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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Inspired from https://github.com/rwightman/pytorch-image-models
from contextlib import contextmanager
import torch
from .states import swap_state
class ModelEMA:
"""
Perform EMA on a model. You can switch to the EMA weights temporarily
with the `swap` method.
ema = ModelEMA(model)
with ema.swap():
# compute valid metrics with averaged model.
"""
def __init__(self, model, decay=0.9999, unbias=True, device='cpu'):
self.decay = decay
self.model = model
self.state = {}
self.count = 0
self.device = device
self.unbias = unbias
self._init()
def _init(self):
for key, val in self.model.state_dict().items():
if val.dtype != torch.float32:
continue
device = self.device or val.device
if key not in self.state:
self.state[key] = val.detach().to(device, copy=True)
def update(self):
if self.unbias:
self.count = self.count * self.decay + 1
w = 1 / self.count
else:
w = 1 - self.decay
for key, val in self.model.state_dict().items():
if val.dtype != torch.float32:
continue
device = self.device or val.device
self.state[key].mul_(1 - w)
self.state[key].add_(val.detach().to(device), alpha=w)
@contextmanager
def swap(self):
with swap_state(self.model, self.state):
yield
def state_dict(self):
return {'state': self.state, 'count': self.count}
def load_state_dict(self, state):
self.count = state['count']
for k, v in state['state'].items():
self.state[k].copy_(v)
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