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""" MultiStep LR Scheduler |
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Basic multi step LR schedule with warmup, noise. |
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""" |
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import torch |
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import bisect |
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from timm.scheduler.scheduler import Scheduler |
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from typing import List |
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class MultiStepLRScheduler(Scheduler): |
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""" |
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""" |
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def __init__( |
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self, |
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optimizer: torch.optim.Optimizer, |
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decay_t: List[int], |
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decay_rate: float = 1., |
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warmup_t=0, |
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warmup_lr_init=0, |
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warmup_prefix=True, |
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t_in_epochs=True, |
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noise_range_t=None, |
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noise_pct=0.67, |
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noise_std=1.0, |
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noise_seed=42, |
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initialize=True, |
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) -> None: |
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super().__init__( |
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optimizer, |
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param_group_field="lr", |
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t_in_epochs=t_in_epochs, |
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noise_range_t=noise_range_t, |
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noise_pct=noise_pct, |
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noise_std=noise_std, |
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noise_seed=noise_seed, |
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initialize=initialize, |
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) |
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self.decay_t = decay_t |
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self.decay_rate = decay_rate |
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self.warmup_t = warmup_t |
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self.warmup_lr_init = warmup_lr_init |
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self.warmup_prefix = warmup_prefix |
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if self.warmup_t: |
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] |
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super().update_groups(self.warmup_lr_init) |
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else: |
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self.warmup_steps = [1 for _ in self.base_values] |
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def get_curr_decay_steps(self, t): |
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return bisect.bisect_right(self.decay_t, t + 1) |
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def _get_lr(self, t: int) -> List[float]: |
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if t < self.warmup_t: |
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lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] |
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else: |
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if self.warmup_prefix: |
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t = t - self.warmup_t |
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lrs = [v * (self.decay_rate ** self.get_curr_decay_steps(t)) for v in self.base_values] |
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return lrs |
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