Thank you for your great repo!
I've used this package in nearly all of my projects. Gradually, I found that the most urgent and useful feature will be a nested argument parser. That is:
python my_app.py --arg1.subarg1.subsubarg1 xxx --arg1.subarg2 xxx --arg2.subarg1 xxx
I don't know whether there is a workaround. Or do you have any plans to add this feature?:smile:
Currently, my workaround is similar to this:
class TrainArgs(Tap):
tr_ctn: bool = None #: Flag for training continue.
ctn_epoch: int = None #: Start epoch for continue training.
class DatasetArgs(Tap):
dataset_name: str = None #: Name of the chosen dataset.
dataloader_name: str = None #: Name of the chosen dataloader. The default is BaseDataLoader.
class ModelArgs(Tap):
r"""
Correspond to ``model`` configs in config files.
"""
model_name: str = None #: Name of the chosen GNN.
model_layer: int = None #: Number of the GNN layer.
class OODArgs(Tap):
r"""
Correspond to ``ood`` configs in config files.
"""
ood_alg: str = None #: Name of the chosen OOD algorithm.
ood_param: float = None #: OOD algorithms' hyperparameter(s). Currently, most of algorithms use it as a float value.
class AutoArgs(Tap):
config_root: str = None #: The root of input configuration files.
launcher: str = None #: The launcher name.
class CommonArgs(Tap):
r"""
Correspond to general configs in config files.
"""
config_path: str = None #: (Required) The path for the config file.
task: Literal['train', 'test'] = None #: Running mode. Allowed: 'train' and 'test'.
# For code auto-complete
train: TrainArgs = None #: For code auto-complete
model: ModelArgs = None #: For code auto-complete
dataset: DatasetArgs = None #: For code auto-complete
ood: OODArgs = None #: For code auto-complete
def process_args(self) -> None:
super().process_args()
self.dataset = DatasetArgs().parse_args(args=self.argv, known_only=True)
self.train = TrainArgs().parse_args(args=self.argv, known_only=True)
self.model = ModelArgs().parse_args(args=self.argv, known_only=True)
self.ood = OODArgs().parse_args(args=self.argv, known_only=True)
def args_parser(argv: list=None):
common_args = CommonArgs(argv=argv).parse_args(args=argv, known_only=True)
return common_argsTherefore, I can use something like args.model.model_name in my code with autocomplete. But I cannot parse them in a similar way from the argv, e.g., --model.model_name XXX.
Thank you for your great repo!
I've used this package in nearly all of my projects. Gradually, I found that the most urgent and useful feature will be a nested argument parser. That is:
I don't know whether there is a workaround. Or do you have any plans to add this feature?:smile:
Currently, my workaround is similar to this:
Therefore, I can use something like
args.model.model_namein my code with autocomplete. But I cannot parse them in a similar way from the argv, e.g.,--model.model_name XXX.