ExponentialMovingAverage

class mmengine.model.ExponentialMovingAverage(model, momentum=0.0002, interval=1, device=None, update_buffers=False)

Implements the exponential moving average (EMA) of the model.

All parameters are updated by the formula as below:

\[Xema_{t+1} = (1 - momentum) * Xema_{t} + momentum * X_t\]

Note

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.

Parameters

• 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.

Return type

None

avg_func(averaged_param, source_param, steps)

Compute the moving average of the parameters using exponential moving average.

Parameters

• averaged_param (Tensor) – The averaged parameters.

• source_param (Tensor) – The source parameters.

• steps (int) – The number of times the parameters have been updated.

Return type

None