Class AbsTask

espnet2/tasks/abs_task.py:207–2553

class AbsTask(ABC):
    # Use @staticmethod, or @classmethod,
    # instead of instance method to avoid God classes

    # If you need more than one optimizers, change this value in inheritance
    num_optimizers: int = 1
    trainer = Trainer
    class_choices_list: List[ClassChoices] = []

    def __init__(self):
        raise RuntimeError("This class can't be instantiated.")

    @classmethod
    @abstractmethod
    def add_task_arguments(cls, parser: argparse.ArgumentParser):
        pass

    @classmethod
    @abstractmethod
    def build_collate_fn(
        cls, args: argparse.Namespace, train: bool
    ) -> Callable[[Sequence[Dict[str, np.ndarray]]], Dict[str, torch.Tensor]]:
        """Return "collate_fn", which is a callable object and given to DataLoader.

        >>> from torch.utils.data import DataLoader
        >>> loader = DataLoader(collate_fn=cls.build_collate_fn(args, train=True), ...)

        In many cases, you can use our common collate_fn.
        """
        raise NotImplementedError

    @classmethod
    @abstractmethod
    def build_preprocess_fn(
        cls, args: argparse.Namespace, train: bool
    ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
        raise NotImplementedError

    @classmethod
    @abstractmethod
    def required_data_names(
        cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        """Define the required names by Task

        This function is used by
        >>> cls.check_task_requirements()
        If your model is defined as following,

        >>> from espnet2.train.abs_espnet_model import AbsESPnetModel
        >>> class Model(AbsESPnetModel):
        ...     def forward(self, input, output, opt=None):  pass

        then "required_data_names" should be as

        >>> required_data_names = ('input', 'output')
        """
        raise NotImplementedError