| import numpy as np | |
| class LambdaWarmUpCosineScheduler: | |
| """ | |
| note: use with a base_lr of 1.0 | |
| """ | |
| def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): | |
| self.lr_warm_up_steps = warm_up_steps | |
| self.lr_start = lr_start | |
| self.lr_min = lr_min | |
| self.lr_max = lr_max | |
| self.lr_max_decay_steps = max_decay_steps | |
| self.last_lr = 0. | |
| self.verbosity_interval = verbosity_interval | |
| def schedule(self, n): | |
| if self.verbosity_interval > 0: | |
| if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") | |
| if n < self.lr_warm_up_steps: | |
| lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start | |
| self.last_lr = lr | |
| return lr | |
| else: | |
| t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) | |
| t = min(t, 1.0) | |
| lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( | |
| 1 + np.cos(t * np.pi)) | |
| self.last_lr = lr | |
| return lr | |
| def __call__(self, n): | |
| return self.schedule(n) | |