torch.utils.data

.. automodule:: torch.utils.data

At the heart of PyTorch data loading utility is the :class:torch.utils.data.DataLoader
class. It represents a Python iterable over a dataset, with support for

• map-style and iterable-style datasets <Dataset Types_>_,

• customizing data loading order <Data Loading Order and Sampler_>_,

• automatic batching <Loading Batched and Non-Batched Data_>_,

• single- and multi-process data loading <Single- and Multi-process Data Loading_>_,

• automatic memory pinning <Memory Pinning_>_.

These options are configured by the constructor arguments of a
:class:~torch.utils.data.DataLoader, which has signature::

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None, *, prefetch_factor=2,
           persistent_workers=False)

The sections below describe in details the effects and usages of these options.

Dataset Types

The most important argument of :class:~torch.utils.data.DataLoader
constructor is :attr:dataset, which indicates a dataset object to load data
from. PyTorch supports two different types of datasets:

• map-style datasets <Map-style datasets_>_,

• iterable-style datasets <Iterable-style datasets_>_.

Map-style datasets
^^^^^^^^^^^^^^^^^^

A map-style dataset is one that implements the :meth:__getitem__ and
:meth:__len__ protocols, and represents a map from (possibly non-integral)
indices/keys to data samples.

For example, such a dataset, when accessed with dataset[idx], could read
the idx-th image and its corresponding label from a folder on the disk.

See :class:~torch.utils.data.Dataset for more details.

Iterable-style datasets
^^^^^^^^^^^^^^^^^^^^^^^

An iterable-style dataset is an instance of a subclass of :class:~torch.utils.data.IterableDataset
that implements the :meth:__iter__ protocol, and represents an iterable over
data samples. This type of datasets is particularly suitable for cases where
random reads are expensive or even improbable, and where the batch size depends
on the fetched data.

For example, such a dataset, when called iter(dataset), could return a
stream of data reading from a database, a remote server, or even logs generated
in real time.

See :class:~torch.utils.data.IterableDataset for more details.

.. note:: When using an :class:~torch.utils.data.IterableDataset with
multi-process data loading <Multi-process data loading_>_. The same
dataset object is replicated on each worker process, and thus the
replicas must be configured differently to avoid duplicated data. See
:class:~torch.utils.data.IterableDataset documentations for how to
achieve this.

Data Loading Order and :class:~torch.utils.data.Sampler

For iterable-style datasets <Iterable-style datasets_>_, data loading order
is entirely controlled by the user-defined iterable. This allows easier
implementations of chunk-reading and dynamic batch size (e.g., by yielding a
batched sample at each time).

The rest of this section concerns the case with
map-style datasets <Map-style datasets_>_. :class:torch.utils.data.Sampler
classes are used to specify the sequence of indices/keys used in data loading.
They represent iterable objects over the indices to datasets. E.g., in the
common case with stochastic gradient decent (SGD), a
:class:~torch.utils.data.Sampler could randomly permute a list of indices
and yield each one at a time, or yield a small number of them for mini-batch
SGD.

A sequential or shuffled sampler will be automatically constructed based on the :attr:shuffle argument to a :class:~torch.utils.data.DataLoader.
Alternatively, users may use the :attr:sampler argument to specify a
custom :class:~torch.utils.data.Sampler object that at each time yields
the next index/key to fetch.

A custom :class:~torch.utils.data.Sampler that yields a list of batch
indices at a time can be passed as the :attr:batch_sampler argument.
Automatic batching can also be enabled via :attr:batch_size and
:attr:drop_last arguments. See
the next section <Loading Batched and Non-Batched Data_>_ for more details
on this.

.. note::
Neither :attr:sampler nor :attr:batch_sampler is compatible with
iterable-style datasets, since such datasets have no notion of a key or an
index.

Loading Batched and Non-Batched Data

:class:~torch.utils.data.DataLoader supports automatically collating
individual fetched data samples into batches via arguments
:attr:batch_size, :attr:drop_last, and :attr:batch_sampler.

