74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
# Copyright 2021 NVIDIA CORPORATION
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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# Copyright 2019 Ross Wightman
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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"""
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Exponential Moving Average (EMA) of model updates
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"""
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from collections import OrderedDict
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from copy import deepcopy
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import torch
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import torch.nn as nn
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class ModelEma(nn.Module):
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""" Model Exponential Moving Average V2
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Keep a moving average of everything in the model state_dict (parameters and buffers).
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V2 of this module is simpler, it does not match params/buffers based on name but simply
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iterates in order. It works with torchscript (JIT of full model).
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"""
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def __init__(self, model, decay=0.999, device=None):
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super().__init__()
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# make a copy of the model for accumulating moving average of weights
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self.module = deepcopy(model)
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self.module.eval()
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self.decay = decay
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self.device = device # perform ema on different device from model if set
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if self.device is not None:
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self.module.to(device=device)
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def update(self, model):
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update_fn=lambda ema_v, model_v: self.decay * ema_v + (1. - self.decay) * model_v
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with torch.no_grad():
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for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
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if self.device is not None:
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model_v = model_v.to(device=self.device)
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ema_v.copy_(update_fn(ema_v, model_v))
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def set(self, model):
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with torch.no_grad():
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for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
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if self.device is not None:
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model_v = model_v.to(device=self.device)
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ema_v.copy_( model_v )
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def forward(self, x):
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return self.module(x)
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