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100 lines
3.6 KiB
100 lines
3.6 KiB
3 years ago
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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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import paddle
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from paddle.optimizer.lr import LRScheduler, MultiStepDecay, LambdaDecay
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from .builder import LRSCHEDULERS
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LRSCHEDULERS.register(MultiStepDecay)
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@LRSCHEDULERS.register()
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class NonLinearDecay(LRScheduler):
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def __init__(self, learning_rate, lr_decay, last_epoch=-1):
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self.lr_decay = lr_decay
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super(NonLinearDecay, self).__init__(learning_rate, last_epoch)
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def get_lr(self):
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lr = self.base_lr / (1.0 + self.lr_decay * self.last_epoch)
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return lr
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@LRSCHEDULERS.register()
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class LinearDecay(LambdaDecay):
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def __init__(self, learning_rate, start_epoch, decay_epochs,
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iters_per_epoch):
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def lambda_rule(epoch):
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epoch = epoch // iters_per_epoch
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lr_l = 1.0 - max(0,
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epoch + 1 - start_epoch) / float(decay_epochs + 1)
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return lr_l
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super().__init__(learning_rate, lambda_rule)
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@LRSCHEDULERS.register()
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class CosineAnnealingRestartLR(LRScheduler):
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""" Cosine annealing with restarts learning rate scheme.
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An example config from configs/edvr_l_blur_wo_tsa.yaml:
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learning_rate: !!float 4e-4
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periods: [150000, 150000, 150000, 150000]
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restart_weights: [1, 1, 1, 1]
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eta_min: !!float 1e-7
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It has four cycles, each has 150000 iterations. At 150000th, 300000th,
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450000th, the scheduler will restart with the weights in restart_weights.
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Args:
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learning_rate (float): Base learning rate.
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periods (list): Period for each cosine anneling cycle.
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restart_weights (list): Restart weights at each restart iteration.
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Default: [1].
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eta_min (float): The mimimum learning rate of the cosine anneling cycle. Default: 0.
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last_epoch (int): Used in paddle.nn._LRScheduler. Default: -1.
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"""
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def __init__(self,
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learning_rate,
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periods,
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restart_weights=[1],
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eta_min=0,
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last_epoch=-1):
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self.periods = periods
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self.restart_weights = restart_weights
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self.eta_min = eta_min
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assert (len(self.periods) == len(self.restart_weights)
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), 'periods and restart_weights should have the same length.'
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self.cumulative_period = [
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sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
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]
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super(CosineAnnealingRestartLR, self).__init__(learning_rate,
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last_epoch)
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def get_lr(self):
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for i, period in enumerate(self.cumulative_period):
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if self.last_epoch <= period:
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index = i
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break
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current_weight = self.restart_weights[index]
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nearest_restart = 0 if index == 0 else self.cumulative_period[index - 1]
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current_period = self.periods[index]
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lr = self.eta_min + current_weight * 0.5 * (
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self.base_lr - self.eta_min) * (1 + math.cos(math.pi * (
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(self.last_epoch - nearest_restart) / current_period)))
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return lr
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