.. currentmodule:: torchvision.transforms
Torchvision supports common computer vision transformations in the
torchvision.transforms and torchvision.transforms.v2 modules. Transforms
can be used to transform or augment data for training or inference of different
tasks (image classification, detection, segmentation, video classification).
# Image Classification
import torch
from torchvision.transforms import v2
H, W = 32, 32
img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8)
transforms = v2.Compose([
v2.RandomResizedCrop(size=(224, 224), antialias=True),
v2.RandomHorizontalFlip(p=0.5),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
img = transforms(img)# Detection (re-using imports and transforms from above)
from torchvision import tv_tensors
img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8)
boxes = torch.randint(0, H // 2, size=(3, 4))
boxes[:, 2:] += boxes[:, :2]
boxes = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=(H, W))
# The same transforms can be used!
img, boxes = transforms(img, boxes)
# And you can pass arbitrary input structures
output_dict = transforms({"image": img, "boxes": boxes})Transforms are typically passed as the transform or transforms argument
to the :ref:`Datasets <datasets>`.
Whether you're new to Torchvision transforms, or you're already experienced with them, we encourage you to start with :ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` in order to learn more about what can be done with the new v2 transforms.
Then, browse the sections in below this page for general information and performance tips. The available transforms and functionals are listed in the :ref:`API reference <v2_api_ref>`.
More information and tutorials can also be found in our :ref:`example gallery <gallery>`, e.g. :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py` or :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`.
Most transformations accept both PIL images and tensor inputs. Both CPU and CUDA tensors are supported. The result of both backends (PIL or Tensors) should be very close. In general, we recommend relying on the tensor backend :ref:`for performance <transforms_perf>`. The :ref:`conversion transforms <conversion_transforms>` may be used to convert to and from PIL images, or for converting dtypes and ranges.
Tensor image are expected to be of shape (C, H, W), where C is the
number of channels, and H and W refer to height and width. Most
transforms support batched tensor input. A batch of Tensor images is a tensor of
shape (N, C, H, W), where N is a number of images in the batch. The
:ref:`v2 <v1_or_v2>` transforms generally accept an arbitrary number of leading
dimensions (..., C, H, W) and can handle batched images or batched videos.
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. Typically, images of dtype
torch.uint8 are expected to have values in [0, 255].
Use :class:`~torchvision.transforms.v2.ToDtype` to convert both the dtype and range of the inputs.
TL;DR We recommending using the torchvision.transforms.v2 transforms
instead of those in torchvision.transforms. They're faster and they can do
more things. Just change the import and you should be good to go. Moving
forward, new features and improvements will only be considered for the v2
transforms.
In Torchvision 0.15 (March 2023), we released a new set of transforms available
in the torchvision.transforms.v2 namespace. These transforms have a lot of
advantages compared to the v1 ones (in torchvision.transforms):
- They can transform images but also bounding boxes, masks, or videos. This provides support for tasks beyond image classification: detection, segmentation, video classification, etc. See :ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` and :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
- They support more transforms like :class:`~torchvision.transforms.v2.CutMix` and :class:`~torchvision.transforms.v2.MixUp`. See :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py`.
- They're :ref:`faster <transforms_perf>`.
- They support arbitrary input structures (dicts, lists, tuples, etc.).
- Future improvements and features will be added to the v2 transforms only.
These transforms are fully backward compatible with the v1 ones, so if
you're already using tranforms from torchvision.transforms, all you need to
do to is to update the import to torchvision.transforms.v2. In terms of
output, there might be negligible differences due to implementation differences.
We recommend the following guidelines to get the best performance out of the transforms:
- Rely on the v2 transforms from
torchvision.transforms.v2 - Use tensors instead of PIL images
- Use
torch.uint8dtype, especially for resizing - Resize with bilinear or bicubic mode
This is what a typical transform pipeline could look like:
from torchvision.transforms import v2
transforms = v2.Compose([
v2.ToImage(), # Convert to tensor, only needed if you had a PIL image
v2.ToDtype(torch.uint8, scale=True), # optional, most input are already uint8 at this point
# ...
v2.RandomResizedCrop(size=(224, 224), antialias=True), # Or Resize(antialias=True)
# ...
v2.ToDtype(torch.float32, scale=True), # Normalize expects float input
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])The above should give you the best performance in a typical training environment
that relies on the :class:`torch.utils.data.DataLoader` with num_workers >
0.
