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import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
class MultiStepRestartLR(_LRScheduler):
""" MultiStep with restarts learning rate scheme.
Args:
optimizer (torch.nn.optimizer): Torch optimizer.
milestones (list): Iterations that will decrease learning rate.
gamma (float): Decrease ratio. Default: 0.1.
restarts (list): Restart iterations. Default: [0].
restart_weights (list): Restart weights at each restart iteration.
Default: [1].
last_epoch (int): Used in _LRScheduler. Default: -1.
"""
def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1):
self.milestones = Counter(milestones)
self.gamma = gamma
self.restarts = restarts
self.restart_weights = restart_weights
assert len(self.restarts) == len(self.restart_weights), 'restarts and their weights do not match.'
super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch in self.restarts:
weight = self.restart_weights[self.restarts.index(self.last_epoch)]
return [group['initial_lr'] * weight for group in self.optimizer.param_groups]
if self.last_epoch not in self.milestones:
return [group['lr'] for group in self.optimizer.param_groups]
return [group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups]
def get_position_from_periods(iteration, cumulative_period):
"""Get the position from a period list.
It will return the index of the right-closest number in the period list.
For example, the cumulative_period = [100, 200, 300, 400],
if iteration == 50, return 0;
if iteration == 210, return 2;
if iteration == 300, return 2.
Args:
iteration (int): Current iteration.
cumulative_period (list[int]): Cumulative period list.
Returns:
int: The position of the right-closest number in the period list.
"""
for i, period in enumerate(cumulative_period):
if iteration <= period:
return i
class CosineAnnealingRestartLR(_LRScheduler):
""" Cosine annealing with restarts learning rate scheme.
An example of config:
periods = [10, 10, 10, 10]
restart_weights = [1, 0.5, 0.5, 0.5]
eta_min=1e-7
It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
scheduler will restart with the weights in restart_weights.
Args:
optimizer (torch.nn.optimizer): Torch optimizer.
periods (list): Period for each cosine anneling cycle.
restart_weights (list): Restart weights at each restart iteration.
Default: [1].
eta_min (float): The mimimum lr. Default: 0.
last_epoch (int): Used in _LRScheduler. Default: -1.
"""
def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=0, last_epoch=-1):
self.periods = periods
self.restart_weights = restart_weights
self.eta_min = eta_min
assert (len(self.periods) == len(
self.restart_weights)), 'periods and restart_weights should have the same length.'
self.cumulative_period = [sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))]
super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
idx = get_position_from_periods(self.last_epoch, self.cumulative_period)
current_weight = self.restart_weights[idx]
nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
current_period = self.periods[idx]
return [
self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
(1 + math.cos(math.pi * ((self.last_epoch - nearest_restart) / current_period)))
for base_lr in self.base_lrs
]
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