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# Chainer SGDR, Cosine Annealing
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from math import cos, pi | |
import numpy | |
from chainer.training import extension | |
class CosineAnnealing(extension.Extension): | |
def __init__(self, lr_max, lr_min=0, T_0=1, T_mult=2, | |
optimizer=None): | |
super(CosineAnnealing, self).__init__() | |
self.lr_max = lr_max | |
self.lr_min = lr_min | |
self.T_0 = T_0 | |
self.T_mult = T_mult | |
self.optimizer = optimizer | |
self._t = 0 | |
self._last_lr = None | |
self._last_t_i = None | |
self._last_T_i = None | |
def initialize(self, trainer): | |
optimizer = self._get_optimizer(trainer) | |
if self._last_lr is not None: # resuming from a snapshot | |
self._update_lr(optimizer, self._last_lr) | |
self._update_t_i(optimizer, self._last_t_i) | |
self._update_T_i(optimizer, self._last_T_i) | |
else: | |
self._update_lr(optimizer, self.lr_max) | |
self._update_t_i(optimizer, 1) | |
self._update_T_i(optimizer, self.T_0) | |
def __call__(self, trainer): | |
self._t += 1 | |
_t = self._t # global iteration | |
lr_max = self.lr_max | |
lr_min = self.lr_min | |
T_0 = self.T_0 | |
T_mult = self.T_mult | |
optimizer = self._get_optimizer(trainer) | |
t_cmsm = _t - numpy.cumsum([T_0*(T_mult**i) for i in range(10)]) | |
# cycle | |
i = numpy.where(t_cmsm<0)[0][0] | |
# local iteration (in cycle i, at global iteration t) | |
T_i = T_0 * (T_mult**i) | |
t_i = int(_t - (T_i - T_0)/(T_mult - 1)) + 1 | |
lr = lr_min + (lr_max-lr_min) * 0.5 * (1 + cos(pi*(t_i-1)/T_i)) | |
self._update_lr(optimizer, lr) | |
self._update_t_i(optimizer, t_i) | |
self._update_T_i(optimizer, T_i) | |
def serialize(self, serializer): | |
self._t = serializer('_t', self._t) | |
self._last_lr = serializer('_last_lr', self._last_lr) | |
self._last_t_i = serializer('_last_t_i', self._last_t_i) | |
self._last_T_i = serializer('_last_T_i', self._last_T_i) | |
if isinstance(self._last_lr, numpy.ndarray): | |
self._last_lr = numpy.asscalar(self._last_lr) | |
def _get_optimizer(self, trainer): | |
return self.optimizer or trainer.updater.get_optimizer('main') | |
def _update_lr(self, optimizer, value): | |
setattr(optimizer, 'lr', value) | |
self._last_lr = value | |
def _update_t_i(self, optimizer, value): | |
setattr(optimizer, 't_i', value) | |
self._last_t_i = value | |
def _update_T_i(self, optimizer, value): | |
setattr(optimizer, 'T_i', value) | |
self._last_T_i = value |
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