Transforms tend to be sensitive to the input strides / memory format. Some
transforms will be faster with channels-first images while others prefer
channels-last. Like torch operators, most transforms will preserve the
memory format of the input, but this may not always be respected due to
implementation details. You may want to experiment a bit if you're chasing the
very best performance. Using :func:`torch.compile` on individual transforms may
also help factoring out the memory format variable (e.g. on
:class:`~torchvision.transforms.v2.Normalize`). Note that we're talking about
memory format, not :ref:`tensor shape <conventions>`.
Note that resize transforms like :class:`~torchvision.transforms.v2.Resize` and :class:`~torchvision.transforms.v2.RandomResizedCrop` typically prefer channels-last input and tend not to benefit from :func:`torch.compile` at this time.
Transforms are available as classes like
:class:`~torchvision.transforms.v2.Resize`, but also as functionals like
:func:`~torchvision.transforms.v2.functional.resize` in the
torchvision.transforms.v2.functional namespace.
This is very much like the :mod:`torch.nn` package which defines both classes
and functional equivalents in :mod:`torch.nn.functional`.
The functionals support PIL images, pure tensors, or :ref:`TVTensors
<tv_tensors>`, e.g. both resize(image_tensor) and resize(boxes) are
valid.
Note
Random transforms like :class:`~torchvision.transforms.v2.RandomCrop` will
randomly sample some parameter each time they're called. Their functional
counterpart (:func:`~torchvision.transforms.v2.functional.crop`) does not do
any kind of random sampling and thus have a slighlty different
parametrization. The get_params() class method of the transforms class
can be used to perform parameter sampling when using the functional APIs.
The torchvision.transforms.v2.functional namespace also contains what we
call the "kernels". These are the low-level functions that implement the
core functionalities for specific types, e.g. resize_bounding_boxes or
`resized_crop_mask. They are public, although not documented. Check the
code
to see which ones are available (note that those starting with a leading
underscore are not public!). Kernels are only really useful if you want
:ref:`torchscript support <transforms_torchscript>` for types like bounding
boxes or masks.
Most transform classes and functionals support torchscript. For composing transforms, use :class:`torch.nn.Sequential` instead of :class:`~torchvision.transforms.v2.Compose`:
transforms = torch.nn.Sequential(
CenterCrop(10),
Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
)
scripted_transforms = torch.jit.script(transforms)Warning
v2 transforms support torchscript, but if you call torch.jit.script() on
a v2 class transform, you'll actually end up with its (scripted) v1
equivalent. This may lead to slightly different results between the
scripted and eager executions due to implementation differences between v1
and v2.
If you really need torchscript support for the v2 transforms, we recommend
scripting the functionals from the
torchvision.transforms.v2.functional namespace to avoid surprises.
Also note that the functionals only support torchscript for pure tensors, which are always treated as images. If you need torchscript support for other types like bounding boxes or masks, you can rely on the :ref:`low-level kernels <functional_transforms>`.
For any custom transformations to be used with torch.jit.script, they should
be derived from torch.nn.Module.
See also: :ref:`sphx_glr_auto_examples_others_plot_scripted_tensor_transforms.py`.
