minerva.schedulers.warmup_cosine_annealing
==========================================

.. py:module:: minerva.schedulers.warmup_cosine_annealing


Classes
-------

.. autoapisummary::

   minerva.schedulers.warmup_cosine_annealing.WarmupCosineAnnealingLR


Module Contents
---------------

.. py:class:: WarmupCosineAnnealingLR(optimizer, warmup_epochs, total_epochs, min_lr = 0, last_epoch = -1)

   Bases: :py:obj:`torch.optim.lr_scheduler._LRScheduler`


   A custom learning rate scheduler that combines linear warmup with cosine
   annealing. The learning rate increases linearly over the first 'warmup_epochs',
   and then decreases until 'total_epochs' following a cosine curve.

   Initializes the scheduler.

   Parameters
   ----------
   optimizer : torch.optim.optimizer.Optimizer
       Wrapped optimizer.
   warmup_epochs : int
       Number of epochs for linear warmup.
   total_epochs : int
       Total number of training epochs.
   min_lr : float
       Minimum learning rate expected at the end of the cosine annealing.
   last_epoch: int
       Index of the last epoch. Used for resuming training.


   .. py:method:: get_lr()

      Compute the next learning rate for each of the optimizer's
      :attr:`~torch.optim.Optimizer.param_groups`.

      Returns:
          list[float | Tensor]: A :class:`list` of learning rates for each of
          the optimizer's :attr:`~torch.optim.Optimizer.param_groups` with the
          same types as their current ``group["lr"]``\s.

      .. note::
          If you're trying to inspect the most recent learning rate, use
          :meth:`get_last_lr()` instead.

      .. note::
          The returned :class:`~torch.Tensor`\s are copies, and never alias
          the optimizer's ``group["lr"]``\s.



   .. py:attribute:: min_lr
      :value: 0



   .. py:attribute:: total_epochs


   .. py:attribute:: warmup_epochs