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49 lines
1.6 KiB
49 lines
1.6 KiB
6 years ago
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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_epochs: int,
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decay_rate: float = 1.,
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warmup_updates=0,
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warmup_lr_init=0,
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initialize=True) -> None:
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super().__init__(optimizer, param_group_field="lr", initialize=initialize)
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self.decay_epochs = decay_epochs
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self.decay_rate = decay_rate
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self.warmup_updates = warmup_updates
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self.warmup_lr_init = warmup_lr_init
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if self.warmup_updates:
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self.warmup_active = warmup_updates > 0 # this state updates with num_updates
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates 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_epoch_values(self, epoch: int):
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if not self.warmup_active:
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lrs = [v * (self.decay_rate ** ((epoch + 1) // self.decay_epochs))
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for v in self.base_values]
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else:
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lrs = None # no epoch updates while warming up
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return lrs
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def get_update_values(self, num_updates: int):
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if num_updates < self.warmup_updates:
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lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
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else:
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self.warmup_active = False # warmup cancelled by first update past warmup_update count
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lrs = None # no change on update afte warmup stage
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return lrs
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