Source code for torch.optim.lr_scheduler
# mypy: allow-untyped-defs
import math
import types
import warnings
from bisect import bisect_right
from collections import Counter
from functools import partial
from typing import (
Any,
Callable,
cast,
Dict,
Iterable,
List,
Literal,
Optional,
Sequence,
SupportsFloat,
TypedDict,
Union,
)
from weakref import ref
from torch import inf, Tensor
from .optimizer import Optimizer
__all__ = [
"LambdaLR",
"MultiplicativeLR",
"StepLR",
"MultiStepLR",
"ConstantLR",
"LinearLR",
"ExponentialLR",
"SequentialLR",
"CosineAnnealingLR",
"ChainedScheduler",
"ReduceLROnPlateau",
"CyclicLR",
"CosineAnnealingWarmRestarts",
"OneCycleLR",
"PolynomialLR",
"LRScheduler",
]
EPOCH_DEPRECATION_WARNING = (
"The epoch parameter in `scheduler.step()` was not necessary and is being "
"deprecated where possible. Please use `scheduler.step()` to step the "
"scheduler. During the deprecation, if epoch is different from None, the "
"closed form is used instead of the new chainable form, where available. "
"Please open an issue if you are unable to replicate your use case: "
"https://github.com/pytorch/pytorch/issues/new/choose."
)
def _check_verbose_deprecated_warning(verbose):
"""Raises a warning when verbose is not the default value."""
if verbose != "deprecated":
warnings.warn(
"The verbose parameter is deprecated. Please use get_last_lr() "
"to access the learning rate.",
UserWarning,
)
return verbose
return False
def _format_param(name: str, optimizer: Optimizer, param):
"""Return correctly formatted lr/momentum for each param group."""
def _copy(_param):
return _param.clone() if isinstance(_param, Tensor) else _param
if isinstance(param, (list, tuple)):
if len(param) != len(optimizer.param_groups):
raise ValueError(
f"{name} must have the same length as optimizer.param_groups. "
f"{name} has {len(param)} values, param_groups has {len(optimizer.param_groups)}."
)
else:
param = [param] * len(optimizer.param_groups)
return list(map(_copy, param))
class LRScheduler:
_get_lr_called_within_step: bool = False
def __init__(self, optimizer: Optimizer, last_epoch=-1, verbose="deprecated"):
# Attach optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
self.optimizer = optimizer
# Initialize epoch and base learning rates
if last_epoch == -1:
for group in optimizer.param_groups:
initial_lr = group["lr"]
if isinstance(initial_lr, Tensor):
initial_lr = initial_lr.clone()
group.setdefault("initial_lr", initial_lr)
else:
for i, group in enumerate(optimizer.param_groups):
if "initial_lr" not in group:
raise KeyError(
"param 'initial_lr' is not specified "
f"in param_groups[{i}] when resuming an optimizer"
)
self.base_lrs: List[float] = [
group["initial_lr"] for group in optimizer.param_groups
]
self.last_epoch = last_epoch
# Following https://github.com/pytorch/pytorch/issues/20124
# We would like to ensure that `lr_scheduler.step()` is called after
# `optimizer.step()`
def patch_track_step_called(opt: Optimizer):
if hasattr(opt.step, "_wrapped_by_lr_sched"):
# we've already patched
return opt.step
def wrap_step(step_fn):
opt_ref = ref(self.optimizer)
func = step_fn.__func__
def wrapper(*args, **kwargs):
opt = opt_ref()
opt._opt_called = True # type: ignore[union-attr]
return func.__get__(opt, opt.__class__)(*args, **kwargs)
wrapper._wrapped_by_lr_sched = True # type: ignore[attr-defined]
return wrapper
opt.step = wrap_step(opt.step) # type: ignore[method-assign]
patch_track_step_called(self.optimizer)
self.verbose = _check_verbose_deprecated_warning(verbose)
self._initial_step()
def _initial_step(self):
"""Initialize step counts and performs a step"""
self._step_count = 0
self.step()
def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
"""
return {
key: value for key, value in self.__dict__.items() if key != "optimizer"
}
def load_state_dict(self, state_dict: Dict[str, Any]):
"""Loads the schedulers state.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
self.__dict__.update(state_dict)
def get_last_lr(self) -> List[float]:
"""Return last computed learning rate by current scheduler."""
return self._last_lr
def get_lr(self) -> List[float]:
# Compute learning rate using chainable form of the scheduler
raise NotImplementedError
def print_lr(
self,
is_verbose: bool,
group: Dict[str, Any],
lr: float,
epoch: Optional[int] = None,
):
"""Display the current learning rate.
.. deprecated:: 2.4
``print_lr()`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
"""
warnings.warn(
"`LRScheduler.print_lr()` is being deprecated. To fetch the learning rate, "
"please use `get_last_lr()` instead. For more details, "
"see https://github.com/pytorch/pytorch/issues/99270.",
UserWarning,
)
if is_verbose:
if epoch is None:
print(f"Adjusting learning rate of group {group} to {lr:.4e}.")
else:
epoch_str = ("%.2f" if isinstance(epoch, float) else "%.5d") % epoch
print(
f"Epoch {epoch_str}: adjusting learning rate of group {group} to {lr:.4e}."
)
def step(self, epoch: Optional[int] = None):
# Raise a warning if old pattern is detected
# https://github.com/pytorch/pytorch/issues/20124
if self._step_count == 1:
if not hasattr(self.optimizer.step, "_wrapped_by_lr_sched"):
warnings.warn(
"Seems like `optimizer.step()` has been overridden after learning rate scheduler "
"initialization. Please, make sure to call `optimizer.step()` before "
"`lr_scheduler.step()`. See more details at "
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
UserWarning,
)
# Just check if there were two first lr_scheduler.step() calls before optimizer.step()
elif not getattr(self.optimizer, "_opt_called", False):
warnings.warn(
"Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
"In PyTorch 1.1.0 and later, you should call them in the opposite order: "
"`optimizer.step()` before `lr_scheduler.step()`. Failure to do this "
"will result in PyTorch skipping the first value of the learning rate schedule. "
"See more details at "
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
UserWarning,
)
self._step_count += 1
with _enable_get_lr_call(self):
if epoch is None:
self.last_epoch += 1
values = self.get_lr()
else:
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
self.last_epoch = epoch
if hasattr(self, "_get_closed_form_lr"):
values = cast(List[float], self._get_closed_form_lr())
else:
values = self.get_lr()
for i, data in enumerate(zip(self.optimizer.param_groups, values)):
param_group, lr = data
if isinstance(param_group["lr"], Tensor):
lr_val = lr.item() if isinstance(lr, Tensor) else lr # type: ignore[attr-defined]
param_group["lr"].fill_(lr_val)
else:
param_group["lr"] = lr
self._last_lr: List[float] = [
group["lr"] for group in self.optimizer.param_groups
]
def _warn_get_lr_called_within_step(lr_scheduler: LRScheduler):
if not lr_scheduler._get_lr_called_within_step:
warnings.warn(
"To get the last learning rate computed by the scheduler, "
"please use `get_last_lr()`.",
UserWarning,
stacklevel=2,
)
# Including _LRScheduler for backwards compatibility
# Subclass instead of assign because we want __name__ of _LRScheduler to be _LRScheduler (assigning would make it LRScheduler).
class _LRScheduler(LRScheduler):
pass
class _enable_get_lr_call:
def __init__(self, o: LRScheduler):
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
[docs]class LambdaLR(LRScheduler):
"""Sets the learning rate of each parameter group to the initial lr
times a given function. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
lr_lambda (function or list): A function which computes a multiplicative
factor given an integer parameter epoch, or a list of such
functions, one for each group in optimizer.param_groups.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer has two groups.
>>> lambda1 = lambda epoch: epoch // 30
>>> lambda2 = lambda epoch: 0.95 ** epoch
>>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
last_epoch=-1,
verbose="deprecated",
):
self.optimizer = optimizer
self.lr_lambdas: List[Callable[[int], float]]
if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
else:
if len(lr_lambda) != len(optimizer.param_groups):
raise ValueError(
f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
)
self.lr_lambdas = list(lr_lambda)
super().__init__(optimizer, last_epoch, verbose)
[docs] def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
The learning rate lambda functions will only be saved if they are callable objects
and not if they are functions or lambdas.
When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
"""
state_dict = {
key: value
for key, value in self.__dict__.items()
if key not in ("optimizer", "lr_lambdas")
}
state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)
for idx, fn in enumerate(self.lr_lambdas):
if not isinstance(fn, types.FunctionType):
state_dict["lr_lambdas"][idx] = fn.__dict__.copy()
return state_dict
[docs] def load_state_dict(self, state_dict):
"""Loads the schedulers state.
When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
lr_lambdas = state_dict.pop("lr_lambdas")
self.__dict__.update(state_dict)
# Restore state_dict keys in order to prevent side effects
# https://github.com/pytorch/pytorch/issues/32756
state_dict["lr_lambdas"] = lr_lambdas
for idx, fn in enumerate(lr_lambdas):
if fn is not None:
self.lr_lambdas[idx].__dict__.update(fn)
def get_lr(self):
_warn_get_lr_called_within_step(self)
return [
base_lr * lmbda(self.last_epoch)
for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)
]
[docs]class MultiplicativeLR(LRScheduler):
"""Multiply the learning rate of each parameter group by the factor given
in the specified function. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
lr_lambda (function or list): A function which computes a multiplicative
factor given an integer parameter epoch, or a list of such
functions, one for each group in optimizer.param_groups.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> lmbda = lambda epoch: 0.95
>>> scheduler = MultiplicativeLR(optimizer, lr_lambda=lmbda)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
last_epoch=-1,
verbose="deprecated",
):
self.optimizer = optimizer
self.lr_lambdas: List[Callable[[int], float]]
if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
else:
if len(lr_lambda) != len(optimizer.param_groups):
raise ValueError(
f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
)
self.lr_lambdas = list(lr_lambda)
super().__init__(optimizer, last_epoch, verbose)
[docs] def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
The learning rate lambda functions will only be saved if they are callable objects
and not if they are functions or lambdas.
"""
state_dict = {
key: value
for key, value in self.__dict__.items()
if key not in ("optimizer", "lr_lambdas")
}
state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)
for idx, fn in enumerate(self.lr_lambdas):
if not isinstance(fn, types.FunctionType):
state_dict["lr_lambdas"][idx] = fn.__dict__.copy()
return state_dict
[docs] def load_state_dict(self, state_dict):
"""Loads the schedulers state.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
lr_lambdas = state_dict.pop("lr_lambdas")
self.__dict__.update(state_dict)
# Restore state_dict keys in order to prevent side effects
# https://github.com/pytorch/pytorch/issues/32756
state_dict["lr_lambdas"] = lr_lambdas
for idx, fn in enumerate(lr_lambdas):
if fn is not None:
self.lr_lambdas[idx].__dict__.update(fn)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch > 0:
return [
group["lr"] * lmbda(self.last_epoch)
for lmbda, group in zip(self.lr_lambdas, self.optimizer.param_groups)
]
else:
return [group["lr"] for group in self.optimizer.param_groups]
[docs]class StepLR(LRScheduler):
"""Decays the learning rate of each parameter group by gamma every
step_size epochs. Notice that such decay can happen simultaneously with
other changes to the learning rate from outside this scheduler. When
last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
step_size (int): Period of learning rate decay.
gamma (float): Multiplicative factor of learning rate decay.
Default: 0.1.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 60
>>> # lr = 0.0005 if 60 <= epoch < 90
>>> # ...
>>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
step_size: int,
gamma=0.1,
last_epoch=-1,
verbose="deprecated",
):
self.step_size = step_size
self.gamma = gamma
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if (self.last_epoch == 0) or (self.last_epoch % self.step_size != 0):
return [group["lr"] for group in self.optimizer.param_groups]
return [group["lr"] * self.gamma for group in self.optimizer.param_groups]
def _get_closed_form_lr(self):
return [
base_lr * self.gamma ** (self.last_epoch // self.step_size)
for base_lr in self.base_lrs
]
[docs]class MultiStepLR(LRScheduler):
"""Decays the learning rate of each parameter group by gamma once the
number of epoch reaches one of the milestones. Notice that such decay can
happen simultaneously with other changes to the learning rate from outside
this scheduler. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
milestones (list): List of epoch indices. Must be increasing.
gamma (float): Multiplicative factor of learning rate decay.
Default: 0.1.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 80
>>> # lr = 0.0005 if epoch >= 80
>>> scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
milestones: Iterable[int],
gamma=0.1,
last_epoch=-1,
verbose="deprecated",
):
self.milestones = Counter(milestones)
self.gamma = gamma
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
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_closed_form_lr(self):
milestones = sorted(self.milestones.elements())
return [
base_lr * self.gamma ** bisect_right(milestones, self.last_epoch)
for base_lr in self.base_lrs
]
[docs]class ConstantLR(LRScheduler):
"""Multiply the learning rate of each parameter group by a small constant factor until the
number of epoch reaches a pre-defined milestone: total_iters.
Notice that such multiplication of the small constant factor can
happen simultaneously with other changes to the learning rate from outside this scheduler.
When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
factor (float): The number we multiply learning rate until the milestone. Default: 1./3.
total_iters (int): The number of steps that the scheduler multiplies the learning rate by the factor.
