PyTorch 的 Dataset 类介绍

dataset 类

功能与作用

1. 在PyTorch中，Dataset 类是torch.utils.data模块的一部分，它是一个抽象的基类，用于定义了数据集加载和处理的标准接口。通过继承这个类并实现其方法，可以创建自定义的数据集来适应各种机器学习任务。

基本结构介绍

1. 抽象基类定义：是一个泛型类，使用 Generic[_T_co] 来表示它可以接受一个协变类型参数 _T_co。这个类是所有数据集类的基类，它定义了数据集应该遵循的基本接口。
   Dataset 类的主要组成部分：

   • 文档字符串（docstring）：提供了关于类的使用和实现的详细说明。它指出所有映射键到数据样本的数据集都应该继承这个类，并且应该覆盖 __getitem__ 方法来支持给定键的数据样本获取。它还提到子类可以选择性地覆盖 __len__ 方法来返回数据集的大小，这在很多情况下是有用的，比如在 Sampler 实现和 DataLoader 的默认选项中。此外，子类也可以选择性地实现 __getitems__ 方法来加速批量样本加载。

   • __getitem__ 方法：这是一个抽象方法，子类必须实现它。这个方法应该根据给定的索引返回对应的数据样本。如果子类没有实现这个方法，尝试获取数据样本时会抛出 NotImplementedError。

   • __getitems__ 方法：这个方法被注释掉了，但它是可选的，用于加速批量样本的加载。如果实现，它应该接受一个样本索引列表，并返回一个样本列表。

   • __add__ 方法：这个方法允许将两个 Dataset 对象相加，返回一个新的 ConcatDataset 对象，该对象将两个数据集合并为一个连续的数据集。

   • __len__ 方法的注释：这部分注释说明为什么没有为 Dataset 类提供一个默认的 __len__ 方法。正如之前解释的，如果子类没有实现 __len__ 方法，那么在尝试获取数据集大小时会抛出 TypeError，这是一种强制子类提供实现的方式。

class Dataset(Generic[_T_co]):
    r"""An abstract class representing a :class:`Dataset`.

    All datasets that represent a map from keys to data samples should subclass
    it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
    data sample for a given key. Subclasses could also optionally overwrite
    :meth:`__len__`, which is expected to return the size of the dataset by many
    :class:`~torch.utils.data.Sampler` implementations and the default options
    of :class:`~torch.utils.data.DataLoader`. Subclasses could also
    optionally implement :meth:`__getitems__`, for speedup batched samples
    loading. This method accepts list of indices of samples of batch and returns
    list of samples.

    .. note::
      :class:`~torch.utils.data.DataLoader` by default constructs an index
      sampler that yields integral indices.  To make it work with a map-style
      dataset with non-integral indices/keys, a custom sampler must be provided.
    """

    def __getitem__(self, index) -> _T_co:
        raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")

    # def __getitems__(self, indices: List) -> List[_T_co]:
    # Not implemented to prevent false-positives in fetcher check in
    # torch.utils.data._utils.fetch._MapDatasetFetcher

    def __add__(self, other: "Dataset[_T_co]") -> "ConcatDataset[_T_co]":
        return ConcatDataset([self, other])

    # No `def __len__(self)` default?
    # See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
    # in pytorch/torch/utils/data/sampler.py

2. Dataset 类定义了以下两个核心方法，任何自定义数据集都需要实现这些方法：

• __len__(self)：返回数据集中的样本总数。
• __getitem__(self, idx)：根据给定的索引idx返回一个样本。这个样本可以是一个数据点，也可以是一个数据点及其对应的标签。

3. 继承自Dataset的其他常用类：
   • TensorDataset：用于处理由张量构成的数据集。它将输入张量和目标张量组合在一起，形成一个数据集。
   • ImageFolder：用于从文件系统中加载图像数据集。它假设每个子目录代表一个类别，并将每个图像文件作为一个样本。
   • ConcatDataset：用于将多个数据集合并成一个大的数据集。
   • Subset：用于从一个大数据集中选择一个子集。
   • ChainDataset：用于将多个数据集串联起来，使得它们可以像一个数据集一样被迭代。

