#396 Trainer constructor cleanup

Merged
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.metrics.metric_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchmetrics</span> <span class="kn">import</span> <span class="n">MetricCollection</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.utils</span> <span class="kn">import</span> <span class="n">AverageMeter</span>
<div class="viewcode-block" id="get_logging_values"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.get_logging_values">[docs]</a><span class="k">def</span> <span class="nf">get_logging_values</span><span class="p">(</span><span class="n">loss_loggings</span><span class="p">:</span> <span class="n">AverageMeter</span><span class="p">,</span> <span class="n">metrics</span><span class="p">:</span> <span class="n">MetricCollection</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    @param loss_loggings: AverageMeter running average for the loss items</span>
<span class="sd">    @param metrics: MetricCollection object for running user specified metrics</span>
<span class="sd">    @param criterion the object loss_loggings average meter is monitoring, when set to None- only the metrics values are</span>
<span class="sd">    computed and returned.</span>
<span class="sd">    @return: tuple of the computed values</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">criterion</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">loss_loggingg_avg</span> <span class="o">=</span> <span class="n">loss_loggings</span><span class="o">.</span><span class="n">average</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">loss_loggingg_avg</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
            <span class="n">loss_loggingg_avg</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">loss_loggingg_avg</span><span class="p">])</span>
        <span class="n">logging_vals</span> <span class="o">=</span> <span class="n">loss_loggingg_avg</span> <span class="o">+</span> <span class="n">get_metrics_results_tuple</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logging_vals</span> <span class="o">=</span> <span class="n">get_metrics_results_tuple</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">logging_vals</span></div>
<div class="viewcode-block" id="get_metrics_titles"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.get_metrics_titles">[docs]</a><span class="k">def</span> <span class="nf">get_metrics_titles</span><span class="p">(</span><span class="n">metrics_collection</span><span class="p">:</span> <span class="n">MetricCollection</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    @param metrics_collection: MetricCollection object for running user specified metrics</span>
<span class="sd">    @return: list of all the names of the computed values list(str)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">titles</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics_collection</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">metric_name</span> <span class="o">==</span> <span class="s2">&quot;additional_items&quot;</span><span class="p">:</span>
            <span class="k">continue</span>
        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="s2">&quot;component_names&quot;</span><span class="p">):</span>
            <span class="n">titles</span> <span class="o">+=</span> <span class="n">metric</span><span class="o">.</span><span class="n">component_names</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">titles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric_name</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">titles</span></div>
<div class="viewcode-block" id="get_metrics_results_tuple"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.get_metrics_results_tuple">[docs]</a><span class="k">def</span> <span class="nf">get_metrics_results_tuple</span><span class="p">(</span><span class="n">metrics_collection</span><span class="p">:</span> <span class="n">MetricCollection</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    @param metrics_collection: metrics collection of the user specified metrics</span>
<span class="sd">    @type metrics_collection</span>
<span class="sd">    @return: tuple of metrics values</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">metrics_collection</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">results_tuple</span> <span class="o">=</span> <span class="p">()</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">results_tuple</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">flatten_metrics_dict</span><span class="p">(</span><span class="n">metrics_collection</span><span class="o">.</span><span class="n">compute</span><span class="p">())</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
    <span class="k">return</span> <span class="n">results_tuple</span></div>
<div class="viewcode-block" id="flatten_metrics_dict"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.flatten_metrics_dict">[docs]</a><span class="k">def</span> <span class="nf">flatten_metrics_dict</span><span class="p">(</span><span class="n">metrics_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    :param metrics_dict - dictionary of metric values where values can also be dictionaries containing subvalues</span>
<span class="sd">    (in the case of compound metrics)</span>
<span class="sd">    @return: flattened dict of metric values i.e {metric1_name: metric1_value...}</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">flattened</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_val</span> <span class="ow">in</span> <span class="n">metrics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">metric_name</span> <span class="o">==</span> <span class="s2">&quot;additional_items&quot;</span><span class="p">:</span>
            <span class="k">continue</span>
        <span class="c1"># COLLECT ALL OF THE COMPONENTS IN THE CASE OF COMPOUND METRICS</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">metric_val</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">sub_metric_name</span><span class="p">,</span> <span class="n">sub_metric_val</span> <span class="ow">in</span> <span class="n">metric_val</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">flattened</span><span class="p">[</span><span class="n">sub_metric_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">sub_metric_val</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">flattened</span><span class="p">[</span><span class="n">metric_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">metric_val</span>
    <span class="k">return</span> <span class="n">flattened</span></div>
<div class="viewcode-block" id="get_metrics_dict"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.get_metrics_dict">[docs]</a><span class="k">def</span> <span class="nf">get_metrics_dict</span><span class="p">(</span><span class="n">metrics_tuple</span><span class="p">,</span> <span class="n">metrics_collection</span><span class="p">,</span> <span class="n">loss_logging_item_names</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Returns a dictionary with the epoch results as values and their names as keys.</span>
<span class="sd">    @param metrics_tuple: the result tuple</span>
<span class="sd">    @param metrics_collection: MetricsCollection</span>
<span class="sd">    @param loss_logging_item_names: loss component&#39;s names.</span>
<span class="sd">    @return: dict</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">keys</span> <span class="o">=</span> <span class="n">loss_logging_item_names</span> <span class="o">+</span> <span class="n">get_metrics_titles</span><span class="p">(</span><span class="n">metrics_collection</span><span class="p">)</span>
    <span class="n">metrics_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">metrics_tuple</span><span class="p">)))</span>
    <span class="k">return</span> <span class="n">metrics_dict</span></div>
<div class="viewcode-block" id="get_train_loop_description_dict"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.metric_utils.get_train_loop_description_dict">[docs]</a><span class="k">def</span> <span class="nf">get_train_loop_description_dict</span><span class="p">(</span><span class="n">metrics_tuple</span><span class="p">,</span> <span class="n">metrics_collection</span><span class="p">,</span> <span class="n">loss_logging_item_names</span><span class="p">,</span> <span class="o">**</span><span class="n">log_items</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Returns a dictionary with the epoch&#39;s logging items as values and their names as keys, with the purpose of</span>
<span class="sd">     passing it as a description to tqdm&#39;s progress bar.</span>
<span class="sd">    @param metrics_tuple: the result tuple</span>
<span class="sd">    @param metrics_collection: MetricsCollection</span>
<span class="sd">    @param loss_logging_item_names: loss component&#39;s names.</span>
<span class="sd">    @param log_items additional logging items to be rendered.</span>
<span class="sd">    @return: dict</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">log_items</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">get_metrics_dict</span><span class="p">(</span><span class="n">metrics_tuple</span><span class="p">,</span> <span class="n">metrics_collection</span><span class="p">,</span> <span class="n">loss_logging_item_names</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">log_items</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</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">log_items</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">log_items</span></div>
</pre></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
