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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import warnings
import torch
from torch.utils.data import DataLoader as _TorchDataLoader
from torch.utils.data import Dataset
from monai.data.meta_obj import get_track_meta
from monai.data.utils import list_data_collate, set_rnd, worker_init_fn
__all__ = ["DataLoader"]
class DataLoader(_TorchDataLoader):
"""
Provides an iterable over the given `dataset`. It inherits the PyTorch
DataLoader and adds enhanced `collate_fn` and `worker_fn` by default.
Although this class could be configured to be the same as
`torch.utils.data.DataLoader`, its default configuration is
recommended, mainly for the following extra features:
- It handles MONAI randomizable objects with appropriate random state
managements for deterministic behaviour.
- It is aware of the patch-based transform (such as
:py:class:`monai.transforms.RandSpatialCropSamplesDict`) samples for
preprocessing with enhanced data collating behaviour.
See: :py:class:`monai.transforms.Compose`.
For more details about :py:class:`torch.utils.data.DataLoader`, please see:
https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader.
For example, to construct a randomized dataset and iterate with the data loader:
.. code-block:: python
import torch
from monai.data import DataLoader
from monai.transforms import Randomizable
class RandomDataset(torch.utils.data.Dataset, Randomizable):
def __getitem__(self, index):
return self.R.randint(0, 1000, (1,))
def __len__(self):
return 16
dataset = RandomDataset()
dataloader = DataLoader(dataset, batch_size=2, num_workers=4)
for epoch in range(2):
for i, batch in enumerate(dataloader):
print(epoch, i, batch.data.numpy().flatten().tolist())
Args:
dataset: dataset from which to load the data.
num_workers: how many subprocesses to use for data
loading. ``0`` means that the data will be loaded in the main process.
(default: ``0``)
collate_fn: default to :py:func:`monai.data.utils.list_data_collate`.
worker_init_fn: default to :py:func:`monai.data.utils.worker_init_fn`.
kwargs: other parameters for PyTorch DataLoader.
"""
def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:
if num_workers == 0:
# when num_workers > 0, random states are determined by worker_init_fn
# this is to make the behavior consistent when num_workers == 0
# torch.int64 doesn't work well on some versions of windows
_g = torch.random.default_generator if kwargs.get("generator") is None else kwargs["generator"]
init_seed = _g.initial_seed()
_seed = torch.empty((), dtype=torch.int64).random_(generator=_g).item()
set_rnd(dataset, int(_seed))
_g.manual_seed(init_seed)
if "collate_fn" not in kwargs:
kwargs["collate_fn"] = list_data_collate
if "worker_init_fn" not in kwargs:
kwargs["worker_init_fn"] = worker_init_fn
if (
"multiprocessing_context" in kwargs
and kwargs["multiprocessing_context"] == "spawn"
and not get_track_meta()
):
warnings.warn(
"Please be aware: Return type of the dataloader will not be a Tensor as expected but"
" a MetaTensor instead! This is because 'spawn' creates a new process where _TRACK_META"
" is initialized to True again. Context:_TRACK_META is set to False and"
" multiprocessing_context to spawn"
)
super().__init__(dataset=dataset, num_workers=num_workers, **kwargs)
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