使用方法

1. 自定义一个数据处理类：

from torch.utils.data import Dataset, DataLoader

class CustomDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

# 假设我们有一些自定义数据和标签
custom_data = [...]
custom_labels = [...]

# 创建自定义数据集
custom_dataset = CustomDataset(custom_data, custom_labels)

# 使用DataLoader来迭代自定义数据集
custom_data_loader = DataLoader(custom_dataset, batch_size=20, shuffle=True)

for batch_idx, (data, target) in enumerate(custom_data_loader):
    # 在这里处理你的数据和目标
    pass

dataset 类源码

1. 源码：https://github.com/pytorch/pytorch/blob/main/torch/utils/data/dataset.py

# mypy: allow-untyped-defs
import bisect
import itertools
import math
import warnings
from typing import (
    cast,
    Dict,
    Generic,
    Iterable,
    List,
    Optional,
    Sequence,
    Tuple,
    TypeVar,
    Union,
)
from typing_extensions import deprecated

# No 'default_generator' in torch/__init__.pyi
from torch import default_generator, Generator, randperm, Tensor


__all__ = [
    "Dataset",
    "IterableDataset",
    "TensorDataset",
    "StackDataset",
    "ConcatDataset",
    "ChainDataset",
    "Subset",
    "random_split",
]


_T = TypeVar("_T")
_T_co = TypeVar("_T_co", covariant=True)
_T_dict = Dict[str, _T_co]
_T_tuple = Tuple[_T_co, ...]
_T_stack = TypeVar("_T_stack", _T_tuple, _T_dict)


class Dataset(Generic[_T_co]):
    r"""An abstract class representing a :class:`Dataset`.

    All datasets that represent a map from keys to data samples should subclass
    it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
    data sample for a given key. Subclasses could also optionally overwrite
    :meth:`__len__`, which is expected to return the size of the dataset by many
    :class:`~torch.utils.data.Sampler` implementations and the default options
    of :class:`~torch.utils.data.DataLoader`. Subclasses could also
    optionally implement :meth:`__getitems__`, for speedup batched samples
    loading. This method accepts list of indices of samples of batch and returns
    list of samples.

    .. note::
      :class:`~torch.utils.data.DataLoader` by default constructs an index
      sampler that yields integral indices.  To make it work with a map-style
      dataset with non-integral indices/keys, a custom sampler must be provided.
    """

    def __getitem__(self, index) -> _T_co:
        raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")

    # def __getitems__(self, indices: List) -> List[_T_co]:
    # Not implemented to prevent false-positives in fetcher check in
    # torch.utils.data._utils.fetch._MapDatasetFetcher

    def __add__(self, other: "Dataset[_T_co]") -> "ConcatDataset[_T_co]":
        return ConcatDataset([self, other])

    # No `def __len__(self)` default?
    # See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
    # in pytorch/torch/utils/data/sampler.py


class IterableDataset(Dataset[_T_co], Iterable[_T_co]):
    r"""An iterable Dataset.

    All datasets that represent an iterable of data samples should subclass it.
    Such form of datasets is particularly useful when data come from a stream.

    All subclasses should overwrite :meth:`__iter__`, which would return an
    iterator of samples in this dataset.

    When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
    item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader`
    iterator. When :attr:`num_workers > 0`, each worker process will have a
    different copy of the dataset object, so it is often desired to configure
    each copy independently to avoid having duplicate data returned from the
    workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
    process, returns information about the worker. It can be used in either the
    dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
    :attr:`worker_init_fn` option to modify each copy's behavior.

    Example 1: splitting workload across all workers in :meth:`__iter__`::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
        >>> # xdoctest: +SKIP("Fails on MacOS12")
        >>> class MyIterableDataset(torch.utils.data.IterableDataset):
        ...     def __init__(self, start, end):
        ...         super(MyIterableDataset).__init__()
        ...         assert end > start, "this example code only works with end >= start"
        ...         self.start = start
        ...         self.end = end
        ...
        ...     def __iter__(self):
        ...         worker_info = torch.utils.data.get_worker_info()
        ...         if worker_info is None:  # single-process data loading, return the full iterator
        ...             iter_start = self.start
        ...             iter_end = self.end
        ...         else:  # in a worker process
        ...             # split workload
        ...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
        ...             worker_id = worker_info.id
        ...             iter_start = self.start + worker_id * per_worker
        ...             iter_end = min(iter_start + per_worker, self.end)
        ...         return iter(range(iter_start, iter_end))
        ...
        >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
        >>> ds = MyIterableDataset(start=3, end=7)

