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53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
# 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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from typing import Dict
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from paddle.optimizer.lr import LRScheduler
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from . import custom_lr
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def build_lr(cfg: Dict, num_iters: int) -> LRScheduler:
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"""Build a learning rate scheduler accroding to ```OPTIMIZER``` configuration, and it always pass into the optimizer.
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In configuration:
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learning_rate:
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name: 'PiecewiseDecay'
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boundaries: [20, 60]
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values: [0.00025, 0.000025, 0.0000025]
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Args:
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cfg (Dict): learning rate configuration.
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num_iters (int): The number of iterations that may be used when calculating the learning rate
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Returns:
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LRScheduler: learning rate scheduler.
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"""
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cfg_copy = cfg.copy()
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#when learning_rate is LRScheduler
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if cfg_copy.get('learning_rate') and isinstance(cfg_copy['learning_rate'],
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dict):
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cfg_copy['learning_rate'] = build_lr(
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cfg_copy['learning_rate'],
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num_iters) #not support only inner iter_step
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lr_name = cfg_copy.pop('name')
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if cfg_copy.get('iter_step'):
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cfg_copy['num_iters'] = num_iters
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cfg_copy.pop('iter_step')
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return getattr(custom_lr, lr_name)(**cfg_copy)
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