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| # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from __future__ import unicode_literals | |
| import copy | |
| import paddle | |
| __all__ = ['build_optimizer'] | |
| def build_lr_scheduler(lr_config, epochs, step_each_epoch): | |
| from . import learning_rate | |
| lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch}) | |
| lr_name = lr_config.pop('name', 'Const') | |
| lr = getattr(learning_rate, lr_name)(**lr_config)() | |
| return lr | |
| def build_optimizer(config, epochs, step_each_epoch, model): | |
| from . import regularizer, optimizer | |
| config = copy.deepcopy(config) | |
| # step1 build lr | |
| lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch) | |
| # step2 build regularization | |
| if 'regularizer' in config and config['regularizer'] is not None: | |
| reg_config = config.pop('regularizer') | |
| reg_name = reg_config.pop('name') | |
| if not hasattr(regularizer, reg_name): | |
| reg_name += 'Decay' | |
| reg = getattr(regularizer, reg_name)(**reg_config)() | |
| elif 'weight_decay' in config: | |
| reg = config.pop('weight_decay') | |
| else: | |
| reg = None | |
| # step3 build optimizer | |
| optim_name = config.pop('name') | |
| if 'clip_norm' in config: | |
| clip_norm = config.pop('clip_norm') | |
| grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm) | |
| elif 'clip_norm_global' in config: | |
| clip_norm = config.pop('clip_norm_global') | |
| grad_clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=clip_norm) | |
| else: | |
| grad_clip = None | |
| optim = getattr(optimizer, optim_name)(learning_rate=lr, | |
| weight_decay=reg, | |
| grad_clip=grad_clip, | |
| **config) | |
| return optim(model), lr | |