# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from paddle.optimizer.lr import LRScheduler
class CyclicalCosineDecay(LRScheduler):
def __init__(
self, learning_rate, T_max, cycle=1, last_epoch=-1, eta_min=0.0, verbose=False
):
"""
Cyclical cosine learning rate decay
A learning rate which can be referred in https://arxiv.org/pdf/2012.12645.pdf
Args:
learning rate(float): learning rate
T_max(int): maximum epoch num
cycle(int): period of the cosine decay
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
eta_min(float): minimum learning rate during training
verbose(bool): whether to print learning rate for each epoch
"""
super(CyclicalCosineDecay, self).__init__(learning_rate, last_epoch, verbose)
self.cycle = cycle
self.eta_min = eta_min
def get_lr(self):
if self.last_epoch == 0:
return self.base_lr
reletive_epoch = self.last_epoch % self.cycle
lr = self.eta_min + 0.5 * (self.base_lr - self.eta_min) * (
1 + math.cos(math.pi * reletive_epoch / self.cycle)
)
return lr
class OneCycleDecay(LRScheduler):
"""
One Cycle learning rate decay
A learning rate which can be referred in https://arxiv.org/abs/1708.07120
Code refered in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
"""
def __init__(
self,
max_lr,
epochs=None,
steps_per_epoch=None,
pct_start=0.3,
anneal_strategy="cos",
div_factor=25.0,
final_div_factor=1e4,
three_phase=False,
last_epoch=-1,
verbose=False,
):
# Validate total_steps
if epochs <= 0 or not isinstance(epochs, int):
raise ValueError(
"Expected positive integer epochs, but got {}".format(epochs)
)
if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int):
raise ValueError(
"Expected positive integer steps_per_epoch, but got {}".format(
steps_per_epoch
)
)
self.total_steps = epochs * steps_per_epoch
self.max_lr = max_lr
self.initial_lr = self.max_lr / div_factor
self.min_lr = self.initial_lr / final_div_factor
if three_phase:
self._schedule_phases = [
{
"end_step": float(pct_start * self.total_steps) - 1,
"start_lr": self.initial_lr,
"end_lr": self.max_lr,
},
{
"end_step": float(2 * pct_start * self.total_steps) - 2,
"start_lr": self.max_lr,
"end_lr": self.initial_lr,
},
{
"end_step": self.total_steps - 1,
"start_lr": self.initial_lr,
"end_lr": self.min_lr,
},
]
else:
self._schedule_phases = [
{
"end_step": float(pct_start * self.total_steps) - 1,
"start_lr": self.initial_lr,
"end_lr": self.max_lr,
},
{
"end_step": self.total_steps - 1,
"start_lr": self.max_lr,
"end_lr": self.min_lr,
},
]
# Validate pct_start
if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
raise ValueError(
"Expected float between 0 and 1 pct_start, but got {}".format(pct_start)
)
# Validate anneal_strategy
if anneal_strategy not in ["cos", "linear"]:
raise ValueError(
"anneal_strategy must by one of 'cos' or 'linear', instead got {}".format(
anneal_strategy
)
)
elif anneal_strategy == "cos":
self.anneal_func = self._annealing_cos
elif anneal_strategy == "linear":
self.anneal_func = self._annealing_linear
super(OneCycleDecay, self).__init__(max_lr, last_epoch, verbose)
def _annealing_cos(self, start, end, pct):
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = math.cos(math.pi * pct) + 1
return end + (start - end) / 2.0 * cos_out
def _annealing_linear(self, start, end, pct):
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return (end - start) * pct + start
def get_lr(self):
computed_lr = 0.0
step_num = self.last_epoch
if step_num > self.total_steps:
raise ValueError(
"Tried to step {} times. The specified number of total steps is {}".format(
step_num + 1, self.total_steps
)
)
start_step = 0
for i, phase in enumerate(self._schedule_phases):
end_step = phase["end_step"]
if step_num <= end_step or i == len(self._schedule_phases) - 1:
pct = (step_num - start_step) / (end_step - start_step)
computed_lr = self.anneal_func(phase["start_lr"], phase["end_lr"], pct)
break
start_step = phase["end_step"]
return computed_lr
class TwoStepCosineDecay(LRScheduler):
def __init__(
self, learning_rate, T_max1, T_max2, eta_min=0, last_epoch=-1, verbose=False
):
if not isinstance(T_max1, int):
raise TypeError(
"The type of 'T_max1' in 'CosineAnnealingDecay' must be 'int', but received %s."
% type(T_max1)
)
if not isinstance(T_max2, int):
raise TypeError(
"The type of 'T_max2' in 'CosineAnnealingDecay' must be 'int', but received %s."
% type(T_max2)
)
if not isinstance(eta_min, (float, int)):
raise TypeError(
"The type of 'eta_min' in 'CosineAnnealingDecay' must be 'float, int', but received %s."
% type(eta_min)
)
assert T_max1 > 0 and isinstance(
T_max1, int
), " 'T_max1' must be a positive integer."
assert T_max2 > 0 and isinstance(
T_max2, int
), " 'T_max1' must be a positive integer."
self.T_max1 = T_max1
self.T_max2 = T_max2
self.eta_min = float(eta_min)
super(TwoStepCosineDecay, self).__init__(learning_rate, last_epoch, verbose)
def get_lr(self):
if self.last_epoch <= self.T_max1:
if self.last_epoch == 0:
return self.base_lr
elif (self.last_epoch - 1 - self.T_max1) % (2 * self.T_max1) == 0:
return (
self.last_lr
+ (self.base_lr - self.eta_min)
* (1 - math.cos(math.pi / self.T_max1))
/ 2
)
return (1 + math.cos(math.pi * self.last_epoch / self.T_max1)) / (
1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max1)
) * (self.last_lr - self.eta_min) + self.eta_min
else:
if (self.last_epoch - 1 - self.T_max2) % (2 * self.T_max2) == 0:
return (
self.last_lr
+ (self.base_lr - self.eta_min)
* (1 - math.cos(math.pi / self.T_max2))
/ 2
)
return (1 + math.cos(math.pi * self.last_epoch / self.T_max2)) / (
1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max2)
) * (self.last_lr - self.eta_min) + self.eta_min
def _get_closed_form_lr(self):
if self.last_epoch <= self.T_max1:
return (
self.eta_min
+ (self.base_lr - self.eta_min)
* (1 + math.cos(math.pi * self.last_epoch / self.T_max1))
/ 2
)
else:
return (
self.eta_min
+ (self.base_lr - self.eta_min)
* (1 + math.cos(math.pi * self.last_epoch / self.T_max2))
/ 2
)