <span class="n">target_image</span><span class="p">,</span> <span class="n">target_class</span> <span class="o">=</span> <span class="n">iou_targets</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
    344. 

  344.         <span class="n">x_y_grid</span> <span class="o">=</span> <span class="n">iou_targets</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">grid_points_num</span>
    345. 

  345.         <span class="n">x_grid_idx</span><span class="p">,</span> <span class="n">y_grid_idx</span> <span class="o">=</span> <span class="n">x_y_grid</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
    346. 

  346.         <span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">target_image</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">y_grid_idx</span><span class="p">,</span> <span class="n">x_grid_idx</span><span class="p">))</span>
    347. 

  347. 

  348.         <span class="c1"># GIoU</span>
    349. 

  349.         <span class="n">x_y_grid</span> <span class="o">-=</span> <span class="n">x_y_grid</span><span class="o">.</span><span class="n">floor</span><span class="p">()</span>
    350. 

  350.         <span class="n">target_bbox</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x_y_grid</span><span class="p">,</span> <span class="n">gwh</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
    351. 

  351.         <span class="n">anchor_vector_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">anchor_vec</span><span class="p">[</span><span class="n">anchors</span><span class="p">])</span>
    352. 

  352. 

  353.         <span class="c1"># Class</span>
    354. 

  354.         <span class="n">target_classes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target_class</span><span class="p">)</span>
    355. 

  355.         <span class="k">if</span> <span class="n">target_class</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
    356. 

  356.             <span class="k">if</span> <span class="ow">not</span> <span class="n">target_class</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">detection_net</span><span class="o">.</span><span class="n">num_classes</span><span class="p">:</span>
    357. 

  357.                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Labeled Class is out of bounds of the classes list&#39;</span><span class="p">)</span>
    358. 

  358. 

  359.     <span class="k">return</span> <span class="n">target_classes</span><span class="p">,</span> <span class="n">target_bbox</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">anchor_vector_list</span></div>
    360. 

  360. 

  361. 

  362. <div class="viewcode-block" id="yolo_v3_non_max_suppression"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.yolo_v3_non_max_suppression">[docs]</a><span class="k">def</span> <span class="nf">yolo_v3_non_max_suppression</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">conf_thres</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">nms_thres</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">):</span>  <span class="c1"># noqa: C901</span>
    363. 

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

  364. <span class="sd">    non_max_suppression - Removes detections with lower object confidence score than &#39;conf_thres&#39;</span>
    365. 

  365. <span class="sd">                          Non-Maximum Suppression to further filter detections.</span>
    366. 

  366. <span class="sd">        :param prediction:      the raw prediction as produced by the yolo_v3 network</span>
    367. 

  367. <span class="sd">        :param conf_thres:      confidence threshold - only prediction with confidence score higher than the threshold</span>
    368. 

  368. <span class="sd">                                will be considered</span>
    369. 

  369. <span class="sd">        :param nms_thres:       IoU threshold for the nms algorithm</span>
    370. 

  370. <span class="sd">        :param device:          the device to move all output tensors into</span>
    371. 

  371. <span class="sd">        :return:  (x1, y1, x2, y2, object_conf, class_conf, class)</span>
    372. 

  372. <span class="sd">    &quot;&quot;&quot;</span>
    373. 

  373.     <span class="c1"># MINIMUM AND MAXIMIUM BOX WIDTH AND HEIGHT IN PIXELS</span>
    374. 

  374.     <span class="n">min_wh</span> <span class="o">=</span> <span class="mi">2</span>
    375. 

  375.     <span class="n">max_wh</span> <span class="o">=</span> <span class="mi">10000</span>
    376. 

  376. 

  377.     <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">prediction</span><span class="p">)</span>
    378. 

  378.     <span class="k">for</span> <span class="n">image_i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">prediction</span><span class="p">):</span>
    379. 

  379.         <span class="c1"># MULTIPLY CONF BY CLASS CONF TO GET COMBINED CONFIDENCE</span>
    380. 

  380.         <span class="n">class_conf</span><span class="p">,</span> <span class="n">class_pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    381. 

  381.         <span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*=</span> <span class="n">class_conf</span>
    382. 

  382. 

  383.         <span class="c1"># IGNORE ANYTHING UNDER conf_thres</span>
    384. 

  384.         <span class="n">i</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">conf_thres</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">min_wh</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">max_wh</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> \
    385. 

  385.             <span class="n">torch</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    386. 

  386.         <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    387. 

  387. 

  388.         <span class="c1"># NOTHING IS OVER THE THRESHOLD</span>
    389. 

  389.         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    390. 

  390.             <span class="k">continue</span>
    391. 

  391. 

  392.         <span class="n">class_conf</span> <span class="o">=</span> <span class="n">class_conf</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    393. 

  393.         <span class="n">class_pred</span> <span class="o">=</span> <span class="n">class_pred</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
    394. 

  394. 

  395.         <span class="c1"># BOX (CENTER X, CENTER Y, WIDTH, HEIGHT) TO (X1, Y1, X2, Y2)</span>
    396. 

  396.         <span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])</span>
    397. 

  397. 

  398.         <span class="c1"># DETECTIONS ORDERED AS (x1y1x2y2, obj_conf, class_conf, class_pred)</span>
    399. 

  399.         <span class="n">pred</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">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">5</span><span class="p">],</span> <span class="n">class_conf</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">class_pred</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
    400. 

  400. 

  401.         <span class="c1"># SORT DETECTIONS BY DECREASING CONFIDENCE SCORES</span>
    402. 

  402.         <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[(</span><span class="o">-</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">argsort</span><span class="p">()]</span>
    403. 

  403. 

  404.         <span class="c1"># &#39;OR&#39;, &#39;AND&#39;, &#39;MERGE&#39;, &#39;VISION&#39;, &#39;VISION_BATCHED&#39;</span>
    405. 

  405.         <span class="c1"># MERGE is highest mAP, VISION is fastest</span>
    406. 

  406.         <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;MERGE&#39;</span> <span class="k">if</span> <span class="n">conf_thres</span> <span class="o">&lt;=</span> <span class="mf">0.01</span> <span class="k">else</span> <span class="s1">&#39;VISION&#39;</span>
    407. 

  407. 

  408.         <span class="c1"># BATCHED NMS</span>
    409. 

  409.         <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;VISION_BATCHED&#39;</span><span class="p">:</span>
    410. 

  410.             <span class="n">i</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">boxes</span><span class="o">.</span><span class="n">batched_nms</span><span class="p">(</span><span class="n">boxes</span><span class="o">=</span><span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">],</span>
    411. 

  411.                                                   <span class="n">scores</span><span class="o">=</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">],</span>
    412. 

  412.                                                   <span class="n">idxs</span><span class="o">=</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">6</span><span class="p">],</span>
    413. 

  413.                                                   <span class="n">iou_threshold</span><span class="o">=</span><span class="n">nms_thres</span><span class="p">)</span>
    414. 

  414.             <span class="n">output</span><span class="p">[</span><span class="n">image_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    415. 

  415.             <span class="k">continue</span>
    416. 

  416. 

  417.         <span class="c1"># Non-maximum suppression</span>
    418. 

  418.         <span class="n">det_max</span> <span class="o">=</span> <span class="p">[]</span>
    419. 

  419.         <span class="k">for</span> <span class="n">detection_class</span> <span class="ow">in</span> <span class="n">pred</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">unique</span><span class="p">():</span>
    420. 

  420.             <span class="n">dc</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">pred</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">detection_class</span><span class="p">]</span>
    421. 

  421.             <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span>
    422. 

  422.             <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    423. 

  423.                 <span class="c1"># NO NMS REQUIRED FOR A SINGLE CLASS</span>
    424. 

  424.                 <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span>
    425. 

  425.                 <span class="k">continue</span>
    426. 

  426.             <span class="k">elif</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">500</span><span class="p">:</span>
    427. 

  427.                 <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[:</span><span class="mi">500</span><span class="p">]</span>
    428. 

  428. 

  429.             <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;VISION&#39;</span><span class="p">:</span>
    430. 

  430.                 <span class="n">i</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">boxes</span><span class="o">.</span><span class="n">nms</span><span class="p">(</span><span class="n">dc</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">],</span> <span class="n">dc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">nms_thres</span><span class="p">)</span>
    431. 

  431.                 <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    432. 

  432. 

  433.             <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;OR&#39;</span><span class="p">:</span>
    434. 

  434.                 <span class="k">while</span> <span class="n">dc</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
    435. 

  435.                     <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
    436. 

  436.                     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    437. 

  437.                         <span class="k">break</span>
    438. 

  438.                     <span class="n">iou</span> <span class="o">=</span> <span class="n">calculate_bbox_iou_elementwise</span><span class="p">(</span><span class="n">dc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
    439. 

  439.                     <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:][</span><span class="n">iou</span> <span class="o">&lt;</span> <span class="n">nms_thres</span><span class="p">]</span>
    440. 

  440. 

  441.             <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;AND&#39;</span><span class="p">:</span>
    442. 

  442.                 <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
    443. 

  443.                     <span class="n">iou</span> <span class="o">=</span> <span class="n">calculate_bbox_iou_elementwise</span><span class="p">(</span><span class="n">dc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
    444. 

  444.                     <span class="k">if</span> <span class="n">iou</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">:</span>
    445. 

  445.                         <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
    446. 

  446.                     <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:][</span><span class="n">iou</span> <span class="o">&lt;</span> <span class="n">nms_thres</span><span class="p">]</span>
    447. 

  447. 

  448.             <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;MERGE&#39;</span><span class="p">:</span>
    449. 

  449.                 <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">):</span>
    450. 

  450.                     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    451. 

  451.                         <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span>
    452. 

  452.                         <span class="k">break</span>
    453. 

  453.                     <span class="n">i</span> <span class="o">=</span> <span class="n">calculate_bbox_iou_elementwise</span><span class="p">(</span><span class="n">dc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dc</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">nms_thres</span>
    454. 

  454.                     <span class="n">weights</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
    455. 

  455.                     <span class="n">dc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weights</span> <span class="o">*</span> <span class="n">dc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">weights</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    456. 

  456.                     <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
    457. 

  457.                     <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
    458. 

  458. 

  459.             <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;SOFT&#39;</span><span class="p">:</span>
    460. 

  460.                 <span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.5</span>
    461. 

  461.                 <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">):</span>
    462. 

  462.                     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    463. 

  463.                         <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">)</span>
    464. 

  464.                         <span class="k">break</span>
    465. 

  465.                     <span class="n">det_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
    466. 

  466.                     <span class="n">iou</span> <span class="o">=</span> <span class="n">calculate_bbox_iou_elementwise</span><span class="p">(</span><span class="n">dc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
    467. 

  467.                     <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
    468. 

  468.                     <span class="n">dc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">iou</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="n">sigma</span><span class="p">)</span>
    469. 

  469.                     <span class="n">dc</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="n">dc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">conf_thres</span><span class="p">]</span>
    470. 

  470. 

  471.         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">det_max</span><span class="p">):</span>
    472. 

  472.             <span class="n">det_max</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">det_max</span><span class="p">)</span>
    473. 

  473.             <span class="n">output</span><span class="p">[</span><span class="n">image_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">det_max</span><span class="p">[(</span><span class="o">-</span><span class="n">det_max</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">argsort</span><span class="p">()]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    474. 

  474. 

  475.     <span class="k">return</span> <span class="n">output</span></div>
    476. 

  476. 

  477. 

