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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import math
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer,
milestones,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
last_epoch=-1,
):
if not list(milestones) == sorted(milestones):
raise ValueError(
"Milestones should be a list of" " increasing integers. Got {}",
milestones,
)
if warmup_method not in ("constant", "linear"):
raise ValueError(
"Only 'constant' or 'linear' warmup_method accepted"
"got {}".format(warmup_method)
)
self.milestones = milestones
self.gamma = gamma
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
return [
base_lr
* warmup_factor
* self.gamma ** bisect_right(self.milestones, self.last_epoch)
for base_lr in self.base_lrs
]
class WarmupCosineAnnealingLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(
self,
optimizer,
max_iters,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
eta_min = 0,
last_epoch=-1,
):
if warmup_method not in ("constant", "linear"):
raise ValueError(
"Only 'constant' or 'linear' warmup_method accepted"
"got {}".format(warmup_method)
)
self.max_iters = max_iters
self.gamma = gamma
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
self.eta_min = eta_min
super(WarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
return [
base_lr
* warmup_factor
for base_lr in self.base_lrs
]
else:
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * (self.last_epoch - self.warmup_iters) / self.max_iters)) / 2
for base_lr in self.base_lrs
]
class WarmupReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
def __init__(
self,
optimizer,
max_iters,
gamma=0.1,
warmup_factor=1.0 / 3,
warmup_iters=500,
warmup_method="linear",
eta_min = 0,
last_epoch=-1,
patience = 5,
verbose = False,
):
if warmup_method not in ("constant", "linear"):
raise ValueError(
"Only 'constant' or 'linear' warmup_method accepted"
"got {}".format(warmup_method)
)
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
self.eta_min = eta_min
if last_epoch == -1:
for group in optimizer.param_groups:
group.setdefault('initial_lr', group['lr'])
else:
for i, group in enumerate(optimizer.param_groups):
if 'initial_lr' not in group:
raise KeyError("param 'initial_lr' is not specified "
"in param_groups[{}] when resuming an optimizer".format(i))
self.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
super(WarmupReduceLROnPlateau, self).__init__(optimizer, factor=gamma, patience=patience, mode='max', min_lr=eta_min, verbose = verbose)
def step(self, metrics=None):
warmup_factor = 1
if self.last_epoch < self.warmup_iters:
if self.warmup_method == "constant":
warmup_factor = self.warmup_factor
elif self.warmup_method == "linear":
alpha = float(self.last_epoch) / self.warmup_iters
warmup_factor = self.warmup_factor * (1 - alpha) + alpha
if self.last_epoch >= self.warmup_iters-1:
warmup_factor = 1.0
warmup_lrs = [
base_lr
* warmup_factor
for base_lr in self.base_lrs
]
for param_group, lr in zip(self.optimizer.param_groups, warmup_lrs):
param_group['lr'] = lr
self.last_epoch += 1
elif metrics:
super().step(metrics) |