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    89. 

  89.   <section id="super-gradients-training-models-package">
    90. 

  90. <h1>super_gradients.training.models package<a class="headerlink" href="#super-gradients-training-models-package" title="Permalink to this headline"></a></h1>
    91. 

  91. <section id="submodules">
    92. 

  92. <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
    93. 

  93. </section>
    94. 

  94. <section id="module-super_gradients.training.models.all_architectures">
    95. 

  95. <span id="super-gradients-training-models-all-architectures-module"></span><h2>super_gradients.training.models.all_architectures module<a class="headerlink" href="#module-super_gradients.training.models.all_architectures" title="Permalink to this headline"></a></h2>
    96. 

  96. </section>
    97. 

  97. <section id="super-gradients-training-models-csp-darknet53-module">
    98. 

  98. <h2>super_gradients.training.models.csp_darknet53 module<a class="headerlink" href="#super-gradients-training-models-csp-darknet53-module" title="Permalink to this headline"></a></h2>
    99. 

  99. </section>
    100. 

  100. <section id="super-gradients-training-models-darknet53-module">
    101. 

  101. <h2>super_gradients.training.models.darknet53 module<a class="headerlink" href="#super-gradients-training-models-darknet53-module" title="Permalink to this headline"></a></h2>
    102. 

  102. </section>
    103. 

  103. <section id="super-gradients-training-models-ddrnet-module">
    104. 

  104. <h2>super_gradients.training.models.ddrnet module<a class="headerlink" href="#super-gradients-training-models-ddrnet-module" title="Permalink to this headline"></a></h2>
    105. 

  105. </section>
    106. 

  106. <section id="super-gradients-training-models-densenet-module">
    107. 

  107. <h2>super_gradients.training.models.densenet module<a class="headerlink" href="#super-gradients-training-models-densenet-module" title="Permalink to this headline"></a></h2>
    108. 

  108. </section>
    109. 

  109. <section id="super-gradients-training-models-dpn-module">
    110. 

  110. <h2>super_gradients.training.models.dpn module<a class="headerlink" href="#super-gradients-training-models-dpn-module" title="Permalink to this headline"></a></h2>
    111. 

  111. </section>
    112. 

  112. <section id="super-gradients-training-models-efficientnet-module">
    113. 

  113. <h2>super_gradients.training.models.efficientnet module<a class="headerlink" href="#super-gradients-training-models-efficientnet-module" title="Permalink to this headline"></a></h2>
    114. 

  114. </section>
    115. 

  115. <section id="super-gradients-training-models-googlenet-module">
    116. 

  116. <h2>super_gradients.training.models.googlenet module<a class="headerlink" href="#super-gradients-training-models-googlenet-module" title="Permalink to this headline"></a></h2>
    117. 

  117. </section>
    118. 

  118. <section id="super-gradients-training-models-laddernet-module">
    119. 

  119. <h2>super_gradients.training.models.laddernet module<a class="headerlink" href="#super-gradients-training-models-laddernet-module" title="Permalink to this headline"></a></h2>
    120. 

  120. </section>
    121. 

  121. <section id="super-gradients-training-models-lenet-module">
    122. 

  122. <h2>super_gradients.training.models.lenet module<a class="headerlink" href="#super-gradients-training-models-lenet-module" title="Permalink to this headline"></a></h2>
    123. 

  123. </section>
    124. 

  124. <section id="super-gradients-training-models-mobilenet-module">
    125. 

  125. <h2>super_gradients.training.models.mobilenet module<a class="headerlink" href="#super-gradients-training-models-mobilenet-module" title="Permalink to this headline"></a></h2>
    126. 

  126. </section>
    127. 

  127. <section id="super-gradients-training-models-mobilenetv2-module">
    128. 

  128. <h2>super_gradients.training.models.mobilenetv2 module<a class="headerlink" href="#super-gradients-training-models-mobilenetv2-module" title="Permalink to this headline"></a></h2>
    129. 

  129. </section>
    130. 

  130. <section id="super-gradients-training-models-mobilenetv3-module">
    131. 

  131. <h2>super_gradients.training.models.mobilenetv3 module<a class="headerlink" href="#super-gradients-training-models-mobilenetv3-module" title="Permalink to this headline"></a></h2>
    132. 

  132. </section>
    133. 

  133. <section id="super-gradients-training-models-pnasnet-module">
    134. 

  134. <h2>super_gradients.training.models.pnasnet module<a class="headerlink" href="#super-gradients-training-models-pnasnet-module" title="Permalink to this headline"></a></h2>
    135. 

  135. </section>
    136. 

  136. <section id="super-gradients-training-models-preact-resnet-module">
    137. 

  137. <h2>super_gradients.training.models.preact_resnet module<a class="headerlink" href="#super-gradients-training-models-preact-resnet-module" title="Permalink to this headline"></a></h2>
    138. 

  138. </section>
    139. 

  139. <section id="super-gradients-training-models-regnet-module">
    140. 

  140. <h2>super_gradients.training.models.regnet module<a class="headerlink" href="#super-gradients-training-models-regnet-module" title="Permalink to this headline"></a></h2>
    141. 

  141. </section>
    142. 

  142. <section id="super-gradients-training-models-repvgg-module">
    143. 

  143. <h2>super_gradients.training.models.repvgg module<a class="headerlink" href="#super-gradients-training-models-repvgg-module" title="Permalink to this headline"></a></h2>
    144. 

  144. </section>
    145. 

  145. <section id="super-gradients-training-models-resnet-module">
    146. 

  146. <h2>super_gradients.training.models.resnet module<a class="headerlink" href="#super-gradients-training-models-resnet-module" title="Permalink to this headline"></a></h2>
    147. 

  147. </section>
    148. 

  148. <section id="super-gradients-training-models-resnext-module">
    149. 

  149. <h2>super_gradients.training.models.resnext module<a class="headerlink" href="#super-gradients-training-models-resnext-module" title="Permalink to this headline"></a></h2>
    150. 

  150. </section>
    151. 

  151. <section id="super-gradients-training-models-senet-module">
    152. 

  152. <h2>super_gradients.training.models.senet module<a class="headerlink" href="#super-gradients-training-models-senet-module" title="Permalink to this headline"></a></h2>
    153. 

  153. </section>
    154. 

  154. <section id="module-super_gradients.training.models.sg_module">
    155. 

  155. <span id="super-gradients-training-models-sg-module-module"></span><h2>super_gradients.training.models.sg_module module<a class="headerlink" href="#module-super_gradients.training.models.sg_module" title="Permalink to this headline"></a></h2>
    156. 

  156. <dl class="py class">
    157. 

  157. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule">
    158. 

  158. <em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.models.sg_module.</span></span><span class="sig-name descname"><span class="pre">SgModule</span></span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule" title="Permalink to this definition"></a></dt>
    159. 

  159. <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
    160. 

  160. <dl class="py method">
    161. 

  161. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.initialize_param_groups">
    162. 

  162. <span class="sig-name descname"><span class="pre">initialize_param_groups</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_params</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.utils.HpmStruct" title="super_gradients.training.utils.utils.HpmStruct"><span class="pre">super_gradients.training.utils.utils.HpmStruct</span></a></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">list</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.initialize_param_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.initialize_param_groups" title="Permalink to this definition"></a></dt>
    163. 

  163. <dd><dl class="field-list simple">
    164. 

  164. <dt class="field-odd">Returns</dt>
    165. 

  165. <dd class="field-odd"><p>list of dictionaries containing the key ‘named_params’ with a list of named params</p>
    166. 

  166. </dd>
    167. 

  167. </dl>
    168. 

  168. </dd></dl>
    169. 

  169. 

  170. <dl class="py method">
    171. 

  171. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.update_param_groups">
    172. 

  172. <span class="sig-name descname"><span class="pre">update_param_groups</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_groups</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_params</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="super_gradients.training.utils.html#super_gradients.training.utils.utils.HpmStruct" title="super_gradients.training.utils.utils.HpmStruct"><span class="pre">super_gradients.training.utils.utils.HpmStruct</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_batch</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">list</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.update_param_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.update_param_groups" title="Permalink to this definition"></a></dt>
    173. 

  173. <dd><dl class="field-list simple">
    174. 

  174. <dt class="field-odd">Parameters</dt>
    175. 

  175. <dd class="field-odd"><p><strong>param_groups</strong> – list of dictionaries containing the params</p>
    176. 

  176. </dd>
    177. 

  177. <dt class="field-even">Returns</dt>
    178. 

  178. <dd class="field-even"><p>list of dictionaries containing the params</p>
    179. 

  179. </dd>
    180. 

  180. </dl>
    181. 

  181. </dd></dl>
    182. 

  182. 

  183. <dl class="py method">
    184. 

  184. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.get_include_attributes">
    185. 

  185. <span class="sig-name descname"><span class="pre">get_include_attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <span class="pre">list</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.get_include_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.get_include_attributes" title="Permalink to this definition"></a></dt>
    186. 

  186. <dd><p>This function is used by the EMA. When updating the EMA model, some attributes of the main model (used in training)
    187. 

  187. are updated to the EMA model along with the model weights.
    188. 

  188. By default, all attributes are updated except for private attributes (starting with ‘_’)
    189. 

  189. You can either set include_attributes or exclude_attributes. By returning a non empty list from this function,
    190. 

  190. you override the default behaviour and only attributes named in this list will be updated.
    191. 

  191. Note: This will also override the get_exclude_attributes list.</p>
    192. 

  192. <blockquote>
    193. 

  193. <div><dl class="field-list simple">
    194. 

  194. <dt class="field-odd">return</dt>
    195. 

  195. <dd class="field-odd"><p>list of attributes to update from main model to EMA model</p>
    196. 

  196. </dd>
    197. 

  197. </dl>
    198. 

  198. </div></blockquote>
    199. 

  199. </dd></dl>
    200. 

  200. 

  201. <dl class="py method">
    202. 

  202. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.get_exclude_attributes">
    203. 

  203. <span class="sig-name descname"><span class="pre">get_exclude_attributes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <span class="pre">list</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.get_exclude_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.get_exclude_attributes" title="Permalink to this definition"></a></dt>
    204. 

  204. <dd><p>This function is used by the EMA. When updating the EMA model, some attributes of the main model (used in training)
    205. 

  205. are updated to the EMA model along with the model weights.
    206. 

  206. By default, all attributes are updated except for private attributes (starting with ‘_’)
    207. 

  207. You can either set include_attributes or exclude_attributes. By returning a non empty list from this function,
    208. 

  208. you override the default behaviour and attributes named in this list will also be excluded from update.
    209. 

  209. Note: if get_include_attributes is not empty, it will override this list.</p>
    210. 

  210. <blockquote>
    211. 

  211. <div><dl class="field-list simple">
    212. 

  212. <dt class="field-odd">return</dt>
    213. 

  213. <dd class="field-odd"><p>list of attributes to not update from main model to EMA mode</p>
    214. 

  214. </dd>
    215. 

  215. </dl>
    216. 

  216. </div></blockquote>
    217. 

  217. </dd></dl>
    218. 

  218. 

  219. <dl class="py method">
    220. 

  220. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.prep_model_for_conversion">
    221. 

  221. <span class="sig-name descname"><span class="pre">prep_model_for_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">tuple</span><span class="p"><span class="pre">,</span> </span><span class="pre">list</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.prep_model_for_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.prep_model_for_conversion" title="Permalink to this definition"></a></dt>
    222. 

  222. <dd><p>Prepare the model to be converted to ONNX or other frameworks.
    223. 

  223. Typically, this function will freeze the size of layers which is otherwise flexible, replace some modules
    224. 

  224. with convertible substitutes and remove all auxiliary or training related parts.
    225. 

  225. :param input_size: [H,W]</p>
    226. 

  226. </dd></dl>
    227. 

  227. 

  228. <dl class="py method">
    229. 

  229. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.replace_head">
    230. 

  230. <span class="sig-name descname"><span class="pre">replace_head</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/models/sg_module.html#SgModule.replace_head"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.replace_head" title="Permalink to this definition"></a></dt>
    231. 

