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super-gradients
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  <h1>Source code for super_gradients.training.legacy.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">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.init</span> <span class="k">as</span> <span class="nn">init</span>


<div class="viewcode-block" id="prefetch_dataset"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.prefetch_dataset">[docs]</a><span class="k">def</span> <span class="nf">prefetch_dataset</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">half</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</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">tensors</span> <span class="o">=</span> <span class="n">dataset</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">dataloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">dataset</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span>
        <span class="p">)</span>
        <span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">dataloader</span><span class="p">]</span>
        <span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">)]</span>

    <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">half</span><span class="p">:</span>
        <span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">half</span><span class="p">()</span> <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">()</span> <span class="k">else</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>

    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">TensorDataset</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">)</span></div>


<div class="viewcode-block" id="PrefetchDataLoader"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.PrefetchDataLoader">[docs]</a><span class="k">class</span> <span class="nc">PrefetchDataLoader</span><span class="p">:</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">dataloader</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">half</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">loader</span> <span class="o">=</span> <span class="n">dataloader</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iter</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="k">if</span> <span class="n">half</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="o">=</span> <span class="kc">None</span>

    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loader</span><span class="p">)</span>

<div class="viewcode-block" id="PrefetchDataLoader.async_prefech"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.PrefetchDataLoader.async_prefech">[docs]</a>    <span class="k">def</span> <span class="nf">async_prefech</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iter</span><span class="p">)</span>
        <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="o">=</span> <span class="kc">None</span>
            <span class="k">return</span>

        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stream</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">next_data</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="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">next_data</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="o">=</span> <span class="p">[</span>
                    <span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">()</span> <span class="k">else</span> <span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
                        <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span>
                <span class="p">]</span></div>

    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loader</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">async_prefech</span><span class="p">()</span>
        <span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stream</span><span class="p">)</span>
            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_data</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">async_prefech</span><span class="p">()</span>
            <span class="k">yield</span> <span class="n">data</span></div>


<div class="viewcode-block" id="init_params"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.init_params">[docs]</a><span class="k">def</span> <span class="nf">init_params</span><span class="p">(</span><span class="n">net</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Init layer parameters.&quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">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">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
            <span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal</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">mode</span><span class="o">=</span><span class="s1">&#39;fan_out&#39;</span><span class="p">)</span>
            <span class="c1"># if m.bias:</span>
            <span class="c1">#    init.constant(m.bias, -5)</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
            <span class="n">init</span><span class="o">.</span><span class="n">constant</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="mi">1</span><span class="p">)</span>
            <span class="n">init</span><span class="o">.</span><span class="n">constant</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="mi">0</span><span class="p">)</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</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">init</span><span class="o">.</span><span class="n">normal</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">std</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">:</span>
                <span class="n">init</span><span class="o">.</span><span class="n">constant</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="mi">0</span><span class="p">)</span></div>


<div class="viewcode-block" id="format_time"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.format_time">[docs]</a><span class="k">def</span> <span class="nf">format_time</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
    <span class="n">days</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seconds</span> <span class="o">/</span> <span class="mi">3600</span> <span class="o">/</span> <span class="mi">24</span><span class="p">)</span>
    <span class="n">seconds</span> <span class="o">=</span> <span class="n">seconds</span> <span class="o">-</span> <span class="n">days</span> <span class="o">*</span> <span class="mi">3600</span> <span class="o">*</span> <span class="mi">24</span>
    <span class="n">hours</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seconds</span> <span class="o">/</span> <span class="mi">3600</span><span class="p">)</span>
    <span class="n">seconds</span> <span class="o">=</span> <span class="n">seconds</span> <span class="o">-</span> <span class="n">hours</span> <span class="o">*</span> <span class="mi">3600</span>
    <span class="n">minutes</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seconds</span> <span class="o">/</span> <span class="mi">60</span><span class="p">)</span>
    <span class="n">seconds</span> <span class="o">=</span> <span class="n">seconds</span> <span class="o">-</span> <span class="n">minutes</span> <span class="o">*</span> <span class="mi">60</span>
    <span class="n">secondsf</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seconds</span><span class="p">)</span>
    <span class="n">seconds</span> <span class="o">=</span> <span class="n">seconds</span> <span class="o">-</span> <span class="n">secondsf</span>
    <span class="n">millis</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seconds</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">)</span>

    <span class="n">f</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    <span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">days</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">days</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;D&#39;</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">hours</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">hours</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;h&#39;</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">minutes</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">minutes</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;m&#39;</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">secondsf</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">secondsf</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;s&#39;</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">millis</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">millis</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;ms&#39;</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">f</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
        <span class="n">f</span> <span class="o">=</span> <span class="s1">&#39;0ms&#39;</span>
    <span class="k">return</span> <span class="n">f</span></div>


<div class="viewcode-block" id="is_better"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.is_better">[docs]</a><span class="k">def</span> <span class="nf">is_better</span><span class="p">(</span><span class="n">new_metric</span><span class="p">,</span> <span class="n">current_best_metric</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="sd">&quot;&quot;&quot;</span>
<span class="sd">    Determines which of the two metrics is better, the higher if watching acc or lower when watching loss</span>
<span class="sd">    :param new_metric:                 the new metric</span>
<span class="sd">    :param current_best_metric:        the compared to metric</span>
<span class="sd">    :param metric_to_watch:             acc or loss</span>
<span class="sd">    :return: bool, True if new metric is better than current</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">metric_to_watch</span> <span class="o">==</span> <span class="s1">&#39;acc&#39;</span> <span class="ow">and</span> <span class="n">new_metric</span> <span class="o">&gt;</span> <span class="n">current_best_metric</span> <span class="ow">or</span> <span class="p">(</span><span class="n">metric_to_watch</span> <span class="o">==</span> <span class="s1">&#39;loss&#39;</span> <span class="ow">and</span> <span class="n">current_best_metric</span> <span class="o">&gt;</span> <span class="n">new_metric</span><span class="p">)</span></div>


<div class="viewcode-block" id="makedirs_if_not_exists"><a class="viewcode-back" href="../../../../super_gradients.training.legacy.html#super_gradients.training.legacy.utils.makedirs_if_not_exists">[docs]</a><span class="k">def</span> <span class="nf">makedirs_if_not_exists</span><span class="p">(</span><span class="n">dir_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    make new directory in dir_path if it doesn&#39;t exists</span>
<span class="sd">        :param dir_path - full path of directory</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dir_path</span><span class="p">):</span>
        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dir_path</span><span class="p">)</span></div>
</pre></div>

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