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torchvision.datasets
====================
All datasets are subclasses of :class:`torch.utils.data.Dataset`
i.e, they have ``__getitem__`` and ``__len__`` methods implemented.
Hence, they can all be passed to a :class:`torch.utils.data.DataLoader`
which can load multiple samples parallelly using ``torch.multiprocessing`` workers.
For example: ::
imagenet_data = torchvision.datasets.ImageFolder('path/to/imagenet_root/')
data_loader = torch.utils.data.DataLoader(imagenet_data,
batch_size=4,
shuffle=True,
num_workers=args.nThreads)
The following datasets are available:
.. contents:: Datasets
:local:
All the datasets have almost similar API. They all have two common arguments:
``transform`` and ``target_transform`` to transform the input and target respectively.
.. currentmodule:: torchvision.datasets
MNIST
~~~~~
.. autoclass:: MNIST
COCO
~~~~
.. note ::
These require the `COCO API to be installed`_
.. _COCO API to be installed: https://github.com/pdollar/coco/tree/master/PythonAPI
Captions
^^^^^^^^
.. autoclass:: CocoCaptions
:members: __getitem__
:special-members:
Detection
^^^^^^^^^
.. autoclass:: CocoDetection
:members: __getitem__
:special-members:
LSUN
~~~~
.. autoclass:: LSUN
:members: __getitem__
:special-members:
ImageFolder
~~~~~~~~~~~
.. autoclass:: ImageFolder
:members: __getitem__
:special-members:
Imagenet-12
~~~~~~~~~~~
This should simply be implemented with an ``ImageFolder`` dataset.
The data is preprocessed `as described
here <https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset>`__
`Here is an
example <https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py#L48-L62>`__.
CIFAR
~~~~~
.. autoclass:: CIFAR10
:members: __getitem__
:special-members:
STL10
~~~~~
.. autoclass:: STL10
:members: __getitem__
:special-members:
SVHN
~~~~~
.. autoclass:: SVHN
:members: __getitem__
:special-members:
PhotoTour
~~~~~~~~~
.. autoclass:: PhotoTour
:members: __getitem__
:special-members: