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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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.
- import paddle
- class ExponentialMovingAverage(object):
- def __init__(self, decay, model, use_thres_step=False):
- self.step = 0
- self.decay = decay
- self.shadow = dict()
- for k, v in model.state_dict().items():
- self.shadow[k] = paddle.zeros_like(v)
- self.use_thres_step = use_thres_step
- def update(self, model):
- if self.use_thres_step:
- decay = min(self.decay, (1 + self.step) / (10 + self.step))
- else:
- decay = self.decay
- self._decay = decay
- model_dict = model.state_dict()
- for k, v in self.shadow.items():
- v = decay * v + (1 - decay) * model_dict[k]
- v.stop_gradient = True
- self.shadow[k] = v
- self.step += 1
- def apply(self):
- if self.step == 0:
- return self.shadow
- state_dict = dict()
- for k, v in self.shadow.items():
- v = v / (1 - self._decay**self.step)
- v.stop_gradient = True
- state_dict[k] = v
- return state_dict
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