[英]Can I define a method as an attribute?
The topic above is a bit ambiguous, the explaination is below:上面的题目有点含糊,解释如下:
class Trainer:
"""Object used to facilitate training."""
def __init__(
self,
# params: Namespace,
params,
model,
device=torch.device("cpu"),
optimizer=None,
scheduler=None,
wandb_run=None,
early_stopping: callbacks.EarlyStopping = None,
):
# Set params
self.params = params
self.model = model
self.device = device
# self.optimizer = optimizer
self.optimizer = self.get_optimizer()
self.scheduler = scheduler
self.wandb_run = wandb_run
self.early_stopping = early_stopping
# list to contain various train metrics
# TODO: how to add more metrics? wandb log too. Maybe save to model artifacts?
self.history = DefaultDict(list)
@staticmethod
def get_optimizer(
model: models.CustomNeuralNet,
optimizer_params: global_params.OptimizerParams(),
):
"""Get the optimizer for the model.
Args:
model (models.CustomNeuralNet): [description]
optimizer_params (global_params.OptimizerParams): [description]
Returns:
[type]: [description]
"""
return getattr(torch.optim, optimizer_params.optimizer_name)(
model.parameters(), **optimizer_params.optimizer_params
)
Notice that initially I passed in optimizer
in the constructor, where I will be calling it outside this class.请注意,最初我在构造函数中传入了optimizer
,我将在此 class 之外调用它。 However, I now put get_optimizer
inside the class itself (for consistency purpose, but unsure if it is ok).但是,我现在将get_optimizer
放入 class 本身(出于一致性目的,但不确定是否可以)。 So, should I still define self.optimizer = self.get_optimizer()
or just use self.get_optimizer()
at the designated places in the class?那么,我应该仍然定义self.optimizer = self.get_optimizer()
还是只在 class 的指定位置使用self.get_optimizer()
? The former encourages some readability for me.前者鼓励了我的一些可读性。
Addendum: I now put the instance inside the .fit()
method where I will call say 5 times to train the model 5 times.附录:我现在将实例放在.fit()
方法中,我将调用 5 次来训练 model 5 次。 In this scenario, even though there won't be any obvious issue as we are using optimizer once per call, will it still be better to not define self.optimizer
here?在这种情况下,即使我们每次调用都使用一次优化器不会有任何明显的问题,但在这里不定义self.optimizer
会更好吗?
def fit(
self,
train_loader: torch.utils.data.DataLoader,
valid_loader: torch.utils.data.DataLoader,
fold: int = None,
):
"""[summary]
Args:
train_loader (torch.utils.data.DataLoader): [description]
val_loader (torch.utils.data.DataLoader): [description]
fold (int, optional): [description]. Defaults to None.
Returns:
[type]: [description]
"""
self.optimizer = self.get_optimizer(
model=self.model, optimizer_params=OPTIMIZER_PARAMS
)
self.scheduler = self.get_scheduler(
optimizer=self.optimizer, scheduler_params=SCHEDULER_PARAMS
)
There is a difference between the two: calling your get_optimizer
will instantiate a new torch.optim.<optimizer>
every time.两者之间是有区别的:调用你的get_optimizer
都会实例化一个新的torch.optim.<optimizer>
。 In contrast, setting self.optimizer
and accessing it numerous times later will only create a single optimizer instance.相反,设置self.optimizer
并在以后多次访问它只会创建一个优化器实例。
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