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