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from typing import Any, Callable, Dict, Iterable, List, Optional, Union
from .optimizer import Optimizer
class LRScheduler:
optimizer: Optimizer = ...
base_lrs: List[float] = ...
last_epoch: int = ...
verbose: bool = ...
def __init__(
self,
optimizer: Optimizer,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
def state_dict(self) -> Dict[str, Any]: ...
def load_state_dict(self, state_dict: Dict[str, Any]) -> None: ...
def get_last_lr(self) -> List[float]: ...
def get_lr(self) -> float: ...
def step(self, epoch: Optional[int] = ...) -> None: ...
def print_lr(
self,
is_verbose: bool,
group: Dict[str, Any],
lr: float,
epoch: Optional[int] = ...,
) -> None: ...
class _LRScheduler(LRScheduler): ...
class LambdaLR(LRScheduler):
lr_lambdas: List[Callable[[int], float]] = ...
def __init__(
self,
optimizer: Optimizer,
lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class MultiplicativeLR(LRScheduler):
lr_lambdas: List[Callable[[int], float]] = ...
def __init__(
self,
optimizer: Optimizer,
lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]],
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class StepLR(LRScheduler):
step_size: int = ...
gamma: float = ...
def __init__(
self,
optimizer: Optimizer,
step_size: int,
gamma: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class MultiStepLR(LRScheduler):
milestones: Iterable[int] = ...
gamma: float = ...
def __init__(
self,
optimizer: Optimizer,
milestones: Iterable[int],
gamma: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class ConstantLR(LRScheduler):
factor: float = ...
total_iters: int = ...
def __init__(
self,
optimizer: Optimizer,
factor: float = ...,
total_iters: int = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class LinearLR(LRScheduler):
start_factor: float = ...
end_factor: float = ...
total_iters: int = ...
def __init__(
self,
optimizer: Optimizer,
start_factor: float = ...,
end_factor: float = ...,
total_iters: int = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class ExponentialLR(LRScheduler):
gamma: float = ...
def __init__(
self,
optimizer: Optimizer,
gamma: float,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class ChainedScheduler(LRScheduler):
def __init__(self, schedulers: List[LRScheduler]) -> None: ...
class SequentialLR(LRScheduler):
def __init__(
self,
optimizer: Optimizer,
schedulers: List[LRScheduler],
milestones: List[int],
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class CosineAnnealingLR(LRScheduler):
T_max: int = ...
eta_min: float = ...
def __init__(
self,
optimizer: Optimizer,
T_max: int,
eta_min: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class ReduceLROnPlateau(LRScheduler):
factor: float = ...
optimizer: Optimizer = ...
min_lrs: List[float] = ...
patience: int = ...
verbose: bool = ...
cooldown: int = ...
cooldown_counter: int = ...
mode: str = ...
threshold: float = ...
threshold_mode: str = ...
best: Optional[float] = ...
num_bad_epochs: Optional[int] = ...
mode_worse: Optional[float] = ...
eps: float = ...
last_epoch: int = ...
def __init__(
self,
optimizer: Optimizer,
mode: str = ...,
factor: float = ...,
patience: int = ...,
threshold: float = ...,
threshold_mode: str = ...,
cooldown: int = ...,
min_lr: Union[List[float], float] = ...,
eps: float = ...,
verbose: bool = ...,
) -> None: ...
def step(self, metrics: Any, epoch: Optional[int] = ...) -> None: ... # type: ignore[override]
@property
def in_cooldown(self) -> bool: ...
def is_better(self, a: Any, best: Any) -> bool: ...
def state_dict(self) -> Dict[str, Any]: ...
def load_state_dict(self, state_dict: Dict[str, Any]) -> None: ...
class CyclicLR(LRScheduler):
max_lrs: List[float] = ...
total_size: float = ...
step_ratio: float = ...
mode: str = ...
gamma: float = ...
scale_mode: str = ...
cycle_momentum: bool = ...
base_momentums: List[float] = ...
max_momentums: List[float] = ...
def __init__(
self,
optimizer: Optimizer,
base_lr: Union[float, List[float]],
max_lr: Union[float, List[float]],
step_size_up: int = ...,
step_size_down: Optional[int] = ...,
mode: str = ...,
gamma: float = ...,
scale_fn: Optional[Callable[[float], float]] = ...,
scale_mode: str = ...,
cycle_momentum: bool = ...,
base_momentum: float = ...,
max_momentum: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
def scale_fn(self, x: Any) -> float: ...
class CosineAnnealingWarmRestarts(LRScheduler):
T_0: int = ...
T_i: int = ...
T_mult: int = ...
eta_min: float = ...
T_cur: Any = ...
def __init__(
self,
optimizer: Optimizer,
T_0: int,
T_mult: int = ...,
eta_min: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class OneCycleLR(LRScheduler):
total_steps: int = ...
anneal_func: Callable[[float, float, float], float] = ...
cycle_momentum: bool = ...
use_beta1: bool = ...
def __init__(
self,
optimizer: Optimizer,
max_lr: Union[float, List[float]],
total_steps: int = ...,
epochs: int = ...,
steps_per_epoch: int = ...,
pct_start: float = ...,
anneal_strategy: str = ...,
cycle_momentum: bool = ...,
base_momentum: Union[float, List[float]] = ...,
max_momentum: Union[float, List[float]] = ...,
div_factor: float = ...,
final_div_factor: float = ...,
three_phase: bool = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
class PolynomialLR(LRScheduler):
total_iters: int = ...
power: float = ...
def __init__(
self,
optimizer: Optimizer,
total_iters: int = ...,
power: float = ...,
last_epoch: int = ...,
verbose: bool = ...,
) -> None: ...
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