torchvision.transforms¶
Transforms are common image transformations. They can be chained together using Compose
.
Most transform classes have a function equivalent: functional
transforms give finegrained 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 images and tensor images, although some transformations are PILonly and some are tensoronly. The Conversion Transforms may be used to convert to and from PIL images.
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 functional transforms.
The following examples illustrate the use of the available transforms:
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:
# 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.
Scriptable transforms¶
In order to script the transformations, please use torch.nn.Sequential
instead of Compose
.
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
.
Compositions of transforms¶

class
torchvision.transforms.
Compose
(transforms)[source]¶ Composes several transforms together. This transform does not support torchscript. Please, see the note below.
 Parameters
transforms (list of
Transform
objects) – list of transforms to compose.
Example
>>> transforms.Compose([ >>> transforms.CenterCrop(10), >>> transforms.PILToTensor(), >>> transforms.ConvertImageDtype(torch.float), >>> ])
Note
In order to script the transformations, please use
torch.nn.Sequential
as below.>>> 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
, does not require lambda functions orPIL.Image
.Examples using
Compose
:
Transforms on PIL Image and torch.*Tensor¶

class
torchvision.transforms.
CenterCrop
(size)[source]¶ Crops the given image at the center. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
 Parameters
size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
Examples using
CenterCrop
:

class
torchvision.transforms.
ColorJitter
(brightness=0, contrast=0, saturation=0, hue=0)[source]¶ Randomly change the brightness, contrast, saturation and hue of an image. If the image is torch Tensor, it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, mode “1”, “I”, “F” and modes with transparency (alpha channel) are not supported.
 Parameters
brightness (float or tuple of python:float (min, max)) – How much to jitter brightness. brightness_factor is chosen uniformly from [max(0, 1  brightness), 1 + brightness] or the given [min, max]. Should be non negative numbers.
contrast (float or tuple of python:float (min, max)) – How much to jitter contrast. contrast_factor is chosen uniformly from [max(0, 1  contrast), 1 + contrast] or the given [min, max]. Should be non negative numbers.
saturation (float or tuple of python:float (min, max)) – How much to jitter saturation. saturation_factor is chosen uniformly from [max(0, 1  saturation), 1 + saturation] or the given [min, max]. Should be non negative numbers.
hue (float or tuple of python:float (min, max)) – How much to jitter hue. hue_factor is chosen uniformly from [hue, hue] or the given [min, max]. Should have 0<= hue <= 0.5 or 0.5 <= min <= max <= 0.5.
Examples using
ColorJitter
:
forward
(img)[source]¶  Parameters
img (PIL Image or Tensor) – Input image.
 Returns
Color jittered image.
 Return type
PIL Image or Tensor

static
get_params
(brightness: Optional[List[float]], contrast: Optional[List[float]], saturation: Optional[List[float]], hue: Optional[List[float]]) → Tuple[torch.Tensor, Optional[float], Optional[float], Optional[float], Optional[float]][source]¶ Get the parameters for the randomized transform to be applied on image.
 Parameters
brightness (tuple of python:float (min, max), optional) – The range from which the brightness_factor is chosen uniformly. Pass None to turn off the transformation.
contrast (tuple of python:float (min, max), optional) – The range from which the contrast_factor is chosen uniformly. Pass None to turn off the transformation.
saturation (tuple of python:float (min, max), optional) – The range from which the saturation_factor is chosen uniformly. Pass None to turn off the transformation.
hue (tuple of python:float (min, max), optional) – The range from which the hue_factor is chosen uniformly. Pass None to turn off the transformation.
 Returns
The parameters used to apply the randomized transform along with their random order.
 Return type

class
torchvision.transforms.
FiveCrop
(size)[source]¶ Crop the given image into four corners and the central crop. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Note
This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns. See below for an example of how to deal with this.
 Parameters
size (sequence or int) – Desired output size of the crop. If size is an
int
instead of sequence like (h, w), a square crop of size (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
Example
>>> transform = Compose([ >>> FiveCrop(size), # this is a list of PIL Images >>> Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor >>> ]) >>> #In your test loop you can do the following: >>> input, target = batch # input is a 5d tensor, target is 2d >>> bs, ncrops, c, h, w = input.size() >>> result = model(input.view(1, c, h, w)) # fuse batch size and ncrops >>> result_avg = result.view(bs, ncrops, 1).mean(1) # avg over crops
Examples using
FiveCrop
:

class
torchvision.transforms.
Grayscale
(num_output_channels=1)[source]¶ Convert image to grayscale. If the image is torch Tensor, it is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions
 Parameters
num_output_channels (int) – (1 or 3) number of channels desired for output image
 Returns
Grayscale version of the input.
If
num_output_channels == 1
: returned image is single channelIf
num_output_channels == 3
: returned image is 3 channel with r == g == b
 Return type
PIL Image
Examples using
Grayscale
:

class
torchvision.transforms.
Pad
(padding, fill=0, padding_mode='constant')[source]¶ Pad the given image on all sides with the given “pad” value. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means at most 2 leading dimensions for mode reflect and symmetric, at most 3 leading dimensions for mode edge, and an arbitrary number of leading dimensions for mode constant
 Parameters
padding (int or sequence) –
Padding on each border. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
Note
In torchscript mode padding as single int is not supported, use a sequence of length 1:
[padding, ]
.fill (number or str or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or str or tuple value is supported for PIL Image.
padding_mode (str) –
Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
constant: pads with a constant value, this value is specified with fill
edge: pads with the last value at the edge of the image. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]
symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]
Examples using
Pad
:

class
torchvision.transforms.
RandomAffine
(degrees, translate=None, scale=None, shear=None, interpolation=<InterpolationMode.NEAREST: 'nearest'>, fill=0, fillcolor=None, resample=None)[source]¶ Random affine transformation of the image keeping center invariant. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
degrees (sequence or number) – Range of degrees to select from. If degrees is a number instead of sequence like (min, max), the range of degrees will be (degrees, +degrees). Set to 0 to deactivate rotations.
translate (tuple, optional) – tuple of maximum absolute fraction for horizontal and vertical translations. For example translate=(a, b), then horizontal shift is randomly sampled in the range img_width * a < dx < img_width * a and vertical shift is randomly sampled in the range img_height * b < dy < img_height * b. Will not translate by default.
scale (tuple, optional) – scaling factor interval, e.g (a, b), then scale is randomly sampled from the range a <= scale <= b. Will keep original scale by default.
shear (sequence or number, optional) – Range of degrees to select from. If shear is a number, a shear parallel to the x axis in the range (shear, +shear) will be applied. Else if shear is a sequence of 2 values a shear parallel to the x axis in the range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values, a xaxis shear in (shear[0], shear[1]) and yaxis shear in (shear[2], shear[3]) will be applied. Will not apply shear by default.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.fill (sequence or number) – Pixel fill value for the area outside the transformed image. Default is
0
. If given a number, the value is used for all bands respectively.fillcolor (sequence or number, optional) – deprecated argument and will be removed since v0.10.0. Please use the
fill
parameter instead.resample (int, optional) – deprecated argument and will be removed since v0.10.0. Please use the
interpolation
parameter instead.
Examples using
RandomAffine
:
forward
(img)[source]¶ img (PIL Image or Tensor): Image to be transformed.
 Returns
Affine transformed image.
 Return type
PIL Image or Tensor

static
get_params
(degrees: List[float], translate: Optional[List[float]], scale_ranges: Optional[List[float]], shears: Optional[List[float]], img_size: List[int]) → Tuple[float, Tuple[int, int], float, Tuple[float, float]][source]¶ Get parameters for affine transformation
 Returns
params to be passed to the affine transformation

class
torchvision.transforms.
RandomApply
(transforms, p=0.5)[source]¶ Apply randomly a list of transformations with a given probability.
Note
In order to script the transformation, please use
torch.nn.ModuleList
as input instead of list/tuple of transforms as shown below:>>> transforms = transforms.RandomApply(torch.nn.ModuleList([ >>> transforms.ColorJitter(), >>> ]), p=0.3) >>> scripted_transforms = torch.jit.script(transforms)
Make sure to use only scriptable transformations, i.e. that work with
torch.Tensor
, does not require lambda functions orPIL.Image
. Parameters
transforms (sequence or torch.nn.Module) – list of transformations
p (float) – probability
Examples using
RandomApply
:

class
torchvision.transforms.
RandomCrop
(size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant')[source]¶ Crop the given image at a random location. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions, but if nonconstant padding is used, the input is expected to have at most 2 leading dimensions
 Parameters
size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
padding (int or sequence, optional) –
Optional padding on each border of the image. Default is None. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
Note
In torchscript mode padding as single int is not supported, use a sequence of length 1:
[padding, ]
.pad_if_needed (boolean) – It will pad the image if smaller than the desired size to avoid raising an exception. Since cropping is done after padding, the padding seems to be done at a random offset.
fill (number or str or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or str or tuple value is supported for PIL Image.
padding_mode (str) –
Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
constant: pads with a constant value, this value is specified with fill
edge: pads with the last value at the edge of the image. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]
symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]
Examples using
RandomCrop
:
forward
(img)[source]¶  Parameters
img (PIL Image or Tensor) – Image to be cropped.
 Returns
Cropped image.
 Return type
PIL Image or Tensor

class
torchvision.transforms.
RandomGrayscale
(p=0.1)[source]¶ Randomly convert image to grayscale with a probability of p (default 0.1). If the image is torch Tensor, it is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions
 Parameters
p (float) – probability that image should be converted to grayscale.
 Returns
Grayscale version of the input image with probability p and unchanged with probability (1p).  If input image is 1 channel: grayscale version is 1 channel  If input image is 3 channel: grayscale version is 3 channel with r == g == b
 Return type
PIL Image or Tensor

class
torchvision.transforms.
RandomHorizontalFlip
(p=0.5)[source]¶ Horizontally flip the given image randomly with a given probability. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
 Parameters
p (float) – probability of the image being flipped. Default value is 0.5
Examples using
RandomHorizontalFlip
:

class
torchvision.transforms.
RandomPerspective
(distortion_scale=0.5, p=0.5, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, fill=0)[source]¶ Performs a random perspective transformation of the given image with a given probability. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
distortion_scale (float) – argument to control the degree of distortion and ranges from 0 to 1. Default is 0.5.
p (float) – probability of the image being transformed. Default is 0.5.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.fill (sequence or number) – Pixel fill value for the area outside the transformed image. Default is
0
. If given a number, the value is used for all bands respectively.
Examples using
RandomPerspective
:
forward
(img)[source]¶  Parameters
img (PIL Image or Tensor) – Image to be Perspectively transformed.
 Returns
Randomly transformed image.
 Return type
PIL Image or Tensor

static
get_params
(width: int, height: int, distortion_scale: float) → Tuple[List[List[int]], List[List[int]]][source]¶ Get parameters for
perspective
for a random perspective transform. Parameters
 Returns
List containing [topleft, topright, bottomright, bottomleft] of the original image, List containing [topleft, topright, bottomright, bottomleft] of the transformed image.

