refactor: AdamP

pytorch_optimizer/adamp.py

@@ -52,36 +52,36 @@ def __init__(
  self.lr = lr
  self.betas = betas
  self.weight_decay = weight_decay
+ self.eps = eps
  self.wd_ratio = wd_ratio
  self.use_gc = use_gc
- self.eps = eps
 
  self.check_valid_parameters()
 
  defaults: DEFAULTS = dict(
  lr=lr,
  betas=betas,
- eps=eps,
  weight_decay=weight_decay,
  delta=delta,
  wd_ratio=wd_ratio,
  nesterov=nesterov,
+ eps=eps,
  )
- super().__init__(params, defaults)
+ super().__init__(params=params, default=defaults)
 
  def check_valid_parameters(self):
  if self.lr < 0.0:
  raise ValueError(f'Invalid learning rate : {self.lr}')
- if self.weight_decay < 0.0:
- raise ValueError(f'Invalid weight_decay : {self.weight_decay}')
  if not 0.0 <= self.betas[0] < 1.0:
  raise ValueError(f'Invalid beta_0 : {self.betas[0]}')
  if not 0.0 <= self.betas[1] < 1.0:
  raise ValueError(f'Invalid beta_1 : {self.betas[1]}')
- if not 0.0 <= self.wd_ratio < 1.0:
- raise ValueError(f'Invalid wd_ratio : {self.wd_ratio}')
+ if self.weight_decay < 0.0:
+ raise ValueError(f'Invalid weight_decay : {self.weight_decay}')
  if self.eps < 0.0:
  raise ValueError(f'Invalid eps : {self.eps}')
+ if not 0.0 <= self.wd_ratio < 1.0:
+ raise ValueError(f'Invalid wd_ratio : {self.wd_ratio}')
 
  @staticmethod
  def channel_view(x: torch.Tensor) -> torch.Tensor:
@@ -97,7 +97,7 @@ def cosine_similarity(
  y: torch.Tensor,
  eps: float,
  view_func: Callable[[torch.Tensor], torch.Tensor],
- ):
+ ) -> torch.Tensor:
  x = view_func(x)
  y = view_func(y)
  return F.cosine_similarity(x, y, dim=1, eps=eps).abs_()