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103 lines
3.4 KiB
103 lines
3.4 KiB
3 years ago
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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 numpy as np
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import paddle
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class EMA(object):
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"""
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The implementation of Exponential Moving Average for the trainable parameters.
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Args:
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model (nn.Layer): The model for applying EMA.
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decay (float, optional): Decay is used to calculate ema_variable by
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`ema_variable = decay * ema_variable + (1 - decay) * new_variable`.
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Default: 0.99.
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Returns:
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None
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Examples:
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.. code-block:: python
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# 1. Define model and dataset
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# 2. Create EMA
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ema = EMA(model, decay=0.99)
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# 3. Train stage
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for data in dataloader():
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...
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optimizer.step()
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ema.step()
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# 4. Evaluate stage
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ema.apply() # Use the EMA data to replace the origin data
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for data in dataloader():
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...
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ema.restore() # Restore the origin data to the model
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"""
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def __init__(self, model, decay=0.99):
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super().__init__()
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assert isinstance(model, paddle.nn.Layer), \
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"The model should be the instance of paddle.nn.Layer."
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assert decay >= 0 and decay <= 1.0, \
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"The decay = {} should in [0.0, 1.0]".format(decay)
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self._model = model
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self._decay = decay
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self._ema_data = {}
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self._backup_data = {}
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for name, param in self._model.named_parameters():
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if not param.stop_gradient:
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self._ema_data[name] = param.numpy()
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def step(self):
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"""
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Calculate the EMA data for all trainable parameters.
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"""
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for name, param in self._model.named_parameters():
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if not param.stop_gradient:
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assert name in self._ema_data, \
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"The param ({}) isn't in the model".format(name)
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self._ema_data[name] = self._decay * self._ema_data[name] \
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+ (1.0 - self._decay) * param.numpy()
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def apply(self):
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"""
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Save the origin data and use the EMA data to replace the origin data.
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"""
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for name, param in self._model.named_parameters():
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if not param.stop_gradient:
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assert name in self._ema_data, \
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"The param ({}) isn't in the model".format(name)
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self._backup_data[name] = param.numpy()
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param.set_value(self._ema_data[name])
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def restore(self):
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"""
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Restore the origin data to the model.
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"""
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for name, param in self._model.named_parameters():
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if not param.stop_gradient:
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assert name in self._backup_data, \
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"The param ({}) isn't in the model".format(name)
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param.set_value(self._backup_data[name])
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self._backup_data = {}
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