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| import math |
| from typing import Union |
|
|
| import torch |
|
|
| from ...models.utils.list import val2list |
|
|
| __all__ = ["CosineLRwithWarmup", "ConstantLRwithWarmup"] |
|
|
|
|
| class CosineLRwithWarmup(torch.optim.lr_scheduler._LRScheduler): |
| def __init__( |
| self, |
| optimizer: torch.optim.Optimizer, |
| warmup_steps: int, |
| warmup_lr: float, |
| decay_steps: Union[int, list[int]], |
| last_epoch: int = -1, |
| ) -> None: |
| self.warmup_steps = warmup_steps |
| self.warmup_lr = warmup_lr |
| self.decay_steps = val2list(decay_steps) |
| super().__init__(optimizer, last_epoch) |
|
|
| def get_lr(self) -> list[float]: |
| if self.last_epoch < self.warmup_steps: |
| return [ |
| (base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps + self.warmup_lr |
| for base_lr in self.base_lrs |
| ] |
| else: |
| current_steps = self.last_epoch - self.warmup_steps |
| decay_steps = [0] + self.decay_steps |
| idx = len(decay_steps) - 2 |
| for i, decay_step in enumerate(decay_steps[:-1]): |
| if decay_step <= current_steps < decay_steps[i + 1]: |
| idx = i |
| break |
| current_steps -= decay_steps[idx] |
| decay_step = decay_steps[idx + 1] - decay_steps[idx] |
| return [0.5 * base_lr * (1 + math.cos(math.pi * current_steps / decay_step)) for base_lr in self.base_lrs] |
|
|
|
|
| class ConstantLRwithWarmup(torch.optim.lr_scheduler._LRScheduler): |
| def __init__( |
| self, |
| optimizer: torch.optim.Optimizer, |
| warmup_steps: int, |
| warmup_lr: float, |
| last_epoch: int = -1, |
| ) -> None: |
| self.warmup_steps = warmup_steps |
| self.warmup_lr = warmup_lr |
| super().__init__(optimizer, last_epoch) |
|
|
| def get_lr(self) -> list[float]: |
| if self.last_epoch < self.warmup_steps: |
| return [ |
| (base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps + self.warmup_lr |
| for base_lr in self.base_lrs |
| ] |
| else: |
| return self.base_lrs |
|
|
