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pytorch-image-models/scheduler/step_lr.py

51 lines
1.5 KiB

import math
import torch
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(self,
optimizer: torch.optim.Optimizer,
decay_t: int,
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
t_in_epochs=True,
initialize=True) -> None:
super().__init__(optimizer, param_group_field="lr", initialize=initialize)
self.decay_t = decay_t
self.decay_rate = decay_rate
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.t_in_epochs = t_in_epochs
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
lrs = [v * (self.decay_rate ** (t // self.decay_t))
for v in self.base_values]
return lrs
def get_epoch_values(self, epoch: int):
if self.t_in_epochs:
return self._get_lr(epoch)
else:
return None
def get_update_values(self, num_updates: int):
if not self.t_in_epochs:
return self._get_lr(num_updates)
else:
return None