Source code for torch.utils.data.dataset
import bisect
import random
import warnings
from torch._utils import _accumulate
from torch import randperm
# No 'default_generator' in torch/__init__.pyi
from torch import default_generator # type: ignore
from typing import TypeVar, Generic, Iterable, Iterator, Sequence, List, Optional, Tuple
from ... import Tensor, Generator
T_co = TypeVar('T_co', covariant=True)
T = TypeVar('T')
[docs]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`.
.. note::
:class:`~torch.utils.data.DataLoader` by default constructs a 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
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
[docs]class IterableDataset(Dataset[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__`::
>>> 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)))
[3, 4, 5, 6]
>>> # Mult-process loading with two worker processes
>>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 5, 4, 6]
>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
[3, 4, 5, 6]
Example 2: splitting workload across all workers using :attr:`worker_init_fn`::
>>> 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=20, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]
"""
def __iter__(self) -> Iterator[T_co]:
raise NotImplementedError
def __add__(self, other: Dataset[T_co]):
return ChainDataset([self, other])
# No `def __len__(self)` default?
# See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
[docs]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)
[docs]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(ConcatDataset, self).__init__()
# Cannot verify that datasets is Sized
assert len(datasets) > 0, 'datasets should not be an empty iterable' # type: ignore
self.datasets = list(datasets)
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
def cummulative_sizes(self):
warnings.warn("cummulative_sizes attribute is renamed to "
"cumulative_sizes", DeprecationWarning, stacklevel=2)
return self.cumulative_sizes
[docs]class ChainDataset(IterableDataset):
r"""Dataset for chainning multiple :class:`IterableDataset` s.
This class is useful to assemble different existing dataset streams. The
chainning 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(ChainDataset, self).__init__()
self.datasets = datasets
def __iter__(self):
for d in self.datasets:
assert isinstance(d, IterableDataset), "ChainDataset only supports IterableDataset"
for x in d:
yield x
def __len__(self):
total = 0
for d in self.datasets:
assert isinstance(d, IterableDataset), "ChainDataset only supports IterableDataset"
# Cannot verify that all self.datasets are Sized
total += len(d) # type: ignore
return total
[docs]class BufferedShuffleDataset(IterableDataset[T_co]):
r"""Dataset shuffled from the original dataset.
This class is useful to shuffle an existing instance of an IterableDataset.
The buffer with `buffer_size` is filled with the items from the dataset first. Then,
each item will be yielded from the buffer by reservoir sampling via iterator.
`buffer_size` is required to be larger than 0. For `buffer_size == 1`, the
dataset is not shuffled. In order to fully shuffle the whole dataset, `buffer_size`
is required to be greater than or equal to the size of dataset.
When it is used with :class:`~torch.utils.data.DataLoader`, each item in the
dataset will be yielded from the :class:`~torch.utils.data.DataLoader` iterator.
And, the method to set up a random seed is different based on :attr:`num_workers`.
For single-process mode (:attr:`num_workers == 0`), the random seed is required to
be set before the :class:`~torch.utils.data.DataLoader` in the main process.
>>> ds = BufferedShuffleDataset(dataset)
>>> random.seed(...)
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
For multi-process mode (:attr:`num_workers > 0`), the random seed is set by a callable
function in each worker.
>>> ds = BufferedShuffleDataset(dataset)
>>> def init_fn(worker_id):
... random.seed(...)
>>> print(list(torch.utils.data.DataLoader(ds, ..., num_workers=n, worker_init_fn=init_fn)))
Args:
dataset (IterableDataset): The original IterableDataset.
buffer_size (int): The buffer size for shuffling.
"""
dataset: IterableDataset[T_co]
buffer_size: int
def __init__(self, dataset: IterableDataset[T_co], buffer_size: int) -> None:
super(BufferedShuffleDataset, self).__init__()
assert buffer_size > 0, "buffer_size should be larger than 0"
self.dataset = dataset
self.buffer_size = buffer_size
def __iter__(self) -> Iterator[T_co]:
buf: List[T_co] = []
for x in self.dataset:
if len(buf) == self.buffer_size:
idx = random.randint(0, self.buffer_size - 1)
yield buf[idx]
buf[idx] = x
else:
buf.append(x)
random.shuffle(buf)
while buf:
yield buf.pop()
[docs]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):
return self.dataset[self.indices[idx]]
def __len__(self):
return len(self.indices)
[docs]def random_split(dataset: Dataset[T], lengths: Sequence[int],
generator: Optional[Generator] = default_generator) -> List[Subset[T]]:
r"""
Randomly split a dataset into non-overlapping new datasets of given lengths.
Optionally fix the generator for reproducible results, e.g.:
>>> random_split(range(10), [3, 7], generator=torch.Generator().manual_seed(42))
Args:
dataset (Dataset): Dataset to be split
lengths (sequence): lengths of splits to be produced
generator (Generator): Generator used for the random permutation.
"""
# Cannot verify that dataset is Sized
if sum(lengths) != len(dataset): # type: ignore
raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
indices = randperm(sum(lengths), generator=generator).tolist()
return [Subset(dataset, indices[offset - length : offset]) for offset, length in zip(_accumulate(lengths), lengths)]
