33 lines
1,012 B
Python
33 lines
1,012 B
Python
|
import torch
|
||
|
|
|
||
|
|
|
||
|
|
def make_optimize(
|
||
|
|
parameters: list[list[torch.nn.parameter.Parameter]],
|
||
|
|
lr_initial: list[float],
|
||
|
|
eps=1e-10,
|
||
|
|
) -> tuple[
|
||
|
|
list[torch.optim.Adam | None],
|
||
|
|
list[torch.optim.lr_scheduler.ReduceLROnPlateau | None],
|
||
|
|
]:
|
||
|
|
list_optimizer: list[torch.optim.Adam | None] = []
|
||
|
|
list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = []
|
||
|
|
|
||
|
|
assert len(parameters) == len(lr_initial)
|
||
|
|
|
||
|
|
for i in range(0, len(parameters)):
|
||
|
|
if len(parameters[i]) > 0:
|
||
|
|
list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i]))
|
||
|
|
else:
|
||
|
|
list_optimizer.append(None)
|
||
|
|
|
||
|
|
for i in range(0, len(list_optimizer)):
|
||
|
|
if list_optimizer[i] is not None:
|
||
|
|
pass
|
||
|
|
list_lr_scheduler.append(
|
||
|
|
torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
list_lr_scheduler.append(None)
|
||
|
|
|
||
|
|
return (list_optimizer, list_lr_scheduler)
|