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# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------
import torch
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.step_lr import StepLRScheduler
from timm.scheduler.scheduler import Scheduler
def build_scheduler(config, optimizer=None):
lr_scheduler = None
if config.TRAIN.LR_SCHEDULER.NAME == 'cosine':
lr_scheduler = CosLRScheduler()
elif config.TRAIN.LR_SCHEDULER.NAME == 'multistep':
lr_scheduler = StepLRScheduler()
else:
raise NotImplementedError(f"Unkown lr scheduler: {config.TRAIN.LR_SCHEDULER.NAME}")
return lr_scheduler
import math
class CosLRScheduler():
def __init__(self) -> None:
pass
def step_update(self, optimizer, epoch, config):
"""Decay the learning rate with half-cycle cosine after warmup"""
if epoch < config.TRAIN.WARMUP_EPOCHS:
lr = (config.TRAIN.BASE_LR-config.TRAIN.WARMUP_LR) * epoch / config.TRAIN.WARMUP_EPOCHS + config.TRAIN.WARMUP_LR
else:
lr = config.TRAIN.MIN_LR + (config.TRAIN.BASE_LR - config.TRAIN.MIN_LR) * 0.5 * \
(1. + math.cos(math.pi * (epoch - config.TRAIN.WARMUP_EPOCHS ) / (config.TRAIN.EPOCHS - config.TRAIN.WARMUP_EPOCHS )))
for param_group in optimizer.param_groups:
if "lr_scale" in param_group:
param_group["lr"] = lr * param_group["lr_scale"]
else:
param_group["lr"] = lr
return lr
class LinearLRScheduler(Scheduler):
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lr_min_rate: float,
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.t_initial = t_initial
self.lr_min_rate = lr_min_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:
t = t - self.warmup_t
total_t = self.t_initial - self.warmup_t
lrs = [v - ((v - v * self.lr_min_rate) * (t / total_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
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