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