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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
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