#378 Feature/sg 281 add kd notebook

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  <h1>Source code for super_gradients.training.utils.sg_model_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">sys</span>
<span class="kn">import</span> <span class="nn">socket</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
<span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Process</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Dict</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">treelib</span> <span class="kn">import</span> <span class="n">Tree</span>
<span class="kn">from</span> <span class="nn">termcolor</span> <span class="kn">import</span> <span class="n">colored</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.exceptions.dataset_exceptions</span> <span class="kn">import</span> <span class="n">UnsupportedBatchItemsFormat</span>
<span class="c1"># TODO: These utils should move to sg_model package as internal (private) helper functions</span>
<span class="n">IS_BETTER_COLOR</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span> <span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span> <span class="s2">&quot;red&quot;</span><span class="p">}</span>
<span class="n">IS_GREATER_SYMBOLS</span> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span> <span class="s2">&quot;↗&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span> <span class="s2">&quot;↘&quot;</span><span class="p">}</span>
<div class="viewcode-block" id="MonitoredValue"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.MonitoredValue">[docs]</a><span class="nd">@dataclass</span>
<span class="k">class</span> <span class="nc">MonitoredValue</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;Store a value and some indicators relative to its past iterations.</span>
<span class="sd">    The value can be a metric/loss, and the iteration can be epochs/batch.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span>
    <span class="n">greater_is_better</span><span class="p">:</span> <span class="nb">bool</span>
    <span class="n">current</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">previous</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">best</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">change_from_previous</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">change_from_best</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">is_better_than_previous</span><span class="p">(</span><span class="bp">self</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="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_best</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">None</span>
        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_previous</span> <span class="o">&gt;=</span> <span class="mi">0</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_previous</span> <span class="o">&lt;</span> <span class="mi">0</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">is_best_value</span><span class="p">(</span><span class="bp">self</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="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_best</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">None</span>
        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">greater_is_better</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_best</span> <span class="o">&gt;=</span> <span class="mi">0</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change_from_best</span> <span class="o">&lt;</span> <span class="mi">0</span></div>
<div class="viewcode-block" id="update_monitored_value"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.update_monitored_value">[docs]</a><span class="k">def</span> <span class="nf">update_monitored_value</span><span class="p">(</span><span class="n">previous_monitored_value</span><span class="p">:</span> <span class="n">MonitoredValue</span><span class="p">,</span> <span class="n">new_value</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">MonitoredValue</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;Update the given ValueToMonitor object (could be a loss or a metric) with the new value</span>
<span class="sd">    :param previous_monitored_value: The stats about the value that is monitored throughout epochs.</span>
<span class="sd">    :param new_value: The value of the current epoch that will be used to update previous_monitored_value</span>
<span class="sd">    :return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">previous_value</span><span class="p">,</span> <span class="n">previous_best_value</span> <span class="o">=</span> <span class="n">previous_monitored_value</span><span class="o">.</span><span class="n">current</span><span class="p">,</span> <span class="n">previous_monitored_value</span><span class="o">.</span><span class="n">best</span>
    <span class="n">name</span><span class="p">,</span> <span class="n">greater_is_better</span> <span class="o">=</span> <span class="n">previous_monitored_value</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">previous_monitored_value</span><span class="o">.</span><span class="n">greater_is_better</span>
    <span class="k">if</span> <span class="n">previous_best_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">previous_best_value</span> <span class="o">=</span> <span class="n">previous_value</span>
    <span class="k">elif</span> <span class="n">greater_is_better</span><span class="p">:</span>
        <span class="n">previous_best_value</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">previous_value</span><span class="p">,</span> <span class="n">previous_best_value</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">previous_best_value</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">previous_value</span><span class="p">,</span> <span class="n">previous_best_value</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">previous_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">change_from_previous</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">change_from_best</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">change_from_previous</span> <span class="o">=</span> <span class="n">new_value</span> <span class="o">-</span> <span class="n">previous_value</span>