Default: 5.
last_epoch (int): The index of the last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.025 if epoch == 0
>>> # lr = 0.025 if epoch == 1
>>> # lr = 0.025 if epoch == 2
>>> # lr = 0.025 if epoch == 3
>>> # lr = 0.05 if epoch >= 4
>>> scheduler = ConstantLR(optimizer, factor=0.5, total_iters=4)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
factor=1.0 / 3,
total_iters=5,
last_epoch=-1,
verbose="deprecated",
):
if factor > 1.0 or factor < 0:
raise ValueError(
"Constant multiplicative factor expected to be between 0 and 1."
)
self.factor = factor
self.total_iters = total_iters
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch == 0:
return [group["lr"] * self.factor for group in self.optimizer.param_groups]
if self.last_epoch != self.total_iters:
return [group["lr"] for group in self.optimizer.param_groups]
return [
group["lr"] * (1.0 / self.factor) for group in self.optimizer.param_groups
]
def _get_closed_form_lr(self):
return [
base_lr
* (self.factor + (self.last_epoch >= self.total_iters) * (1 - self.factor))
for base_lr in self.base_lrs
]
[docs]class LinearLR(LRScheduler):
"""Decays the learning rate of each parameter group by linearly changing small
multiplicative factor until the number of epoch reaches a pre-defined milestone: total_iters.
Notice that such decay can happen simultaneously with other changes to the learning rate
from outside this scheduler. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
start_factor (float): The number we multiply learning rate in the first epoch.
The multiplication factor changes towards end_factor in the following epochs.
Default: 1./3.
end_factor (float): The number we multiply learning rate at the end of linear changing
process. Default: 1.0.
total_iters (int): The number of iterations that multiplicative factor reaches to 1.
Default: 5.
last_epoch (int): The index of the last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.025 if epoch == 0
>>> # lr = 0.03125 if epoch == 1
>>> # lr = 0.0375 if epoch == 2
>>> # lr = 0.04375 if epoch == 3
>>> # lr = 0.05 if epoch >= 4
>>> scheduler = LinearLR(optimizer, start_factor=0.5, total_iters=4)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
start_factor=1.0 / 3,
end_factor=1.0,
total_iters=5,
last_epoch=-1,
verbose="deprecated",
):
if start_factor > 1.0 or start_factor <= 0:
raise ValueError(
"Starting multiplicative factor expected to be greater than 0 and less or equal to 1."
)
if end_factor > 1.0 or end_factor < 0:
raise ValueError(
"Ending multiplicative factor expected to be between 0 and 1."
)
self.start_factor = start_factor
self.end_factor = end_factor
self.total_iters = total_iters
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch == 0:
return [
group["lr"] * self.start_factor for group in self.optimizer.param_groups
]
if self.last_epoch > self.total_iters:
return [group["lr"] for group in self.optimizer.param_groups]
return [
group["lr"]
* (
1.0
+ (self.end_factor - self.start_factor)
/ (
self.total_iters * self.start_factor
+ (self.last_epoch - 1) * (self.end_factor - self.start_factor)
)
)
for group in self.optimizer.param_groups
]
def _get_closed_form_lr(self):
return [
base_lr
* (
self.start_factor
+ (self.end_factor - self.start_factor)
* min(self.total_iters, self.last_epoch)
/ self.total_iters
)
for base_lr in self.base_lrs
]
[docs]class ExponentialLR(LRScheduler):
"""Decays the learning rate of each parameter group by gamma every epoch.
When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
gamma (float): Multiplicative factor of learning rate decay.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
"""
def __init__(
self, optimizer: Optimizer, gamma: float, last_epoch=-1, verbose="deprecated"
):
self.gamma = gamma
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch == 0:
return [group["lr"] for group in self.optimizer.param_groups]
return [group["lr"] * self.gamma for group in self.optimizer.param_groups]
def _get_closed_form_lr(self):
return [base_lr * self.gamma**self.last_epoch for base_lr in self.base_lrs]
[docs]class SequentialLR(LRScheduler):
"""Receives the list of schedulers that is expected to be called sequentially during
optimization process and milestone points that provides exact intervals to reflect
which scheduler is supposed to be called at a given epoch.
Args:
optimizer (Optimizer): Wrapped optimizer.
schedulers (list): List of chained schedulers.
milestones (list): List of integers that reflects milestone points.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): Does nothing.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 1. for all groups
>>> # lr = 0.1 if epoch == 0
>>> # lr = 0.1 if epoch == 1
>>> # lr = 0.9 if epoch == 2
>>> # lr = 0.81 if epoch == 3
>>> # lr = 0.729 if epoch == 4
>>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
>>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
>>> scheduler = SequentialLR(optimizer, schedulers=[scheduler1, scheduler2], milestones=[2])
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
schedulers: List[LRScheduler],
milestones: List[int],
last_epoch=-1,
verbose="deprecated",
):
if len(schedulers) < 1:
raise ValueError(
f"{self.__class__.__name__} expects at least one scheduler, but got no scheduler."
)
for scheduler_idx, scheduler in enumerate(schedulers):
if not hasattr(scheduler, "optimizer"):
raise TypeError(
f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
)
if isinstance(scheduler, ReduceLROnPlateau):
raise ValueError(
f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
"requires additional kwargs to be specified when calling `step`, "
f"but got one at index {scheduler_idx} in the given schedulers sequence."
)
if optimizer != scheduler.optimizer:
raise ValueError(
f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
f"which is different from {optimizer.__class__.__name__}."
)
if len(milestones) != len(schedulers) - 1:
raise ValueError(
"Sequential Schedulers expects number of schedulers provided to be one more "
f"than the number of milestone points, but got number of schedulers {len(schedulers)} and the "
f"number of milestones to be equal to {len(milestones)}"
)
_check_verbose_deprecated_warning(verbose)
self._schedulers = schedulers
self._milestones = milestones
self.last_epoch = last_epoch + 1
self.optimizer = optimizer
# Reset learning rates back to initial values
for group in self.optimizer.param_groups:
group["lr"] = group["initial_lr"]
# "Undo" the step performed by other schedulers
for scheduler in self._schedulers:
scheduler.last_epoch -= 1
# Perform the initial step for only the first scheduler
self._schedulers[0]._initial_step()
self._last_lr = schedulers[0].get_last_lr()
def step(self):
self.last_epoch += 1
idx = bisect_right(self._milestones, self.last_epoch)
scheduler = self._schedulers[idx]
if idx > 0 and self._milestones[idx - 1] == self.last_epoch:
scheduler.step(0)
else:
scheduler.step()
self._last_lr = scheduler.get_last_lr()
[docs] def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
The wrapped scheduler states will also be saved.
"""
state_dict = {
key: value
for key, value in self.__dict__.items()
if key not in ("optimizer", "_schedulers")
}
state_dict["_schedulers"] = [None] * len(self._schedulers)
for idx, s in enumerate(self._schedulers):
state_dict["_schedulers"][idx] = s.state_dict()
return state_dict
[docs] def load_state_dict(self, state_dict):
"""Loads the schedulers state.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
_schedulers = state_dict.pop("_schedulers")
self.__dict__.update(state_dict)
# Restore state_dict keys in order to prevent side effects
# https://github.com/pytorch/pytorch/issues/32756
state_dict["_schedulers"] = _schedulers
for idx, s in enumerate(_schedulers):
self._schedulers[idx].load_state_dict(s)
[docs]class PolynomialLR(LRScheduler):
"""Decays the learning rate of each parameter group using a polynomial function
in the given total_iters. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
total_iters (int): The number of steps that the scheduler decays the learning rate. Default: 5.
power (float): The power of the polynomial. Default: 1.0.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP("undefined vars")
>>> # Assuming optimizer uses lr = 0.001 for all groups
>>> # lr = 0.001 if epoch == 0
>>> # lr = 0.00075 if epoch == 1
>>> # lr = 0.00050 if epoch == 2
>>> # lr = 0.00025 if epoch == 3
>>> # lr = 0.0 if epoch >= 4
>>> scheduler = PolynomialLR(optimizer, total_iters=4, power=1.0)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self,
optimizer: Optimizer,
total_iters=5,
power=1.0,
last_epoch=-1,
verbose="deprecated",
):
self.total_iters = total_iters
self.power = power
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch == 0 or self.last_epoch > self.total_iters:
return [group["lr"] for group in self.optimizer.param_groups]
decay_factor = (
(1.0 - self.last_epoch / self.total_iters)
/ (1.0 - (self.last_epoch - 1) / self.total_iters)
) ** self.power
return [group["lr"] * decay_factor for group in self.optimizer.param_groups]
def _get_closed_form_lr(self):
return [
(
base_lr
* (1.0 - min(self.total_iters, self.last_epoch) / self.total_iters)
** self.power
)
for base_lr in self.base_lrs
]
[docs]class CosineAnnealingLR(LRScheduler):
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.
Args:
optimizer (Optimizer): Wrapped optimizer.
T_max (int): Maximum number of iterations.
eta_min (float): Minimum learning rate. Default: 0.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
"""
def __init__(
self,
optimizer: Optimizer,
T_max: int,
eta_min=0,
last_epoch=-1,
verbose="deprecated",
):
self.T_max = T_max
self.eta_min = eta_min
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
if self.last_epoch == 0:
return [group["lr"] for group in self.optimizer.param_groups]
elif self._step_count == 1 and self.last_epoch > 0:
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos((self.last_epoch) * math.pi / self.T_max))
/ 2
for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
]
elif (self.last_epoch - 1 - self.T_max) % (2 * self.T_max) == 0:
return [
group["lr"]
+ (base_lr - self.eta_min) * (1 - math.cos(math.pi / self.T_max)) / 2
for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
]
return [
(1 + math.cos(math.pi * self.last_epoch / self.T_max))
/ (1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max))
* (group["lr"] - self.eta_min)
+ self.eta_min
for group in self.optimizer.param_groups
]
def _get_closed_form_lr(self):
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * self.last_epoch / self.T_max))
/ 2
for base_lr in self.base_lrs
]
[docs]class ChainedScheduler(LRScheduler):
"""Chains list of learning rate schedulers. It takes a sequence of chainable learning
rate schedulers and performs consecutive step() functions belonging to them by just
one call.
Args:
schedulers (sequence): sequence of chained schedulers.
optimizer (Optimizer, optional): Wrapped optimizer. Default: None.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 1. for all groups
>>> # lr = 0.09 if epoch == 0
>>> # lr = 0.081 if epoch == 1
>>> # lr = 0.729 if epoch == 2
>>> # lr = 0.6561 if epoch == 3
>>> # lr = 0.59049 if epoch >= 4
>>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
>>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
>>> scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(
self, schedulers: Sequence[LRScheduler], optimizer: Optional[Optimizer] = None
):
if len(schedulers) < 1:
raise ValueError(
f"{self.__class__.__name__} expects at least one scheduler to be chained, but got no scheduler."
)
optimizer = optimizer or schedulers[0].optimizer
for scheduler_idx, scheduler in enumerate(schedulers):
if not hasattr(scheduler, "optimizer"):
raise TypeError(
f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
)
if isinstance(scheduler, ReduceLROnPlateau):
raise ValueError(
f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
"requires additional kwargs to be specified when calling `step`, "
f"but got one at index {scheduler_idx} in the given schedulers sequence."
)
if optimizer != scheduler.optimizer:
raise ValueError(
f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
f"which is different from {optimizer.__class__.__name__}."
)
self._schedulers = schedulers
self.optimizer = optimizer
self._last_lr = [
group["lr"] for group in self._schedulers[-1].optimizer.param_groups
]
def step(self):
for scheduler in self._schedulers:
scheduler.step()
self._last_lr = [
group["lr"] for group in self._schedulers[-1].optimizer.param_groups
]
[docs] def state_dict(self):
"""Returns the state of the scheduler as a :class:`dict`.
It contains an entry for every variable in self.__dict__ which
is not the optimizer.
The wrapped scheduler states will also be saved.
"""
state_dict = {
key: value
for key, value in self.__dict__.items()
if key not in ("optimizer", "_schedulers")
}
state_dict["_schedulers"] = [None] * len(self._schedulers)
for idx, s in enumerate(self._schedulers):
state_dict["_schedulers"][idx] = s.state_dict()
return state_dict
[docs] def load_state_dict(self, state_dict):
"""Loads the schedulers state.
Args:
state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.
"""
_schedulers = state_dict.pop("_schedulers")
self.__dict__.update(state_dict)
# Restore state_dict keys in order to prevent side effects
# https://github.com/pytorch/pytorch/issues/32756
state_dict["_schedulers"] = _schedulers
for idx, s in enumerate(_schedulers):
self._schedulers[idx].load_state_dict(s)
[docs]class ReduceLROnPlateau(LRScheduler):
"""Reduce learning rate when a metric has stopped improving.
Models often benefit from reducing the learning rate by a factor
of 2-10 once learning stagnates. This scheduler reads a metrics
quantity and if no improvement is seen for a 'patience' number
of epochs, the learning rate is reduced.
Args:
optimizer (Optimizer): Wrapped optimizer.
mode (str): One of `min`, `max`. In `min` mode, lr will
be reduced when the quantity monitored has stopped
decreasing; in `max` mode it will be reduced when the
quantity monitored has stopped increasing. Default: 'min'.
factor (float): Factor by which the learning rate will be
reduced. new_lr = lr * factor. Default: 0.1.
patience (int): The number of allowed epochs with no improvement after
which the learning rate will be reduced.
For example, consider the case of having no patience (`patience = 0`).
In the first epoch, a baseline is established and is always considered good as there's no previous baseline.
In the second epoch, if the performance is worse than the baseline,
we have what is considered an intolerable epoch.
Since the count of intolerable epochs (1) is greater than the patience level (0),
the learning rate is reduced at the end of this epoch.
From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch
if the performance is worse than the baseline. If the performance improves or remains the same,
the learning rate is not adjusted.
Default: 10.
threshold (float): Threshold for measuring the new optimum,
to only focus on significant changes. Default: 1e-4.
threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
dynamic_threshold = best * ( 1 + threshold ) in 'max'
mode or best * ( 1 - threshold ) in `min` mode.
In `abs` mode, dynamic_threshold = best + threshold in
`max` mode or best - threshold in `min` mode. Default: 'rel'.
cooldown (int): Number of epochs to wait before resuming
normal operation after lr has been reduced. Default: 0.
min_lr (float or list): A scalar or a list of scalars. A
lower bound on the learning rate of all param groups
or each group respectively. Default: 0.
eps (float): Minimal decay applied to lr. If the difference
between new and old lr is smaller than eps, the update is
ignored. Default: 1e-8.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> scheduler = ReduceLROnPlateau(optimizer, 'min')
>>> for epoch in range(10):
>>> train(...)
>>> val_loss = validate(...)
>>> # Note that step should be called after validate()
>>> scheduler.step(val_loss)
"""
def __init__(
self,
optimizer: Optimizer,
mode: Literal["min", "max"] = "min",
factor=0.1,
patience=10,
threshold=1e-4,
threshold_mode: Literal["rel", "abs"] = "rel",
cooldown=0,
min_lr: Union[List[float], float] = 0,
eps=1e-8,
verbose="deprecated",
):
if factor >= 1.0:
raise ValueError("Factor should be < 1.0.")
self.factor = factor
# Attach optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
self.optimizer = optimizer
if isinstance(min_lr, (list, tuple)):
if len(min_lr) != len(optimizer.param_groups):
raise ValueError(
f"expected {len(optimizer.param_groups)} min_lrs, got {len(min_lr)}"
)
self.min_lrs = list(min_lr)
else:
self.min_lrs = [min_lr] * len(optimizer.param_groups)
self.patience = patience
self.verbose = _check_verbose_deprecated_warning(verbose)
self.cooldown = cooldown
self.cooldown_counter = 0
self.mode = mode
self.threshold = threshold
self.threshold_mode = threshold_mode
self.best: float
self.num_bad_epochs: int
self.mode_worse: float # the worse value for the chosen mode
self.eps = eps
self.last_epoch = 0
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
self._init_is_better(
mode=mode, threshold=threshold, threshold_mode=threshold_mode
)
self._reset()
def _reset(self):
"""Resets num_bad_epochs counter and cooldown counter."""
self.best = self.mode_worse
self.cooldown_counter = 0
self.num_bad_epochs = 0
def step(self, metrics: SupportsFloat, epoch=None): # type: ignore[override]
# convert `metrics` to float, in case it's a zero-dim Tensor
current = float(metrics)
if epoch is None:
epoch = self.last_epoch + 1
else:
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
self.last_epoch = epoch
if self.is_better(current, self.best):
self.best = current
self.num_bad_epochs = 0
else:
self.num_bad_epochs += 1
if self.in_cooldown:
self.cooldown_counter -= 1
self.num_bad_epochs = 0 # ignore any bad epochs in cooldown
if self.num_bad_epochs > self.patience:
self._reduce_lr(epoch)
self.cooldown_counter = self.cooldown
self.num_bad_epochs = 0
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
def _reduce_lr(self, epoch):
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group["lr"])
new_lr = max(old_lr * self.factor, self.min_lrs[i])
if old_lr - new_lr > self.eps:
param_group["lr"] = new_lr
@property
def in_cooldown(self):
return self.cooldown_counter > 0
def is_better(self, a, best):
if self.mode == "min" and self.threshold_mode == "rel":
rel_epsilon = 1.0 - self.threshold
return a < best * rel_epsilon
elif self.mode == "min" and self.threshold_mode == "abs":
return a < best - self.threshold
elif self.mode == "max" and self.threshold_mode == "rel":
rel_epsilon = self.threshold + 1.0
return a > best * rel_epsilon
else: # mode == 'max' and epsilon_mode == 'abs':
return a > best + self.threshold
def _init_is_better(self, mode, threshold, threshold_mode):
if mode not in {"min", "max"}:
raise ValueError("mode " + mode + " is unknown!")
if threshold_mode not in {"rel", "abs"}:
raise ValueError("threshold mode " + threshold_mode + " is unknown!")
if mode == "min":
self.mode_worse = inf
else: # mode == 'max':
self.mode_worse = -inf
self.mode = mode
self.threshold = threshold
self.threshold_mode = threshold_mode
def state_dict(self):
return {
key: value for key, value in self.__dict__.items() if key != "optimizer"
}
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
self._init_is_better(
mode=self.mode, threshold=self.threshold, threshold_mode=self.threshold_mode
)
[docs]class CyclicLR(LRScheduler):
r"""Sets the learning rate of each parameter group according to
cyclical learning rate policy (CLR). The policy cycles the learning
rate between two boundaries with a constant frequency, as detailed in
the paper `Cyclical Learning Rates for Training Neural Networks`_.
The distance between the two boundaries can be scaled on a per-iteration
or per-cycle basis.
Cyclical learning rate policy changes the learning rate after every batch.
`step` should be called after a batch has been used for training.
This class has three built-in policies, as put forth in the paper:
* "triangular": A basic triangular cycle without amplitude scaling.
* "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
* "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
at each cycle iteration.
This implementation was adapted from the github repo: `bckenstler/CLR`_
Args:
optimizer (Optimizer): Wrapped optimizer.
base_lr (float or list): Initial learning rate which is the
lower boundary in the cycle for each parameter group.
max_lr (float or list): Upper learning rate boundaries in the cycle
for each parameter group. Functionally,
it defines the cycle amplitude (max_lr - base_lr).
The lr at any cycle is the sum of base_lr
and some scaling of the amplitude; therefore
max_lr may not actually be reached depending on
scaling function.
step_size_up (int): Number of training iterations in the
increasing half of a cycle. Default: 2000
step_size_down (int): Number of training iterations in the
decreasing half of a cycle. If step_size_down is None,
it is set to step_size_up. Default: None
mode (str): One of {triangular, triangular2, exp_range}.
Values correspond to policies detailed above.
If scale_fn is not None, this argument is ignored.
Default: 'triangular'
gamma (float): Constant in 'exp_range' scaling function:
gamma**(cycle iterations)
Default: 1.0
scale_fn (function): Custom scaling policy defined by a single
argument lambda function, where
0 <= scale_fn(x) <= 1 for all x >= 0.
If specified, then 'mode' is ignored.
Default: None
scale_mode (str): {'cycle', 'iterations'}.
Defines whether scale_fn is evaluated on
cycle number or cycle iterations (training
iterations since start of cycle).
Default: 'cycle'
cycle_momentum (bool): If ``True``, momentum is cycled inversely
to learning rate between 'base_momentum' and 'max_momentum'.
Default: True
base_momentum (float or list): Lower momentum boundaries in the cycle
for each parameter group. Note that momentum is cycled inversely
to learning rate; at the peak of a cycle, momentum is
'base_momentum' and learning rate is 'max_lr'.
Default: 0.8
max_momentum (float or list): Upper momentum boundaries in the cycle
for each parameter group. Functionally,
it defines the cycle amplitude (max_momentum - base_momentum).
The momentum at any cycle is the difference of max_momentum
and some scaling of the amplitude; therefore
base_momentum may not actually be reached depending on
scaling function. Note that momentum is cycled inversely
to learning rate; at the start of a cycle, momentum is 'max_momentum'
and learning rate is 'base_lr'
Default: 0.9
last_epoch (int): The index of the last batch. This parameter is used when
resuming a training job. Since `step()` should be invoked after each
batch instead of after each epoch, this number represents the total
number of *batches* computed, not the total number of epochs computed.
When last_epoch=-1, the schedule is started from the beginning.
Default: -1
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01, max_lr=0.1)
>>> data_loader = torch.utils.data.DataLoader(...)
>>> for epoch in range(10):
>>> for batch in data_loader:
>>> train_batch(...)
>>> scheduler.step()
.. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
.. _bckenstler/CLR: https://github.com/bckenstler/CLR
"""
def __init__(
self,
optimizer: Optimizer,
base_lr: Union[float, List[float]],
max_lr: Union[float, List[float]],
step_size_up=2000,
step_size_down: Optional[int] = None,
mode: Literal["triangular", "triangular2", "exp_range"] = "triangular",
gamma=1.0,
scale_fn: Optional[Callable[[float], float]] = None,
scale_mode: Literal["cycle", "iterations"] = "cycle",
cycle_momentum=True,
base_momentum=0.8,
max_momentum=0.9,
last_epoch=-1,
verbose="deprecated",
):
# Attach optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
self.optimizer = optimizer
base_lrs = _format_param("base_lr", optimizer, base_lr)
if last_epoch == -1:
for lr, group in zip(base_lrs, optimizer.param_groups):
if isinstance(group["lr"], Tensor):
lr_val = lr.item() if isinstance(lr, Tensor) else lr
group["lr"].fill_(lr_val)
else:
group["lr"] = lr
self.max_lrs = _format_param("max_lr", optimizer, max_lr)
step_size_up = float(step_size_up)
step_size_down = (
float(step_size_down) if step_size_down is not None else step_size_up
)
self.total_size = step_size_up + step_size_down
self.step_ratio = step_size_up / self.total_size
if mode not in ["triangular", "triangular2", "exp_range"] and scale_fn is None:
raise ValueError("mode is invalid and scale_fn is None")
self.mode = mode
self.gamma = gamma
self._scale_fn_ref: Callable[[float], float]
self._scale_fn_custom = scale_fn
self.scale_mode = scale_mode
self._init_scale_fn()
self.cycle_momentum = cycle_momentum
if cycle_momentum:
if (
"momentum" not in optimizer.defaults
and "betas" not in optimizer.defaults
):
raise ValueError(
"optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
)
self.use_beta1 = "betas" in self.optimizer.defaults
self.base_momentums = _format_param(
"base_momentum", optimizer, base_momentum
)
self.max_momentums = _format_param("max_momentum", optimizer, max_momentum)
if last_epoch == -1:
for m_momentum, b_momentum, group in zip(
self.max_momentums, self.base_momentums, optimizer.param_groups
):
if self.use_beta1:
group["betas"] = (m_momentum, *group["betas"][1:])
else:
group["momentum"] = m_momentum
group["max_momentum"] = m_momentum
group["base_momentum"] = b_momentum
super().__init__(optimizer, last_epoch, verbose)
self.base_lrs = base_lrs
def _init_scale_fn(self):
if self._scale_fn_custom is not None:
return
if self.mode == "triangular":
self._scale_fn_ref = self._triangular_scale_fn
self.scale_mode = "cycle"
elif self.mode == "triangular2":
self._scale_fn_ref = self._triangular2_scale_fn
self.scale_mode = "cycle"
elif self.mode == "exp_range":
self._scale_fn_ref = partial(self._exp_range_scale_fn, self.gamma)
self.scale_mode = "iterations"
def scale_fn(self, x) -> float:
if self._scale_fn_custom is not None:
return self._scale_fn_custom(x)
else:
return self._scale_fn_ref(x) # static method
@staticmethod
def _triangular_scale_fn(x: float) -> float:
return 1.0
@staticmethod
def _triangular2_scale_fn(x: float) -> float:
return 1 / (2.0 ** (x - 1))
@staticmethod
def _exp_range_scale_fn(gamma: float, x: float) -> float:
return gamma**x
[docs] def get_lr(self):
"""Calculates the learning rate at batch index. This function treats
`self.last_epoch` as the last batch index.
If `self.cycle_momentum` is ``True``, this function has a side effect of
updating the optimizer's momentum.
"""
_warn_get_lr_called_within_step(self)
cycle = math.floor(1 + self.last_epoch / self.total_size)
x = 1.0 + self.last_epoch / self.total_size - cycle
if x <= self.step_ratio:
scale_factor = x / self.step_ratio
else:
scale_factor = (x - 1) / (self.step_ratio - 1)
lrs = []
for base_lr, max_lr in zip(self.base_lrs, self.max_lrs):
base_height = (max_lr - base_lr) * scale_factor
if self.scale_mode == "cycle":
lr = base_lr + base_height * self.scale_fn(cycle)
else:
lr = base_lr + base_height * self.scale_fn(self.last_epoch)
lrs.append(lr)
if self.cycle_momentum:
momentums = []
for base_momentum, max_momentum in zip(
self.base_momentums, self.max_momentums
):
base_height = (max_momentum - base_momentum) * scale_factor
if self.scale_mode == "cycle":
momentum = max_momentum - base_height * self.scale_fn(cycle)
else:
momentum = max_momentum - base_height * self.scale_fn(
self.last_epoch
)
momentums.append(momentum)
for param_group, momentum in zip(self.optimizer.param_groups, momentums):
if self.use_beta1:
param_group["betas"] = (momentum, *param_group["betas"][1:])
else:
param_group["momentum"] = momentum
return lrs
def state_dict(self):
state = super().state_dict()
# We are dropping the `_scale_fn_ref` attribute because it is a
# `weakref.WeakMethod` and can't be pickled.
state.pop("_scale_fn_ref", None)
fn = state.pop("_scale_fn_custom")
state["_scale_fn_custom"] = None
if fn is not None and not isinstance(fn, types.FunctionType):
# The _scale_fn_custom will only be saved if it is a callable object
# and not if it is a function or lambda.
state["_scale_fn_custom"] = fn.__dict__.copy()
return state
def load_state_dict(self, state_dict):
fn = state_dict.pop("_scale_fn_custom")
super().load_state_dict(state_dict)
if fn is not None:
self._scale_fn_custom.__dict__.update(fn)
self._init_scale_fn()
[docs]class CosineAnnealingWarmRestarts(LRScheduler):
r"""Set the learning rate of each parameter group using a cosine annealing
schedule, where :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
is the number of epochs since the last restart and :math:`T_{i}` is the number
of epochs between two warm restarts in SGDR:
.. math::
\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
\cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
It has been proposed in
`SGDR: Stochastic Gradient Descent with Warm Restarts`_.
Args:
optimizer (Optimizer): Wrapped optimizer.
T_0 (int): Number of iterations until the first restart.
T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1.
eta_min (float, optional): Minimum learning rate. Default: 0.
last_epoch (int, optional): The index of the last epoch. Default: -1.
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
"""
def __init__(
self,
optimizer: Optimizer,
T_0: int,
T_mult=1,
eta_min=0,
last_epoch=-1,
verbose="deprecated",
):
if T_0 <= 0 or not isinstance(T_0, int):
raise ValueError(f"Expected positive integer T_0, but got {T_0}")
if T_mult < 1 or not isinstance(T_mult, int):
raise ValueError(f"Expected integer T_mult >= 1, but got {T_mult}")
if not isinstance(eta_min, (float, int)):
raise ValueError(
f"Expected float or int eta_min, but got {eta_min} of type {type(eta_min)}"
)
self.T_0 = T_0
self.T_i = T_0
self.T_mult = T_mult
self.eta_min = eta_min
self.T_cur = last_epoch
super().__init__(optimizer, last_epoch, verbose)
def get_lr(self):
_warn_get_lr_called_within_step(self)
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * self.T_cur / self.T_i))
/ 2
for base_lr in self.base_lrs
]
[docs] def step(self, epoch=None):
"""Step could be called after every batch update
Example:
>>> # xdoctest: +SKIP("Undefined vars")
>>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> iters = len(dataloader)
>>> for epoch in range(20):
>>> for i, sample in enumerate(dataloader):
>>> inputs, labels = sample['inputs'], sample['labels']
>>> optimizer.zero_grad()
>>> outputs = net(inputs)
>>> loss = criterion(outputs, labels)
>>> loss.backward()
>>> optimizer.step()
>>> scheduler.step(epoch + i / iters)
This function can be called in an interleaved way.
Example:
>>> # xdoctest: +SKIP("Undefined vars")
>>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> for epoch in range(20):
>>> scheduler.step()
>>> scheduler.step(26)
>>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
"""
if epoch is None and self.last_epoch < 0:
epoch = 0
if epoch is None:
epoch = self.last_epoch + 1
self.T_cur = self.T_cur + 1
if self.T_cur >= self.T_i:
self.T_cur = self.T_cur - self.T_i
self.T_i = self.T_i * self.T_mult
else:
if epoch < 0:
raise ValueError(f"Expected non-negative epoch, but got {epoch}")
if epoch >= self.T_0:
if self.T_mult == 1:
self.T_cur = epoch % self.T_0
else:
n = int(
math.log(
(epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult
)
)
self.T_cur = epoch - self.T_0 * (self.T_mult**n - 1) / (
self.T_mult - 1
)
self.T_i = self.T_0 * self.T_mult ** (n)
else:
self.T_i = self.T_0
self.T_cur = epoch
self.last_epoch = math.floor(epoch)
with _enable_get_lr_call(self):
for i, data in enumerate(zip(self.optimizer.param_groups, self.get_lr())):
param_group, lr = data
param_group["lr"] = lr
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
class _SchedulePhase(TypedDict):
end_step: float
start_lr: str
end_lr: str
start_momentum: str
end_momentum: str
[docs]class OneCycleLR(LRScheduler):
r"""Sets the learning rate of each parameter group according to the
1cycle learning rate policy. The 1cycle policy anneals the learning
rate from an initial learning rate to some maximum learning rate and then
from that maximum learning rate to some minimum learning rate much lower
than the initial learning rate.
This policy was initially described in the paper `Super-Convergence:
Very Fast Training of Neural Networks Using Large Learning Rates`_.
The 1cycle learning rate policy changes the learning rate after every batch.
`step` should be called after a batch has been used for training.
This scheduler is not chainable.
Note also that the total number of steps in the cycle can be determined in one
of two ways (listed in order of precedence):
#. A value for total_steps is explicitly provided.
#. A number of epochs (epochs) and a number of steps per epoch
(steps_per_epoch) are provided.
In this case, the number of total steps is inferred by
total_steps = epochs * steps_per_epoch
You must either provide a value for total_steps or provide a value for both
epochs and steps_per_epoch.
The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
claims that "unpublished work has shown even better results by using only two phases". To
mimic the behaviour of the original paper instead, set ``three_phase=True``.
Args:
optimizer (Optimizer): Wrapped optimizer.
max_lr (float or list): Upper learning rate boundaries in the cycle
for each parameter group.
total_steps (int): The total number of steps in the cycle. Note that
if a value is not provided here, then it must be inferred by providing
a value for epochs and steps_per_epoch.
Default: None
epochs (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle
if a value for total_steps is not provided.
Default: None
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the
cycle if a value for total_steps is not provided.
Default: None
pct_start (float): The percentage of the cycle (in number of steps) spent
increasing the learning rate.
Default: 0.3
anneal_strategy (str): {'cos', 'linear'}
Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
linear annealing.
Default: 'cos'
cycle_momentum (bool): If ``True``, momentum is cycled inversely
to learning rate between 'base_momentum' and 'max_momentum'.
Default: True
base_momentum (float or list): Lower momentum boundaries in the cycle
for each parameter group. Note that momentum is cycled inversely
to learning rate; at the peak of a cycle, momentum is
'base_momentum' and learning rate is 'max_lr'.
Default: 0.85
max_momentum (float or list): Upper momentum boundaries in the cycle
for each parameter group. Functionally,
it defines the cycle amplitude (max_momentum - base_momentum).
Note that momentum is cycled inversely
to learning rate; at the start of a cycle, momentum is 'max_momentum'
and learning rate is 'base_lr'
Default: 0.95
div_factor (float): Determines the initial learning rate via
initial_lr = max_lr/div_factor
Default: 25
final_div_factor (float): Determines the minimum learning rate via
min_lr = initial_lr/final_div_factor
Default: 1e4
three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
learning rate according to 'final_div_factor' instead of modifying the second
phase (the first two phases will be symmetrical about the step indicated by
'pct_start').
last_epoch (int): The index of the last batch. This parameter is used when
resuming a training job. Since `step()` should be invoked after each
batch instead of after each epoch, this number represents the total
number of *batches* computed, not the total number of epochs computed.
When last_epoch=-1, the schedule is started from the beginning.
Default: -1
verbose (bool | str): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. deprecated:: 2.2
``verbose`` is deprecated. Please use ``get_last_lr()`` to access the
learning rate.
Example:
>>> # xdoctest: +SKIP
>>> data_loader = torch.utils.data.DataLoader(...)
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10)
>>> for epoch in range(10):
>>> for batch in data_loader:
>>> train_batch(...)
>>> optimizer.step()
>>> scheduler.step()
.. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
https://arxiv.org/abs/1708.07120
"""
def __init__(
self,
optimizer: Optimizer,
max_lr: Union[float, List[float]],
total_steps: Optional[int] = None,
epochs: Optional[int] = None,
steps_per_epoch: Optional[int] = None,
pct_start=0.3,
anneal_strategy: Literal["cos", "linear"] = "cos",
cycle_momentum=True,
base_momentum: Union[float, List[float]] = 0.85,
max_momentum: Union[float, List[float]] = 0.95,
div_factor=25.0,
final_div_factor=1e4,
three_phase=False,
last_epoch=-1,
verbose="deprecated",
):
# Validate optimizer
if not isinstance(optimizer, Optimizer):
raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
self.optimizer = optimizer
# Validate total_steps
if total_steps is not None:
if total_steps <= 0 or not isinstance(total_steps, int):
raise ValueError(
f"Expected positive integer total_steps, but got {total_steps}"
)
self.total_steps = total_steps
elif epochs is not None and steps_per_epoch is not None:
if not isinstance(epochs, int) or epochs <= 0:
raise ValueError(f"Expected positive integer epochs, but got {epochs}")
if not isinstance(steps_per_epoch, int) or steps_per_epoch <= 0:
raise ValueError(
f"Expected positive integer steps_per_epoch, but got {steps_per_epoch}"
)
self.total_steps = epochs * steps_per_epoch
else:
raise ValueError(
"You must define either total_steps OR (epochs AND steps_per_epoch)"
)
self._schedule_phases: List[_SchedulePhase]
if three_phase:
self._schedule_phases = [
{
"end_step": float(pct_start * self.total_steps) - 1,
"start_lr": "initial_lr",
"end_lr": "max_lr",
"start_momentum": "max_momentum",
"end_momentum": "base_momentum",
},
{
"end_step": float(2 * pct_start * self.total_steps) - 2,
"start_lr": "max_lr",
"end_lr": "initial_lr",
"start_momentum": "base_momentum",
"end_momentum": "max_momentum",
},
{
"end_step": self.total_steps - 1,
"start_lr": "initial_lr",
"end_lr": "min_lr",
"start_momentum": "max_momentum",
"end_momentum": "max_momentum",
},
]
else:
self._schedule_phases = [
{
"end_step": float(pct_start * self.total_steps) - 1,
"start_lr": "initial_lr",
"end_lr": "max_lr",
"start_momentum": "max_momentum",
"end_momentum": "base_momentum",
},
{
"end_step": self.total_steps - 1,
"start_lr": "max_lr",
"end_lr": "min_lr",
"start_momentum": "base_momentum",
"end_momentum": "max_momentum",
},
]
# Validate pct_start
if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
raise ValueError(
f"Expected float between 0 and 1 pct_start, but got {pct_start}"
)
# Validate anneal_strategy
if anneal_strategy not in ["cos", "linear"]:
raise ValueError(
f"anneal_strategy must be one of 'cos' or 'linear', instead got {anneal_strategy}"
)
else:
self._anneal_func_type = anneal_strategy
# Initialize learning rate variables
max_lrs = _format_param("max_lr", self.optimizer, max_lr)
if last_epoch == -1:
for idx, group in enumerate(self.optimizer.param_groups):
group["initial_lr"] = max_lrs[idx] / div_factor
group["max_lr"] = max_lrs[idx]
group["min_lr"] = group["initial_lr"] / final_div_factor
# Initialize momentum variables
self.cycle_momentum = cycle_momentum
if self.cycle_momentum:
if (
"momentum" not in self.optimizer.defaults
and "betas" not in self.optimizer.defaults
):
raise ValueError(
"optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
)
self.use_beta1 = "betas" in self.optimizer.defaults
max_momentums = _format_param("max_momentum", optimizer, max_momentum)
base_momentums = _format_param("base_momentum", optimizer, base_momentum)
if last_epoch == -1:
for m_momentum, b_momentum, group in zip(
max_momentums, base_momentums, optimizer.param_groups
):
if self.use_beta1:
group["betas"] = (m_momentum, *group["betas"][1:])
else:
group["momentum"] = m_momentum
group["max_momentum"] = m_momentum
group["base_momentum"] = b_momentum
super().__init__(optimizer, last_epoch, verbose)
def _anneal_func(self, *args, **kwargs):
if hasattr(self, "_anneal_func_type"):
if self._anneal_func_type == "cos":
return self._annealing_cos(*args, **kwargs)
elif self._anneal_func_type == "linear":
return self._annealing_linear(*args, **kwargs)
else:
raise ValueError(f"Unknown _anneal_func_type: {self._anneal_func_type}")
else:
# For BC
return self.anneal_func(*args, **kwargs) # type: ignore[attr-defined]
@staticmethod
def _annealing_cos(start, end, pct):
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = math.cos(math.pi * pct) + 1
return end + (start - end) / 2.0 * cos_out
@staticmethod
def _annealing_linear(start, end, pct):
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return (end - start) * pct + start
def get_lr(self):
_warn_get_lr_called_within_step(self)
lrs = []
step_num = self.last_epoch
if step_num > self.total_steps:
raise ValueError(
f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}" # noqa: UP032
)
for group in self.optimizer.param_groups:
start_step = 0.0
for i, phase in enumerate(self._schedule_phases):
end_step = phase["end_step"]
if step_num <= end_step or i == len(self._schedule_phases) - 1:
pct = (step_num - start_step) / (end_step - start_step)
computed_lr = self._anneal_func(
group[phase["start_lr"]], group[phase["end_lr"]], pct
)
if self.cycle_momentum:
computed_momentum = self._anneal_func(
group[phase["start_momentum"]],
group[phase["end_momentum"]],
pct,
)
break
start_step = phase["end_step"]
lrs.append(computed_lr) # type: ignore[possibly-undefined]
if self.cycle_momentum:
if self.use_beta1:
group["betas"] = (computed_momentum, *group["betas"][1:]) # type: ignore[possibly-undefined]
else:
group[
"momentum"
] = computed_momentum # type: ignore[possibly-undefined]
return lrs
