#396 Trainer constructor cleanup

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Ghost merged 1 commits into Deci-AI:master from deci-ai:feature/SG-266_clean_trainer_ctor
  
    
        
          
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  <h1>Source code for super_gradients.training.utils.weight_averaging_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pkg_resources</span>
<span class="kn">from</span> <span class="nn">super_gradients.training</span> <span class="kn">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">core_utils</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.utils</span> <span class="kn">import</span> <span class="n">move_state_dict_to_device</span>
<div class="viewcode-block" id="ModelWeightAveraging"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.weight_averaging_utils.ModelWeightAveraging">[docs]</a><span class="k">class</span> <span class="nc">ModelWeightAveraging</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Utils class for managing the averaging of the best several snapshots into a single model.</span>
<span class="sd">    A snapshot dictionary file and the average model will be saved / updated at every epoch and evaluated only when</span>
<span class="sd">    training is completed. The snapshot file will only be deleted upon completing the training.</span>
<span class="sd">    The snapshot dict will be managed on cpu.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <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">ckpt_dir</span><span class="p">,</span>
                 <span class="n">greater_is_better</span><span class="p">,</span>
                 <span class="n">source_ckpt_folder_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metric_to_watch</span><span class="o">=</span><span class="s1">&#39;acc&#39;</span><span class="p">,</span>
                 <span class="n">metric_idx</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">load_checkpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">number_of_models_to_average</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
                 <span class="n">model_checkpoints_location</span><span class="o">=</span><span class="s1">&#39;local&#39;</span>
                 <span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Init the ModelWeightAveraging</span>
<span class="sd">        :param checkpoint_dir: the directory where the checkpoints are saved</span>
<span class="sd">        :param metric_to_watch: monitoring loss or acc, will be identical to that which determines best_model</span>
<span class="sd">        :param metric_idx:</span>
<span class="sd">        :param load_checkpoint: whether to load pre-existing snapshot dict.</span>
<span class="sd">        :param number_of_models_to_average: number of models to average</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">source_ckpt_folder_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">source_ckpt_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">source_ckpt_folder_name</span><span class="p">,</span> <span class="s1">&#39;averaging_snapshots.pkl&#39;</span><span class="p">)</span>
            <span class="n">source_ckpt_file</span> <span class="o">=</span> <span class="n">pkg_resources</span><span class="o">.</span><span class="n">resource_filename</span><span class="p">(</span><span class="s1">&#39;checkpoints&#39;</span><span class="p">,</span> <span class="n">source_ckpt_file</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">averaging_snapshots_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ckpt_dir</span><span class="p">,</span> <span class="s1">&#39;averaging_snapshots.pkl&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">number_of_models_to_average</span> <span class="o">=</span> <span class="n">number_of_models_to_average</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">metric_to_watch</span> <span class="o">=</span> <span class="n">metric_to_watch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">metric_idx</span> <span class="o">=</span> <span class="n">metric_idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span> <span class="o">=</span> <span class="n">greater_is_better</span>
        <span class="c1"># if continuing training, copy previous snapshot dict if exist</span>
        <span class="k">if</span> <span class="n">load_checkpoint</span> <span class="ow">and</span> <span class="n">source_ckpt_folder_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">source_ckpt_file</span><span class="p">):</span>
            <span class="n">averaging_snapshots_dict</span> <span class="o">=</span> <span class="n">core_utils</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">ckpt_destination_dir</span><span class="o">=</span><span class="n">ckpt_dir</span><span class="p">,</span>
                                                                  <span class="n">source_ckpt_folder_name</span><span class="o">=</span><span class="n">source_ckpt_folder_name</span><span class="p">,</span>
                                                                  <span class="n">ckpt_filename</span><span class="o">=</span><span class="s2">&quot;averaging_snapshots.pkl&quot;</span><span class="p">,</span>
                                                                  <span class="n">load_weights_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                                  <span class="n">model_checkpoints_location</span><span class="o">=</span><span class="n">model_checkpoints_location</span><span class="p">,</span>
                                                                  <span class="n">overwrite_local_ckpt</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">averaging_snapshots_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;snapshot&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">):</span> <span class="kc">None</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="bp">self</span><span class="o">.</span><span class="n">number_of_models_to_average</span><span class="p">)}</span>
            <span class="c1"># if metric to watch is acc, hold a zero array, if loss hold inf array</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span><span class="p">:</span>
                <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshots_metric&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</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="bp">self</span><span class="o">.</span><span class="n">number_of_models_to_average</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshots_metric&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</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="bp">self</span><span class="o">.</span><span class="n">number_of_models_to_average</span><span class="p">)</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">averaging_snapshots_dict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">averaging_snapshots_file</span><span class="p">)</span>
<div class="viewcode-block" id="ModelWeightAveraging.update_snapshots_dict"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.weight_averaging_utils.ModelWeightAveraging.update_snapshots_dict">[docs]</a>    <span class="k">def</span> <span class="nf">update_snapshots_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">validation_results_tuple</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Update the snapshot dict and returns the updated average model for saving</span>
<span class="sd">        :param model: the latest model</span>