  478. <div class="viewcode-block" id="change_bbox_bounds_for_image_size"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.change_bbox_bounds_for_image_size">[docs]</a><span class="k">def</span> <span class="nf">change_bbox_bounds_for_image_size</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">img_shape</span><span class="p">):</span>
    479. 

  479.     <span class="c1"># CLIP BOUNDING XYXY BOUNDING BOXES TO IMAGE SHAPE (HEIGHT, WIDTH)</span>
    480. 

  480.     <span class="n">boxes</span><span class="p">[:,</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="o">=</span> <span class="n">boxes</span><span class="p">[:,</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="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">img_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    481. 

  481.     <span class="n">boxes</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> <span class="o">=</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">img_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    482. 

  482.     <span class="k">return</span> <span class="n">boxes</span></div>
    483. 

  483. 

  484. 

  485. <div class="viewcode-block" id="rescale_bboxes_for_image_size"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.rescale_bboxes_for_image_size">[docs]</a><span class="k">def</span> <span class="nf">rescale_bboxes_for_image_size</span><span class="p">(</span><span class="n">current_image_shape</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">original_image_shape</span><span class="p">,</span> <span class="n">ratio_pad</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    486. 

  486.     <span class="sd">&quot;&quot;&quot;</span>
    487. 

  487. <span class="sd">    rescale_bboxes_for_image_size - Changes the bboxes to fit the original image</span>
    488. 

  488. <span class="sd">        :param current_image_shape:</span>
    489. 

  489. <span class="sd">        :param bbox:</span>
    490. 

  490. <span class="sd">        :param original_image_shape:</span>
    491. 

  491. <span class="sd">        :param ratio_pad:</span>
    492. 

  492. <span class="sd">        :return:</span>
    493. 

  493. <span class="sd">    &quot;&quot;&quot;</span>
    494. 

  494.     <span class="k">if</span> <span class="n">ratio_pad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
    495. 

  495.         <span class="n">gain</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">current_image_shape</span><span class="p">)</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">original_image_shape</span><span class="p">)</span>
    496. 

  496.         <span class="c1"># WH PADDING</span>
    497. 

  497.         <span class="n">pad</span> <span class="o">=</span> <span class="p">(</span><span class="n">current_image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">original_image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">gain</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> \
    498. 

  498.               <span class="p">(</span><span class="n">current_image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">original_image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">gain</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
    499. 

  499.     <span class="k">else</span><span class="p">:</span>
    500. 

  500.         <span class="n">gain</span> <span class="o">=</span> <span class="n">ratio_pad</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
    501. 

  501.         <span class="n">pad</span> <span class="o">=</span> <span class="n">ratio_pad</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    502. 

  502. 

  503.     <span class="c1"># X PADDING</span>
    504. 

  504.     <span class="n">bbox</span><span class="p">[:,</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="o">-=</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    505. 

  505. 

  506.     <span class="c1"># Y PADDING</span>
    507. 

  507.     <span class="n">bbox</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> <span class="o">-=</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    508. 

  508.     <span class="n">bbox</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">/=</span> <span class="n">gain</span>
    509. 

  509.     <span class="n">bbox</span> <span class="o">=</span> <span class="n">change_bbox_bounds_for_image_size</span><span class="p">(</span><span class="n">bbox</span><span class="p">,</span> <span class="n">original_image_shape</span><span class="p">)</span>
    510. 

  510.     <span class="k">return</span> <span class="n">bbox</span></div>
    511. 

  511. 

  512. 

  513. <div class="viewcode-block" id="DetectionPostPredictionCallback"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.DetectionPostPredictionCallback">[docs]</a><span class="k">class</span> <span class="nc">DetectionPostPredictionCallback</span><span class="p">(</span><span class="n">ABC</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    514. 

  514. 

  515.     <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="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
    516. 

  516.         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
    517. 

  517. 

  518. <div class="viewcode-block" id="DetectionPostPredictionCallback.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.DetectionPostPredictionCallback.forward">[docs]</a>    <span class="nd">@abstractmethod</span>
    519. 

  519.     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    520. 

  520.         <span class="sd">&quot;&quot;&quot;</span>
    521. 

  521. 

  522. <span class="sd">        :param x:       the output of your model</span>
    523. 

  523. <span class="sd">        :param device:  the device to move all output tensors into</span>
    524. 

  524. <span class="sd">        :return:        a list with length batch_size, each item in the list is a detections</span>
    525. 

  525. <span class="sd">                        with shape: nx6 (x1, y1, x2, y2, confidence, class) where x and y are in range [0,1]</span>
    526. 

  526. <span class="sd">        &quot;&quot;&quot;</span>
    527. 

  527.         <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div>
    528. 

  528. 

  529. 

  530. <div class="viewcode-block" id="YoloV3NonMaxSuppression"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.YoloV3NonMaxSuppression">[docs]</a><span class="k">class</span> <span class="nc">YoloV3NonMaxSuppression</span><span class="p">(</span><span class="n">DetectionPostPredictionCallback</span><span class="p">):</span>
    531. 

  531. 

  532.     <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">conf</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">,</span> <span class="n">nms_thres</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">max_predictions</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
    533. 

  533.         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
    534. 

  534.         <span class="bp">self</span><span class="o">.</span><span class="n">conf</span> <span class="o">=</span> <span class="n">conf</span>
    535. 

  535.         <span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span> <span class="o">=</span> <span class="n">max_predictions</span>
    536. 

  536.         <span class="bp">self</span><span class="o">.</span><span class="n">nms_thres</span> <span class="o">=</span> <span class="n">nms_thres</span>
    537. 

  537. 

  538. <div class="viewcode-block" id="YoloV3NonMaxSuppression.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.YoloV3NonMaxSuppression.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    539. 

  539.         <span class="k">return</span> <span class="n">yolo_v3_non_max_suppression</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">conf_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">,</span> <span class="n">nms_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nms_thres</span><span class="p">)</span></div></div>
    540. 

  540. 

  541. 

  542. <div class="viewcode-block" id="IouThreshold"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.IouThreshold">[docs]</a><span class="k">class</span> <span class="nc">IouThreshold</span><span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="n">Enum</span><span class="p">):</span>
    543. 

  543.     <span class="n">MAP_05</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
    544. 

  544.     <span class="n">MAP_05_TO_095</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">)</span>
    545. 

  545. 

  546. <div class="viewcode-block" id="IouThreshold.is_range"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.IouThreshold.is_range">[docs]</a>    <span class="k">def</span> <span class="nf">is_range</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
    547. 

  547.         <span class="k">return</span> <span class="bp">self</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span></div></div>
    548. 

  548. 

  549. 

  550. <div class="viewcode-block" id="scale_img"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.scale_img">[docs]</a><span class="k">def</span> <span class="nf">scale_img</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">ratio</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">pad_to_original_img_size</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    551. 

  551.     <span class="sd">&quot;&quot;&quot;</span>
    552. 

  552. <span class="sd">    Scales the image by ratio (image dims is (batch_size, channels, height, width)</span>
    553. 

  553. <span class="sd">    Taken from Yolov5 Ultralitics repo&quot;&quot;&quot;</span>
    554. 

  554.     <span class="k">if</span> <span class="n">ratio</span> <span class="o">==</span> <span class="mf">1.0</span><span class="p">:</span>
    555. 

  555.         <span class="k">return</span> <span class="n">img</span>
    556. 

  556.     <span class="k">else</span><span class="p">:</span>
    557. 

  557.         <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
    558. 

  558.         <span class="n">rescaled_size</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">h</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="n">w</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">))</span>
    559. 

  559.         <span class="n">img</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">img</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">rescaled_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>
    560. 

  560.         <span class="c1"># PAD THE IMAGE TO BE A MULTIPLIER OF grid_size. O.W. PAD IT TO THE ORIGINAL IMAGE SIZE</span>
    561. 

  561.         <span class="k">if</span> <span class="ow">not</span> <span class="n">pad_to_original_img_size</span><span class="p">:</span>
    562. 

  562.             <span class="c1"># THE MULTIPLIER WHICH THE DIMENSION MUST BE DIVISIBLE BY</span>
    563. 

  563.             <span class="n">grid_size</span> <span class="o">=</span> <span class="mi">32</span>
    564. 

  564.             <span class="c1"># COMPUTE THE NEW SIZE OF THE IMAGE TO RETURN</span>
    565. 

  565.             <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">ratio</span> <span class="o">/</span> <span class="n">grid_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">grid_size</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)]</span>
    566. 

  566.         <span class="c1"># PAD THE IMAGE TO FIT w, h (EITHER THE ORIGINAL SIZE OR THE NEW SIZE</span>
    567. 

  567.         <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span> <span class="o">-</span> <span class="n">rescaled_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="n">h</span> <span class="o">-</span> <span class="n">rescaled_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">value</span><span class="o">=</span><span class="mf">0.447</span><span class="p">)</span>  <span class="c1"># value = imagenet mean</span></div>
    568. 

  568. 

  569. 

  570. <div class="viewcode-block" id="fuse_conv_and_bn"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.fuse_conv_and_bn">[docs]</a><span class="nd">@deprecated</span><span class="p">(</span><span class="n">reason</span><span class="o">=</span><span class="s2">&quot;use @torch.nn.utils.fuse_conv_bn_eval(conv, bn) instead&quot;</span><span class="p">)</span>
    571. 

  571. <span class="k">def</span> <span class="nf">fuse_conv_and_bn</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="n">bn</span><span class="p">):</span>
    572. 

  572.     <span class="sd">&quot;&quot;&quot;Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/</span>
    573. 

  573. <span class="sd">    Taken from Yolov5 Ultralitics repo&quot;&quot;&quot;</span>
    574. 

  574. 

  575.     <span class="c1"># init</span>
    576. 

  576.     <span class="n">fusedconv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">conv</span><span class="o">.</span><span class="n">in_channels</span><span class="p">,</span>
    577. 

  577.                           <span class="n">conv</span><span class="o">.</span><span class="n">out_channels</span><span class="p">,</span>
    578. 

  578.                           <span class="n">kernel_size</span><span class="o">=</span><span class="n">conv</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">,</span>
    579. 

  579.                           <span class="n">stride</span><span class="o">=</span><span class="n">conv</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
    580. 

  580.                           <span class="n">padding</span><span class="o">=</span><span class="n">conv</span><span class="o">.</span><span class="n">padding</span><span class="p">,</span>
    581. 

  581.                           <span class="n">groups</span><span class="o">=</span><span class="n">conv</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span>
    582. 

  582.                           <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
    583. 

  583. 

  584.     <span class="c1"># prepare filters</span>
    585. 

  585.     <span class="n">w_conv</span> <span class="o">=</span> <span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">conv</span><span class="o">.</span><span class="n">out_channels</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    586. 

  586.     <span class="n">w_bn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">eps</span> <span class="o">+</span> <span class="n">bn</span><span class="o">.</span><span class="n">running_var</span><span class="p">)))</span>
    587. 

  587.     <span class="n">fusedconv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">w_bn</span><span class="p">,</span> <span class="n">w_conv</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">fusedconv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">size</span><span class="p">()))</span>
    588. 

  588. 

  589.     <span class="c1"># prepare spatial bias</span>
    590. 

  590.     <span class="n">b_conv</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="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="n">conv</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">conv</span><span class="o">.</span><span class="n">bias</span>
    591. 

  591.     <span class="n">b_bn</span> <span class="o">=</span> <span class="n">bn</span><span class="o">.</span><span class="n">bias</span> <span class="o">-</span> <span class="n">bn</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">running_mean</span><span class="p">)</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">running_var</span> <span class="o">+</span> <span class="n">bn</span><span class="o">.</span><span class="n">eps</span><span class="p">))</span>
    592. 

  592.     <span class="n">fusedconv</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">w_bn</span><span class="p">,</span> <span class="n">b_conv</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">1</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="o">+</span> <span class="n">b_bn</span><span class="p">)</span>
    593. 

  593. 

  594.     <span class="k">return</span> <span class="n">fusedconv</span></div>
    595. 

  595. 

  596. 

  597. <div class="viewcode-block" id="check_anchor_order"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.check_anchor_order">[docs]</a><span class="k">def</span> <span class="nf">check_anchor_order</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
    598. 

  598.     <span class="sd">&quot;&quot;&quot;Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary</span>
    599. 

  599. <span class="sd">    Taken from Yolov5 Ultralitics repo&quot;&quot;&quot;</span>
    600. 

  600.     <span class="n">anchor_area</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">anchor_grid</span><span class="o">.</span><span class="n">prod</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">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    601. 

  601.     <span class="n">delta_area</span> <span class="o">=</span> <span class="n">anchor_area</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">anchor_area</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    602. 

  602.     <span class="n">delta_stride</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">stride</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">m</span><span class="o">.</span><span class="n">stride</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># delta s</span>
    603. 

  603.     <span class="c1"># IF THE SIGN OF THE SUBTRACTION IS DIFFERENT =&gt; THE STRIDE IS NOT ALIGNED WITH ANCHORS =&gt; m.anchors ARE REVERSED</span>
    604. 

  604.     <span class="k">if</span> <span class="n">delta_area</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">delta_stride</span><span class="o">.</span><span class="n">sign</span><span class="p">():</span>
    605. 

  605.         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Reversing anchor order&#39;</span><span class="p">)</span>
    606. 

  606.         <span class="n">m</span><span class="o">.</span><span class="n">anchors</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">anchors</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    607. 

  607.         <span class="n">m</span><span class="o">.</span><span class="n">anchor_grid</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">anchor_grid</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
    608. 

  608. 

  609. 

  610. <div class="viewcode-block" id="box_iou"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.box_iou">[docs]</a><span class="k">def</span> <span class="nf">box_iou</span><span class="p">(</span><span class="n">box1</span><span class="p">,</span> <span class="n">box2</span><span class="p">):</span>
    611. 

  611.     <span class="c1"># https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py</span>
    612. 

  612.     <span class="sd">&quot;&quot;&quot;</span>
    613. 

  613. <span class="sd">    Return intersection-over-union (Jaccard index) of boxes.</span>
    614. 

  614. <span class="sd">    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.</span>
    615. 

  615. <span class="sd">    Arguments:</span>
    616. 

  616. <span class="sd">        box1 (Tensor[N, 4])</span>
    617. 

  617. <span class="sd">        box2 (Tensor[M, 4])</span>
    618. 

  618. <span class="sd">    Returns:</span>
    619. 

  619. <span class="sd">        iou (Tensor[N, M]): the NxM matrix containing the pairwise</span>
    620. 

  620. <span class="sd">            IoU values for every element in boxes1 and boxes2</span>
    621. 

  621. <span class="sd">    Taken from Yolov5 Ultralitics repo</span>
    622. 

  622. <span class="sd">    &quot;&quot;&quot;</span>
    623. 

  623. 

  624.     <span class="k">def</span> <span class="nf">box_area</span><span class="p">(</span><span class="n">box</span><span class="p">):</span>
    625. 

  625.         <span class="c1"># box = 4xn</span>
    626. 

  626.         <span class="k">return</span> <span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">box</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">box</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    627. 

  627. 

  628.     <span class="n">area1</span> <span class="o">=</span> <span class="n">box_area</span><span class="p">(</span><span class="n">box1</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
    629. 

  629.     <span class="n">area2</span> <span class="o">=</span> <span class="n">box_area</span><span class="p">(</span><span class="n">box2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
    630. 

  630. 

  631.     <span class="c1"># inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)</span>
    632. 

  632.     <span class="n">inter</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">box1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">:],</span> <span class="n">box2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:])</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">box1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">],</span> <span class="n">box2</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
    633. 

  633.     <span class="k">return</span> <span class="n">inter</span> <span class="o">/</span> <span class="p">(</span><span class="n">area1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">area2</span> <span class="o">-</span> <span class="n">inter</span><span class="p">)</span>  <span class="c1"># iou = inter / (area1 + area2 - inter)</span></div>
    634. 

  634. 

  635. 

  636. <div class="viewcode-block" id="non_max_suppression"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.non_max_suppression">[docs]</a><span class="k">def</span> <span class="nf">non_max_suppression</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">conf_thres</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">iou_thres</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">merge</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    637. 

  637.                         <span class="n">agnostic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>  <span class="c1"># noqa: C901</span>
    638. 

  638.     <span class="sd">&quot;&quot;&quot;Performs Non-Maximum Suppression (NMS) on inference results</span>
    639. 

  639. <span class="sd">        :param prediction: raw model prediction</span>
    640. 

  640. <span class="sd">        :param conf_thres: below the confidence threshold - prediction are discarded</span>
    641. 

  641. <span class="sd">        :param iou_thres: IoU threshold for the nms algorithm</span>
    642. 

  642. <span class="sd">        :param merge: Merge boxes using weighted mean</span>
    643. 

  643. <span class="sd">        :param classes: (optional list) filter by class</span>
    644. 

  644. <span class="sd">        :param agnostic: Determines if is class agnostic. i.e. may display a box with 2 predictions</span>
    645. 

  645. <span class="sd">        :return:  (x1, y1, x2, y2, object_conf, class_conf, class)</span>
    646. 

  646. <span class="sd">    Returns:</span>
    647. 

  647. <span class="sd">         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)</span>
    648. 

  648. <span class="sd">    &quot;&quot;&quot;</span>
    649. 

  649.     <span class="c1"># TODO: INVESTIGATE THE COMMENTED OUT PARTS AND DECIDE IF TO ERASE OR UNCOMMENT</span>
    650. 

  650.     <span class="n">number_of_classes</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">5</span>
    651. 

  651.     <span class="n">candidates_above_thres</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">conf_thres</span>
    652. 

  652. 

  653.     <span class="c1"># Settings</span>
    654. 

  654.     <span class="c1"># min_box_width_and_height = 2</span>
    655. 

  655.     <span class="n">max_box_width_and_height</span> <span class="o">=</span> <span class="mi">4096</span>
    656. 

  656.     <span class="n">max_num_of_detections</span> <span class="o">=</span> <span class="mi">300</span>
    657. 

  657.     <span class="n">require_redundant_detections</span> <span class="o">=</span> <span class="kc">True</span>
    658. 

  658.     <span class="n">multi_label_per_box</span> <span class="o">=</span> <span class="n">number_of_classes</span> <span class="o">&gt;</span> <span class="mi">1</span>  <span class="c1"># (adds 0.5ms/img)</span>
    659. 

  659.     <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">prediction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    660. 

  660.     <span class="k">for</span> <span class="n">image_idx</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">prediction</span><span class="p">):</span>
    661. 

  661.         <span class="c1"># Apply constraints</span>
    662. 

  662.         <span class="c1"># pred[((pred[..., 2:4] &lt; min_box_width_and_height) | (pred[..., 2:4] &gt; max_box_width_and_height)).any(1), 4] = 0  # width-height</span>
    663. 

  663.         <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">candidates_above_thres</span><span class="p">[</span><span class="n">image_idx</span><span class="p">]]</span>  <span class="c1"># confidence</span>
    664. 

  664. 

  665.         <span class="c1"># If none remain process next image</span>
    666. 

  666.         <span class="k">if</span> <span class="ow">not</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
    667. 

  667.             <span class="k">continue</span>
    668. 

  668. 

  669.         <span class="c1"># Compute confidence = object_conf * class_conf</span>
    670. 

  670.         <span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:]</span> <span class="o">*=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
    671. 

  671.         <span class="c1"># Box (center x, center y, width, height) to (x1, y1, x2, y2)</span>
    672. 

  672.         <span class="n">box</span> <span class="o">=</span> <span class="n">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])</span>
    673. 

  673. 

  674.         <span class="c1"># Detections matrix nx6 (xyxy, conf, cls)</span>
    675. 

  675.         <span class="k">if</span> <span class="n">multi_label_per_box</span><span class="p">:</span>
    676. 

  676.             <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:]</span> <span class="o">&gt;</span> <span class="n">conf_thres</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">as_tuple</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
    677. 

  677.             <span class="n">pred</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">box</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">j</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">.</span><span class="n">float</span><span class="p">()),</span> <span class="mi">1</span><span class="p">)</span>
    678. 

  678.         <span class="k">else</span><span class="p">:</span>  <span class="c1"># best class only</span>
    679. 

  679.             <span class="n">conf</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    680. 

  680.             <span class="n">pred</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">box</span><span class="p">,</span> <span class="n">conf</span><span class="p">,</span> <span class="n">j</span><span class="o">.</span><span class="n">float</span><span class="p">()),</span> <span class="mi">1</span><span class="p">)[</span><span class="n">conf</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">&gt;</span> <span class="n">conf_thres</span><span class="p">]</span>
    681. 

  681. 

  682.         <span class="c1"># Filter by class</span>
    683. 

  683.         <span class="k">if</span> <span class="n">classes</span><span class="p">:</span>
    684. 

  684.             <span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[(</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">pred</span><span class="o">.</span><span class="n">device</span><span class="p">))</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
    685. 

  685. 

  686.         <span class="c1"># Apply finite constraint</span>
    687. 

  687.         <span class="c1"># if not torch.isfinite(x).all():</span>
    688. 

  688.         <span class="c1">#     x = x[torch.isfinite(x).all(1)]</span>
    689. 

  689. 

  690.         <span class="c1"># If none remain process next image</span>
    691. 

  691.         <span class="n">number_of_boxes</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    692. 

  692.         <span class="k">if</span> <span class="ow">not</span> <span class="n">number_of_boxes</span><span class="p">:</span>
    693. 

  693.             <span class="k">continue</span>
    694. 

  694. 

  695.         <span class="c1"># Sort by confidence</span>
    696. 

  696.         <span class="c1"># x = x[x[:, 4].argsort(descending=True)]</span>
    697. 

  697. 

  698.         <span class="c1"># Batched NMS</span>
    699. 

  699.         <span class="c1"># CREATE AN OFFSET OF THE PREDICTIVE BOX OF DIFFERENT CLASSES IF not agnostic</span>
    700. 

  700.         <span class="n">offset</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">0</span> <span class="k">if</span> <span class="n">agnostic</span> <span class="k">else</span> <span class="n">max_box_width_and_height</span><span class="p">)</span>
    701. 

  701.         <span class="n">boxes</span><span class="p">,</span> <span class="n">scores</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="n">offset</span><span class="p">,</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
    702. 

  702.         <span class="n">idx_to_keep</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">torchvision</span><span class="o">.</span><span class="n">nms</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">iou_thres</span><span class="p">)</span>
    703. 

  703.         <span class="k">if</span> <span class="n">idx_to_keep</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="n">max_num_of_detections</span><span class="p">:</span>  <span class="c1"># limit number of detections</span>
    704. 

  704.             <span class="n">idx_to_keep</span> <span class="o">=</span> <span class="n">idx_to_keep</span><span class="p">[:</span><span class="n">max_num_of_detections</span><span class="p">]</span>
    705. 

  705.         <span class="k">if</span> <span class="n">merge</span> <span class="ow">and</span> <span class="p">(</span><span class="mi">1</span> <span class="o">&lt;</span> <span class="n">number_of_boxes</span> <span class="o">&lt;</span> <span class="mi">3000</span><span class="p">):</span>
    706. 

  706.             <span class="k">try</span><span class="p">:</span>  <span class="c1"># update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)</span>
    707. 

  707.                 <span class="n">iou</span> <span class="o">=</span> <span class="n">box_iou</span><span class="p">(</span><span class="n">boxes</span><span class="p">[</span><span class="n">idx_to_keep</span><span class="p">],</span> <span class="n">boxes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">iou_thres</span>  <span class="c1"># iou matrix</span>
    708. 

  708.                 <span class="n">box_weights</span> <span class="o">=</span> <span class="n">iou</span> <span class="o">*</span> <span class="n">scores</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span>
    709. 

  709.                 <span class="c1"># MERGED BOXES</span>
    710. 

  710.                 <span class="n">pred</span><span class="p">[</span><span class="n">idx_to_keep</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">box_weights</span><span class="p">,</span> <span class="n">pred</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">/</span> <span class="n">box_weights</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    711. 

  711.                 <span class="k">if</span> <span class="n">require_redundant_detections</span><span class="p">:</span>
    712. 

  712.                     <span class="n">idx_to_keep</span> <span class="o">=</span> <span class="n">idx_to_keep</span><span class="p">[</span><span class="n">iou</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">]</span>
    713. 

  713.             <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>  <span class="c1"># possible CUDA error https://github.com/ultralytics/yolov3/issues/1139</span>
    714. 

  714.                 <span class="nb">print</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">idx_to_keep</span><span class="p">,</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">idx_to_keep</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    715. 

  715.                 <span class="k">pass</span>
    716. 

  716. 

  717.         <span class="n">output</span><span class="p">[</span><span class="n">image_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">idx_to_keep</span><span class="p">]</span>
    718. 

  718. 

  719.     <span class="k">return</span> <span class="n">output</span></div>
    720. 

  720. 

  721. 

  722. <div class="viewcode-block" id="check_img_size_divisibilty"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.check_img_size_divisibilty">[docs]</a><span class="k">def</span> <span class="nf">check_img_size_divisibilty</span><span class="p">(</span><span class="n">img_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">stride</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">):</span>
    723. 

  723.     <span class="sd">&quot;&quot;&quot;</span>
    724. 

  724. <span class="sd">    :param img_size: Int, the size of the image (H or W).</span>
    725. 

  725. <span class="sd">    :param stride: Int, the number to check if img_size is divisible by.</span>
    726. 

  726. <span class="sd">    :return: (True, None) if img_size is divisble by stride, (False, Suggestions) if it&#39;s not.</span>
    727. 

  727. <span class="sd">        Note: Suggestions are the two closest numbers to img_size that *are* divisible by stride.</span>
    728. 

  728. <span class="sd">        For example if img_size=321, stride=32, it will return (False,(352, 320)).</span>
    729. 

  729. <span class="sd">    &quot;&quot;&quot;</span>
    730. 

  730.     <span class="n">new_size</span> <span class="o">=</span> <span class="n">make_divisible</span><span class="p">(</span><span class="n">img_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">stride</span><span class="p">))</span>
    731. 

  731.     <span class="k">if</span> <span class="n">new_size</span> <span class="o">!=</span> <span class="n">img_size</span><span class="p">:</span>
    732. 

  732.         <span class="k">return</span> <span class="kc">False</span><span class="p">,</span> <span class="p">(</span><span class="n">new_size</span><span class="p">,</span> <span class="n">make_divisible</span><span class="p">(</span><span class="n">img_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">stride</span><span class="p">),</span> <span class="n">ceil</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
    733. 

  733.     <span class="k">else</span><span class="p">:</span>
    734. 

  734.         <span class="k">return</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">None</span></div>
    735. 

  735. 

  736. 

  737. <div class="viewcode-block" id="make_divisible"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.make_divisible">[docs]</a><span class="k">def</span> <span class="nf">make_divisible</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">ceil</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    738. 

  738.     <span class="sd">&quot;&quot;&quot;</span>
    739. 

  739. <span class="sd">    Returns x evenly divisible by divisor.</span>
    740. 

  740. <span class="sd">    If ceil=True it will return the closest larger number to the original x, and ceil=False the closest smaller number.</span>
    741. 

  741. <span class="sd">    &quot;&quot;&quot;</span>
    742. 

  742.     <span class="k">if</span> <span class="n">ceil</span><span class="p">:</span>
    743. 

  743.         <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="n">divisor</span><span class="p">)</span> <span class="o">*</span> <span class="n">divisor</span>
    744. 

  744.     <span class="k">else</span><span class="p">:</span>
    745. 

  745.         <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="n">divisor</span><span class="p">)</span> <span class="o">*</span> <span class="n">divisor</span></div>
    746. 

  746. 

  747. 

  748. <div class="viewcode-block" id="matrix_non_max_suppression"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.matrix_non_max_suppression">[docs]</a><span class="k">def</span> <span class="nf">matrix_non_max_suppression</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">conf_thres</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">kernel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">,</span>
    749. 

  749.                                <span class="n">sigma</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">max_num_of_detections</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">500</span><span class="p">):</span>
    750. 

  750.     <span class="sd">&quot;&quot;&quot;Performs Matrix Non-Maximum Suppression (NMS) on inference results</span>
    751. 

  751. <span class="sd">        https://arxiv.org/pdf/1912.04488.pdf</span>
    752. 

  752. <span class="sd">        :param pred: raw model prediction (in test mode) - a Tensor of shape [batch, num_predictions, 85]</span>
    753. 

  753. <span class="sd">        where each item format is (x, y, w, h, object_conf, class_conf, ... 80 classes score ...)</span>
    754. 

  754. <span class="sd">        :param conf_thres: below the confidence threshold - prediction are discarded</span>
    755. 

  755. <span class="sd">        :param kernel: type of kernel to use [&#39;gaussian&#39;, &#39;linear&#39;]</span>
    756. 

  756. <span class="sd">        :param sigma: sigma for the gussian kernel</span>
    757. 

  757. <span class="sd">        :param max_num_of_detections: maximum number of boxes to output</span>
    758. 

  758. <span class="sd">        :return:  list of (x1, y1, x2, y2, object_conf, class_conf, class)</span>
    759. 

  759. 

  760. <span class="sd">    Returns:</span>
    761. 

  761. <span class="sd">         detections list with shape: (x1, y1, x2, y2, conf, cls)</span>
    762. 

  762. <span class="sd">    &quot;&quot;&quot;</span>
    763. 

  763.     <span class="c1"># MULTIPLY CONF BY CLASS CONF TO GET COMBINED CONFIDENCE</span>
    764. 

  764.     <span class="n">class_conf</span><span class="p">,</span> <span class="n">class_pred</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">:]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
    765. 

  765.     <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*=</span> <span class="n">class_conf</span>
    766. 

  766. 

  767.     <span class="c1"># BOX (CENTER X, CENTER Y, WIDTH, HEIGHT) TO (X1, Y1, X2, Y2)</span>
    768. 

  768.     <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])</span>
    769. 

  769. 

  770.     <span class="c1"># DETECTIONS ORDERED AS (x1y1x2y2, obj_conf, class_conf, class_pred)</span>
    771. 

  771.     <span class="n">pred</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">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">5</span><span class="p">],</span> <span class="n">class_pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
    772. 

  772. 

  773.     <span class="c1"># SORT DETECTIONS BY DECREASING CONFIDENCE SCORES</span>
    774. 

  774.     <span class="n">sort_ind</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
    775. 

  775.     <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">sort_ind</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">pred</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="mi">0</span><span class="p">:</span><span class="n">max_num_of_detections</span><span class="p">]</span>
    776. 

  776. 

  777.     <span class="n">ious</span> <span class="o">=</span> <span class="n">calc_bbox_iou_matrix</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span>
    778. 

  778. 

  779.     <span class="n">ious</span> <span class="o">=</span> <span class="n">ious</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    780. 

  780. 

  781.     <span class="c1"># CREATE A LABELS MASK, WE WANT ONLY BOXES WITH THE SAME LABEL TO AFFECT EACH OTHER</span>
    782. 

  782.     <span class="n">labels</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">:]</span>
    783. 

  783.     <span class="n">labeles_matrix</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span> <span class="o">==</span> <span class="n">labels</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    784. 

  784. 

  785.     <span class="n">ious</span> <span class="o">*=</span> <span class="n">labeles_matrix</span>
    786. 

  786.     <span class="n">ious_cmax</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">ious</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    787. 

  787.     <span class="n">ious_cmax</span> <span class="o">=</span> <span class="n">ious_cmax</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">max_num_of_detections</span><span class="p">)</span>
    788. 

  788. 

  789.     <span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
    790. 

  790.         <span class="n">decay_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">*</span> <span class="p">(</span><span class="n">ious</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
    791. 

  791.         <span class="n">compensate_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">*</span> <span class="p">(</span><span class="n">ious_cmax</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
    792. 

  792.         <span class="n">decay</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="p">(</span><span class="n">decay_matrix</span> <span class="o">/</span> <span class="n">compensate_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    793. 

  793.     <span class="k">else</span><span class="p">:</span>
    794. 

  794.         <span class="n">decay</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ious</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">ious_cmax</span><span class="p">)</span>
    795. 

  795.         <span class="n">decay</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">decay</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    796. 

  796. 

  797.     <span class="n">pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*=</span> <span class="n">decay</span>
    798. 

  798. 

  799.     <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">pred</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">conf_thres</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
    800. 

  800. 

  801.     <span class="k">return</span> <span class="n">output</span></div>
    802. 

  802. 

  803. 

  804. <div class="viewcode-block" id="NMS_Type"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.NMS_Type">[docs]</a><span class="k">class</span> <span class="nc">NMS_Type</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">Enum</span><span class="p">):</span>
    805. 

  805.     <span class="sd">&quot;&quot;&quot;</span>
    806. 

  806. <span class="sd">    Type of non max suppression algorithm that can be used for post processing detection</span>
    807. 

  807. <span class="sd">    &quot;&quot;&quot;</span>
    808. 

  808.     <span class="n">ITERATIVE</span> <span class="o">=</span> <span class="s1">&#39;iterative&#39;</span>
    809. 

  809.     <span class="n">MATRIX</span> <span class="o">=</span> <span class="s1">&#39;matrix&#39;</span></div>
    810. 

  810. 

  811. 

  812. <div class="viewcode-block" id="calc_batch_prediction_accuracy"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.calc_batch_prediction_accuracy">[docs]</a><span class="k">def</span> <span class="nf">calc_batch_prediction_accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">targets</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">height</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">width</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>  <span class="c1"># noqa: C901</span>
    813. 

  813.                                    <span class="n">iou_thres</span><span class="p">:</span> <span class="n">IouThreshold</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
    814. 

  814.     <span class="sd">&quot;&quot;&quot;</span>
    815. 

  815. 

  816. <span class="sd">    :param output:       list (of length batch_size) of Tensors of shape (num_detections, 6)</span>
    817. 

  817. <span class="sd">                         format:     (x1, y1, x2, y2, confidence, class_label) where x1,y1,x2,y2 are according to image size</span>
    818. 

  818. <span class="sd">    :param targets:      targets for all images of shape (total_num_targets, 6)</span>
    819. 

  819. <span class="sd">                         format:     (image_index, x, y, w, h, label) where x,y,w,h are in range [0,1]</span>
    820. 

  820. <span class="sd">    :param height,width: dimensions of the image</span>
    821. 

  821. <span class="sd">    :param iou_thres:    Threshold to compute the mAP</span>
    822. 

  822. <span class="sd">    :param device:       &#39;cuda&#39;\&#39;cpu&#39; - where the computations are made</span>
    823. 

  823. <span class="sd">    :return:</span>
    824. 

  824. <span class="sd">    &quot;&quot;&quot;</span>
    825. 

  825.     <span class="n">batch_metrics</span> <span class="o">=</span> <span class="p">[]</span>
    826. 

  826.     <span class="n">batch_images_counter</span> <span class="o">=</span> <span class="mi">0</span>
    827. 

  827.     <span class="n">device</span> <span class="o">=</span> <span class="n">targets</span><span class="o">.</span><span class="n">device</span>
    828. 

  828. 

  829.     <span class="k">if</span> <span class="ow">not</span> <span class="n">iou_thres</span><span class="o">.</span><span class="n">is_range</span><span class="p">():</span>
    830. 

  830.         <span class="n">num_ious</span> <span class="o">=</span> <span class="mi">1</span>
    831. 

  831.         <span class="n">ious</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">iou_thres</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    832. 

  832.     <span class="k">else</span><span class="p">:</span>
    833. 

  833.         <span class="n">num_ious</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">((</span><span class="n">iou_thres</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">iou_thres</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="mf">0.05</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span>
    834. 

  834.         <span class="n">ious</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">iou_thres</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">iou_thres</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">num_ious</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    835. 

  835. 

  836.     <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">output</span><span class="p">):</span>
    837. 

  837.         <span class="n">labels</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">targets</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span>
    838. 

  838.         <span class="n">labels_num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
    839. 

  839.         <span class="n">target_class</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">if</span> <span class="n">labels_num</span> <span class="k">else</span> <span class="p">[]</span>
    840. 

  840.         <span class="n">batch_images_counter</span> <span class="o">+=</span> <span class="mi">1</span>
    841. 

  841. 

  842.         <span class="k">if</span> <span class="n">pred</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
    843. 

  843.             <span class="k">if</span> <span class="n">labels_num</span><span class="p">:</span>
    844. 

  844.                 <span class="n">batch_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
    845. 

  845.                     <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_ious</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">target_class</span><span class="p">))</span>
    846. 

  846.             <span class="k">continue</span>
    847. 

  847. 

  848.         <span class="c1"># CHANGE bboxes TO FIT THE IMAGE SIZE</span>
    849. 

  849.         <span class="n">change_bbox_bounds_for_image_size</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">))</span>
    850. 

  850. 

  851.         <span class="c1"># ZEROING ALL OF THE bbox PREDICTIONS BEFORE MAX IOU FILTERATION</span>
    852. 

  852.         <span class="n">correct</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="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="p">),</span> <span class="n">num_ious</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
    853. 

  853.         <span class="k">if</span> <span class="n">labels_num</span><span class="p">:</span>
    854. 

  854.             <span class="n">detected</span> <span class="o">=</span> <span class="p">[]</span>
    855. 

  855.             <span class="n">tcls_tensor</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
    856. 

  856. 

  857.             <span class="n">target_bboxes</span> <span class="o">=</span> <span class="n">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">labels</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span>
    858. 

  858.             <span class="n">target_bboxes</span><span class="p">[:,</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="o">*=</span> <span class="n">width</span>
    859. 

  859.             <span class="n">target_bboxes</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> <span class="o">*=</span> <span class="n">height</span>
    860. 

  860. 

  861.             <span class="c1"># SEARCH FOR CORRECT PREDICTIONS</span>
    862. 

  862.             <span class="c1"># Per target class</span>
    863. 

  863.             <span class="k">for</span> <span class="bp">cls</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">tcls_tensor</span><span class="p">):</span>
    864. 

  864.                 <span class="n">target_index</span> <span class="o">=</span> <span class="p">(</span><span class="bp">cls</span> <span class="o">==</span> <span class="n">tcls_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">as_tuple</span><span class="o">=</span><span class="kc">False</span><span class="p">)</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>
    865. 

  865.                 <span class="n">pred_index</span> <span class="o">=</span> <span class="p">(</span><span class="bp">cls</span> <span class="o">==</span> <span class="n">pred</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">])</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">as_tuple</span><span class="o">=</span><span class="kc">False</span><span class="p">)</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>
    866. 

  866. 

  867.                 <span class="c1"># Search for detections</span>
    868. 

  868.                 <span class="k">if</span> <span class="n">pred_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
    869. 

  869.                     <span class="c1"># Prediction to target ious</span>
    870. 

  870.                     <span class="n">iou</span><span class="p">,</span> <span class="n">i</span> <span class="o">=</span> <span class="n">box_iou</span><span class="p">(</span><span class="n">pred</span><span class="p">[</span><span class="n">pred_index</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">],</span> <span class="n">target_bboxes</span><span class="p">[</span><span class="n">target_index</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># best ious, indices</span>
    871. 

  871. 

  872.                     <span class="c1"># Append detections</span>
    873. 

  873.                     <span class="n">detected_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
    874. 

  874.                     <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">(</span><span class="n">iou</span> <span class="o">&gt;</span> <span class="n">ious</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">as_tuple</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    875. 

  875.                         <span class="n">detected_target</span> <span class="o">=</span> <span class="n">target_index</span><span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="n">j</span><span class="p">]]</span>
    876. 

  876.                         <span class="k">if</span> <span class="n">detected_target</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">detected_set</span><span class="p">:</span>
    877. 

  877.                             <span class="n">detected_set</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">detected_target</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
    878. 

  878.                             <span class="n">detected</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">detected_target</span><span class="p">)</span>
    879. 

  879.                             <span class="n">correct</span><span class="p">[</span><span class="n">pred_index</span><span class="p">[</span><span class="n">j</span><span class="p">]]</span> <span class="o">=</span> <span class="n">iou</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">ious</span>  <span class="c1"># iou_thres is 1xn</span>
    880. 

  880.                             <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected</span><span class="p">)</span> <span class="o">==</span> <span class="n">labels_num</span><span class="p">:</span>  <span class="c1"># all targets already located in image</span>
    881. 

  881.                                 <span class="k">break</span>
    882. 

  882. 

  883.         <span class="c1"># APPEND STATISTICS (CORRECT, CONF, PCLS, TCLS)</span>
    884. 

  884.         <span class="n">batch_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">correct</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="n">pred</span><span class="p">[:,</span> <span class="mi">4</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="n">pred</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="n">target_class</span><span class="p">))</span>
    885. 

  885. 

  886.     <span class="k">return</span> <span class="n">batch_metrics</span><span class="p">,</span> <span class="n">batch_images_counter</span></div>
    887. 

  887. 

  888. 

  889. <div class="viewcode-block" id="AnchorGenerator"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.AnchorGenerator">[docs]</a><span class="k">class</span> <span class="nc">AnchorGenerator</span><span class="p">:</span>
    890. 

  890.     <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>
    891. 

  891. 

  892.     <span class="nd">@staticmethod</span>
    893. 

  893.     <span class="k">def</span> <span class="nf">_metric</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">):</span>
    894. 

  894.         <span class="sd">&quot;&quot;&quot; measure how &#39;far&#39; each object is from the closest anchor</span>
    895. 

  895. <span class="sd">            :returns a matrix n by number of objects and the measurements to the closest anchor for each object</span>
    896. 

  896. <span class="sd">        &quot;&quot;&quot;</span>
    897. 

  897.         <span class="n">r</span> <span class="o">=</span> <span class="n">objects</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">/</span> <span class="n">anchors</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span>
    898. 

  898.         <span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">amin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">r</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
    899. 

  899.         <span class="k">return</span> <span class="n">matrix</span><span class="p">,</span> <span class="n">matrix</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    900. 

  900. 

  901.     <span class="nd">@staticmethod</span>
    902. 

  902.     <span class="k">def</span> <span class="nf">_anchor_fitness</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">):</span>
    903. 

  903.         <span class="sd">&quot;&quot;&quot; how well the anchors fit the objects&quot;&quot;&quot;</span>
    904. 

  904.         <span class="n">_</span><span class="p">,</span> <span class="n">best</span> <span class="o">=</span> <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_metric</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">)</span>
    905. 

  905.         <span class="k">return</span> <span class="p">(</span><span class="n">best</span> <span class="o">*</span> <span class="p">(</span><span class="n">best</span> <span class="o">&gt;</span> <span class="n">thresh</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>  <span class="c1"># fitness</span>
    906. 

  906. 

  907.     <span class="nd">@staticmethod</span>
    908. 

  908.     <span class="k">def</span> <span class="nf">_print_results</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">num_anchors</span><span class="p">,</span> <span class="n">img_size</span><span class="p">):</span>
    909. 

  909.         <span class="c1"># SORT SMALL TO LARGE (BY AREA)</span>
    910. 

  910.         <span class="n">anchors</span> <span class="o">=</span> <span class="n">anchors</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">anchors</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="mi">1</span><span class="p">))]</span>
    911. 

  911.         <span class="n">x</span><span class="p">,</span> <span class="n">best</span> <span class="o">=</span> <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_metric</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">)</span>
    912. 

  912.         <span class="n">best_possible_recall</span> <span class="o">=</span> <span class="p">(</span><span class="n">best</span> <span class="o">&gt;</span> <span class="n">thresh</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
    913. 

  913.         <span class="n">anchors_above_thesh</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">thresh</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">*</span> <span class="n">num_anchors</span>
    914. 

  914. 

  915.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
    916. 

  916.             <span class="sa">f</span><span class="s1">&#39;thr=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">best_possible_recall</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1"> best possible recall, </span><span class="si">{</span><span class="n">anchors_above_thesh</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> anchors past thr&#39;</span><span class="p">)</span>
    917. 

  917.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;num_anchors=</span><span class="si">{</span><span class="n">num_anchors</span><span class="si">}</span><span class="s1">, img_size=</span><span class="si">{</span><span class="n">img_size</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    918. 

  918.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
    919. 

  919.             <span class="sa">f</span><span class="s1">&#39; metric_all=</span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">best</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">-mean/best, past_thr=</span><span class="si">{</span><span class="n">x</span><span class="p">[</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">thresh</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">-mean: &#39;</span><span class="p">)</span>
    920. 

  920. 

  921.         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">mean</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">anchors</span><span class="p">):</span>
    922. 

  922.             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">,</span><span class="si">%i</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">round</span><span class="p">(</span><span class="n">mean</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
    923. 

  923.                   <span class="n">end</span><span class="o">=</span><span class="s1">&#39;,  &#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">anchors</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>  <span class="c1"># use in *.cfg</span>
    924. 

  924. 

  925.     <span class="nd">@staticmethod</span>
    926. 

  926.     <span class="k">def</span> <span class="nf">_plot_object_distribution</span><span class="p">(</span><span class="n">objects</span><span class="p">,</span> <span class="n">anchors</span><span class="p">):</span>
    927. 

  927.         <span class="n">selected</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">objects</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="n">size</span><span class="o">=</span><span class="n">objects</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">50</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    928. 

  928. 

  929.         <span class="n">distance_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">objects</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">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">T</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="mi">1</span><span class="p">))</span>
    930. 

  930.         <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">distance_matrix</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    931. 

