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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import paddle
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from paddle import nn
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scheduler_classes = dict(
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ReduceOnPlateau=paddle.optimizer.lr.ReduceOnPlateau,
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lambda_decay=paddle.optimizer.lr.LambdaDecay,
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step_decay=paddle.optimizer.lr.StepDecay,
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multistep_decay=paddle.optimizer.lr.MultiStepDecay,
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exponential_decay=paddle.optimizer.lr.ExponentialDecay,
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CosineAnnealingDecay=paddle.optimizer.lr.CosineAnnealingDecay, )
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optim_classes = dict(
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adadelta=paddle.optimizer.Adadelta,
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adagrad=paddle.optimizer.Adagrad,
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adam=paddle.optimizer.Adam,
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adamax=paddle.optimizer.Adamax,
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adamw=paddle.optimizer.AdamW,
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lamb=paddle.optimizer.Lamb,
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momentum=paddle.optimizer.Momentum,
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rmsprop=paddle.optimizer.RMSProp,
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sgd=paddle.optimizer.SGD, )
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def build_optimizers(
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model: nn.Layer,
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optim='adadelta',
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max_grad_norm=None,
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learning_rate=0.01,
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weight_decay=None,
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epsilon=1.0e-6, ) -> paddle.optimizer:
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optim_class = optim_classes.get(optim)
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if optim_class is None:
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raise ValueError(f"must be one of {list(optim_classes)}: {optim}")
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else:
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grad_clip = None
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if max_grad_norm:
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grad_clip = paddle.nn.ClipGradByGlobalNorm(max_grad_norm)
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optim_dict = {}
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optim_dict['parameters'] = model.parameters()
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optim_dict['learning_rate'] = learning_rate
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optim_dict['grad_clip'] = grad_clip
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optim_dict['weight_decay'] = weight_decay
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if optim_class not in {'momentum', 'sgd'}:
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optim_dict['epsilon'] = epsilon
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optimizers = optim_class(**optim_dict)
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return optimizers
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