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  <h1>Source code for super_gradients.training.models.sg_module</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>

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

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


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

<div class="viewcode-block" id="SgModule.initialize_param_groups"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.initialize_param_groups">[docs]</a>    <span class="k">def</span> <span class="nf">initialize_param_groups</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">training_params</span><span class="p">:</span> <span class="n">HpmStruct</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>

<span class="sd">        :return: list of dictionaries containing the key &#39;named_params&#39; with a list of named params</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[{</span><span class="s1">&#39;named_params&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()}]</span></div>

<div class="viewcode-block" id="SgModule.update_param_groups"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.update_param_groups">[docs]</a>    <span class="k">def</span> <span class="nf">update_param_groups</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param_groups</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">lr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">epoch</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">iter</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">training_params</span><span class="p">:</span> <span class="n">HpmStruct</span><span class="p">,</span>
                            <span class="n">total_batch</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> \
            <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>

<span class="sd">        :param param_groups: list of dictionaries containing the params</span>
<span class="sd">        :return: list of dictionaries containing the params</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">for</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="n">param_groups</span><span class="p">:</span>
            <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;lr&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lr</span>
        <span class="k">return</span> <span class="n">param_groups</span></div>

<div class="viewcode-block" id="SgModule.get_include_attributes"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.get_include_attributes">[docs]</a>    <span class="k">def</span> <span class="nf">get_include_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This function is used by the EMA. When updating the EMA model, some attributes of the main model (used in training)</span>
<span class="sd">        are updated to the EMA model along with the model weights.</span>
<span class="sd">        By default, all attributes are updated except for private attributes (starting with &#39;_&#39;)</span>
<span class="sd">        You can either set include_attributes or exclude_attributes. By returning a non empty list from this function,</span>
<span class="sd">        you override the default behaviour and only attributes named in this list will be updated.</span>
<span class="sd">        Note: This will also override the get_exclude_attributes list.</span>
<span class="sd">            :return: list of attributes to update from main model to EMA model</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[]</span></div>

<div class="viewcode-block" id="SgModule.get_exclude_attributes"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.get_exclude_attributes">[docs]</a>    <span class="k">def</span> <span class="nf">get_exclude_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This function is used by the EMA. When updating the EMA model, some attributes of the main model (used in training)</span>
<span class="sd">        are updated to the EMA model along with the model weights.</span>
<span class="sd">        By default, all attributes are updated except for private attributes (starting with &#39;_&#39;)</span>
<span class="sd">        You can either set include_attributes or exclude_attributes. By returning a non empty list from this function,</span>
<span class="sd">        you override the default behaviour and attributes named in this list will also be excluded from update.</span>
<span class="sd">        Note: if get_include_attributes is not empty, it will override this list.</span>
<span class="sd">            :return: list of attributes to not update from main model to EMA mode</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="p">[]</span></div>

<div class="viewcode-block" id="SgModule.prep_model_for_conversion"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.prep_model_for_conversion">[docs]</a>    <span class="k">def</span> <span class="nf">prep_model_for_conversion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Prepare the model to be converted to ONNX or other frameworks.</span>
<span class="sd">        Typically, this function will freeze the size of layers which is otherwise flexible, replace some modules</span>
<span class="sd">        with convertible substitutes and remove all auxiliary or training related parts.</span>
<span class="sd">        :param input_size: [H,W]</span>
<span class="sd">        &quot;&quot;&quot;</span></div>

<div class="viewcode-block" id="SgModule.replace_head"><a class="viewcode-back" href="../../../../super_gradients.training.models.html#super_gradients.training.models.sg_module.SgModule.replace_head">[docs]</a>    <span class="k">def</span> <span class="nf">replace_head</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Replace final layer for pretrained models. Since this varies between architectures, we leave it to the inheriting</span>
<span class="sd">        class to implement.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div>
</pre></div>

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