ExponentialMovingAverage¶
- class mmengine.model.ExponentialMovingAverage(model, momentum=0.0002, interval=1, device=None, update_buffers=False)[源代码]¶
Implements the exponential moving average (EMA) of the model.
All parameters are updated by the formula as below:
\[Xema_{t+1} = (1 - momentum) * Xema_{t} + momentum * X_t\]备注
This
momentum
argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, \(Xema_{t+1}\) is the moving average and \(X_t\) is the new observed value. The value of momentum is usually a small number, allowing observed values to slowly update the ema parameters.- 参数
model (nn.Module) – The model to be averaged.
momentum (float) – The momentum used for updating ema parameter. Defaults to 0.0002. Ema’s parameter are updated with the formula \(averaged\_param = (1-momentum) * averaged\_param + momentum * source\_param\).
interval (int) – Interval between two updates. Defaults to 1.
device (torch.device, optional) – If provided, the averaged model will be stored on the
device
. Defaults to None.update_buffers (bool) – if True, it will compute running averages for both the parameters and the buffers of the model. Defaults to False.
- 返回类型
None
- avg_func(averaged_param, source_param, steps)[源代码]¶
Compute the moving average of the parameters using exponential moving average.
- 参数
averaged_param (Tensor) – The averaged parameters.
source_param (Tensor) – The source parameters.
steps (int) – The number of times the parameters have been updated.
- 返回类型
None