489 lines
19 KiB
Python
489 lines
19 KiB
Python
import bisect
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import itertools
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import math
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import warnings
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from typing import (
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cast,
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Dict,
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Generic,
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Iterable,
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List,
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Optional,
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Sequence,
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Tuple,
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TypeVar,
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Union,
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)
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# No 'default_generator' in torch/__init__.pyi
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from torch import default_generator, randperm
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from ... import Generator, Tensor
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__all__ = [
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"Dataset",
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"IterableDataset",
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"TensorDataset",
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"StackDataset",
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"ConcatDataset",
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"ChainDataset",
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"Subset",
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"random_split",
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]
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T_co = TypeVar("T_co", covariant=True)
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T = TypeVar("T")
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T_dict = Dict[str, T_co]
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T_tuple = Tuple[T_co, ...]
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T_stack = TypeVar("T_stack", T_tuple, T_dict)
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class Dataset(Generic[T_co]):
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r"""An abstract class representing a :class:`Dataset`.
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All datasets that represent a map from keys to data samples should subclass
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it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
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data sample for a given key. Subclasses could also optionally overwrite
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:meth:`__len__`, which is expected to return the size of the dataset by many
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:class:`~torch.utils.data.Sampler` implementations and the default options
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of :class:`~torch.utils.data.DataLoader`. Subclasses could also
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optionally implement :meth:`__getitems__`, for speedup batched samples
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loading. This method accepts list of indices of samples of batch and returns
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list of samples.
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.. note::
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:class:`~torch.utils.data.DataLoader` by default constructs an index
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sampler that yields integral indices. To make it work with a map-style
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dataset with non-integral indices/keys, a custom sampler must be provided.
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"""
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def __getitem__(self, index) -> T_co:
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raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")
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# def __getitems__(self, indices: List) -> List[T_co]:
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# Not implemented to prevent false-positives in fetcher check in
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# torch.utils.data._utils.fetch._MapDatasetFetcher
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def __add__(self, other: "Dataset[T_co]") -> "ConcatDataset[T_co]":
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return ConcatDataset([self, other])
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# No `def __len__(self)` default?
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# See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
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# in pytorch/torch/utils/data/sampler.py
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class IterableDataset(Dataset[T_co], Iterable[T_co]):
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r"""An iterable Dataset.
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All datasets that represent an iterable of data samples should subclass it.
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Such form of datasets is particularly useful when data come from a stream.
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All subclasses should overwrite :meth:`__iter__`, which would return an
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iterator of samples in this dataset.
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When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
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item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader`
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iterator. When :attr:`num_workers > 0`, each worker process will have a
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different copy of the dataset object, so it is often desired to configure
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each copy independently to avoid having duplicate data returned from the
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workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
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process, returns information about the worker. It can be used in either the
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dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
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:attr:`worker_init_fn` option to modify each copy's behavior.
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Example 1: splitting workload across all workers in :meth:`__iter__`::
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>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
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>>> # xdoctest: +SKIP("Fails on MacOS12")
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>>> class MyIterableDataset(torch.utils.data.IterableDataset):
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... def __init__(self, start, end):
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... super(MyIterableDataset).__init__()
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... assert end > start, "this example code only works with end >= start"
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... self.start = start
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... self.end = end
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...
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... def __iter__(self):
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... worker_info = torch.utils.data.get_worker_info()
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... if worker_info is None: # single-process data loading, return the full iterator
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... iter_start = self.start
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... iter_end = self.end
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... else: # in a worker process
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... # split workload
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... per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
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... worker_id = worker_info.id
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... iter_start = self.start + worker_id * per_worker
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... iter_end = min(iter_start + per_worker, self.end)
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... return iter(range(iter_start, iter_end))
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...
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>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
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>>> ds = MyIterableDataset(start=3, end=7)
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>>> # Single-process loading
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
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[tensor([3]), tensor([4]), tensor([5]), tensor([6])]
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>>> # xdoctest: +REQUIRES(POSIX)
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>>> # Mult-process loading with two worker processes
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>>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6].
