66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
""" MultiStep LR Scheduler
|
|
|
|
Basic multi step LR schedule with warmup, noise.
|
|
"""
|
|
import torch
|
|
import bisect
|
|
from timm.scheduler.scheduler import Scheduler
|
|
from typing import List
|
|
|
|
class MultiStepLRScheduler(Scheduler):
|
|
"""
|
|
"""
|
|
|
|
def __init__(self,
|
|
optimizer: torch.optim.Optimizer,
|
|
decay_t: List[int],
|
|
decay_rate: float = 1.,
|
|
warmup_t=0,
|
|
warmup_lr_init=0,
|
|
t_in_epochs=True,
|
|
noise_range_t=None,
|
|
noise_pct=0.67,
|
|
noise_std=1.0,
|
|
noise_seed=42,
|
|
initialize=True,
|
|
) -> None:
|
|
super().__init__(
|
|
optimizer, param_group_field="lr",
|
|
noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
|
|
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_curr_decay_steps(self, t):
|
|
# find where in the array t goes,
|
|
# assumes self.decay_t is sorted
|
|
return bisect.bisect_right(self.decay_t, t+1)
|
|
|
|
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 ** self.get_curr_decay_steps(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
|