<span class="sd">        :param validation_results_tuple: performance of the latest model</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">averaging_snapshots_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_averaging_snapshots_dict</span><span class="p">()</span>
        <span class="c1"># IF CURRENT MODEL IS BETTER, TAKING HIS PLACE IN ACC LIST AND OVERWRITE THE NEW AVERAGE</span>
        <span class="n">require_update</span><span class="p">,</span> <span class="n">update_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_better</span><span class="p">(</span><span class="n">averaging_snapshots_dict</span><span class="p">,</span> <span class="n">validation_results_tuple</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">require_update</span><span class="p">:</span>
            <span class="c1"># moving state dict to cpu</span>
            <span class="n">new_sd</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
            <span class="n">new_sd</span> <span class="o">=</span> <span class="n">move_state_dict_to_device</span><span class="p">(</span><span class="n">new_sd</span><span class="p">,</span> <span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
            <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshot&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">update_ind</span><span class="p">)]</span> <span class="o">=</span> <span class="n">new_sd</span>
            <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshots_metric&#39;</span><span class="p">][</span><span class="n">update_ind</span><span class="p">]</span> <span class="o">=</span> <span class="n">validation_results_tuple</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">metric_idx</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">averaging_snapshots_dict</span></div>
<div class="viewcode-block" id="ModelWeightAveraging.get_average_model"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.weight_averaging_utils.ModelWeightAveraging.get_average_model">[docs]</a>    <span class="k">def</span> <span class="nf">get_average_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">validation_results_tuple</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the averaged model</span>
<span class="sd">        :param model: will be used to determine arch</span>
<span class="sd">        :param validation_results_tuple: if provided, will update the average model before returning</span>
<span class="sd">        :param target_device: if provided, return sd on target device</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># If validation tuple is provided, update the average model</span>
        <span class="k">if</span> <span class="n">validation_results_tuple</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">averaging_snapshots_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_snapshots_dict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">validation_results_tuple</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">averaging_snapshots_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_averaging_snapshots_dict</span><span class="p">()</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">averaging_snapshots_dict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">averaging_snapshots_file</span><span class="p">)</span>
        <span class="n">average_model_sd</span> <span class="o">=</span> <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshot0&#39;</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">n_model</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_models_to_average</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshot&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n_model</span><span class="p">)]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">net_sd</span> <span class="o">=</span> <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshot&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n_model</span><span class="p">)]</span>
                <span class="c1"># USING MOVING AVERAGE</span>
                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">average_model_sd</span><span class="p">:</span>
                    <span class="n">average_model_sd</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">true_divide</span><span class="p">(</span>
                        <span class="n">average_model_sd</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_model</span> <span class="o">+</span> <span class="n">net_sd</span><span class="p">[</span><span class="n">key</span><span class="p">],</span>
                        <span class="p">(</span><span class="n">n_model</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">average_model_sd</span></div>
<div class="viewcode-block" id="ModelWeightAveraging.cleanup"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.weight_averaging_utils.ModelWeightAveraging.cleanup">[docs]</a>    <span class="k">def</span> <span class="nf">cleanup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Delete snapshot file when reaching the last epoch</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">averaging_snapshots_file</span><span class="p">)</span></div>
    <span class="k">def</span> <span class="nf">_is_better</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">averaging_snapshots_dict</span><span class="p">,</span> <span class="n">validation_results_tuple</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Determines if the new model is better according to the specified metrics</span>
<span class="sd">        :param averaging_snapshots_dict: snapshot dict</span>
<span class="sd">        :param validation_results_tuple: latest model performance</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">snapshot_metric_array</span> <span class="o">=</span> <span class="n">averaging_snapshots_dict</span><span class="p">[</span><span class="s1">&#39;snapshots_metric&#39;</span><span class="p">]</span>
        <span class="n">val</span> <span class="o">=</span> <span class="n">validation_results_tuple</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">metric_idx</span><span class="p">]</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span><span class="p">:</span>
            <span class="n">update_ind</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">snapshot_metric_array</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">update_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">snapshot_metric_array</span><span class="p">)</span>
        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span> <span class="ow">and</span> <span class="n">val</span> <span class="o">&gt;</span> <span class="n">snapshot_metric_array</span><span class="p">[</span><span class="n">update_ind</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span>
                <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span> <span class="ow">and</span> <span class="n">val</span> <span class="o">&lt;</span> <span class="n">snapshot_metric_array</span><span class="p">[</span><span class="n">update_ind</span><span class="p">]):</span>
            <span class="k">return</span> <span class="kc">True</span><span class="p">,</span> <span class="n">update_ind</span>
        <span class="k">return</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">None</span>
    <span class="k">def</span> <span class="nf">_get_averaging_snapshots_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">averaging_snapshots_file</span><span class="p">)</span></div>
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Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#396 Trainer constructor cleanup

Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git
