def make(self, params, **kwargs):
argument = override(
kwargs,
lr=self.lr,
betas=self.betas,
eps=self.eps,
weight_decay=self.weight_decay,
)
return tensor_optim.LAMB(params, **argument)
def make(self, optimizer, **kwargs):
argument = override(
kwargs,
lr_min=self.lr_min,
lr_max=self.lr_max,
n_iter=self.n_iter,
linear=self.linear,
)
return lr_scheduler.lr_finder(optimizer, **argument)
def make(self, params, **kwargs):
argument = override(
kwargs,
lr=self.lr,
betas=self.betas,
eps=self.eps,
weight_decay=self.weight_decay,
amsgrad=self.amsgrad,
)
return optim.AdamW(params, **argument)
def make(self, params, **kwargs):
argument = override(
kwargs,
lr=self.lr,
momentum=self.momentum,
dampening=self.dampening,
weight_decay=self.weight_decay,
nesterov=self.nesterov,
)
return optim.SGD(params, **argument)
def make(self, optimizer, **kwargs):
argument = override(
kwargs,
lr=self.lr,
milestones=self.milestones,
gamma=self.gamma,
warmup=self.warmup,
warmup_multiplier=self.warmup_multiplier,
)
return lr_scheduler.step_scheduler(optimizer, **argument)
def make(self, optimizer, **kwargs):
argument = override(
kwargs,
lr=self.lr,
step=self.step,
max_iter=self.max_iter,
gamma=self.gamma,
warmup=self.warmup,
warmup_multiplier=self.warmup_multiplier,
)
return lr_scheduler.exp_scheduler(optimizer, **argument)
def make(self, **kwargs):
argument = override(
kwargs,
project=self.project,
group=self.group,
name=self.name,
notes=self.notes,
resume=self.resume,
tags=self.tags,
id=self.id,
)
return checker.WandB(**argument)
def make(self, **kwargs):
argument = override(
kwargs,
bucket=self.bucket,
path=self.path,
access_key=self.access_key,
secret_key=self.secret_key,
keep=self.keep,
endpoint=self.endpoint,
show_progress=self.show_progress,
)
return checker.S3(**argument)
def make(self, optimizer, **kwargs):
argument = override(
kwargs,
lr=self.lr,
n_iter=self.n_iter,
initial_multiplier=self.initial_multiplier,
final_multiplier=self.final_multiplier,
warmup=self.warmup,
plateau=self.plateau,
decay=self.decay,
)
return lr_scheduler.cycle_scheduler(optimizer, **argument)
def make(self, params, **kwargs):
argument = override(
kwargs,
lr=self.lr,
alpha=self.alpha,
eps=self.eps,
weight_decay=self.weight_decay,
momentum=self.momentum,
centered=self.centered,
decoupled_decay=self.decoupled_decay,
lr_in_momentum=self.lr_in_momentum,
)
return tensor_optim.RMSpropTF(params, **argument)
def make(self, **kwargs):
argument = override(kwargs, path=self.path, keep=self.keep)
return checker.Local(**argument)
