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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import math | |
| from torch.optim import Optimizer | |
| from torch.optim.lr_scheduler import _LRScheduler | |
| class CosineLRScheduler(_LRScheduler): | |
| """Cosine LR scheduler. | |
| Args: | |
| optimizer (Optimizer): Torch optimizer. | |
| warmup_steps (int): Number of warmup steps. | |
| total_steps (int): Total number of steps. | |
| lr_min_ratio (float): Minimum learning rate. | |
| cycle_length (float): Cycle length. | |
| """ | |
| def __init__(self, optimizer: Optimizer, total_steps: int, warmup_steps: int, | |
| lr_min_ratio: float = 0.0, cycle_length: float = 1.0): | |
| self.warmup_steps = warmup_steps | |
| assert self.warmup_steps >= 0 | |
| self.total_steps = total_steps | |
| assert self.total_steps >= 0 | |
| self.lr_min_ratio = lr_min_ratio | |
| self.cycle_length = cycle_length | |
| super().__init__(optimizer) | |
| def _get_sched_lr(self, lr: float, step: int): | |
| if step < self.warmup_steps: | |
| lr_ratio = step / self.warmup_steps | |
| lr = lr_ratio * lr | |
| elif step <= self.total_steps: | |
| s = (step - self.warmup_steps) / (self.total_steps - self.warmup_steps) | |
| lr_ratio = self.lr_min_ratio + 0.5 * (1 - self.lr_min_ratio) * \ | |
| (1. + math.cos(math.pi * s / self.cycle_length)) | |
| lr = lr_ratio * lr | |
| else: | |
| lr_ratio = self.lr_min_ratio | |
| lr = lr_ratio * lr | |
| return lr | |
| def get_lr(self): | |
| return [self._get_sched_lr(lr, self.last_epoch) for lr in self.base_lrs] | |