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63 lines
2.2 KiB
Python
63 lines
2.2 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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import copy
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import paddle
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__all__ = ['build_optimizer']
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def build_lr_scheduler(lr_config, epochs, step_each_epoch):
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from . import learning_rate
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lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch})
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lr_name = lr_config.pop('name', 'Const')
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lr = getattr(learning_rate, lr_name)(**lr_config)()
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return lr
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def build_optimizer(config, epochs, step_each_epoch, model):
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from . import regularizer, optimizer
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config = copy.deepcopy(config)
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# step1 build lr
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lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)
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# step2 build regularization
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if 'regularizer' in config and config['regularizer'] is not None:
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reg_config = config.pop('regularizer')
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reg_name = reg_config.pop('name')
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if not hasattr(regularizer, reg_name):
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reg_name += 'Decay'
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reg = getattr(regularizer, reg_name)(**reg_config)()
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elif 'weight_decay' in config:
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reg = config.pop('weight_decay')
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else:
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reg = None
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# step3 build optimizer
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optim_name = config.pop('name')
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if 'clip_norm' in config:
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clip_norm = config.pop('clip_norm')
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grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
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else:
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grad_clip = None
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optim = getattr(optimizer, optim_name)(learning_rate=lr,
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weight_decay=reg,
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grad_clip=grad_clip,
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**config)
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return optim(model), lr
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