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>>> # xdoctest: +IGNORE_WANT("non deterministic")
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
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[tensor([3]), tensor([5]), tensor([4]), tensor([6])]
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>>> # With even more workers
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>>> # xdoctest: +IGNORE_WANT("non deterministic")
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=12)))
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[tensor([3]), tensor([5]), tensor([4]), tensor([6])]
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Example 2: splitting workload across all workers using :attr:`worker_init_fn`::
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>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
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>>> class MyIterableDataset(torch.utils.data.IterableDataset):
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... def __init__(self, start, end):
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... super(MyIterableDataset).__init__()
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... assert end > start, "this example code only works with end >= start"
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... self.start = start
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... self.end = end
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...
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... def __iter__(self):
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... return iter(range(self.start, self.end))
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...
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>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
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>>> ds = MyIterableDataset(start=3, end=7)
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>>> # Single-process loading
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
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[3, 4, 5, 6]
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>>>
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>>> # Directly doing multi-process loading yields duplicate data
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
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[3, 3, 4, 4, 5, 5, 6, 6]
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>>> # Define a `worker_init_fn` that configures each dataset copy differently
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>>> def worker_init_fn(worker_id):
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... worker_info = torch.utils.data.get_worker_info()
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... dataset = worker_info.dataset # the dataset copy in this worker process
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... overall_start = dataset.start
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... overall_end = dataset.end
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... # configure the dataset to only process the split workload
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... per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
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... worker_id = worker_info.id
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... dataset.start = overall_start + worker_id * per_worker
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... dataset.end = min(dataset.start + per_worker, overall_end)
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...
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>>> # Mult-process loading with the custom `worker_init_fn`
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>>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6].
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
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[3, 5, 4, 6]
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>>> # With even more workers
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>>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn)))
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[3, 4, 5, 6]
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"""
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def __add__(self, other: Dataset[T_co]):
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return ChainDataset([self, other])
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# No `def __len__(self)` default? Subclasses raise `TypeError` when needed.
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# See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
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class TensorDataset(Dataset[Tuple[Tensor, ...]]):
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r"""Dataset wrapping tensors.
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Each sample will be retrieved by indexing tensors along the first dimension.
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Args:
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*tensors (Tensor): tensors that have the same size of the first dimension.
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"""
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tensors: Tuple[Tensor, ...]
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def __init__(self, *tensors: Tensor) -> None:
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assert all(
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tensors[0].size(0) == tensor.size(0) for tensor in tensors
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), "Size mismatch between tensors"
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self.tensors = tensors
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def __getitem__(self, index):
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return tuple(tensor[index] for tensor in self.tensors)
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def __len__(self):
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return self.tensors[0].size(0)
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class StackDataset(Dataset[T_stack]):
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r"""Dataset as a stacking of multiple datasets.
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This class is useful to assemble different parts of complex input data, given as datasets.
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Example:
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>>> # xdoctest: +SKIP
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>>> images = ImageDataset()
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>>> texts = TextDataset()
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>>> tuple_stack = StackDataset(images, texts)
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>>> tuple_stack[0] == (images[0], texts[0])
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>>> dict_stack = StackDataset(image=images, text=texts)
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>>> dict_stack[0] == {'image': images[0], 'text': texts[0]}
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Args:
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*args (Dataset): Datasets for stacking returned as tuple.
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**kwargs (Dataset): Datasets for stacking returned as dict.
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"""
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datasets: Union[tuple, dict]
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def __init__(self, *args: Dataset[T_co], **kwargs: Dataset[T_co]) -> None:
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if args:
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if kwargs:
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raise ValueError(
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"Supported either ``tuple``- (via ``args``) or"
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"``dict``- (via ``kwargs``) like input/output, but both types are given."
