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808036c | 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 46 47 48 49 50 | import torch
class EMAWrapper(object):
"""A wrapper class for exponential moving average of model weights."""
def __init__(
self, model: torch.nn.Module, decay: float = 0.999, mutable_param_keywords=None
):
"""
model: a pytorch model to apply EMA
decay: a scaler to indicate the decay rate
mutable_param_keywords: keywords of parameters to apply EMA decay, other params will stay untouched
"""
self.model = model
self.decay = decay
self.mutable_param_keywords = [
s.strip() for s in mutable_param_keywords if s.strip()
]
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
self.shadow[name] = param.data.clone()
def update(self):
for name, param in self.model.named_parameters():
if self.mutable_param_keywords and not any(
[keyword in name for keyword in self.mutable_param_keywords]
):
continue
assert name in self.shadow
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[
name
]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
assert name in self.shadow
self.backup[name] = param.data
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
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