        >>> # Single-process loading
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
        [tensor([3]), tensor([4]), tensor([5]), tensor([6])]

        >>> # xdoctest: +REQUIRES(POSIX)
        >>> # Mult-process loading with two worker processes
        >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
        >>> # xdoctest: +IGNORE_WANT("non deterministic")
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
        [tensor([3]), tensor([5]), tensor([4]), tensor([6])]

        >>> # With even more workers
        >>> # xdoctest: +IGNORE_WANT("non deterministic")
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12)))
        [tensor([3]), tensor([5]), tensor([4]), tensor([6])]

    Example 2: splitting workload across all workers using :attr:`worker_init_fn`::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
        >>> class MyIterableDataset(torch.utils.data.IterableDataset):
        ...     def __init__(self, start, end):
        ...         super(MyIterableDataset).__init__()
        ...         assert end > start, "this example code only works with end >= start"
        ...         self.start = start
        ...         self.end = end
        ...
        ...     def __iter__(self):
        ...         return iter(range(self.start, self.end))
        ...
        >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
        >>> ds = MyIterableDataset(start=3, end=7)

        >>> # Single-process loading
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
        [3, 4, 5, 6]
        >>>
        >>> # Directly doing multi-process loading yields duplicate data
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
        [3, 3, 4, 4, 5, 5, 6, 6]

        >>> # Define a `worker_init_fn` that configures each dataset copy differently
        >>> def worker_init_fn(worker_id):
        ...     worker_info = torch.utils.data.get_worker_info()
        ...     dataset = worker_info.dataset  # the dataset copy in this worker process
        ...     overall_start = dataset.start
        ...     overall_end = dataset.end
        ...     # configure the dataset to only process the split workload
        ...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
        ...     worker_id = worker_info.id
        ...     dataset.start = overall_start + worker_id * per_worker
        ...     dataset.end = min(dataset.start + per_worker, overall_end)
        ...

        >>> # Mult-process loading with the custom `worker_init_fn`
        >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
        [3, 5, 4, 6]

        >>> # With even more workers
        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn)))
        [3, 4, 5, 6]
    """

    def __add__(self, other: Dataset[_T_co]):
        return ChainDataset([self, other])

    # No `def __len__(self)` default? Subclasses raise `TypeError` when needed.
    # See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]


class TensorDataset(Dataset[Tuple[Tensor, ...]]):
    r"""Dataset wrapping tensors.

    Each sample will be retrieved by indexing tensors along the first dimension.

    Args:
        *tensors (Tensor): tensors that have the same size of the first dimension.
    """

    tensors: Tuple[Tensor, ...]

    def __init__(self, *tensors: Tensor) -> None:
        assert all(
            tensors[0].size(0) == tensor.size(0) for tensor in tensors
        ), "Size mismatch between tensors"
        self.tensors = tensors

    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)

    def __len__(self):
        return self.tensors[0].size(0)


class StackDataset(Dataset[_T_stack]):
    r"""Dataset as a stacking of multiple datasets.

    This class is useful to assemble different parts of complex input data, given as datasets.

    Example:
        >>> # xdoctest: +SKIP
        >>> images = ImageDataset()
        >>> texts = TextDataset()
        >>> tuple_stack = StackDataset(images, texts)
        >>> tuple_stack[0] == (images[0], texts[0])
        >>> dict_stack = StackDataset(image=images, text=texts)
        >>> dict_stack[0] == {'image': images[0], 'text': texts[0]}