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)
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self._length = len(args[0]) # type: ignore[arg-type]
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if any(self._length != len(dataset) for dataset in args): # type: ignore[arg-type]
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raise ValueError("Size mismatch between datasets")
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self.datasets = args
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elif kwargs:
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tmp = list(kwargs.values())
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self._length = len(tmp[0]) # type: ignore[arg-type]
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if any(self._length != len(dataset) for dataset in tmp): # type: ignore[arg-type]
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raise ValueError("Size mismatch between datasets")
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self.datasets = kwargs
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else:
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raise ValueError("At least one dataset should be passed")
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def __getitem__(self, index):
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if isinstance(self.datasets, dict):
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return {k: dataset[index] for k, dataset in self.datasets.items()}
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return tuple(dataset[index] for dataset in self.datasets)
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def __getitems__(self, indices: list):
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# add batched sampling support when parent datasets supports it.
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if isinstance(self.datasets, dict):
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dict_batch: List[T_dict] = [{} for _ in indices]
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for k, dataset in self.datasets.items():
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if callable(getattr(dataset, "__getitems__", None)):
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items = dataset.__getitems__(indices) # type: ignore[attr-defined]
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if len(items) != len(indices):
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raise ValueError(
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"Nested dataset's output size mismatch."
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f" Expected {len(indices)}, got {len(items)}"
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)
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for data, d_sample in zip(items, dict_batch):
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d_sample[k] = data
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else:
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for idx, d_sample in zip(indices, dict_batch):
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d_sample[k] = dataset[idx]
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return dict_batch
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# tuple data
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list_batch: List[list] = [[] for _ in indices]
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for dataset in self.datasets:
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if callable(getattr(dataset, "__getitems__", None)):
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items = dataset.__getitems__(indices) # type: ignore[attr-defined]
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if len(items) != len(indices):
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raise ValueError(
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"Nested dataset's output size mismatch."
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f" Expected {len(indices)}, got {len(items)}"
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)
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for data, t_sample in zip(items, list_batch):
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t_sample.append(data)
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else:
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for idx, t_sample in zip(indices, list_batch):
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t_sample.append(dataset[idx])
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tuple_batch: List[T_tuple] = [tuple(sample) for sample in list_batch]
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return tuple_batch
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def __len__(self):
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return self._length
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class ConcatDataset(Dataset[T_co]):
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r"""Dataset as a concatenation of multiple datasets.
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This class is useful to assemble different existing datasets.
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Args:
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datasets (sequence): List of datasets to be concatenated
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"""
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datasets: List[Dataset[T_co]]
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cumulative_sizes: List[int]
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@staticmethod
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def cumsum(sequence):
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r, s = [], 0
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for e in sequence:
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l = len(e)
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r.append(l + s)
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s += l
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return r
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def __init__(self, datasets: Iterable[Dataset]) -> None:
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super().__init__()
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self.datasets = list(datasets)
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assert len(self.datasets) > 0, "datasets should not be an empty iterable" # type: ignore[arg-type]
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for d in self.datasets:
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assert not isinstance(
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d, IterableDataset
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), "ConcatDataset does not support IterableDataset"
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self.cumulative_sizes = self.cumsum(self.datasets)
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def __len__(self):
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return self.cumulative_sizes[-1]
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def __getitem__(self, idx):
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if idx < 0:
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if -idx > len(self):
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raise ValueError(
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"absolute value of index should not exceed dataset length"
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)
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idx = len(self) + idx
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dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
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if dataset_idx == 0:
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sample_idx = idx
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else:
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sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
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return self.datasets[dataset_idx][sample_idx]
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@property
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def cummulative_sizes(self):
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warnings.warn(
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"cummulative_sizes attribute is renamed to " "cumulative_sizes",
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DeprecationWarning,
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stacklevel=2,
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)
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return self.cumulative_sizes
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class ChainDataset(IterableDataset):
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r"""Dataset for chaining multiple :class:`IterableDataset` s.
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This class is useful to assemble different existing dataset streams. The
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chaining operation is done on-the-fly, so concatenating large-scale
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datasets with this class will be efficient.
