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          <li><a href="../index.html">Module code</a> &raquo;</li>





  
  

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      <li>pathflowai.schedulers</li>





  
  

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  <h1>Source code for pathflowai.schedulers</h1><div class="highlight"><pre>





  
  

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<span></span><span class="sd">&quot;&quot;&quot;</span>





  
  

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<span class="sd">schedulers.py</span>





  
  

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<span class="sd">=======================</span>





  
  

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<span class="sd">Modulates the learning rate during the training process.</span>





  
  

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<span class="sd">&quot;&quot;&quot;</span>





  
  

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<span class="kn">import</span> <span class="nn">torch</span>





  
  

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<span class="kn">import</span> <span class="nn">math</span>





  
  

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<span class="kn">from</span> <span class="nn">torch.optim.lr_scheduler</span> <span class="k">import</span> <span class="n">ExponentialLR</span>





  
  

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<div class="viewcode-block" id="CosineAnnealingWithRestartsLR"><a class="viewcode-back" href="../../index.html#pathflowai.schedulers.CosineAnnealingWithRestartsLR">[docs]</a><span class="k">class</span> <span class="nc">CosineAnnealingWithRestartsLR</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">_LRScheduler</span><span class="p">):</span>





  
  

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    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Set the learning rate of each parameter group using a cosine annealing</span>





  
  

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<span class="sd">   schedule, where :math:`\eta_{max}` is set to the initial lr and</span>





  
  

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<span class="sd">   :math:`T_{cur}` is the number of epochs since the last restart in SGDR:</span>





  
  

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<span class="sd">    .. math::</span>





  
  

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<span class="sd">        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 +</span>





  
  

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<span class="sd">       \cos(\frac{T_{cur}}{T_{max}}\pi))</span>





  
  

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<span class="sd">    When last_epoch=-1, sets initial lr as lr.</span>





  
  

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<span class="sd">    It has been proposed in</span>





  
  

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<span class="sd">   `SGDR: Stochastic Gradient Descent with Warm Restarts`_. This implements</span>





  
  

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<span class="sd">   the cosine annealing part of SGDR, the restarts and number of iterations multiplier.</span>





  
  

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<span class="sd">    Args:</span>





  
  

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<span class="sd">       optimizer (Optimizer): Wrapped optimizer.</span>





  
  

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<span class="sd">       T_max (int): Maximum number of iterations.</span>





  
  

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<span class="sd">       T_mult (float): Multiply T_max by this number after each restart. Default: 1.</span>





  
  

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<span class="sd">       eta_min (float): Minimum learning rate. Default: 0.</span>





  
  

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<span class="sd">       last_epoch (int): The index of last epoch. Default: -1.</span>





  
  

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<span class="sd">    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:</span>





  
  

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<span class="sd">       https://arxiv.org/abs/1608.03983</span>





  
  

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<span class="sd">   &quot;&quot;&quot;</span>





  
  

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    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">T_max</span><span class="p">,</span> <span class="n">eta_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">last_epoch</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">T_mult</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">alpha_decay</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">T_max</span> <span class="o">=</span> <span class="n">T_max</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">T_mult</span> <span class="o">=</span> <span class="n">T_mult</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restart_every</span> <span class="o">=</span> <span class="n">T_max</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">eta_min</span> <span class="o">=</span> <span class="n">eta_min</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restarts</span> <span class="o">=</span> <span class="mi">0</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restarted_at</span> <span class="o">=</span> <span class="mi">0</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha_decay</span>





  
  

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        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">last_epoch</span><span class="p">)</span>





  
  

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    <span class="k">def</span> <span class="nf">restart</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restarts</span> <span class="o">+=</span> <span class="mi">1</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restart_every</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">restart_every</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">T_mult</span><span class="p">))</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">restarted_at</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_epoch</span>





  
  

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    <span class="k">def</span> <span class="nf">cosine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_lr</span><span class="p">):</span>





  
  

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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_min</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">restarts</span> <span class="o">*</span> <span class="p">(</span><span class="n">base_lr</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_min</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_n</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">restart_every</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>





  
  

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    <span class="nd">@property</span>





  
  

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    <span class="k">def</span> <span class="nf">step_n</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>





  
  

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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_epoch</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">restarted_at</span>





  
  

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    <span class="k">def</span> <span class="nf">get_lr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>





  
  

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        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_n</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">restart_every</span><span class="p">:</span>





  
  

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            <span class="bp">self</span><span class="o">.</span><span class="n">restart</span><span class="p">()</span>





  
  

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        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cosine</span><span class="p">(</span><span class="n">base_lr</span><span class="p">)</span> <span class="k">for</span> <span class="n">base_lr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_lrs</span><span class="p">]</span></div>





  
  

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<div class="viewcode-block" id="Scheduler"><a class="viewcode-back" href="../../index.html#pathflowai.schedulers.Scheduler">[docs]</a><span class="k">class</span> <span class="nc">Scheduler</span><span class="p">:</span>





  
  

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    <span class="sd">&quot;&quot;&quot;Scheduler class that modulates learning rate of torch optimizers over epochs.</span>





