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YaoFANGUK
162
backend/ppocr/optimizer/lr_scheduler.py
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162
backend/ppocr/optimizer/lr_scheduler.py
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from paddle.optimizer.lr import LRScheduler
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class CyclicalCosineDecay(LRScheduler):
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def __init__(self,
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learning_rate,
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T_max,
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cycle=1,
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last_epoch=-1,
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eta_min=0.0,
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verbose=False):
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"""
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Cyclical cosine learning rate decay
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A learning rate which can be referred in https://arxiv.org/pdf/2012.12645.pdf
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Args:
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learning rate(float): learning rate
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T_max(int): maximum epoch num
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cycle(int): period of the cosine decay
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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eta_min(float): minimum learning rate during training
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verbose(bool): whether to print learning rate for each epoch
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"""
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super(CyclicalCosineDecay, self).__init__(learning_rate, last_epoch,
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verbose)
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self.cycle = cycle
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self.eta_min = eta_min
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def get_lr(self):
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if self.last_epoch == 0:
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return self.base_lr
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reletive_epoch = self.last_epoch % self.cycle
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lr = self.eta_min + 0.5 * (self.base_lr - self.eta_min) * \
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(1 + math.cos(math.pi * reletive_epoch / self.cycle))
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return lr
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class OneCycleDecay(LRScheduler):
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"""
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One Cycle learning rate decay
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A learning rate which can be referred in https://arxiv.org/abs/1708.07120
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Code refered in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
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"""
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def __init__(self,
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max_lr,
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epochs=None,
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steps_per_epoch=None,
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pct_start=0.3,
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anneal_strategy='cos',
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div_factor=25.,
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final_div_factor=1e4,
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three_phase=False,
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last_epoch=-1,
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verbose=False):
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# Validate total_steps
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if epochs <= 0 or not isinstance(epochs, int):
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raise ValueError(
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"Expected positive integer epochs, but got {}".format(epochs))
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if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int):
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raise ValueError(
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"Expected positive integer steps_per_epoch, but got {}".format(
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steps_per_epoch))
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self.total_steps = epochs * steps_per_epoch
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self.max_lr = max_lr
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self.initial_lr = self.max_lr / div_factor
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self.min_lr = self.initial_lr / final_div_factor
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if three_phase:
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self._schedule_phases = [
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{
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'end_step': float(pct_start * self.total_steps) - 1,
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'start_lr': self.initial_lr,
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'end_lr': self.max_lr,
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},
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{
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'end_step': float(2 * pct_start * self.total_steps) - 2,
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'start_lr': self.max_lr,
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'end_lr': self.initial_lr,
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},
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{
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'end_step': self.total_steps - 1,
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'start_lr': self.initial_lr,
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'end_lr': self.min_lr,
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},
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]
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else:
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self._schedule_phases = [
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{
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'end_step': float(pct_start * self.total_steps) - 1,
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'start_lr': self.initial_lr,
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'end_lr': self.max_lr,
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},
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{
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'end_step': self.total_steps - 1,
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'start_lr': self.max_lr,
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'end_lr': self.min_lr,
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},
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]
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# Validate pct_start
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if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
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raise ValueError(
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"Expected float between 0 and 1 pct_start, but got {}".format(
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pct_start))
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# Validate anneal_strategy
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if anneal_strategy not in ['cos', 'linear']:
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raise ValueError(
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"anneal_strategy must by one of 'cos' or 'linear', instead got {}".
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format(anneal_strategy))
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elif anneal_strategy == 'cos':
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self.anneal_func = self._annealing_cos
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elif anneal_strategy == 'linear':
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self.anneal_func = self._annealing_linear
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super(OneCycleDecay, self).__init__(max_lr, last_epoch, verbose)
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def _annealing_cos(self, start, end, pct):
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"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
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cos_out = math.cos(math.pi * pct) + 1
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return end + (start - end) / 2.0 * cos_out
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def _annealing_linear(self, start, end, pct):
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"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
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return (end - start) * pct + start
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def get_lr(self):
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computed_lr = 0.0
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step_num = self.last_epoch
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if step_num > self.total_steps:
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raise ValueError(
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"Tried to step {} times. The specified number of total steps is {}"
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.format(step_num + 1, self.total_steps))
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start_step = 0
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for i, phase in enumerate(self._schedule_phases):
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end_step = phase['end_step']
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if step_num <= end_step or i == len(self._schedule_phases) - 1:
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pct = (step_num - start_step) / (end_step - start_step)
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computed_lr = self.anneal_func(phase['start_lr'],
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phase['end_lr'], pct)
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break
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start_step = phase['end_step']
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return computed_lr
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