        <span class="n">change_from_best</span> <span class="o">=</span> <span class="n">new_value</span> <span class="o">-</span> <span class="n">previous_best_value</span>
    <span class="k">return</span> <span class="n">MonitoredValue</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">current</span><span class="o">=</span><span class="n">new_value</span><span class="p">,</span> <span class="n">previous</span><span class="o">=</span><span class="n">previous_value</span><span class="p">,</span> <span class="n">best</span><span class="o">=</span><span class="n">previous_best_value</span><span class="p">,</span>
                          <span class="n">change_from_previous</span><span class="o">=</span><span class="n">change_from_previous</span><span class="p">,</span> <span class="n">change_from_best</span><span class="o">=</span><span class="n">change_from_best</span><span class="p">,</span>
                          <span class="n">greater_is_better</span><span class="o">=</span><span class="n">greater_is_better</span><span class="p">)</span></div>
<div class="viewcode-block" id="update_monitored_values_dict"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.update_monitored_values_dict">[docs]</a><span class="k">def</span> <span class="nf">update_monitored_values_dict</span><span class="p">(</span><span class="n">monitored_values_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">MonitoredValue</span><span class="p">],</span>
                                 <span class="n">new_values_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">MonitoredValue</span><span class="p">]:</span>
    <span class="sd">&quot;&quot;&quot;Update the given ValueToMonitor object (could be a loss or a metric) with the new value</span>
<span class="sd">    :param monitored_values_dict: Dict mapping value names to their stats throughout epochs.</span>
<span class="sd">    :param new_values_dict: Dict mapping value names to their new (i.e. current epoch) value.</span>
<span class="sd">    :return: Updated monitored_values_dict</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">monitored_value_name</span> <span class="ow">in</span> <span class="n">monitored_values_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">monitored_values_dict</span><span class="p">[</span><span class="n">monitored_value_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">update_monitored_value</span><span class="p">(</span>
            <span class="n">new_value</span><span class="o">=</span><span class="n">new_values_dict</span><span class="p">[</span><span class="n">monitored_value_name</span><span class="p">],</span>
            <span class="n">previous_monitored_value</span><span class="o">=</span><span class="n">monitored_values_dict</span><span class="p">[</span><span class="n">monitored_value_name</span><span class="p">],</span>
        <span class="p">)</span>
    <span class="k">return</span> <span class="n">monitored_values_dict</span></div>
<div class="viewcode-block" id="display_epoch_summary"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.display_epoch_summary">[docs]</a><span class="k">def</span> <span class="nf">display_epoch_summary</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="n">n_digits</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                          <span class="n">train_monitored_values</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">MonitoredValue</span><span class="p">],</span>
                          <span class="n">valid_monitored_values</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">MonitoredValue</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;Display a summary of loss/metric of interest, for a given epoch.</span>
<span class="sd">        :param epoch: the number of epoch.</span>
<span class="sd">        :param n_digits: number of digits to display on screen for float values</span>
<span class="sd">        :param train_monitored_values: mapping of loss/metric with their stats that will be displayed</span>
<span class="sd">        :param valid_monitored_values: mapping of loss/metric with their stats that will be displayed</span>
<span class="sd">        :return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">_format_to_str</span><span class="p">(</span><span class="n">val</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">n_digits</span><span class="p">))</span>
    <span class="k">def</span> <span class="nf">_generate_tree</span><span class="p">(</span><span class="n">value_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">monitored_value</span><span class="p">:</span> <span class="n">MonitoredValue</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tree</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;Generate a tree that represents the stats of a given loss/metric.&quot;&quot;&quot;</span>
        <span class="n">current</span> <span class="o">=</span> <span class="n">_format_to_str</span><span class="p">(</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">current</span><span class="p">)</span>
        <span class="n">root_id</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">hash</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">value_name</span><span class="si">}</span><span class="s2"> = </span><span class="si">{</span><span class="n">current</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">())</span>
        <span class="n">tree</span> <span class="o">=</span> <span class="n">Tree</span><span class="p">()</span>