.. autosummary::
:toctree: generated/
:template: class.rst
v2.Resize
v2.ScaleJitter
v2.RandomShortestSize
v2.RandomResize
Functionals
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.resize
.. autosummary::
:toctree: generated/
:template: class.rst
v2.RandomCrop
v2.RandomResizedCrop
v2.RandomIoUCrop
v2.CenterCrop
v2.FiveCrop
v2.TenCrop
Functionals
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.crop
v2.functional.resized_crop
v2.functional.ten_crop
v2.functional.center_crop
v2.functional.five_crop
.. autosummary::
:toctree: generated/
:template: class.rst
v2.RandomHorizontalFlip
v2.RandomVerticalFlip
v2.Pad
v2.RandomZoomOut
v2.RandomRotation
v2.RandomAffine
v2.RandomPerspective
v2.ElasticTransform
Functionals
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.horizontal_flip
v2.functional.vertical_flip
v2.functional.pad
v2.functional.rotate
v2.functional.affine
v2.functional.perspective
v2.functional.elastic
.. autosummary::
:toctree: generated/
:template: class.rst
v2.ColorJitter
v2.RandomChannelPermutation
v2.RandomPhotometricDistort
v2.Grayscale
v2.RGB
v2.RandomGrayscale
v2.GaussianBlur
v2.GaussianNoise
v2.RandomInvert
v2.RandomPosterize
v2.RandomSolarize
v2.RandomAdjustSharpness
v2.RandomAutocontrast
v2.RandomEqualize
Functionals
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.permute_channels
v2.functional.rgb_to_grayscale
v2.functional.grayscale_to_rgb
v2.functional.to_grayscale
v2.functional.gaussian_blur
v2.functional.gaussian_noise
v2.functional.invert
v2.functional.posterize
v2.functional.solarize
v2.functional.adjust_sharpness
v2.functional.autocontrast
v2.functional.adjust_contrast
v2.functional.equalize
v2.functional.adjust_brightness
v2.functional.adjust_saturation
v2.functional.adjust_hue
v2.functional.adjust_gamma
.. autosummary::
:toctree: generated/
:template: class.rst
v2.Compose
v2.RandomApply
v2.RandomChoice
v2.RandomOrder
.. autosummary::
:toctree: generated/
:template: class.rst
v2.LinearTransformation
v2.Normalize
v2.RandomErasing
v2.Lambda
v2.SanitizeBoundingBoxes
v2.ClampBoundingBoxes
v2.UniformTemporalSubsample
v2.JPEG
Functionals
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.normalize
v2.functional.erase
v2.functional.sanitize_bounding_boxes
v2.functional.clamp_bounding_boxes
v2.functional.uniform_temporal_subsample
v2.functional.jpeg
Note
Beware, some of these conversion transforms below will scale the values
while performing the conversion, while some may not do any scaling. By
scaling, we mean e.g. that a uint8 -> float32 would map the [0,
255] range into [0, 1] (and vice-versa). See :ref:`range_and_dtype`.
.. autosummary::
:toctree: generated/
:template: class.rst
v2.ToImage
v2.ToPureTensor
v2.PILToTensor
v2.ToPILImage
v2.ToDtype
v2.ConvertBoundingBoxFormat
functionals
.. autosummary::
:toctree: generated/
:template: functional.rst
v2.functional.to_image
v2.functional.pil_to_tensor
v2.functional.to_pil_image
v2.functional.to_dtype
v2.functional.convert_bounding_box_format
Deprecated
.. autosummary::
:toctree: generated/
:template: class.rst
v2.ToTensor
v2.functional.to_tensor
v2.ConvertImageDtype
v2.functional.convert_image_dtype
AutoAugment 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
v2.AutoAugment
v2.RandAugment
v2.TrivialAugmentWide
v2.AugMix
CutMix and MixUp are special transforms that are meant to be used on batches rather than on individual images, because they are combining pairs of images together. These can be used after the dataloader (once the samples are batched), or part of a collation function. See :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage examples.
.. autosummary::
:toctree: generated/
:template: class.rst
v2.CutMix
v2.MixUp
.. autosummary::
:toctree: generated/
:template: class.rst
v2.Transform
.. autosummary::
:toctree: generated/
:template: function.rst
v2.functional.register_kernel
v2.query_size
v2.query_chw
v2.get_bounding_boxes
.. autosummary::
:toctree: generated/
:template: class.rst
Resize
RandomCrop
RandomResizedCrop
CenterCrop
FiveCrop
TenCrop
Pad
RandomRotation
RandomAffine
RandomPerspective
ElasticTransform
RandomHorizontalFlip
RandomVerticalFlip
.. autosummary::
:toctree: generated/
:template: class.rst
ColorJitter
Grayscale
RandomGrayscale
GaussianBlur
RandomInvert
RandomPosterize
RandomSolarize
RandomAdjustSharpness
RandomAutocontrast
RandomEqualize
.. autosummary::
:toctree: generated/
:template: class.rst
Compose
RandomApply
RandomChoice
RandomOrder
.. autosummary::
:toctree: generated/
:template: class.rst
LinearTransformation
Normalize
RandomErasing
Lambda
Note
Beware, some of these conversion transforms below will scale the values
while performing the conversion, while some may not do any scaling. By
scaling, we mean e.g. that a uint8 -> float32 would map the [0,
255] range into [0, 1] (and vice-versa). See :ref:`range_and_dtype`.
.. autosummary::
:toctree: generated/
:template: class.rst
ToPILImage
ToTensor
PILToTensor
ConvertImageDtype
AutoAugment 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
.. currentmodule:: torchvision.transforms.functional
.. 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