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b6ff324 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # ------------------------------------------------------------------------
# Copyright (c) 2024-present, BAAI. 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.
# ------------------------------------------------------------------------
"""Exponential Moving Average (EMA) of model updates."""
import copy
import torch
class ModelEMA(torch.nn.Module):
"""Model Exponential Moving Average."""
def __init__(self, model, decay=0.99, update_every=100, device="gpu"):
super().__init__()
self.decay = decay
self.update_every = update_every
self.model = copy.deepcopy(model).eval()
self.model._apply(lambda t: t.float() if t.requires_grad else t) if decay < 1 else None
[setattr(p, "requires_grad", False) for p in self.model.parameters()]
self.model.cpu() if device == "cpu" else None
def forward(self, *args, **kwargs):
return self.model(*args, **kwargs)
@torch.no_grad()
def update(self, model):
for ema_v, model_v in zip(self.model.parameters(), model.parameters()):
if not model_v.requires_grad:
continue
new_value = model_v.data.float()
value = ema_v.to(device=new_value.device)
ema_v.copy_(value.mul_(self.decay).add_(new_value, alpha=1 - self.decay))
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