Automatic batching (default)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This is the most common case, and corresponds to fetching a minibatch of
data and collating them into batched samples, i.e., containing Tensors with
one dimension being the batch dimension (usually the first).

When :attr:batch_size (default 1) is not None, the data loader yields
batched samples instead of individual samples. :attr:batch_size and
:attr:drop_last arguments are used to specify how the data loader obtains
batches of dataset keys. For map-style datasets, users can alternatively
specify :attr:batch_sampler, which yields a list of keys at a time.

.. note::
The :attr:batch_size and :attr:drop_last arguments essentially are used
to construct a :attr:batch_sampler from :attr:sampler. For map-style
datasets, the :attr:sampler is either provided by user or constructed
based on the :attr:shuffle argument. For iterable-style datasets, the
:attr:sampler is a dummy infinite one. See
this section <Data Loading Order and Sampler_>_ on more details on
samplers.

.. note::
When fetching from
iterable-style datasets <Iterable-style datasets_>_ with
multi-processing <Multi-process data loading_>_, the :attr:drop_last
argument drops the last non-full batch of each worker's dataset replica.

After fetching a list of samples using the indices from sampler, the function
passed as the :attr:collate_fn argument is used to collate lists of samples
into batches.

In this case, loading from a map-style dataset is roughly equivalent with::

for indices in batch_sampler:
    yield collate_fn([dataset[i] for i in indices])

and loading from an iterable-style dataset is roughly equivalent with::

dataset_iter = iter(dataset)
for indices in batch_sampler:
    yield collate_fn([next(dataset_iter) for _ in indices])

A custom :attr:collate_fn can be used to customize collation, e.g., padding
sequential data to max length of a batch. See
this section <dataloader-collate_fn_>_ on more about :attr:collate_fn.

Disable automatic batching
^^^^^^^^^^^^^^^^^^^^^^^^^^

In certain cases, users may want to handle batching manually in dataset code,
or simply load individual samples. For example, it could be cheaper to directly
load batched data (e.g., bulk reads from a database or reading continuous
chunks of memory), or the batch size is data dependent, or the program is
designed to work on individual samples. Under these scenarios, it's likely
better to not use automatic batching (where :attr:collate_fn is used to
collate the samples), but let the data loader directly return each member of
the :attr:dataset object.

When both :attr:batch_size and :attr:batch_sampler are None (default
value for :attr:batch_sampler is already None), automatic batching is
disabled. Each sample obtained from the :attr:dataset is processed with the
function passed as the :attr:collate_fn argument.

When automatic batching is disabled, the default :attr:collate_fn simply
converts NumPy arrays into PyTorch Tensors, and keeps everything else untouched.

In this case, loading from a map-style dataset is roughly equivalent with::

for index in sampler:
    yield collate_fn(dataset[index])

and loading from an iterable-style dataset is roughly equivalent with::

for data in iter(dataset):
    yield collate_fn(data)

See this section <dataloader-collate_fn_>_ on more about :attr:collate_fn.

.. _dataloader-collate_fn:

Working with :attr:collate_fn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The use of :attr:collate_fn is slightly different when automatic batching is
enabled or disabled.

When automatic batching is disabled, :attr:collate_fn is called with
each individual data sample, and the output is yielded from the data loader
iterator. In this case, the default :attr:collate_fn simply converts NumPy
arrays in PyTorch tensors.

When automatic batching is enabled, :attr:collate_fn is called with a list
of data samples at each time. It is expected to collate the input samples into
a batch for yielding from the data loader iterator. The rest of this section
describes behavior of the default :attr:collate_fn in this case.

For instance, if each data sample consists of a 3-channel image and an integral
class label, i.e., each element of the dataset returns a tuple
(image, class_index), the default :attr:collate_fn collates a list of
such tuples into a single tuple of a batched image tensor and a batched class
label Tensor. In particular, the default :attr:collate_fn has the following
properties:

• It always prepends a new dimension as the batch dimension.

• It automatically converts NumPy arrays and Python numerical values into
  PyTorch Tensors.

• It preserves the data structure, e.g., if each sample is a dictionary, it
  outputs a dictionary with the same set of keys but batched Tensors as values
  (or lists if the values can not be converted into Tensors). Same
  for list s, tuple s, namedtuple s, etc.

Users may use customized :attr:collate_fn to achieve custom batching, e.g.,
collating along a dimension other than the first, padding sequences of
various lengths, or adding support for custom data types.

Single- and Multi-process Data Loading

A :class:~torch.utils.data.DataLoader uses single-process data loading by
default.

Within a Python process, the
Global Interpreter Lock (GIL) <https://wiki.python.org/moin/GlobalInterpreterLock>_
prevents true fully parallelizing Python code across threads. To avoid blocking
computation code with data loading, PyTorch provides an easy switch to perform
multi-process data loading by simply setting the argument :attr:num_workers
to a positive integer.

Single-process data loading (default)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In this mode, data fetching is done in the same process a
:class:~torch.utils.data.DataLoader is initialized. Therefore, data loading
may block computing. However, this mode may be preferred when resource(s) used
for sharing data among processes (e.g., shared memory, file descriptors) is
limited, or when the entire dataset is small and can be loaded entirely in
memory. Additionally, single-process loading often shows more readable error
traces and thus is useful for debugging.

Multi-process data loading
^^^^^^^^^^^^^^^^^^^^^^^^^^

Setting the argument :attr:num_workers as a positive integer will
turn on multi-process data loading with the specified number of loader worker
processes.

In this mode, each time an iterator of a :class:~torch.utils.data.DataLoader
is created (e.g., when you call enumerate(dataloader)), :attr:num_workers
worker processes are created. At this point, the :attr:dataset,
:attr:collate_fn, and :attr:worker_init_fn are passed to each
worker, where they are used to initialize, and fetch data. This means that
dataset access together with its internal IO, transforms
(including :attr:collate_fn) runs in the worker process.

:func:torch.utils.data.get_worker_info() returns various useful information
in a worker process (including the worker id, dataset replica, initial seed,
etc.), and returns None in main process. Users may use this function in
dataset code and/or :attr:worker_init_fn to individually configure each
dataset replica, and to determine whether the code is running in a worker
process. For example, this can be particularly helpful in sharding the dataset.

For map-style datasets, the main process generates the indices using
:attr:sampler and sends them to the workers. So any shuffle randomization is
done in the main process which guides loading by assigning indices to load.

For iterable-style datasets, since each worker process gets a replica of the
:attr:dataset object, naive multi-process loading will often result in
duplicated data. Using :func:torch.utils.data.get_worker_info() and/or
:attr:worker_init_fn, users may configure each replica independently. (See
:class:~torch.utils.data.IterableDataset documentations for how to achieve
this. ) For similar reasons, in multi-process loading, the :attr:drop_last
argument drops the last non-full batch of each worker's iterable-style dataset
replica.

Workers are shut down once the end of the iteration is reached, or when the
iterator becomes garbage collected.

.. warning::
It is generally not recommended to return CUDA tensors in multi-process
loading because of many subtleties in using CUDA and sharing CUDA tensors in
multiprocessing (see :ref:multiprocessing-cuda-note). Instead, we recommend
using automatic memory pinning <Memory Pinning_>_ (i.e., setting
:attr:pin_memory=True), which enables fast data transfer to CUDA-enabled
GPUs.

Platform-specific behaviors
"""""""""""""""""""""""""""

Since workers rely on Python :py:mod:multiprocessing, worker launch behavior is
different on Windows compared to Unix.

• On Unix, :func:fork() is the default :py:mod:multiprocessing start method.
  Using :func:fork, child workers typically can access the :attr:dataset and
  Python argument functions directly through the cloned address space.

