1.原理：
EMA也就是指数移动平均(Exponential moving average)。其公式非常简单，如下所示：
\(\theta_{\text{EMA}, t+1} = (1 - \lambda) \cdot \theta_{\text{EMA}, t} + \lambda \cdot \theta_{t}\)
\(\theta_{t}\)是t时刻的网络参数，\(\theta_{\text{EMA}, t}\)是t时刻滑动平均后的网络参数，那么t+1时刻的滑动平均结果就是这两者的加权融合。这里 \(\lambda\)通常会取接近于1的数，比如0.9995，数字越大平均的效果就比较强。
值得注意的是，这里可以看成有两个模型，基础模型其参数按照常规的前后向传播来更新，另外一个模型则是基础模型的滑动平均版本，它并不直接参与前后向传播，仅仅是利用基础模型的参数结果来更新自己。
EMA为什么会有效呢？大概是因为在训练的时候，会使用验证集来衡量模型精度，但其实验证集精度并不和测试集一致，在训练后期阶段，模型可能已经在测试集最佳精度附近波动，所以使用滑动平均的结果会比使用单一结果更加可靠。感兴趣的话可以看看这几篇论文,论文1,论文2,论文3。
2.实现：
Pytorch其实已经为我们实现了这一功能，为了避免自己造轮子可能引入的错误，这里直接学习一下官方的代码。这个类的名称就叫做AveragedModel。代码如下所示。
我们需要做的是提供avg_fn这个函数，avg_fn用来指定以何种方式进行平均。
class AveragedModel(Module):
"""
You can also use custom averaging functions with `avg_fn` parameter.
If no averaging function is provided, the default is to compute
equally-weighted average of the weights.
"""
def __init__(self, model, device=None, avg_fn=None, use_buffers=False):
super(AveragedModel, self).__init__()
self.module = deepcopy(model)
if device is not None:
self.module = self.module.to(device)
self.register_buffer('n_averaged',
torch.tensor(0, dtype=torch.long, device=device))
if avg_fn is None:
def avg_fn(averaged_model_parameter, model_parameter, num_averaged):
return averaged_model_parameter + \
(model_parameter - averaged_model_parameter) / (num_averaged + 1)
self.avg_fn = avg_fn
self.use_buffers = use_buffers
def forward(self, *args, **kwargs):
return self.module(*args, **kwargs)
def update_parameters(self, model):
self_param = (
itertools.chain(self.module.parameters(), self.module.buffers())
if self.use_buffers else self.parameters()
)
model_param = (
itertools.chain(model.parameters(), model.buffers())
if self.use_buffers else model.parameters()
)
for p_swa, p_model in zip(self_param, model_param):
device = p_swa.device
p_model_ = p_model.detach().to(device)
if self.n_averaged == 0:
p_swa.detach().copy_(p_model_)
else:
p_swa.detach().copy_(self.avg_fn(p_swa.detach(), p_model_,
self.n_averaged.to(device)))
self.n_averaged += 1
@torch.no_grad()
def update_bn(loader, model, device=None):
r"""Updates BatchNorm running_mean, running_var buffers in the model.
It performs one pass over data in `loader` to estimate the activation
statistics for BatchNorm layers in the model.
Args:
loader (torch.utils.data.DataLoader): dataset loader to compute the
activation statistics on. Each data batch should be either a
tensor, or a list/tuple whose first element is a tensor
containing data.
model (torch.nn.Module): model for which we seek to update BatchNorm
statistics.
device (torch.device, optional): If set, data will be transferred to
:attr:`device` before being passed into :attr:`model`.
Example:
>>> loader, model = ...
>>> torch.optim.swa_utils.update_bn(loader, model)
.. note::
The `update_bn` utility assumes that each data batch in :attr:`loader`
is either a tensor or a list or tuple of tensors; in the latter case it
is assumed that :meth:`model.forward()` should be called on the first
element of the list or tuple corresponding to the data batch.
"""
momenta = {}
for module in model.modules():
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
module.running_mean = torch.zeros_like(module.running_mean)
module.running_var = torch.ones_like(module.running_var)
momenta[module] = module.momentum
if not momenta:
return
was_training = model.training
model.train()
for module in momenta.keys():
module.momentum = None
module.num_batches_tracked *= 0
for input in loader:
if isinstance(input, (list, tuple)):
input = input[0]
if device is not None:
input = input.to(device)
model(input)
for bn_module in momenta.keys():
bn_module.momentum = momenta[bn_module]
model.train(was_training)
这里同样参考官方的示例代码，给出滑动平均的实现。ExponentialMovingAverage继承了AveragedModel，并且复写了init方法，其实更直接的方法是将ema_avg函数作为参数传递给AveragedModel，这里可能是为了可读性，避免出现一个孤零零的ema_avg函数。
class ExponentialMovingAverage(torch.optim.swa_utils.AveragedModel):
"""Maintains moving averages of model parameters using an exponential decay.
``ema_avg = decay * avg_model_param + (1 - decay) * model_param``
`torch.optim.swa_utils.AveragedModel <https://pytorch.org/docs/stable/optim.html#custom-averaging-strategies>`_
is used to compute the EMA.
"""
def __init__(self, model, decay, device="cpu"):
def ema_avg(avg_model_param, model_param, num_averaged):
return decay * avg_model_param + (1 - decay) * model_param
super().__init__(model, device, ema_avg, use_buffers=True)
如何使用呢？方式是比较简单的，首先是利用当前模型创建出一个滑动平均模型。
model_ema = utils.ExponentialMovingAverage(model, device=device, decay=ema_decay)
然后是进行基础模型的前后向传播，更新结束后再对滑动平均版的模型进行参数更新。
output = model(image)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
model_ema.update_parameters(model)