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64 lines
2.3 KiB
64 lines
2.3 KiB
3 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import numpy as np
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class ExponentialMovingAverage():
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"""
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Exponential Moving Average
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Code was heavily based on https://github.com/Wanger-SJTU/SegToolbox.Pytorch/blob/master/lib/utils/ema.py
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"""
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def __init__(self, model, decay, thres_steps=True):
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self._model = model
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self._decay = decay
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self._thres_steps = thres_steps
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self._shadow = {}
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self._backup = {}
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def register(self):
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self._update_step = 0
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for name, param in self._model.named_parameters():
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if param.stop_gradient is False:
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self._shadow[name] = param.numpy().copy()
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def update(self):
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decay = min(self._decay, (1 + self._update_step) / (
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10 + self._update_step)) if self._thres_steps else self._decay
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for name, param in self._model.named_parameters():
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if param.stop_gradient is False:
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assert name in self._shadow
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new_val = np.array(param.numpy().copy())
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old_val = np.array(self._shadow[name])
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new_average = decay * old_val + (1 - decay) * new_val
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self._shadow[name] = new_average
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self._update_step += 1
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return decay
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def apply(self):
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for name, param in self._model.named_parameters():
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if param.stop_gradient is False:
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assert name in self._shadow
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self._backup[name] = np.array(param.numpy().copy())
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param.set_value(np.array(self._shadow[name]))
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def restore(self):
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for name, param in self._model.named_parameters():
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if param.stop_gradient is False:
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assert name in self._backup
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param.set_value(self._backup[name])
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self._backup = {}
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