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64 lines
1.9 KiB
64 lines
1.9 KiB
""" Step Scheduler
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Basic step LR schedule with warmup, noise.
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import math
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import torch
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from .scheduler import Scheduler
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class StepLRScheduler(Scheduler):
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"""
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"""
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def __init__(self,
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optimizer: torch.optim.Optimizer,
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decay_t: float,
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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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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, param_group_field="lr",
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noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
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initialize=initialize)
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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.t_in_epochs = t_in_epochs
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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_lr(self, t):
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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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lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values]
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return lrs
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def get_epoch_values(self, epoch: int):
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if self.t_in_epochs:
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return self._get_lr(epoch)
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else:
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return None
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def get_update_values(self, num_updates: int):
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if not self.t_in_epochs:
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return self._get_lr(num_updates)
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else:
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return None
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