  931.         <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">objects</span><span class="p">[</span><span class="n">selected</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">objects</span><span class="p">[</span><span class="n">selected</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">labels</span><span class="p">[</span><span class="n">selected</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
    932. 

  932.         <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">anchors</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="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;P&#39;</span><span class="p">)</span>
    933. 

  933.         <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    934. 

  934. 

  935.     <span class="nd">@staticmethod</span>
    936. 

  936.     <span class="k">def</span> <span class="nf">_generate_anchors</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">num_anchors</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">gen</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
    937. 

  937.         <span class="sd">&quot;&quot;&quot; Creates kmeans-evolved anchors from training dataset</span>
    938. 

  938. <span class="sd">            Based on the implementation by Ultralytics for Yolo V5</span>
    939. 

  939. 

  940. <span class="sd">            :param dataset: a loaded dataset (must be with cached labels and &quot;train_sample_loading_method&quot;:&#39;rectangular&#39;)</span>
    941. 

  941. <span class="sd">            :param num_anchors: number of anchors</span>
    942. 

  942. <span class="sd">            :param thresh: anchor-label wh ratio threshold used to asses if a label can be detected by an anchor.</span>
    943. 

  943. <span class="sd">                    it means that the aspect ratio of the object is not more than thres from the aspect ratio of the anchor.</span>
    944. 

  944. <span class="sd">            :param gen: generations to evolve anchors using genetic algorithm. after kmeans, this algorithm iteratively</span>
    945. 

  945. <span class="sd">                    make minor random changes in the anchors and if a change imporve the anchors-data fit it evolves the</span>
    946. 

  946. <span class="sd">                    anchors.</span>
    947. 

  947. <span class="sd">            :returns anchors array num_anchors by 2 (x,y) normalized to image size</span>
    948. 

  948. <span class="sd">        &quot;&quot;&quot;</span>
    949. 

  949.         <span class="n">_prefix</span> <span class="o">=</span> <span class="s1">&#39;Anchors Generator: &#39;</span>
    950. 

  950.         <span class="n">img_size</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">img_size</span>
    951. 

  951.         <span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">cache_labels</span><span class="p">,</span> <span class="s2">&quot;dataset labels have to be cached before generating anchors&quot;</span>
    952. 

  952. 

  953.         <span class="n">image_shapes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
    954. 

  954.             <span class="p">[</span><span class="n">dataset</span><span class="o">.</span><span class="n">exif_size</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">f</span><span class="p">))</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">img_files</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;Reading image shapes&#39;</span><span class="p">)])</span>
    955. 

  955. 

  956.         <span class="c1"># Get label wh</span>
    957. 

  957.         <span class="n">shapes</span> <span class="o">=</span> <span class="n">img_size</span> <span class="o">*</span> <span class="n">image_shapes</span> <span class="o">/</span> <span class="n">image_shapes</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    958. 

  958.         <span class="n">objects_wh</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">l</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="n">s</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">shapes</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">labels</span><span class="p">)])</span>
    959. 

  959. 

  960.         <span class="c1"># Filter</span>
    961. 

  961.         <span class="n">i</span> <span class="o">=</span> <span class="p">(</span><span class="n">objects_wh</span> <span class="o">&lt;</span> <span class="mf">3.0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    962. 

  962.         <span class="k">if</span> <span class="n">i</span><span class="p">:</span>
    963. 

  963.             <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
    964. 

  964.                 <span class="sa">f</span><span class="s1">&#39;Extremely small objects found. </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">objects_wh</span><span class="p">)</span><span class="si">}</span><span class="s1"> labels are &lt; 3 pixels in size.&#39;</span><span class="p">)</span>
    965. 

  965.         <span class="n">object_wh_filtered</span> <span class="o">=</span> <span class="n">objects_wh</span><span class="p">[(</span><span class="n">objects_wh</span> <span class="o">&gt;=</span> <span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
    966. 

  966. 

  967.         <span class="c1"># Kmeans calculation</span>
    968. 

  968.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Running kmeans for </span><span class="si">{</span><span class="n">num_anchors</span><span class="si">}</span><span class="s1"> anchors on </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">object_wh_filtered</span><span class="p">)</span><span class="si">}</span><span class="s1"> points...&#39;</span><span class="p">)</span>
    969. 

  969.         <span class="n">mean_wh</span> <span class="o">=</span> <span class="n">object_wh_filtered</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># sigmas for whitening</span>
    970. 

  970.         <span class="n">anchors</span><span class="p">,</span> <span class="n">dist</span> <span class="o">=</span> <span class="n">kmeans</span><span class="p">(</span><span class="n">object_wh_filtered</span> <span class="o">/</span> <span class="n">mean_wh</span><span class="p">,</span> <span class="n">num_anchors</span><span class="p">,</span> <span class="nb">iter</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>  <span class="c1"># points, mean distance</span>
    971. 

  971.         <span class="c1"># MEANS WHERE NORMALIZED. SCALE THEM BACK TO IMAGE SIZE</span>
    972. 

  972.         <span class="n">anchors</span> <span class="o">*=</span> <span class="n">mean_wh</span>
    973. 

  973. 

  974.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Initial results&#39;</span><span class="p">)</span>
    975. 

  975.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_print_results</span><span class="p">(</span><span class="n">objects_wh</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">num_anchors</span><span class="p">,</span> <span class="n">img_size</span><span class="p">)</span>
    976. 

  976.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_plot_object_distribution</span><span class="p">(</span><span class="n">objects_wh</span><span class="p">,</span> <span class="n">anchors</span><span class="p">)</span>
    977. 

  977. 

  978.         <span class="c1"># EVOLVE</span>
    979. 

  979.         <span class="n">fitness</span><span class="p">,</span> <span class="n">generations</span><span class="p">,</span> <span class="n">mutation_prob</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_anchor_fitness</span><span class="p">(</span><span class="n">object_wh_filtered</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span>
    980. 

  980.                                                                                      <span class="n">thresh</span><span class="p">),</span> <span class="n">anchors</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span>
    981. 

  981.         <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">gen</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s1">Evolving anchors with Genetic Algorithm:&#39;</span><span class="p">)</span>
    982. 

  982.         <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">:</span>
    983. 

  983.             <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">generations</span><span class="p">)</span>
    984. 

  984.             <span class="k">while</span> <span class="p">(</span><span class="n">v</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>  <span class="c1"># mutate until a change occurs (prevent duplicates)</span>
    985. 

  985.                 <span class="n">v</span> <span class="o">=</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">random</span><span class="p">(</span><span class="n">generations</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">mutation_prob</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span>
    986. 

  986.                     <span class="o">*</span><span class="n">generations</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">)</span>
    987. 

  987.             <span class="n">evolved_anchors</span> <span class="o">=</span> <span class="p">(</span><span class="n">anchors</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
    988. 

  988.             <span class="n">evolved_anchors_fitness</span> <span class="o">=</span> <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_anchor_fitness</span><span class="p">(</span><span class="n">object_wh_filtered</span><span class="p">,</span> <span class="n">evolved_anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span>
    989. 

  989.             <span class="k">if</span> <span class="n">evolved_anchors_fitness</span> <span class="o">&gt;</span> <span class="n">fitness</span><span class="p">:</span>
    990. 

  990.                 <span class="n">fitness</span><span class="p">,</span> <span class="n">anchors</span> <span class="o">=</span> <span class="n">evolved_anchors_fitness</span><span class="p">,</span> <span class="n">evolved_anchors</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    991. 

  991.                 <span class="n">progress_bar</span><span class="o">.</span><span class="n">desc</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s1">Evolving anchors with Genetic Algorithm: fitness = </span><span class="si">{</span><span class="n">fitness</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span>
    992. 

  992. 

  993.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Final results&#39;</span><span class="p">)</span>
    994. 

  994.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_print_results</span><span class="p">(</span><span class="n">objects_wh</span><span class="p">,</span> <span class="n">anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">num_anchors</span><span class="p">,</span> <span class="n">img_size</span><span class="p">)</span>
    995. 

  995.         <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_plot_object_distribution</span><span class="p">(</span><span class="n">objects_wh</span><span class="p">,</span> <span class="n">anchors</span><span class="p">)</span>
    996. 

  996. 

  997.         <span class="n">anchors</span> <span class="o">=</span> <span class="n">anchors</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">anchors</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="mi">1</span><span class="p">))]</span>
    998. 

  998.         <span class="n">anchors_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">anchors</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</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">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    999. 

  999.         <span class="k">return</span> <span class="n">anchors_list</span>
    1000. 

  1000. 

  1001.     <span class="nd">@staticmethod</span>
    1002. 

  1002.     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">num_anchors</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">gen</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
    1003. 

  1003.         <span class="k">return</span> <span class="n">AnchorGenerator</span><span class="o">.</span><span class="n">_generate_anchors</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">num_anchors</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">gen</span><span class="p">)</span></div>
    1004. 

  1004. 

  1005. 

  1006. <div class="viewcode-block" id="plot_coco_datasaet_images_with_detections"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.plot_coco_datasaet_images_with_detections">[docs]</a><span class="k">def</span> <span class="nf">plot_coco_datasaet_images_with_detections</span><span class="p">(</span><span class="n">data_loader</span><span class="p">,</span> <span class="n">num_images_to_plot</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    1007. 

  1007.     <span class="sd">&quot;&quot;&quot;</span>
    1008. 

  1008. <span class="sd">    plot_coco_images</span>
    1009. 

  1009. <span class="sd">        :param data_loader:</span>
    1010. 

  1010. <span class="sd">        :param num_images_to_plot:</span>
    1011. 

  1011. <span class="sd">        :return:</span>
    1012. 

  1012. <span class="sd">    # &quot;&quot;&quot;</span>
    1013. 

  1013.     <span class="n">images_counter</span> <span class="o">=</span> <span class="mi">0</span>
    1014. 

  1014. 

  1015.     <span class="c1"># PLOT ONE image AND ONE GROUND_TRUTH bbox</span>
    1016. 

  1016.     <span class="k">for</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">targets</span> <span class="ow">in</span> <span class="n">data_loader</span><span class="p">:</span>
    1017. 

  1017. 

  1018.         <span class="c1"># PLOTS TRAINING IMAGES OVERLAID WITH TARGETS</span>
    1019. 

  1019.         <span class="n">imgs</span> <span class="o">=</span> <span class="n">imgs</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>
    1020. 

  1020.         <span class="n">targets</span> <span class="o">=</span> <span class="n">targets</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>
    1021. 

  1021. 

  1022.         <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">10</span><span class="p">))</span>
    1023. 

  1023.         <span class="n">batch_size</span><span class="p">,</span> <span class="n">_</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="n">imgs</span><span class="o">.</span><span class="n">shape</span>
    1024. 

  1024. 

  1025.         <span class="c1"># LIMIT PLOT TO 16 IMAGES</span>
    1026. 

  1026.         <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
    1027. 

  1027. 

  1028.         <span class="c1"># NUMBER OF SUBPLOTS</span>
    1029. 

  1029.         <span class="n">ns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">batch_size</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">)</span>
    1030. 

  1030. 

  1031.         <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="n">batch_size</span><span class="p">):</span>
    1032. 

  1032.             <span class="n">boxes</span> <span class="o">=</span> <span class="n">convert_xywh_bbox_to_xyxy</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">targets</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">6</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">detach</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">T</span>
    1033. 

  1033.             <span class="n">boxes</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="o">*=</span> <span class="n">w</span>
    1034. 

  1034.             <span class="n">boxes</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> <span class="o">*=</span> <span class="n">h</span>
    1035. 

  1035.             <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">ns</span><span class="p">,</span> <span class="n">ns</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</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="mi">0</span><span class="p">))</span>
    1036. 

  1036.             <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">boxes</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">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">boxes</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</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="s1">&#39;.-&#39;</span><span class="p">)</span>
    1037. 

  1037.             <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    1038. 

  1038.         <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
    1039. 

  1039.         <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    1040. 

  1040.         <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
    1041. 

  1041. 

  1042.         <span class="n">images_counter</span> <span class="o">+=</span> <span class="mi">1</span>
    1043. 

  1043.         <span class="k">if</span> <span class="n">images_counter</span> <span class="o">==</span> <span class="n">num_images_to_plot</span><span class="p">:</span>
    1044. 

  1044.             <span class="k">break</span></div>
    1045. 

  1045. 

  1046. 

  1047. <div class="viewcode-block" id="undo_image_preprocessing"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.undo_image_preprocessing">[docs]</a><span class="k">def</span> <span class="nf">undo_image_preprocessing</span><span class="p">(</span><span class="n">im_tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    1048. 

  1048.     <span class="sd">&quot;&quot;&quot;</span>
    1049. 

  1049. <span class="sd">    :param im_tensor: images in a batch after preprocessing for inference, RGB, (B, C, H, W)</span>
    1050. 

  1050. <span class="sd">    :return:          images in a batch in cv2 format, BGR, (B, H, W, C)</span>
    1051. 

  1051. <span class="sd">    &quot;&quot;&quot;</span>
    1052. 

  1052.     <span class="n">im_np</span> <span class="o">=</span> <span class="n">im_tensor</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>
    1053. 

  1053.     <span class="n">im_np</span> <span class="o">=</span> <span class="n">im_np</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><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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    1054. 

  1054.     <span class="n">im_np</span> <span class="o">*=</span> <span class="mf">255.</span>
    1055. 

  1055.     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">im_np</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span></div>
    1056. 

  1056. 

  1057. 

  1058. <div class="viewcode-block" id="DetectionVisualization"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.DetectionVisualization">[docs]</a><span class="k">class</span> <span class="nc">DetectionVisualization</span><span class="p">:</span>
    1059. 

  1059.     <span class="nd">@staticmethod</span>
    1060. 

  1060.     <span class="k">def</span> <span class="nf">_generate_color_mapping</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="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">]]:</span>
    1061. 

  1061.         <span class="sd">&quot;&quot;&quot;</span>
    1062. 

  1062. <span class="sd">        Generate a unique BGR color for each class</span>
    1063. 

  1063. <span class="sd">        &quot;&quot;&quot;</span>
    1064. 

  1064.         <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;gist_rainbow&#39;</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
    1065. 

  1065.         <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="n">cmap</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">bytes</span><span class="o">=</span><span class="kc">True</span><span class="p">)[:</span><span class="mi">3</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_classes</span><span class="p">)]</span>
    1066. 

  1066.         <span class="k">return</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">colors</span><span class="p">]</span>
    1067. 

  1067. 

  1068.     <span class="nd">@staticmethod</span>
    1069. 

  1069.     <span class="k">def</span> <span class="nf">_draw_box_title</span><span class="p">(</span><span class="n">color_mapping</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span> <span class="n">class_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">box_thickness</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
    1070. 

  1070.                         <span class="n">image_np</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">x1</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">y1</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">x2</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">y2</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">class_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
    1071. 

  1071.                         <span class="n">pred_conf</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    1072. 

  1072.         <span class="n">color</span> <span class="o">=</span> <span class="n">color_mapping</span><span class="p">[</span><span class="n">class_id</span><span class="p">]</span>
    1073. 

  1073.         <span class="n">class_name</span> <span class="o">=</span> <span class="n">class_names</span><span class="p">[</span><span class="n">class_id</span><span class="p">]</span>
    1074. 

  1074. 

  1075.         <span class="c1"># Draw the box</span>
    1076. 

  1076.         <span class="n">image_np</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">),</span> <span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">),</span> <span class="n">color</span><span class="p">,</span> <span class="n">box_thickness</span><span class="p">)</span>
    1077. 

  1077. 

  1078.         <span class="c1"># Caption with class name and confidence if given</span>
    1079. 

  1079.         <span class="n">text_color</span> <span class="o">=</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span>  <span class="c1"># white</span>
    1080. 

  1080.         <span class="n">title</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">class_name</span><span class="si">}</span><span class="s1">  </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pred_conf</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="k">if</span> <span class="n">pred_conf</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="si">}</span><span class="s1">&#39;</span>
    1081. 

  1081.         <span class="n">image_np</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">-</span> <span class="mi">15</span><span class="p">),</span> <span class="p">(</span><span class="n">x1</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">title</span><span class="p">)</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span> <span class="n">y1</span><span class="p">),</span> <span class="n">color</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">FILLED</span><span class="p">)</span>
    1082. 

  1082.         <span class="n">image_np</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">putText</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">-</span> <span class="n">box_thickness</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="n">text_color</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">lineType</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">LINE_AA</span><span class="p">)</span>
    1083. 

  1083. 

  1084.         <span class="k">return</span> <span class="n">image_np</span>
    1085. 

  1085. 

  1086.     <span class="nd">@staticmethod</span>
    1087. 

  1087.     <span class="k">def</span> <span class="nf">_visualize_image</span><span class="p">(</span><span class="n">image_np</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">pred_boxes</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">target_boxes</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
    1088. 

  1088.                          <span class="n">class_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">box_thickness</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">gt_alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">image_scale</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
    1089. 

  1089.                          <span class="n">checkpoint_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">image_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    1090. 

  1090.         <span class="n">image_np</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">fx</span><span class="o">=</span><span class="n">image_scale</span><span class="p">,</span> <span class="n">fy</span><span class="o">=</span><span class="n">image_scale</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">INTER_NEAREST</span><span class="p">)</span>
    1091. 

  1091.         <span class="n">color_mapping</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">_generate_color_mapping</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">))</span>
    1092. 

  1092. 

  1093.         <span class="c1"># Draw predictions</span>
    1094. 

  1094.         <span class="n">pred_boxes</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">*=</span> <span class="n">image_scale</span>
    1095. 

  1095.         <span class="k">for</span> <span class="n">box</span> <span class="ow">in</span> <span class="n">pred_boxes</span><span class="p">:</span>
    1096. 

  1096.             <span class="n">image_np</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">_draw_box_title</span><span class="p">(</span><span class="n">color_mapping</span><span class="p">,</span> <span class="n">class_names</span><span class="p">,</span> <span class="n">box_thickness</span><span class="p">,</span>
    1097. 

  1097.                                                               <span class="n">image_np</span><span class="p">,</span> <span class="o">*</span><span class="n">box</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
    1098. 

  1098.                                                               <span class="n">class_id</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">box</span><span class="p">[</span><span class="mi">5</span><span class="p">]),</span> <span class="n">pred_conf</span><span class="o">=</span><span class="n">box</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
    1099. 

  1099. 

  1100.         <span class="c1"># Draw ground truths</span>
    1101. 

  1101.         <span class="n">target_boxes_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
    1102. 

  1102.         <span class="k">for</span> <span class="n">box</span> <span class="ow">in</span> <span class="n">target_boxes</span><span class="p">:</span>
    1103. 

  1103.             <span class="n">target_boxes_image</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">_draw_box_title</span><span class="p">(</span><span class="n">color_mapping</span><span class="p">,</span> <span class="n">class_names</span><span class="p">,</span> <span class="n">box_thickness</span><span class="p">,</span>
    1104. 

  1104.                                                                         <span class="n">target_boxes_image</span><span class="p">,</span> <span class="o">*</span><span class="n">box</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span>
    1105. 

  1105.                                                                         <span class="n">class_id</span><span class="o">=</span><span class="n">box</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    1106. 

  1106. 

  1107.         <span class="c1"># Transparent overlay of ground truth boxes</span>
    1108. 

  1108.         <span class="n">mask</span> <span class="o">=</span> <span class="n">target_boxes_image</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
    1109. 

  1109.         <span class="n">image_np</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">addWeighted</span><span class="p">(</span><span class="n">image_np</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">gt_alpha</span><span class="p">,</span> <span class="n">target_boxes_image</span><span class="p">,</span> <span class="n">gt_alpha</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[</span><span class="n">mask</span><span class="p">]</span>
    1110. 

  1110. 

  1111.         <span class="k">if</span> <span class="n">checkpoint_dir</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
    1112. 

  1112.             <span class="k">return</span> <span class="n">image_np</span>
    1113. 

  1113.         <span class="k">else</span><span class="p">:</span>
    1114. 

  1114.             <span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</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">join</span><span class="p">(</span><span class="n">checkpoint_dir</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">image_name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;.jpg&#39;</span><span class="p">),</span> <span class="n">image_np</span><span class="p">)</span>
    1115. 

  1115. 

  1116.     <span class="nd">@staticmethod</span>
    1117. 

  1117.     <span class="k">def</span> <span class="nf">_scaled_ccwh_to_xyxy</span><span class="p">(</span><span class="n">target_boxes</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">h</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">image_scale</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
    1118. 

  1118.         <span class="sd">&quot;&quot;&quot;</span>
    1119. 

  1119. <span class="sd">        Modifies target_boxes inplace</span>
    1120. 

  1120. <span class="sd">        :param target_boxes:    (c1, c2, w, h) boxes in [0, 1] range</span>
    1121. 

  1121. <span class="sd">        :param h:               image height</span>
    1122. 

  1122. <span class="sd">        :param w:               image width</span>
    1123. 

  1123. <span class="sd">        :param image_scale:     desired scale for the boxes w.r.t. w and h</span>
    1124. 

  1124. <span class="sd">        :return:                targets in (x1, y1, x2, y2) format</span>
    1125. 

  1125. <span class="sd">                                in range [0, w * self.image_scale] [0, h * self.image_scale]</span>
    1126. 

  1126. <span class="sd">        &quot;&quot;&quot;</span>
    1127. 

  1127.         <span class="c1"># unscale</span>
    1128. 

  1128.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</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">w</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="n">h</span><span class="p">]])</span>
    1129. 

  1129. 

  1130.         <span class="c1"># x1 = c1 - w // 2; y1 = c2 - h // 2</span>
    1131. 

  1131.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-=</span> <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
    1132. 

  1132.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">-=</span> <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
    1133. 

  1133.         <span class="c1"># x2 = w + x1; y2 = h + y1</span>
    1134. 

  1134.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">+=</span> <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
    1135. 

  1135.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">+=</span> <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
    1136. 

  1136. 

  1137.         <span class="n">target_boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span> <span class="o">*=</span> <span class="n">image_scale</span>
    1138. 

  1138.         <span class="n">target_boxes</span> <span class="o">=</span> <span class="n">target_boxes</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
    1139. 

  1139.         <span class="k">return</span> <span class="n">target_boxes</span>
    1140. 

  1140. 

  1141. <div class="viewcode-block" id="DetectionVisualization.visualize_batch"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.DetectionVisualization.visualize_batch">[docs]</a>    <span class="nd">@staticmethod</span>
    1142. 

  1142.     <span class="k">def</span> <span class="nf">visualize_batch</span><span class="p">(</span><span class="n">image_tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">pred_boxes</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">target_boxes</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
    1143. 

  1143.                         <span class="n">batch_name</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span> <span class="n">class_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">checkpoint_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    1144. 

  1144.                         <span class="n">undo_preprocessing_func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</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="o">=</span> <span class="n">undo_image_preprocessing</span><span class="p">,</span>
    1145. 

  1145.                         <span class="n">box_thickness</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">image_scale</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">gt_alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">.4</span><span class="p">):</span>
    1146. 

  1146.         <span class="sd">&quot;&quot;&quot;</span>
    1147. 

  1147. <span class="sd">        A helper function to visualize detections predicted by a network:</span>
    1148. 

  1148. <span class="sd">        saves images into a given path with a name that is {batch_name}_{imade_idx_in_the_batch}.jpg, one batch per call.</span>
    1149. 

  1149. <span class="sd">        Colors are generated on the fly: uniformly sampled from color wheel to support all given classes.</span>
    1150. 

  1150. 

  1151. <span class="sd">        Adjustable:</span>
    1152. 

  1152. <span class="sd">            * Ground truth box transparency;</span>
    1153. 

  1153. <span class="sd">            * Box width;</span>
    1154. 

  1154. <span class="sd">            * Image size (larger or smaller than what&#39;s provided)</span>
    1155. 

  1155. 

  1156. <span class="sd">        :param image_tensor:            rgb images, (B, H, W, 3)</span>
    1157. 

  1157. <span class="sd">        :param pred_boxes:              boxes after NMS for each image in a batch, each (Num_boxes, 6),</span>
    1158. 

  1158. <span class="sd">                                        values on dim 1 are: x1, y1, x2, y2, confidence, class</span>
    1159. 

  1159. <span class="sd">        :param target_boxes:            (Num_targets, 6), values on dim 1 are: image id in a batch, class, x y w h</span>
    1160. 

  1160. <span class="sd">                                        (coordinates scaled to [0, 1])</span>
    1161. 

  1161. <span class="sd">        :param batch_name:              id of the current batch to use for image naming</span>
    1162. 

  1162. 

  1163. <span class="sd">        :param class_names:             names of all classes, each on its own index</span>
    1164. 

  1164. <span class="sd">        :param checkpoint_dir:          a path where images with boxes will be saved. if None, the result images will</span>
    1165. 

  1165. <span class="sd">                                        be returns as a list of numpy image arrays</span>
    1166. 

  1166. 

  1167. <span class="sd">        :param undo_preprocessing_func: a function to convert preprocessed images tensor into a batch of cv2-like images</span>
    1168. 

  1168. <span class="sd">        :param box_thickness:           box line thickness in px</span>
    1169. 

  1169. <span class="sd">        :param image_scale:             scale of an image w.r.t. given image size,</span>
    1170. 

  1170. <span class="sd">                                        e.g. incoming images are (320x320), use scale = 2. to preview in (640x640)</span>
    1171. 

  1171. <span class="sd">        :param gt_alpha:                a value in [0., 1.] transparency on ground truth boxes,</span>
    1172. 

  1172. <span class="sd">                                        0 for invisible, 1 for fully opaque</span>
    1173. 

  1173. <span class="sd">        &quot;&quot;&quot;</span>
    1174. 

  1174.         <span class="n">image_np</span> <span class="o">=</span> <span class="n">undo_preprocessing_func</span><span class="p">(</span><span class="n">image_tensor</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
    1175. 

  1175.         <span class="n">targets</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">_scaled_ccwh_to_xyxy</span><span class="p">(</span><span class="n">target_boxes</span><span class="o">.</span><span class="n">detach</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">image_np</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">],</span>
    1176. 

  1176.                                                               <span class="n">image_scale</span><span class="p">)</span>
    1177. 

  1177. 

  1178.         <span class="n">out_images</span> <span class="o">=</span> <span class="p">[]</span>
    1179. 

  1179.         <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="n">image_np</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
    1180. 

  1180.             <span class="n">preds</span> <span class="o">=</span> <span class="n">pred_boxes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">detach</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="k">if</span> <span class="n">pred_boxes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
    1181. 

  1181.             <span class="n">targets_cur</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">targets</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">]</span>
    1182. 

  1182. 

  1183.             <span class="n">image_name</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="nb">str</span><span class="p">(</span><span class="n">batch_name</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)])</span>
    1184. 

  1184.             <span class="n">res_image</span> <span class="o">=</span> <span class="n">DetectionVisualization</span><span class="o">.</span><span class="n">_visualize_image</span><span class="p">(</span><span class="n">image_np</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">preds</span><span class="p">,</span> <span class="n">targets_cur</span><span class="p">,</span> <span class="n">class_names</span><span class="p">,</span> <span class="n">box_thickness</span><span class="p">,</span> <span class="n">gt_alpha</span><span class="p">,</span> <span class="n">image_scale</span><span class="p">,</span> <span class="n">checkpoint_dir</span><span class="p">,</span> <span class="n">image_name</span><span class="p">)</span>
    1185. 

  1185.             <span class="k">if</span> <span class="n">res_image</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
    1186. 

  1186.                 <span class="n">out_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res_image</span><span class="p">)</span>
    1187. 

  1187. 

  1188.         <span class="k">return</span> <span class="n">out_images</span></div></div>
    1189. 

  1189. 

  1190. 

  1191. <div class="viewcode-block" id="Anchors"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.detection_utils.Anchors">[docs]</a><span class="k">class</span> <span class="nc">Anchors</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    1192. 

  1192.     <span class="sd">&quot;&quot;&quot;</span>
    1193. 

  1193. <span class="sd">    A wrapper function to hold the anchors used by detection models such as Yolo</span>
    1194. 

  1194. <span class="sd">    &quot;&quot;&quot;</span>
    1195. 

  1195. 

  1196.     <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">anchors_list</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">],</span> <span class="n">strides</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
    1197. 

  1197.         <span class="sd">&quot;&quot;&quot;</span>
    1198. 

  1198. <span class="sd">        :param anchors_list: of the shape [[w1,h1,w2,h2,w3,h3], [w4,h4,w5,h5,w6,h6] .... where each sublist holds</span>
    1199. 

  1199. <span class="sd">            the width and height of the anchors of a specific detection layer.</span>
    1200. 

  1200. <span class="sd">            i.e. for a model with 3 detection layers, each containing 5 anchors the format will be a of 3 sublists of 10 numbers each</span>
    1201. 

  1201. <span class="sd">            The width and height are in pixels (not relative to image size)</span>
    1202. 

  1202. <span class="sd">        :param strides: a list containing the stride of the layers from which the detection heads are fed.</span>
    1203. 

  1203. <span class="sd">            i.e. if the firs detection head is connected to the backbone after the input dimensions were reduces by 8, the first number will be 8</span>
    1204. 

  1204. <span class="sd">        &quot;&quot;&quot;</span>
    1205. 

  1205.         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
    1206. 

  1206. 

  1207.         <span class="bp">self</span><span class="o">.</span><span class="n">__anchors_list</span> <span class="o">=</span> <span class="n">anchors_list</span>
    1208. 

  1208.         <span class="bp">self</span><span class="o">.</span><span class="n">__strides</span> <span class="o">=</span> <span class="n">strides</span>
    1209. 

  1209. 

  1210.         <span class="bp">self</span><span class="o">.</span><span class="n">_check_all_lists</span><span class="p">(</span><span class="n">anchors_list</span><span class="p">)</span>
    1211. 

  1211.         <span class="bp">self</span><span class="o">.</span><span class="n">_check_all_len_equal_and_even</span><span class="p">(</span><span class="n">anchors_list</span><span class="p">)</span>
    1212. 

  1212. 

  1213.         <span class="bp">self</span><span class="o">.</span><span class="n">_stride</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">strides</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    1214. 

  1214.         <span class="n">anchors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">anchors_list</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">anchors_list</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>
    1215. 

  1215.         <span class="bp">self</span><span class="o">.</span><span class="n">_anchors</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">anchors</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stride</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    1216. 

  1216.         <span class="bp">self</span><span class="o">.</span><span class="n">_anchor_grid</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">anchors</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">anchors_list</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="mi">1</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="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    1217. 

  1217. 

  1218.     <span class="nd">@staticmethod</span>
    1219. 

  1219.     <span class="k">def</span> <span class="nf">_check_all_lists</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="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
    1220. 

  1220.         <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">anchors</span><span class="p">:</span>
    1221. 

  1221.             <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">ListConfig</span><span class="p">)):</span>
    1222. 

  1222.                 <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;All objects of anchors_list must be lists&#39;</span><span class="p">)</span>
    1223. 

  1223. 

  1224.     <span class="nd">@staticmethod</span>
    1225. 

  1225.     <span class="k">def</span> <span class="nf">_check_all_len_equal_and_even</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="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
    1226. 

  1226.         <span class="n">len_of_first</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">anchors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    1227. 

  1227.         <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">anchors</span><span class="p">:</span>
    1228. 

  1228.             <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">!=</span> <span class="n">len_of_first</span><span class="p">:</span>
    1229. 

  1229.                 <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;All objects of anchors_list must be of the same even length&#39;</span><span class="p">)</span>
    1230. 

  1230. 

  1231.     <span class="nd">@property</span>
    1232. 

  1232.     <span class="k">def</span> <span class="nf">stride</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">:</span>
    1233. 

  1233.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stride</span>
    1234. 

  1234. 

  1235.     <span class="nd">@property</span>
    1236. 

  1236.     <span class="k">def</span> <span class="nf">anchors</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">:</span>
    1237. 

  1237.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_anchors</span>
    1238. 

  1238. 

  1239.     <span class="nd">@property</span>
    1240. 

  1240.     <span class="k">def</span> <span class="nf">anchor_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">:</span>
    1241. 

  1241.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_anchor_grid</span>
    1242. 

  1242. 

  1243.     <span class="nd">@property</span>
    1244. 

  1244.     <span class="k">def</span> <span class="nf">detection_layers_num</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
    1245. 

  1245.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_anchors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    1246. 

  1246. 

  1247.     <span class="nd">@property</span>
    1248. 

  1248.     <span class="k">def</span> <span class="nf">num_anchors</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
    1249. 

  1249.         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_anchors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    1250. 

  1250. 

  1251.     <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
    1252. 

  1252.         <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;anchors_list: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">__anchors_list</span><span class="si">}</span><span class="s2"> strides: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">__strides</span><span class="si">}</span><span class="s2">&quot;</span></div>
    1253. 

  1253. </pre></div>
    1254. 

  1254. 

  1255.            </div>
    1256. 

  1256.           </div>
    1257. 

  1257.           <footer>
    1258. 

  1258. 

  1259.   <hr/>
    1260. 

  1260. 

  1261.   <div role="contentinfo">
    1262. 

  1262.     <p>&#169; Copyright 2021, SuperGradients team.</p>
    1263. 

  1263.   </div>
    1264. 

  1264. 

  1265.   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
    1266. 

  1266.     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
    1267. 

  1267.     provided by <a href="https://readthedocs.org">Read the Docs</a>.
    1268. 

  1268.    
    1269. 

  1269. 

  1270. </footer>
    1271. 

  1271.         </div>
    1272. 

  1272.       </div>
    1273. 

  1273.     </section>
    1274. 

  1274.   </div>
    1275. 

  1275.   <script>
    1276. 

  1276.       jQuery(function () {
    1277. 

  1277.           SphinxRtdTheme.Navigation.enable(true);
    1278. 

  1278.       });
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  1279.   </script> 
    1280. 

  1280. 

  1281. </body>
    1282. 

  1282. </html>
    1283.
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