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100 lines
3.6 KiB
100 lines
3.6 KiB
# 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, epoch + 1 - |
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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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