class
torchvision.transforms.
RandomResizedCrop
(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: 'bilinear'>)[source]¶ Crop a random portion of image and resize it to a given size.
If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
A crop of the original image is made: the crop has a random area (H * W) and a random aspect ratio. This crop is finally resized to the given size. This is popularly used to train the Inception networks.
 Parameters
size (int or sequence) –
expected output size of the crop, for each edge. If size is an int instead of sequence like (h, w), a square output size
(size, size)
is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).Note
In torchscript mode size as single int is not supported, use a sequence of length 1:
[size, ]
.scale (tuple of python:float) – Specifies the lower and upper bounds for the random area of the crop, before resizing. The scale is defined with respect to the area of the original image.
ratio (tuple of python:float) – lower and upper bounds for the random aspect ratio of the crop, before resizing.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
andInterpolationMode.BICUBIC
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.
Examples using
RandomResizedCrop
:
forward
(img)[source]¶  Parameters
img (PIL Image or Tensor) – Image to be cropped and resized.
 Returns
Randomly cropped and resized image.
 Return type
PIL Image or Tensor

class
torchvision.transforms.
RandomRotation
(degrees, interpolation=<InterpolationMode.NEAREST: 'nearest'>, expand=False, center=None, fill=0, resample=None)[source]¶ Rotate the image by angle. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
degrees (sequence or number) – Range of degrees to select from. If degrees is a number instead of sequence like (min, max), the range of degrees will be (degrees, +degrees).
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.expand (bool, optional) – Optional expansion flag. If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.
center (sequence, optional) – Optional center of rotation, (x, y). Origin is the upper left corner. Default is the center of the image.
fill (sequence or number) – Pixel fill value for the area outside the rotated image. Default is
0
. If given a number, the value is used for all bands respectively.resample (int, optional) – deprecated argument and will be removed since v0.10.0. Please use the
interpolation
parameter instead.
Examples using
RandomRotation
:

class
torchvision.transforms.
RandomSizedCrop
(*args, **kwargs)[source]¶ Note: This transform is deprecated in favor of RandomResizedCrop.

class
torchvision.transforms.
RandomVerticalFlip
(p=0.5)[source]¶ Vertically flip the given image randomly with a given probability. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
 Parameters
p (float) – probability of the image being flipped. Default value is 0.5
Examples using
RandomVerticalFlip
:

class
torchvision.transforms.
Resize
(size, interpolation=<InterpolationMode.BILINEAR: 'bilinear'>, max_size=None, antialias=None)[source]¶ Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Warning
The output image might be different depending on its type: when downsampling, the interpolation of PIL images and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences in the performance of a network. Therefore, it is preferable to train and serve a model with the same input types. See also below the
antialias
parameter, which can help making the output of PIL images and tensors closer. Parameters
size (sequence or int) –
Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
Note
In torchscript mode size as single int is not supported, use a sequence of length 1:
[size, ]
.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
andInterpolationMode.BICUBIC
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.max_size (int, optional) – The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than
max_size
after being resized according tosize
, then the image is resized again so that the longer edge is equal tomax_size
. As a result,size
might be overruled, i.e the smaller edge may be shorter thansize
. This is only supported ifsize
is an int (or a sequence of length 1 in torchscript mode).antialias (bool, optional) –
antialias flag. If
img
is PIL Image, the flag is ignored and antialias is always used. Ifimg
is Tensor, the flag is False by default and can be set to True forInterpolationMode.BILINEAR
only mode. This can help making the output for PIL images and tensors closer.Warning
There is no autodiff support for
antialias=True
option with inputimg
as Tensor.
Examples using
Resize
:

class
torchvision.transforms.
Scale
(*args, **kwargs)[source]¶ Note: This transform is deprecated in favor of Resize.

class
torchvision.transforms.
TenCrop
(size, vertical_flip=False)[source]¶ Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Note
This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns. See below for an example of how to deal with this.
 Parameters
Example
>>> transform = Compose([ >>> TenCrop(size), # this is a list of PIL Images >>> Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor >>> ]) >>> #In your test loop you can do the following: >>> input, target = batch # input is a 5d tensor, target is 2d >>> bs, ncrops, c, h, w = input.size() >>> result = model(input.view(1, c, h, w)) # fuse batch size and ncrops >>> result_avg = result.view(bs, ncrops, 1).mean(1) # avg over crops

class
torchvision.transforms.
GaussianBlur
(kernel_size, sigma=(0.1, 2.0))[source]¶ Blurs image with randomly chosen Gaussian blur. If the image is torch Tensor, it is expected to have […, C, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
kernel_size (int or sequence) – Size of the Gaussian kernel.
sigma (float or tuple of python:float (min, max)) – Standard deviation to be used for creating kernel to perform blurring. If float, sigma is fixed. If it is tuple of float (min, max), sigma is chosen uniformly at random to lie in the given range.
 Returns
Gaussian blurred version of the input image.
 Return type
PIL Image or Tensor
Examples using
GaussianBlur
:
forward
(img: torch.Tensor) → torch.Tensor[source]¶  Parameters
img (PIL Image or Tensor) – image to be blurred.
 Returns
Gaussian blurred image
 Return type
PIL Image or Tensor

class
torchvision.transforms.
RandomInvert
(p=0.5)[source]¶ Inverts the colors of the given image randomly with a given probability. If img is a Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
p (float) – probability of the image being color inverted. Default value is 0.5
Examples using
RandomInvert
:

class
torchvision.transforms.
RandomPosterize
(bits, p=0.5)[source]¶ Posterize the image randomly with a given probability by reducing the number of bits for each color channel. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
Examples using
RandomPosterize
:

class
torchvision.transforms.
RandomSolarize
(threshold, p=0.5)[source]¶ Solarize the image randomly with a given probability by inverting all pixel values above a threshold. If img is a Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
Examples using
RandomSolarize
:

class
torchvision.transforms.
RandomAdjustSharpness
(sharpness_factor, p=0.5)[source]¶ Adjust the sharpness of the image randomly with a given probability. If the image is torch Tensor, it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
Examples using
RandomAdjustSharpness
:

class
torchvision.transforms.
RandomAutocontrast
(p=0.5)[source]¶ Autocontrast the pixels of the given image randomly with a given probability. If the image is torch Tensor, it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
p (float) – probability of the image being autocontrasted. Default value is 0.5
Examples using
RandomAutocontrast
:

class
torchvision.transforms.
RandomEqualize
(p=0.5)[source]¶ Equalize the histogram of the given image randomly with a given probability. If the image is torch Tensor, it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “P”, “L” or “RGB”.
 Parameters
p (float) – probability of the image being equalized. Default value is 0.5
Examples using
RandomEqualize
:
Transforms on PIL Image only¶
Transforms on torch.*Tensor only¶

class
torchvision.transforms.
LinearTransformation
(transformation_matrix, mean_vector)[source]¶ Transform a tensor image with a square transformation matrix and a mean_vector computed offline. This transform does not support PIL Image. Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and subtract mean_vector from it which is then followed by computing the dot product with the transformation matrix and then reshaping the tensor to its original shape.
 Applications:
whitening transformation: Suppose X is a column vector zerocentered data. Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X), perform SVD on this matrix and pass it as transformation_matrix.
 Parameters
transformation_matrix (Tensor) – tensor [D x D], D = C x H x W
mean_vector (Tensor) – tensor [D], D = C x H x W

forward
(tensor: torch.Tensor) → torch.Tensor[source]¶  Parameters
tensor (Tensor) – Tensor image to be whitened.
 Returns
Transformed image.
 Return type
Tensor

class
torchvision.transforms.
Normalize
(mean, std, inplace=False)[source]¶ Normalize a tensor image with mean and standard deviation. This transform does not support PIL Image. Given mean:
(mean[1],...,mean[n])
and std:(std[1],..,std[n])
forn
channels, this transform will normalize each channel of the inputtorch.*Tensor
i.e.,output[channel] = (input[channel]  mean[channel]) / std[channel]
Note
This transform acts out of place, i.e., it does not mutate the input tensor.
 Parameters
mean (sequence) – Sequence of means for each channel.
std (sequence) – Sequence of standard deviations for each channel.
inplace (bool,optional) – Bool to make this operation inplace.
Examples using
Normalize
:
forward
(tensor: torch.Tensor) → torch.Tensor[source]¶  Parameters
tensor (Tensor) – Tensor image to be normalized.
 Returns
Normalized Tensor image.
 Return type
Tensor

class
torchvision.transforms.
RandomErasing
(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False)[source]¶ Randomly selects a rectangle region in an torch Tensor image and erases its pixels. This transform does not support PIL Image. ‘Random Erasing Data Augmentation’ by Zhong et al. See https://arxiv.org/abs/1708.04896
 Parameters
p – probability that the random erasing operation will be performed.
scale – range of proportion of erased area against input image.
ratio – range of aspect ratio of erased area.
value – erasing value. Default is 0. If a single int, it is used to erase all pixels. If a tuple of length 3, it is used to erase R, G, B channels respectively. If a str of ‘random’, erasing each pixel with random values.
inplace – boolean to make this transform inplace. Default set to False.
 Returns
Erased Image.
Example
>>> transform = transforms.Compose([ >>> transforms.RandomHorizontalFlip(), >>> transforms.PILToTensor(), >>> transforms.ConvertImageDtype(torch.float), >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), >>> transforms.RandomErasing(), >>> ])

forward
(img)[source]¶  Parameters
img (Tensor) – Tensor image to be erased.
 Returns
Erased Tensor image.
 Return type
img (Tensor)

static
get_params
(img: torch.Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None) → Tuple[int, int, int, int, torch.Tensor][source]¶ Get parameters for
erase
for a random erasing. Parameters
img (Tensor) – Tensor image to be erased.
scale (sequence) – range of proportion of erased area against input image.
ratio (sequence) – range of aspect ratio of erased area.
value (list, optional) – erasing value. If None, it is interpreted as “random” (erasing each pixel with random values). If
len(value)
is 1, it is interpreted as a number, i.e.value[0]
.
 Returns
params (i, j, h, w, v) to be passed to
erase
for random erasing. Return type

class
torchvision.transforms.
ConvertImageDtype
(dtype: torch.dtype)[source]¶ Convert a tensor image to the given
dtype
and scale the values accordingly This function does not support PIL Image. Parameters
dtype (torch.dpython:type) – Desired data type of the output
Note
When converting from a smaller to a larger integer
dtype
the maximum values are not mapped exactly. If converted back and forth, this mismatch has no effect. Raises
RuntimeError – When trying to cast
torch.float32
totorch.int32
ortorch.int64
as well as for trying to casttorch.float64
totorch.int64
. These conversions might lead to overflow errors since the floating pointdtype
cannot store consecutive integers over the whole range of the integerdtype
.
Examples using
ConvertImageDtype
:
Conversion Transforms¶

class
torchvision.transforms.
ToPILImage
(mode=None)[source]¶ Convert a tensor or an ndarray to PIL Image. This transform does not support torchscript.
Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape H x W x C to a PIL Image while preserving the value range.
 Parameters
mode (PIL.Image mode) – color space and pixel depth of input data (optional). If
mode
isNone
(default) there are some assumptions made about the input data:  If the input has 4 channels, themode
is assumed to beRGBA
.  If the input has 3 channels, themode
is assumed to beRGB
.  If the input has 2 channels, themode
is assumed to beLA
.  If the input has 1 channel, themode
is determined by the data type (i.eint
,float
,short
).
Examples using
ToPILImage
:

class
torchvision.transforms.
ToTensor
[source]¶ Convert a
PIL Image
ornumpy.ndarray
to tensor. This transform does not support torchscript.Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8
In the other cases, tensors are returned without scaling.
Note
Because the input image is scaled to [0.0, 1.0], this transformation should not be used when transforming target image masks. See the references for implementing the transforms for image masks.
Generic Transforms¶
Automatic Augmentation Transforms¶
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 mixedandmatched with existing transforms:

class
torchvision.transforms.
AutoAugmentPolicy
(value)[source]¶ AutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN.
Examples using
AutoAugmentPolicy
:

class
torchvision.transforms.
AutoAugment
(policy: torchvision.transforms.autoaugment.AutoAugmentPolicy = <AutoAugmentPolicy.IMAGENET: 'imagenet'>, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)[source]¶ AutoAugment data augmentation method based on “AutoAugment: Learning Augmentation Strategies from Data”. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
policy (AutoAugmentPolicy) – Desired policy enum defined by
torchvision.transforms.autoaugment.AutoAugmentPolicy
. Default isAutoAugmentPolicy.IMAGENET
.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported.fill (sequence or number, optional) – Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Examples using
AutoAugment
:
forward
(img: torch.Tensor) → torch.Tensor[source]¶ img (PIL Image or Tensor): Image to be transformed.
 Returns
AutoAugmented image.
 Return type
PIL Image or Tensor

static
get_params
(transform_num: int) → Tuple[int, torch.Tensor, torch.Tensor][source]¶ Get parameters for autoaugment transformation
 Returns
params required by the autoaugment transformation
RandAugment is a simple highperforming Data Augmentation technique which improves the accuracy of Image Classification models.

class
torchvision.transforms.
RandAugment
(num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)[source]¶ RandAugment data augmentation method based on “RandAugment: Practical automated data augmentation with a reduced search space”. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
num_ops (int) – Number of augmentation transformations to apply sequentially.
magnitude (int) – Magnitude for all the transformations.
num_magnitude_bins (int) – The number of different magnitude values.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported.fill (sequence or number, optional) – Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Examples using
RandAugment
:
forward
(img: torch.Tensor) → torch.Tensor[source]¶ img (PIL Image or Tensor): Image to be transformed.
 Returns
Transformed image.
 Return type
PIL Image or Tensor
TrivialAugmentWide is a datasetindependent dataaugmentation technique which improves the accuracy of Image Classification models.

class
torchvision.transforms.
TrivialAugmentWide
(num_magnitude_bins: int = 31, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)[source]¶ Datasetindependent dataaugmentation with TrivialAugment Wide, as described in “TrivialAugment: Tuningfree Yet StateoftheArt Data Augmentation” <https://arxiv.org/abs/2103.10158>. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Parameters
num_magnitude_bins (int) – The number of different magnitude values.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported.fill (sequence or number, optional) – Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Examples using
TrivialAugmentWide
:
forward
(img: torch.Tensor) → torch.Tensor[source]¶ img (PIL Image or Tensor): Image to be transformed.
 Returns
Transformed image.
 Return type
PIL Image or Tensor
Functional Transforms¶
Functional transforms give you finegrained 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:
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:
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])

class
torchvision.transforms.functional.
InterpolationMode
(value)[source]¶ Interpolation modes Available interpolation methods are
nearest
,bilinear
,bicubic
,box
,hamming
, andlanczos
.

torchvision.transforms.functional.
adjust_brightness
(img: torch.Tensor, brightness_factor: float) → torch.Tensor[source]¶ Adjust brightness of an image.
 Parameters
img (PIL Image or Tensor) – Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions.
brightness_factor (float) – How much to adjust the brightness. Can be any non negative number. 0 gives a black image, 1 gives the original image while 2 increases the brightness by a factor of 2.
 Returns
Brightness adjusted image.
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
adjust_contrast
(img: torch.Tensor, contrast_factor: float) → torch.Tensor[source]¶ Adjust contrast of an image.
 Parameters
img (PIL Image or Tensor) – Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions.
contrast_factor (float) – How much to adjust the contrast. Can be any non negative number. 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2.
 Returns
Contrast adjusted image.
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
adjust_gamma
(img: torch.Tensor, gamma: float, gain: float = 1) → torch.Tensor[source]¶ Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted based on the following equation:
\[I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}\]See Gamma Correction for more details.
 Parameters
img (PIL Image or Tensor) – PIL Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, modes with transparency (alpha channel) are not supported.
gamma (float) – Non negative real number, same as \(\gamma\) in the equation. gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter.
gain (float) – The constant multiplier.
 Returns
Gamma correction adjusted image.
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
adjust_hue
(img: torch.Tensor, hue_factor: float) → torch.Tensor[source]¶ Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel (H). The image is then converted back to original image mode.
hue_factor is the amount of shift in H channel and must be in the interval [0.5, 0.5].
See Hue for more details.
 Parameters
img (PIL Image or Tensor) – Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image mode “1”, “I”, “F” and modes with transparency (alpha channel) are not supported.
hue_factor (float) – How much to shift the hue channel. Should be in [0.5, 0.5]. 0.5 and 0.5 give complete reversal of hue channel in HSV space in positive and negative direction respectively. 0 means no shift. Therefore, both 0.5 and 0.5 will give an image with complementary colors while 0 gives the original image.
 Returns
Hue adjusted image.
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
adjust_saturation
(img: torch.Tensor, saturation_factor: float) → torch.Tensor[source]¶ Adjust color saturation of an image.
 Parameters
img (PIL Image or Tensor) – Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions.
saturation_factor (float) – How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image while 2 will enhance the saturation by a factor of 2.
 Returns
Saturation adjusted image.
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
adjust_sharpness
(img: torch.Tensor, sharpness_factor: float) → torch.Tensor[source]¶ Adjust the sharpness of an image.
 Parameters
img (PIL Image or Tensor) – Image to be adjusted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions.
sharpness_factor (float) – How much to adjust the sharpness. Can be any non negative number. 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness by a factor of 2.
 Returns
Sharpness adjusted image.
 Return type
PIL Image or Tensor
Examples using
adjust_sharpness
:

torchvision.transforms.functional.
affine
(img: torch.Tensor, angle: float, translate: List[int], scale: float, shear: List[float], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None, resample: Optional[int] = None, fillcolor: Optional[List[float]] = None) → torch.Tensor[source]¶ Apply affine transformation on the image keeping image center invariant. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
img (PIL Image or Tensor) – image to transform.
angle (number) – rotation angle in degrees between 180 and 180, clockwise direction.
translate (sequence of python:integers) – horizontal and vertical translations (postrotation translation)
scale (float) – overall scale
shear (float or sequence) – shear angle value in degrees between 180 to 180, clockwise direction. If a sequence is specified, the first value corresponds to a shear parallel to the x axis, while the second value corresponds to a shear parallel to the y axis.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.fill (sequence or number, optional) –
Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Note
In torchscript mode single int/float value is not supported, please use a sequence of length 1:
[value, ]
.fillcolor (sequence, int, float) – deprecated argument and will be removed since v0.10.0. Please use the
fill
parameter instead.resample (int, optional) – deprecated argument and will be removed since v0.10.0. Please use the
interpolation
parameter instead.
 Returns
Transformed image.
 Return type
PIL Image or Tensor
Examples using
affine
:

torchvision.transforms.functional.
autocontrast
(img: torch.Tensor) → torch.Tensor[source]¶ Maximize contrast of an image by remapping its pixels per channel so that the lowest becomes black and the lightest becomes white.
 Parameters
img (PIL Image or Tensor) – Image on which autocontrast is applied. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Returns
An image that was autocontrasted.
 Return type
PIL Image or Tensor
Examples using
autocontrast
:

torchvision.transforms.functional.
center_crop
(img: torch.Tensor, output_size: List[int]) → torch.Tensor[source]¶ Crops the given image at the center. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
 Parameters
img (PIL Image or Tensor) – Image to be cropped.
output_size (sequence or int) – (height, width) of the crop box. If int or sequence with single int, it is used for both directions.
 Returns
Cropped image.
 Return type
PIL Image or Tensor
Examples using
center_crop
:

torchvision.transforms.functional.
convert_image_dtype
(image: torch.Tensor, dtype: torch.dtype = torch.float32) → torch.Tensor[source]¶ Convert a tensor image to the given
dtype
and scale the values accordingly This function does not support PIL Image. Parameters
image (torch.Tensor) – Image to be converted
dtype (torch.dpython:type) – Desired data type of the output
 Returns
Converted image
 Return type
Tensor
Note
When converting from a smaller to a larger integer
dtype
the maximum values are not mapped exactly. If converted back and forth, this mismatch has no effect. Raises
RuntimeError – When trying to cast
torch.float32
totorch.int32
ortorch.int64
as well as for trying to casttorch.float64
totorch.int64
. These conversions might lead to overflow errors since the floating pointdtype
cannot store consecutive integers over the whole range of the integerdtype
.
Examples using
convert_image_dtype
:

torchvision.transforms.functional.
crop
(img: torch.Tensor, top: int, left: int, height: int, width: int) → torch.Tensor[source]¶ Crop the given image at specified location and output size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then cropped.
 Parameters
img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.
top (int) – Vertical component of the top left corner of the crop box.
left (int) – Horizontal component of the top left corner of the crop box.
height (int) – Height of the crop box.
width (int) – Width of the crop box.
 Returns
Cropped image.
 Return type
PIL Image or Tensor
Examples using
crop
:

torchvision.transforms.functional.
equalize
(img: torch.Tensor) → torch.Tensor[source]¶ Equalize the histogram of an image by applying a nonlinear mapping to the input in order to create a uniform distribution of grayscale values in the output.
 Parameters
img (PIL Image or Tensor) – Image on which equalize is applied. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. The tensor dtype must be
torch.uint8
and values are expected to be in[0, 255]
. If img is PIL Image, it is expected to be in mode “P”, “L” or “RGB”. Returns
An image that was equalized.
 Return type
PIL Image or Tensor
Examples using
equalize
:

torchvision.transforms.functional.
erase
(img: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False) → torch.Tensor[source]¶ Erase the input Tensor Image with given value. This transform does not support PIL Image.
 Parameters
img (Tensor Image) – Tensor image of size (C, H, W) to be erased
i (int) – i in (i,j) i.e coordinates of the upper left corner.
j (int) – j in (i,j) i.e coordinates of the upper left corner.
h (int) – Height of the erased region.
w (int) – Width of the erased region.
v – Erasing value.
inplace (bool, optional) – For inplace operations. By default is set False.
 Returns
Erased image.
 Return type
Tensor Image

torchvision.transforms.functional.
five_crop
(img: torch.Tensor, size: List[int]) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor][source]¶ Crop the given image into four corners and the central crop. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Note
This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your
Dataset
returns. Parameters
img (PIL Image or Tensor) – Image to be cropped.
size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
 Returns
tuple (tl, tr, bl, br, center) Corresponding top left, top right, bottom left, bottom right and center crop.
 Return type
Examples using
five_crop
:

torchvision.transforms.functional.
gaussian_blur
(img: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) → torch.Tensor[source]¶ Performs Gaussian blurring on the image by given kernel. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
img (PIL Image or Tensor) – Image to be blurred
kernel_size (sequence of python:ints or int) –
Gaussian kernel size. Can be a sequence of integers like
(kx, ky)
or a single integer for square kernels.Note
In torchscript mode kernel_size as single int is not supported, use a sequence of length 1:
[ksize, ]
.sigma (sequence of python:floats or float, optional) –
Gaussian kernel standard deviation. Can be a sequence of floats like
(sigma_x, sigma_y)
or a single float to define the same sigma in both X/Y directions. If None, then it is computed usingkernel_size
assigma = 0.3 * ((kernel_size  1) * 0.5  1) + 0.8
. Default, None.Note
In torchscript mode sigma as single float is not supported, use a sequence of length 1:
[sigma, ]
.
 Returns
Gaussian Blurred version of the image.
 Return type
PIL Image or Tensor
Examples using
gaussian_blur
:

torchvision.transforms.functional.
get_image_num_channels
(img: torch.Tensor) → int[source]¶ Returns the number of channels of an image.
 Parameters
img (PIL Image or Tensor) – The image to be checked.
 Returns
The number of channels.
 Return type

torchvision.transforms.functional.
get_image_size
(img: torch.Tensor) → List[int][source]¶ Returns the size of an image as [width, height].
 Parameters
img (PIL Image or Tensor) – The image to be checked.
 Returns
The image size.
 Return type
List[int]

torchvision.transforms.functional.
hflip
(img: torch.Tensor) → torch.Tensor[source]¶ Horizontally flip the given image.
 Parameters
img (PIL Image or Tensor) – Image to be flipped. If img is a Tensor, it is expected to be in […, H, W] format, where … means it can have an arbitrary number of leading dimensions.
 Returns
Horizontally flipped image.
 Return type
PIL Image or Tensor
Examples using
hflip
:

torchvision.transforms.functional.
invert
(img: torch.Tensor) → torch.Tensor[source]¶ Invert the colors of an RGB/grayscale image.
 Parameters
img (PIL Image or Tensor) – Image to have its colors inverted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
 Returns
Color inverted image.
 Return type
PIL Image or Tensor
Examples using
invert
:

torchvision.transforms.functional.
normalize
(tensor: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) → torch.Tensor[source]¶ Normalize a float tensor image with mean and standard deviation. This transform does not support PIL Image.
Note
This transform acts out of place by default, i.e., it does not mutates the input tensor.
See
Normalize
for more details. Parameters
tensor (Tensor) – Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized.
mean (sequence) – Sequence of means for each channel.
std (sequence) – Sequence of standard deviations for each channel.
inplace (bool,optional) – Bool to make this operation inplace.
 Returns
Normalized Tensor image.
 Return type
Tensor
Examples using
normalize
:

torchvision.transforms.functional.
pad
(img: torch.Tensor, padding: List[int], fill: int = 0, padding_mode: str = 'constant') → torch.Tensor[source]¶ Pad the given image on all sides with the given “pad” value. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means at most 2 leading dimensions for mode reflect and symmetric, at most 3 leading dimensions for mode edge, and an arbitrary number of leading dimensions for mode constant
 Parameters
img (PIL Image or Tensor) – Image to be padded.
padding (int or sequence) –
Padding on each border. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
Note
In torchscript mode padding as single int is not supported, use a sequence of length 1:
[padding, ]
.fill (number or str or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or str or tuple value is supported for PIL Image.
padding_mode (str) –
Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
constant: pads with a constant value, this value is specified with fill
edge: pads with the last value at the edge of the image. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]
symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]
 Returns
Padded image.
 Return type
PIL Image or Tensor
Examples using
pad
:

torchvision.transforms.functional.
perspective
(img: torch.Tensor, startpoints: List[List[int]], endpoints: List[List[int]], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>, fill: Optional[List[float]] = None) → torch.Tensor[source]¶ Perform perspective transform of the given image. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
img (PIL Image or Tensor) – Image to be transformed.
startpoints (list of list of python:ints) – List containing four lists of two integers corresponding to four corners
[topleft, topright, bottomright, bottomleft]
of the original image.endpoints (list of list of python:ints) – List containing four lists of two integers corresponding to four corners
[topleft, topright, bottomright, bottomleft]
of the transformed image.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.fill (sequence or number, optional) –
Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Note
In torchscript mode single int/float value is not supported, please use a sequence of length 1:
[value, ]
.
 Returns
transformed Image.
 Return type
PIL Image or Tensor
Examples using
perspective
:

torchvision.transforms.functional.
pil_to_tensor
(pic)[source]¶ Convert a
PIL Image
to a tensor of the same type. This function does not support torchscript.See
PILToTensor
for more details.Note
A deep copy of the underlying array is performed.
 Parameters
pic (PIL Image) – Image to be converted to tensor.
 Returns
Converted image.
 Return type
Tensor

torchvision.transforms.functional.
posterize
(img: torch.Tensor, bits: int) → torch.Tensor[source]¶ Posterize an image by reducing the number of bits for each color channel.
 Parameters
img (PIL Image or Tensor) – Image to have its colors posterized. If img is torch Tensor, it should be of type torch.uint8 and it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
bits (int) – The number of bits to keep for each channel (08).
 Returns
Posterized image.
 Return type
PIL Image or Tensor
Examples using
posterize
:

torchvision.transforms.functional.
resize
(img: torch.Tensor, size: List[int], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>, max_size: Optional[int] = None, antialias: Optional[bool] = None) → torch.Tensor[source]¶ Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Warning
The output image might be different depending on its type: when downsampling, the interpolation of PIL images and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences in the performance of a network. Therefore, it is preferable to train and serve a model with the same input types. See also below the
antialias
parameter, which can help making the output of PIL images and tensors closer. Parameters
img (PIL Image or Tensor) – Image to be resized.
size (sequence or int) –
Desired output size. If size is a sequence like (h, w), the output size will be matched to this. If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio. i.e, if height > width, then image will be rescaled to \(\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)\).
Note
In torchscript mode size as single int is not supported, use a sequence of length 1:
[size, ]
.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
andInterpolationMode.BICUBIC
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.max_size (int, optional) – The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than
max_size
after being resized according tosize
, then the image is resized again so that the longer edge is equal tomax_size
. As a result,size
might be overruled, i.e the smaller edge may be shorter thansize
. This is only supported ifsize
is an int (or a sequence of length 1 in torchscript mode).antialias (bool, optional) –
antialias flag. If
img
is PIL Image, the flag is ignored and antialias is always used. Ifimg
is Tensor, the flag is False by default and can be set to True forInterpolationMode.BILINEAR
only mode. This can help making the output for PIL images and tensors closer.Warning
There is no autodiff support for
antialias=True
option with inputimg
as Tensor.
 Returns
Resized image.
 Return type
PIL Image or Tensor
Examples using
resize
:

torchvision.transforms.functional.
resized_crop
(img: torch.Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>) → torch.Tensor[source]¶ Crop the given image and resize it to desired size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Notably used in
RandomResizedCrop
. Parameters
img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.
top (int) – Vertical component of the top left corner of the crop box.
left (int) – Horizontal component of the top left corner of the crop box.
height (int) – Height of the crop box.
width (int) – Width of the crop box.
size (sequence or int) – Desired output size. Same semantics as
resize
.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
andInterpolationMode.BICUBIC
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.
 Returns
Cropped image.
 Return type
PIL Image or Tensor
Examples using
resized_crop
:

torchvision.transforms.functional.
rgb_to_grayscale
(img: torch.Tensor, num_output_channels: int = 1) → torch.Tensor[source]¶ Convert RGB image to grayscale version of image. If the image is torch Tensor, it is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions
Note
Please, note that this method supports only RGB images as input. For inputs in other color spaces, please, consider using meth:~torchvision.transforms.functional.to_grayscale with PIL Image.
 Parameters
img (PIL Image or Tensor) – RGB Image to be converted to grayscale.
num_output_channels (int) – number of channels of the output image. Value can be 1 or 3. Default, 1.
 Returns
Grayscale version of the image.
if num_output_channels = 1 : returned image is single channel
if num_output_channels = 3 : returned image is 3 channel with r = g = b
 Return type
PIL Image or Tensor

torchvision.transforms.functional.
rotate
(img: torch.Tensor, angle: float, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, expand: bool = False, center: Optional[List[int]] = None, fill: Optional[List[float]] = None, resample: Optional[int] = None) → torch.Tensor[source]¶ Rotate the image by angle. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
 Parameters
img (PIL Image or Tensor) – image to be rotated.
angle (number) – rotation angle value in degrees, counterclockwise.
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported. For backward compatibility integer values (e.g.PIL.Image.NEAREST
) are still acceptable.expand (bool, optional) – Optional expansion flag. If true, expands the output image to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.
center (sequence, optional) – Optional center of rotation. Origin is the upper left corner. Default is the center of the image.
fill (sequence or number, optional) –
Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Note
In torchscript mode single int/float value is not supported, please use a sequence of length 1:
[value, ]
.
 Returns
Rotated image.
 Return type
PIL Image or Tensor
Examples using
rotate
:

torchvision.transforms.functional.
solarize
(img: torch.Tensor, threshold: float) → torch.Tensor[source]¶ Solarize an RGB/grayscale image by inverting all pixel values above a threshold.
 Parameters
img (PIL Image or Tensor) – Image to have its colors inverted. If img is torch Tensor, it is expected to be in […, 1 or 3, H, W] format, where … means it can have an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
threshold (float) – All pixels equal or above this value are inverted.
 Returns
Solarized image.
 Return type
PIL Image or Tensor
Examples using
solarize
:

torchvision.transforms.functional.
ten_crop
(img: torch.Tensor, size: List[int], vertical_flip: bool = False) → List[torch.Tensor][source]¶ Generate ten cropped images from the given image. Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Note
This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your
Dataset
returns. Parameters
img (PIL Image or Tensor) – Image to be cropped.
size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
vertical_flip (bool) – Use vertical flipping instead of horizontal
 Returns
tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip) Corresponding top left, top right, bottom left, bottom right and center crop and same for the flipped image.
 Return type

torchvision.transforms.functional.
to_grayscale
(img, num_output_channels=1)[source]¶ Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image. This transform does not support torch Tensor.
 Parameters
img (PIL Image) – PIL Image to be converted to grayscale.
num_output_channels (int) – number of channels of the output image. Value can be 1 or 3. Default is 1.
 Returns
Grayscale version of the image.
if num_output_channels = 1 : returned image is single channel
if num_output_channels = 3 : returned image is 3 channel with r = g = b
 Return type
PIL Image
Examples using
to_grayscale
:

torchvision.transforms.functional.
to_pil_image
(pic, mode=None)[source]¶ Convert a tensor or an ndarray to PIL Image. This function does not support torchscript.
See
ToPILImage
for more details. Parameters
pic (Tensor or numpy.ndarray) – Image to be converted to PIL Image.
mode (PIL.Image mode) – color space and pixel depth of input data (optional).
 Returns
Image converted to PIL Image.
 Return type
PIL Image
Examples using
to_pil_image
:

torchvision.transforms.functional.
to_tensor
(pic)[source]¶ Convert a
PIL Image
ornumpy.ndarray
to tensor. This function does not support torchscript.See
ToTensor
for more details. Parameters
pic (PIL Image or numpy.ndarray) – Image to be converted to tensor.
 Returns
Converted image.
 Return type
Tensor

torchvision.transforms.functional.
vflip
(img: torch.Tensor) → torch.Tensor[source]¶ Vertically flip the given image.
 Parameters
img (PIL Image or Tensor) – Image to be flipped. If img is a Tensor, it is expected to be in […, H, W] format, where … means it can have an arbitrary number of leading dimensions.
 Returns
Vertically flipped image.
 Return type
PIL Image or Tensor
Examples using
vflip
: