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from typing import Iterable, Any, Optional, Callable, Union, List
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: ...
def load_state_dict(self, state_dict: dict) -> 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, lr: float, epoch: Optional[int] = ...) -> None: ...
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:
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: ...
@property
def in_cooldown(self) -> bool: ...
def is_better(self, a: Any, best: Any) -> bool: ...
def state_dict(self) -> dict: ...
def load_state_dict(self, state_dict: dict) -> 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: Optional[int] = ...
eta_min: Optional[float] = ...
T_cur: Any = ...
def __init__(self, optimizer: Optimizer, T_0: int, T_mult: int = ..., eta_min: float = ..., last_epoch: int = ..., verbose: bool = ...) -> None: ...
def step(self, epoch: Optional[Any] = ...): ...
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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