  
  

  203
  





  
  

  204
  
<span class="sd">   Parameters</span>





  
  

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<span class="sd">   ----------</span>





  
  

  206
  
<span class="sd">   optimizer : type</span>





  
  

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<span class="sd">       torch.Optimizer object</span>





  
  

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<span class="sd">   opts : type</span>





  
  

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<span class="sd">       Options of setting the learning rate scheduler, see default.</span>





  
  

  210
  





  
  

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<span class="sd">   Attributes</span>





  
  

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<span class="sd">   ----------</span>





  
  

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<span class="sd">   schedulers : type</span>





  
  

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<span class="sd">       Different types of schedulers to choose from.</span>





  
  

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<span class="sd">   scheduler_step_fn : type</span>





  
  

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<span class="sd">       How scheduler updates learning rate.</span>





  
  

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<span class="sd">   initial_lr : type</span>





  
  

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<span class="sd">       Initial set learning rate.</span>





  
  

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<span class="sd">   scheduler_choice : type</span>





  
  

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<span class="sd">       What scheduler type was chosen.</span>





  
  

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<span class="sd">   scheduler : type</span>





  
  

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<span class="sd">       Scheduler object chosen that will more directly update optimizer LR.</span>





  
  

  223
  





  
  

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<span class="sd">   &quot;&quot;&quot;</span>





  
  

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    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">opts</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">scheduler</span><span class="o">=</span><span class="s1">&#39;null&#39;</span><span class="p">,</span><span class="n">lr_scheduler_decay</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">T_max</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">eta_min</span><span class="o">=</span><span class="mf">5e-8</span><span class="p">,</span><span class="n">T_mult</span><span class="o">=</span><span class="mi">2</span><span class="p">)):</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">schedulers</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;exp&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">optimizer</span><span class="p">:</span> <span class="n">ExponentialLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">opts</span><span class="p">[</span><span class="s2">&quot;lr_scheduler_decay&quot;</span><span class="p">])),</span>





  
  

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                            <span class="s1">&#39;null&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">optimizer</span><span class="p">:</span> <span class="kc">None</span><span class="p">),</span>





  
  

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                            <span class="s1">&#39;warm_restarts&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">optimizer</span><span class="p">:</span> <span class="n">CosineAnnealingWithRestartsLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><span class="n">opts</span><span class="p">[</span><span class="s1">&#39;T_max&#39;</span><span class="p">],</span> <span class="n">eta_min</span><span class="o">=</span><span class="n">opts</span><span class="p">[</span><span class="s1">&#39;eta_min&#39;</span><span class="p">],</span> <span class="n">last_epoch</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">T_mult</span><span class="o">=</span><span class="n">opts</span><span class="p">[</span><span class="s1">&#39;T_mult&#39;</span><span class="p">]))}</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">scheduler_step_fn</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;exp&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">scheduler</span><span class="p">:</span> <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()),</span>





  
  

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                                  <span class="s1">&#39;warm_restarts&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">scheduler</span><span class="p">:</span> <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()),</span>





  
  

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                                  <span class="s1">&#39;null&#39;</span><span class="p">:(</span><span class="k">lambda</span> <span class="n">scheduler</span><span class="p">:</span> <span class="kc">None</span><span class="p">)}</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">initial_lr</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;lr&#39;</span><span class="p">]</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">scheduler_choice</span> <span class="o">=</span> <span class="n">opts</span><span class="p">[</span><span class="s1">&#39;scheduler&#39;</span><span class="p">]</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">schedulers</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler_choice</span><span class="p">](</span><span class="n">optimizer</span><span class="p">)</span> <span class="k">if</span> <span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>





  
  

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<div class="viewcode-block" id="Scheduler.step"><a class="viewcode-back" href="../../index.html#pathflowai.schedulers.Scheduler.step">[docs]</a>    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>





  
  

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        <span class="sd">&quot;&quot;&quot;Update optimizer learning rate&quot;&quot;&quot;</span>





  
  

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        <span class="bp">self</span><span class="o">.</span><span class="n">scheduler_step_fn</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler_choice</span><span class="p">](</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="p">)</span></div>





  
  

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<div class="viewcode-block" id="Scheduler.get_lr"><a class="viewcode-back" href="../../index.html#pathflowai.schedulers.Scheduler.get_lr">[docs]</a>    <span class="k">def</span> <span class="nf">get_lr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>





  
  

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        <span class="sd">&quot;&quot;&quot;Return current learning rate.</span>





  
  

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<span class="sd">        Returns</span>





  
  

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<span class="sd">        -------</span>





  
  

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<span class="sd">        float</span>





  
  

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<span class="sd">            Current learning rate.</span>





  
  

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<span class="sd">        &quot;&quot;&quot;</span>





  
  

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        <span class="n">lr</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_lr</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler_choice</span> <span class="o">==</span> <span class="s1">&#39;null&#39;</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;lr&#39;</span><span class="p">])</span>





  
  

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





  
  

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</pre></div>





  
  

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