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136 lines
4.9 KiB
136 lines
4.9 KiB
# Copyright (c) 2022 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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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import math |
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import paddle |
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import weakref |
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class ModelEMA(object): |
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""" |
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Exponential Weighted Average for Deep Neutal Networks |
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Args: |
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model (nn.Layer): Detector of model. |
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decay (int): The decay used for updating ema parameter. |
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Ema's parameter are updated with the formula: |
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`ema_param = decay * ema_param + (1 - decay) * cur_param`. |
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Defaults is 0.9998. |
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ema_decay_type (str): type in ['threshold', 'normal', 'exponential'], |
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'threshold' as default. |
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cycle_epoch (int): The epoch of interval to reset ema_param and |
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step. Defaults is -1, which means not reset. Its function is to |
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add a regular effect to ema, which is set according to experience |
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and is effective when the total training epoch is large. |
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ema_black_list (set|list|tuple, optional): The custom EMA black_list. |
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Blacklist of weight names that will not participate in EMA |
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calculation. Default: None. |
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""" |
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def __init__(self, |
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model, |
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decay=0.9998, |
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ema_decay_type='threshold', |
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cycle_epoch=-1, |
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ema_black_list=None): |
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self.step = 0 |
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self.epoch = 0 |
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self.decay = decay |
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self.ema_decay_type = ema_decay_type |
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self.cycle_epoch = cycle_epoch |
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self.ema_black_list = self._match_ema_black_list( |
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model.state_dict().keys(), ema_black_list) |
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self.state_dict = dict() |
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for k, v in model.state_dict().items(): |
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if k in self.ema_black_list: |
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self.state_dict[k] = v |
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else: |
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self.state_dict[k] = paddle.zeros_like(v) |
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self._model_state = { |
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k: weakref.ref(p) |
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for k, p in model.state_dict().items() |
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} |
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def reset(self): |
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self.step = 0 |
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self.epoch = 0 |
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for k, v in self.state_dict.items(): |
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if k in self.ema_black_list: |
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self.state_dict[k] = v |
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else: |
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self.state_dict[k] = paddle.zeros_like(v) |
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def resume(self, state_dict, step=0): |
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for k, v in state_dict.items(): |
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if k in self.state_dict: |
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if self.state_dict[k].dtype == v.dtype: |
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self.state_dict[k] = v |
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else: |
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self.state_dict[k] = v.astype(self.state_dict[k].dtype) |
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self.step = step |
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def update(self, model=None): |
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if self.ema_decay_type == 'threshold': |
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decay = min(self.decay, (1 + self.step) / (10 + self.step)) |
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elif self.ema_decay_type == 'exponential': |
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decay = self.decay * (1 - math.exp(-(self.step + 1) / 2000)) |
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else: |
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decay = self.decay |
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self._decay = decay |
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if model is not None: |
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model_dict = model.state_dict() |
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else: |
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model_dict = {k: p() for k, p in self._model_state.items()} |
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assert all( |
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[v is not None for _, v in model_dict.items()]), 'python gc.' |
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for k, v in self.state_dict.items(): |
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if k not in self.ema_black_list: |
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v = decay * v + (1 - decay) * model_dict[k] |
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v.stop_gradient = True |
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self.state_dict[k] = v |
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self.step += 1 |
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def apply(self): |
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if self.step == 0: |
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return self.state_dict |
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state_dict = dict() |
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for k, v in self.state_dict.items(): |
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if k in self.ema_black_list: |
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v.stop_gradient = True |
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state_dict[k] = v |
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else: |
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if self.ema_decay_type != 'exponential': |
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v = v / (1 - self._decay**self.step) |
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v.stop_gradient = True |
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state_dict[k] = v |
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self.epoch += 1 |
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if self.cycle_epoch > 0 and self.epoch == self.cycle_epoch: |
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self.reset() |
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return state_dict |
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def _match_ema_black_list(self, weight_name, ema_black_list=None): |
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out_list = set() |
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if ema_black_list: |
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for name in weight_name: |
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for key in ema_black_list: |
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if key in name: |
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out_list.add(name) |
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return out_list
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