• On Windows, :func:spawn() is the default :py:mod:multiprocessing start method.
  Using :func:spawn(), another interpreter is launched which runs your main script,
  followed by the internal worker function that receives the :attr:dataset,
  :attr:collate_fn and other arguments through :py:mod:pickle serialization.

This separate serialization means that you should take two steps to ensure you
are compatible with Windows while using multi-process data loading:

• Wrap most of you main script's code within if __name__ == '__main__': block,
  to make sure it doesn't run again (most likely generating error) when each worker
  process is launched. You can place your dataset and :class:~torch.utils.data.DataLoader
  instance creation logic here, as it doesn't need to be re-executed in workers.

• Make sure that any custom :attr:collate_fn, :attr:worker_init_fn
  or :attr:dataset code is declared as top level definitions, outside of the
  __main__ check. This ensures that they are available in worker processes.
  (this is needed since functions are pickled as references only, not bytecode.)

.. _data-loading-randomness:

Randomness in multi-process data loading
""""""""""""""""""""""""""""""""""""""""""

By default, each worker will have its PyTorch seed set to base_seed + worker_id,
where base_seed is a long generated by main process using its RNG (thereby,
consuming a RNG state mandatorily). However, seeds for other libraries may be
duplicated upon initializing workers (e.g., NumPy), causing each worker to return
identical random numbers. (See :ref:this section <dataloader-workers-random-seed> in FAQ.).

In :attr:worker_init_fn, you may access the PyTorch seed set for each worker
with either :func:torch.utils.data.get_worker_info().seed <torch.utils.data.get_worker_info>
or :func:torch.initial_seed(), and use it to seed other libraries before data
loading.

Memory Pinning

Host to GPU copies are much faster when they originate from pinned (page-locked)
memory. See :ref:cuda-memory-pinning for more details on when and how to use
pinned memory generally.

For data loading, passing :attr:pin_memory=True to a
:class:~torch.utils.data.DataLoader will automatically put the fetched data
Tensors in pinned memory, and thus enables faster data transfer to CUDA-enabled
GPUs.

The default memory pinning logic only recognizes Tensors and maps and iterables
containing Tensors. By default, if the pinning logic sees a batch that is a
custom type (which will occur if you have a :attr:collate_fn that returns a
custom batch type), or if each element of your batch is a custom type, the
pinning logic will not recognize them, and it will return that batch (or those
elements) without pinning the memory. To enable memory pinning for custom
batch or data type(s), define a :meth:pin_memory method on your custom
type(s).

See the example below.

Example::

class SimpleCustomBatch:
    def __init__(self, data):
        transposed_data = list(zip(*data))
        self.inp = torch.stack(transposed_data[0], 0)
        self.tgt = torch.stack(transposed_data[1], 0)

    # custom memory pinning method on custom type
    def pin_memory(self):
        self.inp = self.inp.pin_memory()
        self.tgt = self.tgt.pin_memory()
        return self

def collate_wrapper(batch):
    return SimpleCustomBatch(batch)

inps = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
tgts = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
dataset = TensorDataset(inps, tgts)

loader = DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper,
                    pin_memory=True)

for batch_ndx, sample in enumerate(loader):
    print(sample.inp.is_pinned())
    print(sample.tgt.is_pinned())

.. autoclass:: DataLoader
.. autoclass:: Dataset
.. autoclass:: IterableDataset
.. autoclass:: TensorDataset
.. autoclass:: ConcatDataset
.. autoclass:: ChainDataset
.. autoclass:: BufferedShuffleDataset
.. autoclass:: Subset
.. autofunction:: torch.utils.data.get_worker_info
.. autofunction:: torch.utils.data.random_split
.. autoclass:: torch.utils.data.Sampler
.. autoclass:: torch.utils.data.SequentialSampler
.. autoclass:: torch.utils.data.RandomSampler
.. autoclass:: torch.utils.data.SubsetRandomSampler
.. autoclass:: torch.utils.data.WeightedRandomSampler
.. autoclass:: torch.utils.data.BatchSampler
.. autoclass:: torch.utils.data.distributed.DistributedSampler