62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# 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})
|
|
if 'name' in lr_config:
|
|
lr_name = lr_config.pop('name')
|
|
lr = getattr(learning_rate, lr_name)(**lr_config)()
|
|
else:
|
|
lr = lr_config['learning_rate']
|
|
return lr
|
|
|
|
|
|
def build_optimizer(config, epochs, step_each_epoch, parameters):
|
|
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') + 'Decay'
|
|
reg = getattr(regularizer, reg_name)(**reg_config)()
|
|
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)
|
|
else:
|
|
grad_clip = None
|
|
optim = getattr(optimizer, optim_name)(learning_rate=lr,
|
|
weight_decay=reg,
|
|
grad_clip=grad_clip,
|
|
**config)
|
|
return optim(parameters), lr
|