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Args:
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datasets (iterable of IterableDataset): datasets to be chained together
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"""
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def __init__(self, datasets: Iterable[Dataset]) -> None:
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super().__init__()
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self.datasets = datasets
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def __iter__(self):
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for d in self.datasets:
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assert isinstance(
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d, IterableDataset
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), "ChainDataset only supports IterableDataset"
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yield from d
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def __len__(self):
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total = 0
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for d in self.datasets:
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assert isinstance(
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d, IterableDataset
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), "ChainDataset only supports IterableDataset"
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total += len(d) # type: ignore[arg-type]
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return total
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class Subset(Dataset[T_co]):
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r"""
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Subset of a dataset at specified indices.
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Args:
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dataset (Dataset): The whole Dataset
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indices (sequence): Indices in the whole set selected for subset
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"""
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dataset: Dataset[T_co]
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indices: Sequence[int]
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def __init__(self, dataset: Dataset[T_co], indices: Sequence[int]) -> None:
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self.dataset = dataset
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self.indices = indices
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def __getitem__(self, idx):
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if isinstance(idx, list):
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return self.dataset[[self.indices[i] for i in idx]]
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return self.dataset[self.indices[idx]]
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def __getitems__(self, indices: List[int]) -> List[T_co]:
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# add batched sampling support when parent dataset supports it.
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# see torch.utils.data._utils.fetch._MapDatasetFetcher
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if callable(getattr(self.dataset, "__getitems__", None)):
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return self.dataset.__getitems__([self.indices[idx] for idx in indices]) # type: ignore[attr-defined]
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else:
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return [self.dataset[self.indices[idx]] for idx in indices]
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def __len__(self):
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return len(self.indices)
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def random_split(
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dataset: Dataset[T],
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lengths: Sequence[Union[int, float]],
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generator: Optional[Generator] = default_generator,
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) -> List[Subset[T]]:
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r"""
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Randomly split a dataset into non-overlapping new datasets of given lengths.
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If a list of fractions that sum up to 1 is given,
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the lengths will be computed automatically as
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floor(frac * len(dataset)) for each fraction provided.
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After computing the lengths, if there are any remainders, 1 count will be
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distributed in round-robin fashion to the lengths
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until there are no remainders left.
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Optionally fix the generator for reproducible results, e.g.:
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Example:
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>>> # xdoctest: +SKIP
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>>> generator1 = torch.Generator().manual_seed(42)
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>>> generator2 = torch.Generator().manual_seed(42)
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>>> random_split(range(10), [3, 7], generator=generator1)
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>>> random_split(range(30), [0.3, 0.3, 0.4], generator=generator2)
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Args:
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dataset (Dataset): Dataset to be split
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lengths (sequence): lengths or fractions of splits to be produced
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generator (Generator): Generator used for the random permutation.
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"""
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if math.isclose(sum(lengths), 1) and sum(lengths) <= 1:
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subset_lengths: List[int] = []
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for i, frac in enumerate(lengths):
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if frac < 0 or frac > 1:
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raise ValueError(f"Fraction at index {i} is not between 0 and 1")
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n_items_in_split = int(
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math.floor(len(dataset) * frac) # type: ignore[arg-type]
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)
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subset_lengths.append(n_items_in_split)
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remainder = len(dataset) - sum(subset_lengths) # type: ignore[arg-type]
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# add 1 to all the lengths in round-robin fashion until the remainder is 0
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for i in range(remainder):
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idx_to_add_at = i % len(subset_lengths)
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subset_lengths[idx_to_add_at] += 1
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lengths = subset_lengths
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for i, length in enumerate(lengths):
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if length == 0:
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warnings.warn(
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f"Length of split at index {i} is 0. "
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f"This might result in an empty dataset."
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)
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# Cannot verify that dataset is Sized
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if sum(lengths) != len(dataset): # type: ignore[arg-type]
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raise ValueError(
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"Sum of input lengths does not equal the length of the input dataset!"
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)
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|
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)
|
|
]
|