  231. <dd><p>Replace final layer for pretrained models. Since this varies between architectures, we leave it to the inheriting
    232. 

  232. class to implement.</p>
    233. 

  233. </dd></dl>
    234. 

  234. 

  235. <dl class="py attribute">
    236. 

  236. <dt class="sig sig-object py" id="super_gradients.training.models.sg_module.SgModule.training">
    237. 

  237. <span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="pre">:</span> <span class="pre">bool</span></em><a class="headerlink" href="#super_gradients.training.models.sg_module.SgModule.training" title="Permalink to this definition"></a></dt>
    238. 

  238. <dd></dd></dl>
    239. 

  239. 

  240. </dd></dl>
    241. 

  241. 

  242. </section>
    243. 

  243. <section id="super-gradients-training-models-shelfnet-module">
    244. 

  244. <h2>super_gradients.training.models.shelfnet module<a class="headerlink" href="#super-gradients-training-models-shelfnet-module" title="Permalink to this headline"></a></h2>
    245. 

  245. </section>
    246. 

  246. <section id="super-gradients-training-models-shufflenet-module">
    247. 

  247. <h2>super_gradients.training.models.shufflenet module<a class="headerlink" href="#super-gradients-training-models-shufflenet-module" title="Permalink to this headline"></a></h2>
    248. 

  248. </section>
    249. 

  249. <section id="super-gradients-training-models-shufflenetv2-module">
    250. 

  250. <h2>super_gradients.training.models.shufflenetv2 module<a class="headerlink" href="#super-gradients-training-models-shufflenetv2-module" title="Permalink to this headline"></a></h2>
    251. 

  251. </section>
    252. 

  252. <section id="super-gradients-training-models-ssd-module">
    253. 

  253. <h2>super_gradients.training.models.ssd module<a class="headerlink" href="#super-gradients-training-models-ssd-module" title="Permalink to this headline"></a></h2>
    254. 

  254. </section>
    255. 

  255. <section id="super-gradients-training-models-vgg-module">
    256. 

  256. <h2>super_gradients.training.models.vgg module<a class="headerlink" href="#super-gradients-training-models-vgg-module" title="Permalink to this headline"></a></h2>
    257. 

  257. </section>
    258. 

  258. <section id="super-gradients-training-models-yolov3-module">
    259. 

  259. <h2>super_gradients.training.models.yolov3 module<a class="headerlink" href="#super-gradients-training-models-yolov3-module" title="Permalink to this headline"></a></h2>
    260. 

  260. </section>
    261. 

  261. <section id="super-gradients-training-models-yolov5-module">
    262. 

  262. <h2>super_gradients.training.models.yolov5 module<a class="headerlink" href="#super-gradients-training-models-yolov5-module" title="Permalink to this headline"></a></h2>
    263. 

  263. </section>
    264. 

  264. <section id="module-super_gradients.training.models">
    265. 

  265. <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-super_gradients.training.models" title="Permalink to this headline"></a></h2>
    266. 

  266. </section>
    267. 

  267. </section>
    268. 

  268. 

  269. 

  270.            </div>
    271. 

  271.           </div>
    272. 

  272.           <footer>
    273. 

  273. 

  274.   <hr/>
    275. 

  275. 

  276.   <div role="contentinfo">
    277. 

  277.     <p>&#169; Copyright 2021, SuperGradients team.</p>
    278. 

  278.   </div>
    279. 

  279. 

  280.   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
    281. 

  281.     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
    282. 

  282.     provided by <a href="https://readthedocs.org">Read the Docs</a>.
    283. 

  283.    
    284. 

  284. 

  285. </footer>
    286. 

  286.         </div>
    287. 

  287.       </div>
    288. 

  288.     </section>
    289. 

  289.   </div>
    290. 

  290.   <script>
    291. 

  291.       jQuery(function () {
    292. 

  292.           SphinxRtdTheme.Navigation.enable(true);
    293. 

  293.       });
    294. 

  294.   </script> 
    295. 

  295. 

  296. </body>
    297. 

  297. </html>
    298.
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