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base_dataset

BaseDataset

Bases: Dataset

Base class for dataset.

Source code in lightning_boost/data/datasets/base_dataset.py 
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class BaseDataset(Dataset):
    """Base class for dataset."""

    def __init__(
        self,
        root: str,
        download: bool = False,
        transform: Optional[BaseTransform] = None,
        **kwargs
    ) -> None:
        """
        Initializes base dataset.

        Args:
            root (str): Directory, where data is to be stored.
            download (bool, optional): Whether dataset should be downloaded. Defaults to False.
            transform (BaseTransform, optional): Data transform. Defaults to None.
        """

        self.path = Path.joinpath(Path(root), Path(inflection.underscore(type(self).__name__)))
        self.transform = transform
        if download:
            self._download()

    def get_item(self, index: int) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """
        Returns data item at given index.

        Args:
            index (int): Index.

        Raises:
            NotImplementedError: Needs to be implemented for a concrete dataset.

        Returns:
            Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
        """

        raise NotImplementedError

    def __len__(self) -> int:
        """
        Returns size of dataset.

        Raises:
            NotImplementedError: Needs to be implemented for a concrete dataset.

        Returns:
            int: Dataset size.
        """

        raise NotImplementedError

    def download(self) -> None:
        """
        Downloads dataset.
        """

        pass

    def __getitem__(self, index: int) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """
        Returns (transformed) data item at given index.

        Args:
            index (int): Index.

        Returns:
            Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
        """

        inputs, targets = self.get_item(index)

        if self.transform is not None:
            inputs, targets = self.transform(inputs, targets)

        return inputs, targets

    def _download(self) -> None:
        """Downloads dataset (closure function)."""

        if Path.exists(self.path):
            return

        self.path.mkdir(parents=True, exist_ok=True)

        self.download()

__getitem__(index)

Returns (transformed) data item at given index.

Parameters:

Name Type Description Default
index int

Index.

 required

Returns:

Type Description
Tuple[Dict[str, Any], Dict[str, Any]]

Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
Source code in lightning_boost/data/datasets/base_dataset.py 
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def __getitem__(self, index: int) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """
    Returns (transformed) data item at given index.

    Args:
        index (int): Index.

    Returns:
        Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
    """

    inputs, targets = self.get_item(index)

    if self.transform is not None:
        inputs, targets = self.transform(inputs, targets)

    return inputs, targets

__init__(root, download=False, transform=None, **kwargs)

Initializes base dataset.

Parameters:

Name Type Description Default
root str

Directory, where data is to be stored.

 required
download bool

Whether dataset should be downloaded. Defaults to False.

 False
transform BaseTransform

Data transform. Defaults to None.

 None
Source code in lightning_boost/data/datasets/base_dataset.py 
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def __init__(
    self,
    root: str,
    download: bool = False,
    transform: Optional[BaseTransform] = None,
    **kwargs
) -> None:
    """
    Initializes base dataset.

    Args:
        root (str): Directory, where data is to be stored.
        download (bool, optional): Whether dataset should be downloaded. Defaults to False.
        transform (BaseTransform, optional): Data transform. Defaults to None.
    """

    self.path = Path.joinpath(Path(root), Path(inflection.underscore(type(self).__name__)))
    self.transform = transform
    if download:
        self._download()

__len__()

Returns size of dataset.

Raises:

Type Description
NotImplementedError

Needs to be implemented for a concrete dataset.

Returns:

Name Type Description
int int

Dataset size.
Source code in lightning_boost/data/datasets/base_dataset.py 
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def __len__(self) -> int:
    """
    Returns size of dataset.

    Raises:
        NotImplementedError: Needs to be implemented for a concrete dataset.

    Returns:
        int: Dataset size.
    """

    raise NotImplementedError

download()

Downloads dataset.

Source code in lightning_boost/data/datasets/base_dataset.py 
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def download(self) -> None:
    """
    Downloads dataset.
    """

    pass

get_item(index)

Returns data item at given index.

Parameters:

Name Type Description Default
index int

Index.

 required

Raises:

Type Description
NotImplementedError

Needs to be implemented for a concrete dataset.

Returns:

Type Description
Tuple[Dict[str, Any], Dict[str, Any]]

Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
Source code in lightning_boost/data/datasets/base_dataset.py 
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def get_item(self, index: int) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """
    Returns data item at given index.

    Args:
        index (int): Index.

    Raises:
        NotImplementedError: Needs to be implemented for a concrete dataset.

    Returns:
        Tuple[Dict[str, Any], Dict[str, Any]]: Inputs, targets.
    """

    raise NotImplementedError