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  <h1>Source code for super_gradients.training.utils.optimizer_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">torch.nn.modules.batchnorm</span> <span class="kn">import</span> <span class="n">_BatchNorm</span>
<span class="kn">from</span> <span class="nn">torch.nn.modules.conv</span> <span class="kn">import</span> <span class="n">_ConvNd</span>
<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>
<span class="kn">from</span> <span class="nn">super_gradients.common.factories.optimizers_type_factory</span> <span class="kn">import</span> <span class="n">OptimizersTypeFactory</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.params</span> <span class="kn">import</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_SGD</span><span class="p">,</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_ADAM</span><span class="p">,</span> \
    <span class="n">DEFAULT_OPTIMIZER_PARAMS_RMSPROP</span><span class="p">,</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_RMSPROPTF</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils</span> <span class="kn">import</span> <span class="n">get_param</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.optimizers.rmsprop_tf</span> <span class="kn">import</span> <span class="n">RMSpropTF</span>

<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>

<span class="n">OPTIMIZERS_DEFAULT_PARAMS</span> <span class="o">=</span> <span class="p">{</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">:</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_SGD</span><span class="p">,</span>
                             <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">:</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_ADAM</span><span class="p">,</span>
                             <span class="n">optim</span><span class="o">.</span><span class="n">RMSprop</span><span class="p">:</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_RMSPROP</span><span class="p">,</span>
                             <span class="n">RMSpropTF</span><span class="p">:</span> <span class="n">DEFAULT_OPTIMIZER_PARAMS_RMSPROPTF</span><span class="p">}</span>


<div class="viewcode-block" id="separate_zero_wd_params_groups_for_optimizer"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.optimizer_utils.separate_zero_wd_params_groups_for_optimizer">[docs]</a><span class="k">def</span> <span class="nf">separate_zero_wd_params_groups_for_optimizer</span><span class="p">(</span><span class="n">module</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">net_named_params</span><span class="p">,</span> <span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    separate param groups for batchnorm and biases and others with weight decay. return list of param groups in format</span>
<span class="sd">     required by torch Optimizer classes.</span>
<span class="sd">    bias + BN with weight decay=0 and the rest with the given weight decay</span>
<span class="sd">        :param module: train net module.</span>
<span class="sd">        :param net_named_params: list of params groups, output of SgModule.initialize_param_groups</span>
<span class="sd">        :param weight_decay: value to set for the non BN and bias parameters</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># FIXME - replace usage of ids addresses to find batchnorm and biases params.</span>
    <span class="c1">#  This solution iterate 2 times over module parameters, find a way to iterate only one time.</span>
    <span class="n">no_decay_ids</span> <span class="o">=</span> <span class="n">_get_no_decay_param_ids</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="c1"># split param groups for optimizer</span>
    <span class="n">optimizer_param_groups</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="n">net_named_params</span><span class="p">:</span>
        <span class="n">no_decay_params</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">decay_params</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">param_group</span><span class="p">[</span><span class="s2">&quot;named_params&quot;</span><span class="p">]:</span>
            <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> <span class="ow">in</span> <span class="n">no_decay_ids</span><span class="p">:</span>
                <span class="n">no_decay_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">decay_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
        <span class="c1"># append two param groups from the original param group, with and without weight decay.</span>
        <span class="n">extra_optim_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">param_group</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">param_group</span>
                              <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;named_params&quot;</span><span class="p">,</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">]}</span>
        <span class="n">optimizer_param_groups</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">no_decay_params</span><span class="p">,</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">extra_optim_params</span><span class="p">})</span>
        <span class="n">optimizer_param_groups</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">decay_params</span><span class="p">,</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="n">weight_decay</span><span class="p">,</span> <span class="o">**</span><span class="n">extra_optim_params</span><span class="p">})</span>

    <span class="k">return</span> <span class="n">optimizer_param_groups</span></div>