        <span class="n">tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">value_name</span><span class="o">.</span><span class="n">capitalize</span><span class="p">()</span><span class="si">}</span><span class="s2"> = </span><span class="si">{</span><span class="n">current</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">identifier</span><span class="o">=</span><span class="n">root_id</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">monitored_value</span><span class="o">.</span><span class="n">previous</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">previous</span> <span class="o">=</span> <span class="n">_format_to_str</span><span class="p">(</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">previous</span><span class="p">)</span>
            <span class="n">best</span> <span class="o">=</span> <span class="n">_format_to_str</span><span class="p">(</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">best</span><span class="p">)</span>
            <span class="n">change_from_previous</span> <span class="o">=</span> <span class="n">_format_to_str</span><span class="p">(</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">change_from_previous</span><span class="p">)</span>
            <span class="n">change_from_best</span> <span class="o">=</span> <span class="n">_format_to_str</span><span class="p">(</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">change_from_best</span><span class="p">)</span>
            <span class="n">diff_with_prev_colored</span> <span class="o">=</span> <span class="n">colored</span><span class="p">(</span>
                <span class="n">text</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">IS_GREATER_SYMBOLS</span><span class="p">[</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">change_from_previous</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">change_from_previous</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
                <span class="n">color</span><span class="o">=</span><span class="n">IS_BETTER_COLOR</span><span class="p">[</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">is_better_than_previous</span><span class="p">]</span>
            <span class="p">)</span>
            <span class="n">diff_with_best_colored</span> <span class="o">=</span> <span class="n">colored</span><span class="p">(</span>
                <span class="n">text</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">IS_GREATER_SYMBOLS</span><span class="p">[</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">change_from_best</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">change_from_best</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
                <span class="n">color</span><span class="o">=</span><span class="n">IS_BETTER_COLOR</span><span class="p">[</span><span class="n">monitored_value</span><span class="o">.</span><span class="n">is_best_value</span><span class="p">]</span>
            <span class="p">)</span>
            <span class="n">tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span>
                <span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch N-1      = </span><span class="si">{</span><span class="n">previous</span><span class="si">:</span><span class="s2">6</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">diff_with_prev_colored</span><span class="si">:</span><span class="s2">8</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">,</span>
                <span class="n">identifier</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;0_previous_</span><span class="si">{</span><span class="n">root_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
                <span class="n">parent</span><span class="o">=</span><span class="n">root_id</span>
            <span class="p">)</span>
            <span class="n">tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span>
                <span class="n">tag</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Best until now = </span><span class="si">{</span><span class="n">best</span><span class="si">:</span><span class="s2">6</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">diff_with_best_colored</span><span class="si">:</span><span class="s2">8</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">,</span>
                <span class="n">identifier</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;1_best_</span><span class="si">{</span><span class="n">root_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
                <span class="n">parent</span><span class="o">=</span><span class="n">root_id</span>
            <span class="p">)</span>
        <span class="k">return</span> <span class="n">tree</span>
    <span class="n">train_tree</span> <span class="o">=</span> <span class="n">Tree</span><span class="p">()</span>
    <span class="n">train_tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span><span class="s2">&quot;Training&quot;</span><span class="p">,</span> <span class="s2">&quot;Training&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">train_monitored_values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">train_tree</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="s1">&#39;Training&#39;</span><span class="p">,</span> <span class="n">new_tree</span><span class="o">=</span><span class="n">_generate_tree</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">monitored_value</span><span class="o">=</span><span class="n">value</span><span class="p">))</span>
    <span class="n">valid_tree</span> <span class="o">=</span> <span class="n">Tree</span><span class="p">()</span>
    <span class="n">valid_tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span><span class="s2">&quot;Validation&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">valid_monitored_values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">valid_tree</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="s1">&#39;Validation&#39;</span><span class="p">,</span> <span class="n">new_tree</span><span class="o">=</span><span class="n">_generate_tree</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">monitored_value</span><span class="o">=</span><span class="n">value</span><span class="p">))</span>