    Args:
        *args (Dataset): Datasets for stacking returned as tuple.
        **kwargs (Dataset): Datasets for stacking returned as dict.
    """

    datasets: Union[tuple, dict]

    def __init__(self, *args: Dataset[_T_co], **kwargs: Dataset[_T_co]) -> None:
        if args:
            if kwargs:
                raise ValueError(
                    "Supported either ``tuple``- (via ``args``) or"
                    "``dict``- (via ``kwargs``) like input/output, but both types are given."
                )
            self._length = len(args[0])  # type: ignore[arg-type]
            if any(self._length != len(dataset) for dataset in args):  # type: ignore[arg-type]
                raise ValueError("Size mismatch between datasets")
            self.datasets = args
        elif kwargs:
            tmp = list(kwargs.values())
            self._length = len(tmp[0])  # type: ignore[arg-type]
            if any(self._length != len(dataset) for dataset in tmp):  # type: ignore[arg-type]
                raise ValueError("Size mismatch between datasets")
            self.datasets = kwargs
        else:
            raise ValueError("At least one dataset should be passed")

    def __getitem__(self, index):
        if isinstance(self.datasets, dict):
            return {k: dataset[index] for k, dataset in self.datasets.items()}
        return tuple(dataset[index] for dataset in self.datasets)

    def __getitems__(self, indices: list):
        # add batched sampling support when parent datasets supports it.
        if isinstance(self.datasets, dict):
            dict_batch: List[_T_dict] = [{} for _ in indices]
            for k, dataset in self.datasets.items():
                if callable(getattr(dataset, "__getitems__", None)):
                    items = dataset.__getitems__(indices)  # type: ignore[attr-defined]
                    if len(items) != len(indices):
                        raise ValueError(
                            "Nested dataset's output size mismatch."
                            f" Expected {len(indices)}, got {len(items)}"
                        )
                    for data, d_sample in zip(items, dict_batch):
                        d_sample[k] = data
                else:
                    for idx, d_sample in zip(indices, dict_batch):
                        d_sample[k] = dataset[idx]
            return dict_batch

        # tuple data
        list_batch: List[list] = [[] for _ in indices]
        for dataset in self.datasets:
            if callable(getattr(dataset, "__getitems__", None)):
                items = dataset.__getitems__(indices)  # type: ignore[attr-defined]
                if len(items) != len(indices):
                    raise ValueError(
                        "Nested dataset's output size mismatch."
                        f" Expected {len(indices)}, got {len(items)}"
                    )
                for data, t_sample in zip(items, list_batch):
                    t_sample.append(data)
            else:
                for idx, t_sample in zip(indices, list_batch):
                    t_sample.append(dataset[idx])
        tuple_batch: List[_T_tuple] = [tuple(sample) for sample in list_batch]
        return tuple_batch

    def __len__(self):
        return self._length


class ConcatDataset(Dataset[_T_co]):
    r"""Dataset as a concatenation of multiple datasets.

    This class is useful to assemble different existing datasets.

    Args:
        datasets (sequence): List of datasets to be concatenated
    """

    datasets: List[Dataset[_T_co]]
    cumulative_sizes: List[int]

    @staticmethod
    def cumsum(sequence):
        r, s = [], 0
        for e in sequence:
            l = len(e)
            r.append(l + s)
            s += l
        return r

    def __init__(self, datasets: Iterable[Dataset]) -> None:
        super().__init__()
        self.datasets = list(datasets)
        assert len(self.datasets) > 0, "datasets should not be an empty iterable"  # type: ignore[arg-type]
        for d in self.datasets:
            assert not isinstance(
                d, IterableDataset
            ), "ConcatDataset does not support IterableDataset"
        self.cumulative_sizes = self.cumsum(self.datasets)

    def __len__(self):
        return self.cumulative_sizes[-1]

    def __getitem__(self, idx):
        if idx < 0:
            if -idx > len(self):
                raise ValueError(
                    "absolute value of index should not exceed dataset length"
                )
            idx = len(self) + idx
        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
        if dataset_idx == 0:
            sample_idx = idx
        else:
            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
        return self.datasets[dataset_idx][sample_idx]

    @property
    @deprecated(
        "`cummulative_sizes` attribute is renamed to `cumulative_sizes`",
        category=FutureWarning,
    )
    def cummulative_sizes(self):
        return self.cumulative_sizes


class ChainDataset(IterableDataset):
    r"""Dataset for chaining multiple :class:`IterableDataset` s.

    This class is useful to assemble different existing dataset streams. The
    chaining operation is done on-the-fly, so concatenating large-scale
    datasets with this class will be efficient.

