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from torch.optim.lr_scheduler import CosineAnnealingLR as _CosineAnnealingLR
from torch.optim.lr_scheduler import LRScheduler as _LRScheduler
class _enable_get_lr_call:
def __init__(self, o):
self.o = o
def __enter__(self):
self.o._get_lr_called_within_step = True
return self
def __exit__(self, type, value, traceback):
self.o._get_lr_called_within_step = False
class TwoStageScheduler(_LRScheduler):
def __init__(self, optimizer, after_scheduler: _LRScheduler, last_epoch=-1):
self.after_scheduler = after_scheduler
self.finished = False
super().__init__(optimizer, last_epoch)
def state_dict(self):
state_dict = {
key: value for key, value in self.__dict__.items() if key not in "optimizer"
}
if isinstance(state_dict["after_scheduler"], _LRScheduler):
state_dict["after_scheduler_type"] = type(
state_dict["after_scheduler"]
).__name__
state_dict["after_scheduler_dict"] = state_dict[
"after_scheduler"
].state_dict()
del state_dict["after_scheduler"]
else:
raise NotImplementedError()
return state_dict
def load_state_dict(self, state_dict):
if "after_scheduler_dict" not in state_dict:
warn(
"after_scheduler_dict is not found, skip loading after_scheduler. This may cause unexpected behavior."
)
else:
self.after_scheduler.load_state_dict(state_dict["after_scheduler_dict"])
state_dict = {
key: value
for key, value in state_dict.items()
if key not in ("after_scheduler_type", "after_scheduler_dict")
}
super().load_state_dict(state_dict)
class DelayerScheduler(TwoStageScheduler):
"""Starts with a flat lr schedule until it reaches N epochs then applies
the specific scheduler (For example: ReduceLROnPlateau)
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
delay_epochs (int): Number of epochs to keep the initial lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, delay_epochs, after_scheduler, last_epoch=-1):
if delay_epochs < 0:
raise ValueError(f"delay_epochs must >= 0, got {delay_epochs}")
self.delay_epochs = delay_epochs
super().__init__(optimizer, after_scheduler, last_epoch)
def get_lr(self):
if self.last_epoch >= self.delay_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
self.finished = True
with _enable_get_lr_call(self.after_scheduler):
return self.after_scheduler.get_lr()
return self.base_lrs
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.delay_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super(DelayerScheduler, self).step(epoch)
class WarmupScheduler(TwoStageScheduler):
"""Starts with a linear warmup lr schedule until it reaches N epochs then applies
the specific scheduler (For example: ReduceLROnPlateau).
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
warmup_epochs (int): Number of epochs to linearly warmup lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, warmup_epochs, after_scheduler, last_epoch=-1):
self.warmup_epochs = int(warmup_epochs)
super().__init__(optimizer, after_scheduler, last_epoch)
def get_lr(self):
if self.last_epoch >= self.warmup_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
self.finished = True
return self.after_scheduler.get_lr()
return [(self.last_epoch + 1) / self.warmup_epochs * lr for lr in self.base_lrs]
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.warmup_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super().step(epoch)
class WarmupDelayerScheduler(TwoStageScheduler):
"""Starts with a linear warmup lr schedule until it reaches N epochs and a flat lr schedule
until it reaches M epochs then applies the specific scheduler (For example: ReduceLROnPlateau).
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
warmup_epochs (int): Number of epochs to linearly warmup lr until starting applying the scheduler.
delay_epochs (int): Number of epochs to keep the initial lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(
self, optimizer, warmup_epochs, delay_epochs, after_scheduler, last_epoch=-1
):
if delay_epochs < 0:
raise ValueError(f"delay_epochs must >= 0, got {delay_epochs}")
if warmup_epochs < 0:
raise ValueError(f"warmup_epochs must >= 0, got {warmup_epochs}")
self.warmup_epochs = warmup_epochs
self.delay_epochs = delay_epochs
super().__init__(optimizer, after_scheduler, last_epoch)
def get_lr(self):
if self.last_epoch >= self.warmup_epochs + self.delay_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
# reset lr to base_lr
for group, base_lr in zip(self.optimizer.param_groups, self.base_lrs):
group["lr"] = base_lr
self.finished = True
with _enable_get_lr_call(self.after_scheduler):
return self.after_scheduler.get_lr()
elif self.last_epoch >= self.warmup_epochs:
return self.base_lrs
return [(self.last_epoch + 1) / self.warmup_epochs * lr for lr in self.base_lrs]
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.warmup_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super().step(epoch)
class CosineAnnealingLR(_CosineAnnealingLR):
r"""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::
\begin{aligned}
\eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
+ \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
& T_{cur} \neq (2k+1)T_{max}; \\
\eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
\left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
& T_{cur} = (2k+1)T_{max}.
\end{aligned}
When last_epoch=-1, sets initial lr as lr. Notice that because the schedule
is defined recursively, the learning rate can be simultaneously modified
outside this scheduler by other operators. If the learning rate is set
solely by this scheduler, the learning rate at each step becomes:
.. math::
\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
\cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)
It has been proposed in
`SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
implements the cosine annealing part of SGDR, and not the restarts.
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
eta_min (int, optional): Minimum learning rate, defaults to 0.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(
self,
optimizer,
total_steps: int,
eta_min: int = 0,
last_epoch: int = -1,
**kwargs,
):
super().__init__(optimizer, total_steps, eta_min=eta_min, last_epoch=last_epoch)
class CosineAnnealingWarmupLR(WarmupScheduler):
"""Cosine annealing learning rate scheduler with learning rate warmup. A linear warmup schedule will be applied.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
warmup_steps (int, optional): Number of warmup steps, defaults to 0.
eta_min (int, optional): Minimum learning rate, defaults to 0.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(
self,
optimizer,
total_steps: int,
warmup_steps: int = 0,
eta_min: float = 0.0,
last_epoch: int = -1,
):
base_scheduler = _CosineAnnealingLR(
optimizer,
total_steps - warmup_steps,
eta_min=eta_min,
last_epoch=last_epoch,
)
super().__init__(optimizer, warmup_steps, base_scheduler, last_epoch=last_epoch)
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