    <span class="n">summary_tree</span> <span class="o">=</span> <span class="n">Tree</span><span class="p">()</span>
    <span class="n">summary_tree</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SUMMARY OF EPOCH </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;Summary&quot;</span><span class="p">)</span>
    <span class="n">summary_tree</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="s2">&quot;Summary&quot;</span><span class="p">,</span> <span class="n">train_tree</span><span class="p">)</span>
    <span class="n">summary_tree</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="s2">&quot;Summary&quot;</span><span class="p">,</span> <span class="n">valid_tree</span><span class="p">)</span>
    <span class="n">summary_tree</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
<div class="viewcode-block" id="try_port"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.try_port">[docs]</a><span class="k">def</span> <span class="nf">try_port</span><span class="p">(</span><span class="n">port</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    try_port - Helper method for tensorboard port binding</span>
<span class="sd">    :param port:</span>
<span class="sd">    :return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">sock</span> <span class="o">=</span> <span class="n">socket</span><span class="o">.</span><span class="n">socket</span><span class="p">(</span><span class="n">socket</span><span class="o">.</span><span class="n">AF_INET</span><span class="p">,</span> <span class="n">socket</span><span class="o">.</span><span class="n">SOCK_STREAM</span><span class="p">)</span>
    <span class="n">is_port_available</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">sock</span><span class="o">.</span><span class="n">bind</span><span class="p">((</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span> <span class="n">port</span><span class="p">))</span>
        <span class="n">is_port_available</span> <span class="o">=</span> <span class="kc">True</span>
    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Port &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">port</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; is in use&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ex</span><span class="p">))</span>
    <span class="n">sock</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">is_port_available</span></div>
<div class="viewcode-block" id="launch_tensorboard_process"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.launch_tensorboard_process">[docs]</a><span class="k">def</span> <span class="nf">launch_tensorboard_process</span><span class="p">(</span><span class="n">checkpoints_dir_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">sleep_postpone</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">port</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Process</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    launch_tensorboard_process - Default behavior is to scan all free ports from 6006-6016 and try using them</span>
<span class="sd">                                 unless port is defined by the user</span>
<span class="sd">        :param checkpoints_dir_path:</span>
<span class="sd">        :param sleep_postpone:</span>
<span class="sd">        :param port:</span>
<span class="sd">        :return: tuple of tb process, port</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">logdir_path</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">checkpoints_dir_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">absolute</span><span class="p">())</span>
    <span class="n">tb_cmd</span> <span class="o">=</span> <span class="s1">&#39;tensorboard --logdir=&#39;</span> <span class="o">+</span> <span class="n">logdir_path</span> <span class="o">+</span> <span class="s1">&#39; --bind_all&#39;</span>
    <span class="k">if</span> <span class="n">port</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">tb_ports</span> <span class="o">=</span> <span class="p">[</span><span class="n">port</span><span class="p">]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">tb_ports</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">6006</span><span class="p">,</span> <span class="mi">6016</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">tb_port</span> <span class="ow">in</span> <span class="n">tb_ports</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">try_port</span><span class="p">(</span><span class="n">tb_port</span><span class="p">):</span>
            <span class="k">continue</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Starting Tensor-Board process on port: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">tb_port</span><span class="p">))</span>
            <span class="n">tensor_board_process</span> <span class="o">=</span> <span class="n">Process</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">([</span><span class="n">tb_cmd</span> <span class="o">+</span> <span class="s1">&#39; --port=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">tb_port</span><span class="p">)]))</span>
            <span class="n">tensor_board_process</span><span class="o">.</span><span class="n">daemon</span> <span class="o">=</span> <span class="kc">True</span>
            <span class="n">tensor_board_process</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
            <span class="c1"># LET THE TENSORBOARD PROCESS START</span>
            <span class="k">if</span> <span class="n">sleep_postpone</span><span class="p">:</span>
                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">tensor_board_process</span><span class="p">,</span> <span class="n">tb_port</span>
    <span class="c1"># RETURNING IRRELEVANT VALUES</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Failed to initialize Tensor-Board process on port: &#39;</span> <span class="o">+</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">tb_ports</span><span class="p">)))</span>
    <span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span></div>