<span class="k">def</span> <span class="nf">_get_no_decay_param_ids</span><span class="p">(</span><span class="n">module</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="c1"># FIXME - replace usage of ids addresses to find batchnorm and biases params.</span>
    <span class="c1">#  Use other common way to identify torch parameters other than id or layer names</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Iterate over module.modules() and returns params id addresses of batch-norm and biases params.</span>
<span class="sd">    NOTE - ALL MODULES WITH ATTRIBUTES NAMED BIAS AND ARE INSTANCE OF nn.Parameter WILL BE CONSIDERED A BIAS PARAM FOR</span>
<span class="sd">        ZERO WEIGHT DECAY.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">batchnorm_types</span> <span class="o">=</span> <span class="p">(</span><span class="n">_BatchNorm</span><span class="p">,)</span>
    <span class="n">torch_weight_with_bias_types</span> <span class="o">=</span> <span class="p">(</span><span class="n">_ConvNd</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">)</span>
    <span class="n">no_decay_ids</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">named_modules</span><span class="p">():</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">batchnorm_types</span><span class="p">):</span>
            <span class="n">no_decay_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">))</span>
            <span class="n">no_decay_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">))</span>
        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s2">&quot;bias&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">):</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">torch_weight_with_bias_types</span><span class="p">):</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Module class: </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="si">}</span><span class="s2">, have a `bias` parameter attribute but is not instance of&quot;</span>
                               <span class="sa">f</span><span class="s2">&quot; torch primitive modules, this bias parameter will be part of param group with zero&quot;</span>
                               <span class="sa">f</span><span class="s2">&quot; weight decay.&quot;</span><span class="p">)</span>
            <span class="n">no_decay_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">no_decay_ids</span>


<div class="viewcode-block" id="build_optimizer"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.optimizer_utils.build_optimizer">[docs]</a><span class="k">def</span> <span class="nf">build_optimizer</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">training_params</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Wrapper function for initializing the optimizer</span>
<span class="sd">        :param net: the nn_module to build the optimizer for</span>
<span class="sd">        :param lr: initial learning rate</span>
<span class="sd">        :param training_params: training_parameters</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">training_params</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
        <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">OptimizersTypeFactory</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">training_params</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">training_params</span><span class="o">.</span><span class="n">optimizer</span>
    <span class="n">default_optimizer_params</span> <span class="o">=</span> <span class="n">OPTIMIZERS_DEFAULT_PARAMS</span><span class="p">[</span><span class="n">optimizer_cls</span><span class="p">]</span> <span class="k">if</span> <span class="n">optimizer_cls</span> <span class="ow">in</span> <span class="n">OPTIMIZERS_DEFAULT_PARAMS</span> <span class="k">else</span> <span class="p">{}</span>
    <span class="n">training_params</span><span class="o">.</span><span class="n">optimizer_params</span> <span class="o">=</span> <span class="n">get_param</span><span class="p">(</span><span class="n">training_params</span><span class="p">,</span> <span class="s1">&#39;optimizer_params&#39;</span><span class="p">,</span> <span class="n">default_optimizer_params</span><span class="p">)</span>

    <span class="c1"># OPTIMIZER PARAM GROUPS ARE SET USING DEFAULT OR MODEL SPECIFIC INIT</span>
    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">module</span><span class="p">,</span> <span class="s1">&#39;initialize_param_groups&#39;</span><span class="p">):</span>
        <span class="c1"># INITIALIZE_PARAM_GROUPS MUST RETURN A LIST OF DICTS WITH &#39;named_params&#39; AND OPTIMIZER&#39;s ATTRIBUTES PER GROUP</span>
        <span class="n">net_named_params</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">initialize_param_groups</span><span class="p">(</span><span class="n">lr</span><span class="p">,</span> <span class="n">training_params</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">net_named_params</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;named_params&#39;</span><span class="p">:</span> <span class="n">net</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()}]</span>

    <span class="k">if</span> <span class="n">training_params</span><span class="o">.</span><span class="n">zero_weight_decay_on_bias_and_bn</span><span class="p">:</span>
        <span class="n">optimizer_training_params</span> <span class="o">=</span> <span class="n">separate_zero_wd_params_groups_for_optimizer</span><span class="p">(</span>
            <span class="n">net</span><span class="o">.</span><span class="n">module</span><span class="p">,</span> <span class="n">net_named_params</span><span class="p">,</span> <span class="n">training_params</span><span class="o">.</span><span class="n">optimizer_params</span><span class="p">[</span><span class="s1">&#39;weight_decay&#39;</span><span class="p">]</span>
        <span class="p">)</span>

    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># Overwrite groups to include params instead of named params</span>
        <span class="k">for</span> <span class="n">ind_group</span><span class="p">,</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">net_named_params</span><span class="p">):</span>
            <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">param</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;named_params&#39;</span><span class="p">])]</span>
            <span class="k">del</span> <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;named_params&#39;</span><span class="p">]</span>
            <span class="n">net_named_params</span><span class="p">[</span><span class="n">ind_group</span><span class="p">]</span> <span class="o">=</span> <span class="n">param_group</span>
        <span class="n">optimizer_training_params</span> <span class="o">=</span> <span class="n">net_named_params</span>

    <span class="c1"># CREATE AN OPTIMIZER OBJECT AND INITIALIZE IT</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer_cls</span><span class="p">(</span><span class="n">optimizer_training_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="o">**</span><span class="n">training_params</span><span class="o">.</span><span class="n">optimizer_params</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">optimizer</span></div>
</pre></div>

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