ABLkit/ablkit/learning/torch_dataset/classification_dataset.py

"""
Implementation of PyTorch dataset class used for classification.

Copyright (c) 2024 LAMDA.  All rights reserved.
"""

from typing import Any, Callable, List, Tuple, Optional

import torch
from torch.utils.data import Dataset


class ClassificationDataset(Dataset):
    """
    Dataset used for classification task.

    Parameters
    ----------
    X : List[Any]
        The input data.
    Y : List[int]
        The target data.
    transform : Callable[..., Any], optional
        A function/transform that takes an object and returns a transformed version.
        Defaults to None.
    """

    def __init__(self, X: List[Any], Y: List[int], transform: Optional[Callable[..., Any]] = None):
        if (not isinstance(X, list)) or (not isinstance(Y, list)):
            raise ValueError("X and Y should be of type list.")
        if len(X) != len(Y):
            raise ValueError("Length of X and Y must be equal.")

        self.X = X
        self.Y = torch.LongTensor(Y)
        self.transform = transform

    def __len__(self) -> int:
        """
        Return the length of the dataset.

        Returns
        -------
        int
            The length of the dataset.
        """
        return len(self.X)

    def __getitem__(self, index: int) -> Tuple[Any, torch.Tensor]:
        """
        Get the item at the given index.

        Parameters
        ----------
        index : int
            The index of the item to get.

        Returns
        -------
        Tuple[Any, torch.Tensor]
            A tuple containing the object and its label.
        """
        if index >= len(self):
            raise ValueError("index range error")

        x = self.X[index]
        if self.transform is not None:
            x = self.transform(x)

        y = self.Y[index]

        return x, y