    Args:
        datasets (iterable of IterableDataset): datasets to be chained together
    """

    def __init__(self, datasets: Iterable[Dataset]) -> None:
        super().__init__()
        self.datasets = datasets

    def __iter__(self):
        for d in self.datasets:
            assert isinstance(
                d, IterableDataset
            ), "ChainDataset only supports IterableDataset"
            yield from d

    def __len__(self):
        total = 0
        for d in self.datasets:
            assert isinstance(
                d, IterableDataset
            ), "ChainDataset only supports IterableDataset"
            total += len(d)  # type: ignore[arg-type]
        return total


class Subset(Dataset[_T_co]):
    r"""
    Subset of a dataset at specified indices.

    Args:
        dataset (Dataset): The whole Dataset
        indices (sequence): Indices in the whole set selected for subset
    """

    dataset: Dataset[_T_co]
    indices: Sequence[int]

    def __init__(self, dataset: Dataset[_T_co], indices: Sequence[int]) -> None:
        self.dataset = dataset
        self.indices = indices

    def __getitem__(self, idx):
        if isinstance(idx, list):
            return self.dataset[[self.indices[i] for i in idx]]
        return self.dataset[self.indices[idx]]

    def __getitems__(self, indices: List[int]) -> List[_T_co]:
        # add batched sampling support when parent dataset supports it.
        # see torch.utils.data._utils.fetch._MapDatasetFetcher
        if callable(getattr(self.dataset, "__getitems__", None)):
            return self.dataset.__getitems__([self.indices[idx] for idx in indices])  # type: ignore[attr-defined]
        else:
            return [self.dataset[self.indices[idx]] for idx in indices]

    def __len__(self):
        return len(self.indices)


def random_split(
    dataset: Dataset[_T],
    lengths: Sequence[Union[int, float]],
    generator: Optional[Generator] = default_generator,
) -> List[Subset[_T]]:
    r"""
    Randomly split a dataset into non-overlapping new datasets of given lengths.

    If a list of fractions that sum up to 1 is given,
    the lengths will be computed automatically as
    floor(frac * len(dataset)) for each fraction provided.

    After computing the lengths, if there are any remainders, 1 count will be
    distributed in round-robin fashion to the lengths
    until there are no remainders left.

    Optionally fix the generator for reproducible results, e.g.:

    Example:
        >>> # xdoctest: +SKIP
        >>> generator1 = torch.Generator().manual_seed(42)
        >>> generator2 = torch.Generator().manual_seed(42)
        >>> random_split(range(10), [3, 7], generator=generator1)
        >>> random_split(range(30), [0.3, 0.3, 0.4], generator=generator2)

    Args:
        dataset (Dataset): Dataset to be split
        lengths (sequence): lengths or fractions of splits to be produced
        generator (Generator): Generator used for the random permutation.
    """
    if math.isclose(sum(lengths), 1) and sum(lengths) <= 1:
        subset_lengths: List[int] = []
        for i, frac in enumerate(lengths):
            if frac < 0 or frac > 1:
                raise ValueError(f"Fraction at index {i} is not between 0 and 1")
            n_items_in_split = int(
                math.floor(len(dataset) * frac)  # type: ignore[arg-type]
            )
            subset_lengths.append(n_items_in_split)
        remainder = len(dataset) - sum(subset_lengths)  # type: ignore[arg-type]
        # add 1 to all the lengths in round-robin fashion until the remainder is 0
        for i in range(remainder):
            idx_to_add_at = i % len(subset_lengths)
            subset_lengths[idx_to_add_at] += 1
        lengths = subset_lengths
        for i, length in enumerate(lengths):
            if length == 0:
                warnings.warn(
                    f"Length of split at index {i} is 0. "
                    f"This might result in an empty dataset."
                )

    # Cannot verify that dataset is Sized
    if sum(lengths) != len(dataset):  # type: ignore[arg-type]
        raise ValueError(
            "Sum of input lengths does not equal the length of the input dataset!"
        )

    indices = randperm(sum(lengths), generator=generator).tolist()  # type: ignore[arg-type, call-overload]
    lengths = cast(Sequence[int], lengths)
    return [
        Subset(dataset, indices[offset - length : offset])
        for offset, length in zip(itertools.accumulate(lengths), lengths)
    ]