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schedulers.py
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Modulates the learning rate during the training process.
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S)ÚCosineAnnealingWithRestartsLRa¬Set the learning rate of each parameter group using a cosine annealing
	schedule, where :math:`\eta_{max}` is set to the initial lr and
	:math:`T_{cur}` is the number of epochs since the last restart in SGDR:
	 .. math::
		 \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 +
		\cos(\frac{T_{cur}}{T_{max}}\pi))
	 When last_epoch=-1, sets initial lr as lr.
	 It has been proposed in
	`SGDR: Stochastic Gradient Descent with Warm Restarts`_. This implements
	the cosine annealing part of SGDR, the restarts and number of iterations multiplier.
	 Args:
		optimizer (Optimizer): Wrapped optimizer.
		T_max (int): Maximum number of iterations.
		T_mult (float): Multiply T_max by this number after each restart. Default: 1.
		eta_min (float): Minimum learning rate. Default: 0.
		last_epoch (int): The index of last epoch. Default: -1.
	 .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
		https://arxiv.org/abs/1608.03983
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	Parameters
	----------
	optimizer : type
		torch.Optimizer object
	opts : type
		Options of setting the learning rate scheduler, see default.

	Attributes
	----------
	schedulers : type
		Different types of schedulers to choose from.
	scheduler_step_fn : type
		How scheduler updates learning rate.
	initial_lr : type
		Initial set learning rate.
	scheduler_choice : type
		What scheduler type was chosen.
	scheduler : type
		Scheduler object chosen that will more directly update optimizer LR.

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