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| import numpy as np | |
| import math | |
| class LearningRateDecay: | |
| def __init__(self, lr=0.002, warmup_steps=4000.0) -> None: | |
| self.lr = lr | |
| self.warmup_steps = warmup_steps | |
| def __call__(self, global_step) -> float: | |
| step = global_step + 1.0 | |
| lr = ( | |
| self.lr | |
| * self.warmup_steps ** 0.5 | |
| * np.minimum(step * self.warmup_steps ** -1.5, step ** -0.5) | |
| ) | |
| return lr | |
| class SquareRootScheduler: | |
| def __init__(self, lr=0.1): | |
| self.lr = lr | |
| def __call__(self, global_step): | |
| global_step = global_step // 1000 | |
| return self.lr * pow(global_step + 1.0, -0.5) | |
| class CosineScheduler: | |
| def __init__( | |
| self, max_update, base_lr=0.02, final_lr=0, warmup_steps=0, warmup_begin_lr=0 | |
| ): | |
| self.base_lr_orig = base_lr | |
| self.max_update = max_update | |
| self.final_lr = final_lr | |
| self.warmup_steps = warmup_steps | |
| self.warmup_begin_lr = warmup_begin_lr | |
| self.max_steps = self.max_update - self.warmup_steps | |
| def get_warmup_lr(self, global_step): | |
| increase = ( | |
| (self.base_lr_orig - self.warmup_begin_lr) | |
| * float(global_step) | |
| / float(self.warmup_steps) | |
| ) | |
| return self.warmup_begin_lr + increase | |
| def __call__(self, global_step): | |
| if global_step < self.warmup_steps: | |
| return self.get_warmup_lr(global_step) | |
| if global_step <= self.max_update: | |
| self.base_lr = ( | |
| self.final_lr | |
| + (self.base_lr_orig - self.final_lr) | |
| * ( | |
| 1 | |
| + math.cos( | |
| math.pi * (global_step - self.warmup_steps) / self.max_steps | |
| ) | |
| ) | |
| / 2 | |
| ) | |
| return self.base_lr | |
| def adjust_learning_rate(optimizer, global_step): | |
| lr = LearningRateDecay()(global_step=global_step) | |
| for param_group in optimizer.param_groups: | |
| param_group["lr"] = lr | |
| return lr | |