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LR scheduler update:

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* add polynomial decay 'poly'
* cleanup cycle specific args for cosine, poly, and tanh sched, t_mul -> cycle_mul, decay -> cycle_decay, default cycle_limit to 1 in each opt
* add k-decay for cosine and poly sched as per https://arxiv.org/abs/2004.05909
* change default tanh ub/lb to push inflection to later epochs
This commit is contained in:
Ross Wightman 2021-09-01 17:33:11 -07:00
parent 492c0a4e20
commit 29a37e23ee
5 changed files with 188 additions and 53 deletions
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3
timm/scheduler/__init__.py
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@ -1,5 +1,8 @@
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
from .scheduler_factory import create_scheduler

45
timm/scheduler/cosine_lr.py
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@ -1,8 +1,8 @@
""" Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise.
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
@ -22,23 +22,26 @@ class CosineLRScheduler(Scheduler):
Inspiration from
https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
t_mul: float = 1.,
lr_min: float = 0.,
decay_rate: float = 1.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
cycle_limit=0,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
k_decay=1.0,
initialize=True) -> None:
super().__init__(
optimizer, param_group_field="lr",
@ -47,18 +50,19 @@ class CosineLRScheduler(Scheduler):
assert t_initial > 0
assert lr_min >= 0
if t_initial == 1 and t_mul == 1 and decay_rate == 1:
if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1:
_logger.warning("Cosine annealing scheduler will have no effect on the learning "
"rate since t_initial = t_mul = eta_mul = 1.")
self.t_initial = t_initial
self.t_mul = t_mul
self.lr_min = lr_min
self.decay_rate = decay_rate
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
self.t_in_epochs = t_in_epochs
self.k_decay = k_decay
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)
@ -72,22 +76,23 @@ class CosineLRScheduler(Scheduler):
if self.warmup_prefix:
t = t - self.warmup_t
if self.t_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.t_mul), self.t_mul))
t_i = self.t_mul ** i * self.t_initial
t_curr = t - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
gamma = self.decay_rate ** i
lr_min = self.lr_min * gamma
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
k = self.k_decay
if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit):
if i < self.cycle_limit:
lrs = [
lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * t_curr / t_i)) for lr_max in lr_max_values
self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 + math.cos(math.pi * t_curr ** k / t_i ** k))
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min for _ in self.base_values]
@ -107,10 +112,8 @@ class CosineLRScheduler(Scheduler):
return None
def get_cycle_length(self, cycles=0):
if not cycles:
cycles = self.cycle_limit
cycles = max(1, cycles)
if self.t_mul == 1.0:
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.t_mul ** cycles - 1) / (1 - self.t_mul)))
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))

116
timm/scheduler/poly_lr.py Normal file
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@ -0,0 +1,116 @@
""" Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, noise, and k-decay
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
power: float = 0.5,
lr_min: float = 0.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
k_decay=.5,
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)
assert t_initial > 0
assert lr_min >= 0
if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1:
_logger.warning("Cosine annealing scheduler will have no effect on the learning "
"rate since t_initial = t_mul = eta_mul = 1.")
self.t_initial = t_initial
self.power = power
self.lr_min = lr_min
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
self.t_in_epochs = t_in_epochs
self.k_decay = k_decay
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:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
k = self.k_decay
if i < self.cycle_limit:
lrs = [
self.lr_min + (lr_max - self.lr_min) * (1 - t_curr ** k / t_i ** k) ** self.power
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min for _ 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
def get_cycle_length(self, cycles=0):
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))

36
timm/scheduler/scheduler_factory.py
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@ -1,11 +1,12 @@
""" Scheduler Factory
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
from .cosine_lr import CosineLRScheduler
from .tanh_lr import TanhLRScheduler
from .step_lr import StepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
def create_scheduler(args, optimizer):
@ -27,19 +28,22 @@ def create_scheduler(args, optimizer):
noise_std=getattr(args, 'lr_noise_std', 1.),
noise_seed=getattr(args, 'seed', 42),
)
cycle_args = dict(
cycle_mul=getattr(args, 'lr_cycle_mul', 1.),
cycle_decay=getattr(args, 'lr_cycle_decay', 0.1),
cycle_limit=getattr(args, 'lr_cycle_limit', 1),
)
lr_scheduler = None
if args.sched == 'cosine':
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=num_epochs,
t_mul=getattr(args, 'lr_cycle_mul', 1.),
lr_min=args.min_lr,
decay_rate=args.decay_rate,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
cycle_limit=getattr(args, 'lr_cycle_limit', 1),
t_in_epochs=True,
k_decay=getattr(args, 'lr_k_decay', 1.0),
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
@ -47,12 +51,11 @@ def create_scheduler(args, optimizer):
lr_scheduler = TanhLRScheduler(
optimizer,
t_initial=num_epochs,
t_mul=getattr(args, 'lr_cycle_mul', 1.),
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
cycle_limit=getattr(args, 'lr_cycle_limit', 1),
t_in_epochs=True,
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
@ -87,5 +90,18 @@ def create_scheduler(args, optimizer):
cooldown_t=0,
**noise_args,
)
elif args.sched == 'poly':
lr_scheduler = PolyLRScheduler(
optimizer,
power=args.decay_rate, # overloading 'decay_rate' as polynomial power
t_initial=num_epochs,
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
k_decay=getattr(args, 'lr_k_decay', 1.0),
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
return lr_scheduler, num_epochs

41
timm/scheduler/tanh_lr.py
View File

@ -2,7 +2,7 @@
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
@ -24,15 +24,15 @@ class TanhLRScheduler(Scheduler):
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lb: float = -6.,
ub: float = 4.,
t_mul: float = 1.,
lb: float = -7.,
ub: float = 3.,
lr_min: float = 0.,
decay_rate: float = 1.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
cycle_limit=0,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
@ -53,9 +53,9 @@ class TanhLRScheduler(Scheduler):
self.lb = lb
self.ub = ub
self.t_initial = t_initial
self.t_mul = t_mul
self.lr_min = lr_min
self.decay_rate = decay_rate
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
@ -75,27 +75,26 @@ class TanhLRScheduler(Scheduler):
if self.warmup_prefix:
t = t - self.warmup_t
if self.t_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.t_mul), self.t_mul))
t_i = self.t_mul ** i * self.t_initial
t_curr = t - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit):
gamma = self.decay_rate ** i
lr_min = self.lr_min * gamma
if i < self.cycle_limit:
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
tr = t_curr / t_i
lrs = [
lr_min + 0.5 * (lr_max - lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min * (self.decay_rate ** self.cycle_limit) for _ in self.base_values]
lrs = [self.lr_min for _ in self.base_values]
return lrs
def get_epoch_values(self, epoch: int):
@ -111,10 +110,8 @@ class TanhLRScheduler(Scheduler):
return None
def get_cycle_length(self, cycles=0):
if not cycles:
cycles = self.cycle_limit
cycles = max(1, cycles)
if self.t_mul == 1.0:
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.t_mul ** cycles - 1) / (1 - self.t_mul)))
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))