| | |
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| | | |
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| | | class TriStageLR(_LRScheduler, AbsBatchStepScheduler): |
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| | | def __init__( |
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| | | self, |
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| | | optimizer: torch.optim.Optimizer, |
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| | | last_epoch: int = -1, |
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| | | phase_ratio: Optional[List[float]] = None, |
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| | | init_lr_scale: float = 0.01, |
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| | | final_lr_scale: float = 0.01, |
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| | | self, |
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| | | optimizer: torch.optim.Optimizer, |
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| | | last_epoch: int = -1, |
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| | | phase_ratio: Optional[List[float]] = None, |
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| | | init_lr_scale: float = 0.01, |
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| | | final_lr_scale: float = 0.01, |
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| | | ): |
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| | | self.optimizer = optimizer |
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| | | self.last_epoch = last_epoch |
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| | |
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| | | self.decay_steps = int(self.max_update * self.phase_ratio[2]) |
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| | | |
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| | | self.warmup_rate = ( |
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| | | (self.peak_lr - self.init_lr) / self.warmup_steps |
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| | | if self.warmup_steps != 0 |
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| | | else 0 |
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| | | (self.peak_lr - self.init_lr) / self.warmup_steps if self.warmup_steps != 0 else 0 |
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| | | ) |
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| | | self.decay_factor = -math.log(self.final_lr_scale) / self.decay_steps |
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| | | |
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