Method update

ADHMR/lib/models/ema.py:32–51

Update currently maintained parameters. Call this every time the parameters are updated, such as the result of the `optimizer.step()` call. Args: parameters: Iterable of `torch.nn.Parameter`; usually the same set of parameters used to initialize this object.

(self, parameters)

    self.collected_params = []

  def update(self, parameters):
    """
    Update currently maintained parameters.

    Call this every time the parameters are updated, such as the result of
    the `optimizer.step()` call.

    Args:
      parameters: Iterable of `torch.nn.Parameter`; usually the same set of
        parameters used to initialize this object.
    """
    decay = self.decay
    if self.num_updates is not None:
      self.num_updates += 1
      decay = min(decay, (1 + self.num_updates) / (10 + self.num_updates))
    one_minus_decay = 1.0 - decay
    with torch.no_grad():
      parameters = [p for p in parameters if p.requires_grad]
      for s_param, param in zip(self.shadow_params, parameters):
        s_param.sub_(one_minus_decay * (s_param - param))

  def copy_to(self, parameters):
    """