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from torch.optim.lr_scheduler import ReduceLROnPlateau
class WarmupThenReduceLROnPlateau(ReduceLROnPlateau):
def __init__(self, optimizer, warmup_steps, *args, **kwargs):
"""
Args:
optimizer (Optimizer): Optimizer to wrap
warmup_steps: number of steps before reaching base learning rate
*args: Arguments for ReduceLROnPlateau
**kwargs: Arguments for ReduceLROnPlateau
"""
super().__init__(optimizer, *args, **kwargs)
self.warmup_steps = warmup_steps
self.steps_taken = 0
self.base_lrs = list(map(lambda group: group["lr"], optimizer.param_groups))
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group["lr"] = lr
def get_lr(self):
assert self.steps_taken <= self.warmup_steps
return [
base_lr * (self.steps_taken / self.warmup_steps)
for base_lr in self.base_lrs
]
def step(self, metrics=None):
self.steps_taken += 1
if self.steps_taken <= self.warmup_steps:
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group["lr"] = lr
elif metrics is not None:
super().step(metrics)
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