NTU ML2023Spring Part3.14 lifelong learning

示例代码中需要实现 MAS．

class mas(object):
  """
  @article{aljundi2017memory,
      title={Memory Aware Synapses: Learning what (not) to forget},
      author={Aljundi, Rahaf and Babiloni, Francesca and Elhoseiny, Mohamed and Rohrbach, Marcus and Tuytelaars, Tinne},
      booktitle={ECCV},
      year={2018},
      url={https://eccv2018.org/openaccess/content_ECCV_2018/papers/Rahaf_Aljundi_Memory_Aware_Synapses_ECCV_2018_paper.pdf}
  }
  """
  def __init__(self, model: nn.Module, dataloader, device, prev_guards=[None]):
    self.model = model
    self.dataloader = dataloader
    # extract all parameters in models
    self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad}

    # initialize parameters
    self.p_old = {}

    self.device = device

    # save previous guards
    self.previous_guards_list = prev_guards

    # generate Omega(Ω) matrix for MAS
    self._precision_matrices = self.calculate_importance()

    # keep the old parameter in self.p_old
    for n, p in self.params.items():
      self.p_old[n] = p.clone().detach()

  def calculate_importance(self):
    precision_matrices = {}
    # initialize Omega(Ω) matrix（all filled zero）
    for n, p in self.params.items():
      precision_matrices[n] = p.clone().detach().fill_(0)
      for i in range(len(self.previous_guards_list)):
        if self.previous_guards_list[i]:
          precision_matrices[n] += self.previous_guards_list[i][n]

    self.model.eval()
    if self.dataloader is not None:
      num_data = len(self.dataloader)
      for data in self.dataloader:
        self.model.zero_grad()
        output = self.model(data[0].to(self.device))
        ################################################################
        #####  TODO: generate Omega(Ω) matrix for MAS.  #####
        ################################################################
        ################################################################
        loss = output.pow(2).sum()
        loss.backward()

        for n, p in self.model.named_parameters():
          precision_matrices[n].data += abs(p.grad.data) / num_data

      precision_matrices = {n: p for n, p in precision_matrices.items()}
    return precision_matrices

  def penalty(self, model: nn.Module):
    loss = 0
    for n, p in model.named_parameters():
      _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2
      loss += _loss.sum()
    return loss

  def update(self, model):
    # do nothing
    return

其他的都实现好了，也不用做任何调整，跑 2~3h 即可．

这个 MAS 就是逊，连 baseline 都打不过（也可能是实现错了？）．