transforms.rst

.. _transforms:

Transforming and augmenting images
==================================

.. currentmodule:: torchvision.transforms

Transforms are common image transformations available in the
``torchvision.transforms`` module. They can be chained together using
:class:`Compose`.
Most transform classes have a function equivalent: :ref:`functional
transforms <functional_transforms>` give fine-grained control over the
transformations.
This is useful if you have to build a more complex transformation pipeline
(e.g. in the case of segmentation tasks).

Most transformations accept both `PIL <https://pillow.readthedocs.io>`_ images
and tensor images, although some transformations are PIL-only and some are
tensor-only. The :ref:`conversion_transforms` may be used to convert to and from
PIL images, or for converting dtypes and ranges.

The transformations that accept tensor images also accept batches of tensor
images. A Tensor Image is a tensor with ``(C, H, W)`` shape, where ``C`` is a
number of channels, ``H`` and ``W`` are image height and width. A batch of
Tensor Images is a tensor of ``(B, C, H, W)`` shape, where ``B`` is a number
of images in the batch.

The expected range of the values of a tensor image is implicitly defined by
the tensor dtype. Tensor images with a float dtype are expected to have
values in ``[0, 1)``. Tensor images with an integer dtype are expected to
have values in ``[0, MAX_DTYPE]`` where ``MAX_DTYPE`` is the largest value
that can be represented in that dtype.

Randomized transformations will apply the same transformation to all the
images of a given batch, but they will produce different transformations
across calls. For reproducible transformations across calls, you may use
:ref:`functional transforms <functional_transforms>`.

The following examples illustrate the use of the available transforms:

    * :ref:`sphx_glr_auto_examples_plot_transforms.py`

        .. figure:: ../source/auto_examples/images/sphx_glr_plot_transforms_001.png
            :align: center
            :scale: 65%

    * :ref:`sphx_glr_auto_examples_plot_scripted_tensor_transforms.py`

        .. figure:: ../source/auto_examples/images/sphx_glr_plot_scripted_tensor_transforms_001.png
            :align: center
            :scale: 30%

.. warning::

    Since v0.8.0 all random transformations are using torch default random generator to sample random parameters.
    It is a backward compatibility breaking change and user should set the random state as following:

    .. code:: python

        # Previous versions
        # import random
        # random.seed(12)

        # Now
        import torch
        torch.manual_seed(17)

    Please, keep in mind that the same seed for torch random generator and Python random generator will not
    produce the same results.


Transforms scriptability
------------------------

.. TODO: Add note about v2 scriptability (in next PR)

In order to script the transformations, please use ``torch.nn.Sequential`` instead of :class:`Compose`.

.. code:: python

    transforms = torch.nn.Sequential(
        transforms.CenterCrop(10),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    )
    scripted_transforms = torch.jit.script(transforms)

Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor`` and does not require
`lambda` functions or ``PIL.Image``.

For any custom transformations to be used with ``torch.jit.script``, they should be derived from ``torch.nn.Module``.


Geometry
--------

.. autosummary::
    :toctree: generated/
    :template: class.rst

    Resize
    RandomCrop
    RandomResizedCrop
    CenterCrop
    FiveCrop
    TenCrop
    Pad
    RandomAffine
    RandomPerspective
    RandomRotation
    RandomHorizontalFlip
    RandomVerticalFlip

Color
-----

.. autosummary::
    :toctree: generated/
    :template: class.rst

    ColorJitter
    Grayscale
    RandomGrayscale
    GaussianBlur
    RandomInvert
    RandomPosterize
    RandomSolarize
    RandomAdjustSharpness
    RandomAutocontrast
    RandomEqualize

Composition
-----------

.. autosummary::
    :toctree: generated/
    :template: class.rst

    Compose
    RandomApply
    RandomChoice
    RandomOrder

Miscellaneous
-------------

.. autosummary::
    :toctree: generated/
    :template: class.rst

    LinearTransformation
    Normalize
    RandomErasing
    Lambda

.. _conversion_transforms:

Conversion
----------

.. autosummary::
    :toctree: generated/
    :template: class.rst

    ToPILImage
    ToTensor
    PILToTensor
    ConvertImageDtype

Auto-Augmentation
-----------------

`AutoAugment <https://arxiv.org/pdf/1805.09501.pdf>`_ is a common Data Augmentation technique that can improve the accuracy of Image Classification models.
Though the data augmentation policies are directly linked to their trained dataset, empirical studies show that
ImageNet policies provide significant improvements when applied to other datasets.
In TorchVision we implemented 3 policies learned on the following datasets: ImageNet, CIFAR10 and SVHN.
The new transform can be used standalone or mixed-and-matched with existing transforms:

.. autosummary::
    :toctree: generated/
    :template: class.rst

    AutoAugmentPolicy
    AutoAugment
    RandAugment
    TrivialAugmentWide
    AugMix

.. _functional_transforms:

Functional Transforms
---------------------

.. currentmodule:: torchvision.transforms.functional

Functional transforms give you fine-grained control of the transformation pipeline.
As opposed to the transformations above, functional transforms don't contain a random number
generator for their parameters.
That means you have to specify/generate all parameters, but the functional transform will give you
reproducible results across calls.

Example:
you can apply a functional transform with the same parameters to multiple images like this:

.. code:: python

    import torchvision.transforms.functional as TF
    import random

    def my_segmentation_transforms(image, segmentation):
        if random.random() > 0.5:
            angle = random.randint(-30, 30)
            image = TF.rotate(image, angle)
            segmentation = TF.rotate(segmentation, angle)
        # more transforms ...
        return image, segmentation


Example:
you can use a functional transform to build transform classes with custom behavior:

.. code:: python

    import torchvision.transforms.functional as TF
    import random

    class MyRotationTransform:
        """Rotate by one of the given angles."""

        def __init__(self, angles):
            self.angles = angles

        def __call__(self, x):
            angle = random.choice(self.angles)
            return TF.rotate(x, angle)

    rotation_transform = MyRotationTransform(angles=[-30, -15, 0, 15, 30])


.. autosummary::
    :toctree: generated/
    :template: function.rst

    adjust_brightness
    adjust_contrast
    adjust_gamma
    adjust_hue
    adjust_saturation
    adjust_sharpness
    affine
    autocontrast
    center_crop
    convert_image_dtype
    crop
    equalize
    erase
    five_crop
    gaussian_blur
    get_dimensions
    get_image_num_channels
    get_image_size
    hflip
    invert
    normalize
    pad
    perspective
    pil_to_tensor
    posterize
    resize
    resized_crop
    rgb_to_grayscale
    rotate
    solarize
    ten_crop
    to_grayscale
    to_pil_image
    to_tensor
    vflip