<div class="viewcode-block" id="init_summary_writer"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.init_summary_writer">[docs]</a><span class="k">def</span> <span class="nf">init_summary_writer</span><span class="p">(</span><span class="n">tb_dir</span><span class="p">,</span> <span class="n">checkpoint_loaded</span><span class="p">,</span> <span class="n">user_prompt</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Remove previous tensorboard files from directory and launch a tensor board process&quot;&quot;&quot;</span>
    <span class="c1"># If the training is from scratch, Walk through destination folder and delete existing tensorboard logs</span>
    <span class="n">user</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">checkpoint_loaded</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">tb_dir</span><span class="p">):</span>
            <span class="k">if</span> <span class="s1">&#39;events&#39;</span> <span class="ow">in</span> <span class="n">filename</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">user_prompt</span><span class="p">:</span>
                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&quot;</span><span class="si">{}</span><span class="s1">&quot; will not be deleted&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span>
                    <span class="k">continue</span>
                <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
                    <span class="c1"># Verify with user before deleting old tensorboard files</span>
                    <span class="n">user</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">OLDER TENSORBOARD FILES EXISTS IN EXPERIMENT FOLDER:</span><span class="se">\n</span><span class="s1">&quot;</span><span class="si">{}</span><span class="s1">&quot;</span><span class="se">\n</span><span class="s1">&#39;</span>
                                 <span class="s1">&#39;DO YOU WANT TO DELETE THEM? [y/n]&#39;</span>
                                 <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span> <span class="k">if</span> <span class="p">(</span><span class="n">user</span> <span class="o">!=</span> <span class="s1">&#39;n&#39;</span> <span class="ow">or</span> <span class="n">user</span> <span class="o">!=</span> <span class="s1">&#39;y&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">user</span>
                    <span class="k">if</span> <span class="n">user</span> <span class="o">==</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span>
                        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tb_dir</span><span class="p">,</span> <span class="n">filename</span><span class="p">))</span>
                        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DELETED: </span><span class="si">{}</span><span class="s1">!&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span>
                        <span class="k">break</span>
                    <span class="k">elif</span> <span class="n">user</span> <span class="o">==</span> <span class="s1">&#39;n&#39;</span><span class="p">:</span>
                        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&quot;</span><span class="si">{}</span><span class="s1">&quot; will not be deleted&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span>
                        <span class="k">break</span>
                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Unknown answer...&#39;</span><span class="p">)</span>
    <span class="c1"># Launch a tensorboard process</span>
    <span class="k">return</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="n">tb_dir</span><span class="p">)</span></div>
<div class="viewcode-block" id="add_log_to_file"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.add_log_to_file">[docs]</a><span class="k">def</span> <span class="nf">add_log_to_file</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">results_titles_list</span><span class="p">,</span> <span class="n">results_values_list</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">max_epochs</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Add a message to the log file&quot;&quot;&quot;</span>
    <span class="c1"># -Note: opening and closing the file every time is in-efficient. It is done for experimental purposes</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Epoch (</span><span class="si">%d</span><span class="s1">/</span><span class="si">%d</span><span class="s1">)  - &#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">max_epochs</span><span class="p">))</span>
        <span class="k">for</span> <span class="n">result_title</span><span class="p">,</span> <span class="n">result_value</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">results_titles_list</span><span class="p">,</span> <span class="n">results_values_list</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_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">result_value</span> <span class="o">=</span> <span class="n">result_value</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">result_title</span> <span class="o">+</span> <span class="s1">&#39;: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">result_value</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span></div>
<div class="viewcode-block" id="write_training_results"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.write_training_results">[docs]</a><span class="k">def</span> <span class="nf">write_training_results</span><span class="p">(</span><span class="n">writer</span><span class="p">,</span> <span class="n">results_titles_list</span><span class="p">,</span> <span class="n">results_values_list</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Stores the training and validation loss and accuracy for current epoch in a tensorboard file&quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">res_key</span><span class="p">,</span> <span class="n">res_val</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">results_titles_list</span><span class="p">,</span> <span class="n">results_values_list</span><span class="p">):</span>
        <span class="c1"># USE ONLY LOWER-CASE LETTERS AND REPLACE SPACES WITH &#39;_&#39; TO AVOID MANY TITLES FOR THE SAME KEY</span>
        <span class="n">corrected_res_key</span> <span class="o">=</span> <span class="n">res_key</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="s1">&#39;_&#39;</span><span class="p">)</span>
        <span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="n">corrected_res_key</span><span class="p">,</span> <span class="n">res_val</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
    <span class="n">writer</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span></div>
<div class="viewcode-block" id="write_hpms"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.write_hpms">[docs]</a><span class="k">def</span> <span class="nf">write_hpms</span><span class="p">(</span><span class="n">writer</span><span class="p">,</span> <span class="n">hpmstructs</span><span class="o">=</span><span class="p">[],</span> <span class="n">special_conf</span><span class="o">=</span><span class="p">{}):</span>
    <span class="sd">&quot;&quot;&quot;Stores the training and dataset hyper params in the tensorboard file&quot;&quot;&quot;</span>
    <span class="n">hpm_string</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
    <span class="k">for</span> <span class="n">hpm</span> <span class="ow">in</span> <span class="n">hpmstructs</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">hpm</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">hpm_string</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">special_conf</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">hpm_string</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">  </span><span class="se">\n</span><span class="s1">  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
    <span class="n">writer</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s2">&quot;Hyper_parameters&quot;</span><span class="p">,</span> <span class="n">hpm_string</span><span class="p">)</span>
    <span class="n">writer</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span></div>
<span class="c1"># TODO: This should probably move into datasets/datasets_utils.py?</span>
<div class="viewcode-block" id="unpack_batch_items"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.unpack_batch_items">[docs]</a><span class="k">def</span> <span class="nf">unpack_batch_items</span><span class="p">(</span><span class="n">batch_items</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="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Adds support for unpacking batch items in train/validation loop.</span>
<span class="sd">    @param batch_items: (Union[tuple, torch.Tensor]) returned by the data loader, which is expected to be in one of</span>
<span class="sd">         the following formats:</span>
<span class="sd">            1. torch.Tensor or tuple, s.t inputs = batch_items[0], targets = batch_items[1] and len(batch_items) = 2</span>
<span class="sd">            2. tuple: (inputs, targets, additional_batch_items)</span>
<span class="sd">         where inputs are fed to the network, targets are their corresponding labels and additional_batch_items is a</span>
<span class="sd">         dictionary (format {additional_batch_item_i_name: additional_batch_item_i ...}) which can be accessed through</span>
<span class="sd">         the phase context under the attribute additional_batch_item_i_name, using a phase callback.</span>
<span class="sd">    @return: inputs, target, additional_batch_items</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">additional_batch_items</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_items</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">inputs</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">batch_items</span>
    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_items</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
        <span class="n">inputs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">additional_batch_items</span> <span class="o">=</span> <span class="n">batch_items</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="n">UnsupportedBatchItemsFormat</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">additional_batch_items</span></div>
<div class="viewcode-block" id="log_uncaught_exceptions"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.sg_model_utils.log_uncaught_exceptions">[docs]</a><span class="k">def</span> <span class="nf">log_uncaught_exceptions</span><span class="p">(</span><span class="n">logger</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Makes logger log uncaught exceptions</span>
<span class="sd">    @param logger: logging.Logger</span>
<span class="sd">    @return: None</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">handle_exception</span><span class="p">(</span><span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_traceback</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">exc_type</span><span class="p">,</span> <span class="ne">KeyboardInterrupt</span><span class="p">):</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">__excepthook__</span><span class="p">(</span><span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_traceback</span><span class="p">)</span>
            <span class="k">return</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Uncaught exception&quot;</span><span class="p">,</span> <span class="n">exc_info</span><span class="o">=</span><span class="p">(</span><span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_traceback</span><span class="p">))</span>
    <span class="n">sys</span><span class="o">.</span><span class="n">excepthook</span> <span class="o">=</span> <span class="n">handle_exception</span></div>
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#378 Feature/sg 281 add kd notebook

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