torchvision.transforms¶
Transforms are common image transformations. They can be chained together using Compose
.
Additionally, there is the torchvision.transforms.functional
module.
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).

class
torchvision.transforms.
Compose
(transforms)[source]¶ Composes several transforms together.
Parameters: transforms (list of Transform
objects) – list of transforms to compose.Example
>>> transforms.Compose([ >>> transforms.CenterCrop(10), >>> transforms.ToTensor(), >>> ])
Transforms on PIL Image¶

class
torchvision.transforms.
CenterCrop
(size)[source]¶ Crops the given PIL Image at the center.
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.

class
torchvision.transforms.
ColorJitter
(brightness=0, contrast=0, saturation=0, hue=0)[source]¶ Randomly change the brightness, contrast and saturation of an image.
Parameters:  brightness (float) – How much to jitter brightness. brightness_factor is chosen uniformly from [max(0, 1  brightness), 1 + brightness].
 contrast (float) – How much to jitter contrast. contrast_factor is chosen uniformly from [max(0, 1  contrast), 1 + contrast].
 saturation (float) – How much to jitter saturation. saturation_factor is chosen uniformly from [max(0, 1  saturation), 1 + saturation].
 hue (float) – How much to jitter hue. hue_factor is chosen uniformly from [hue, hue]. Should be >=0 and <= 0.5.

class
torchvision.transforms.
FiveCrop
(size)[source]¶ Crop the given PIL Image into four corners and the central crop
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.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

class
torchvision.transforms.
Grayscale
(num_output_channels=1)[source]¶ Convert image to grayscale.
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 channel  If num_output_channels == 3 : returned image is 3 channel with r == g == b Return type: PIL Image

class
torchvision.transforms.
LinearTransformation
(transformation_matrix)[source]¶ Transform a tensor image with a square transformation matrix computed offline.
Given transformation_matrix, will flatten the torch.*Tensor, compute the dot product with the transformation matrix and reshape the tensor to its original shape.
Applications:  whitening: zerocenter the data, compute the data covariance matrix
[D x D] with np.dot(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

class
torchvision.transforms.
Pad
(padding, fill=0, padding_mode='constant')[source]¶ Pad the given PIL Image on all sides with the given “pad” value.
Parameters:  padding (int or tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
 fill – 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
 padding_mode –
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 reflect: pads with reflection of image (without repeating the last value on the edge)
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)
 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]

class
torchvision.transforms.
RandomAffine
(degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0)[source]¶ Random affine transformation of the image keeping center invariant
Parameters:  degrees (sequence or float or int) – 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 desactivate 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 float or int, optional) – 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). Will not apply shear by default
 resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional) – An optional resampling filter. See http://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters If omitted, or if the image has mode “1” or “P”, it is set to PIL.Image.NEAREST.
 fillcolor (int) – Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)

class
torchvision.transforms.
RandomApply
(transforms, p=0.5)[source]¶ Apply randomly a list of transformations with a given probability
Parameters:

class
torchvision.transforms.
RandomChoice
(transforms)[source]¶ Apply single transformation randomly picked from a list

class
torchvision.transforms.
RandomCrop
(size, padding=0, pad_if_needed=False)[source]¶ Crop the given PIL Image at a random location.
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.
 padding (int or sequence, optional) – Optional padding on each border of the image. Default is 0, i.e no padding. If a sequence of length 4 is provided, it is used to pad left, top, right, bottom borders respectively.
 pad_if_needed (boolean) – It will pad the image if smaller than the desired size to avoid raising an exception.

class
torchvision.transforms.
RandomGrayscale
(p=0.1)[source]¶ Randomly convert image to grayscale with a probability of p (default 0.1).
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

class
torchvision.transforms.
RandomHorizontalFlip
(p=0.5)[source]¶ Horizontally flip the given PIL Image randomly with a given probability.
Parameters: p (float) – probability of the image being flipped. Default value is 0.5

class
torchvision.transforms.
RandomOrder
(transforms)[source]¶ Apply a list of transformations in a random order

class
torchvision.transforms.
RandomResizedCrop
(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=2)[source]¶ Crop the given PIL Image to random size and aspect ratio.
A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. This is popularly used to train the Inception networks.
Parameters:  size – expected output size of each edge
 scale – range of size of the origin size cropped
 ratio – range of aspect ratio of the origin aspect ratio cropped
 interpolation – Default: PIL.Image.BILINEAR

class
torchvision.transforms.
RandomRotation
(degrees, resample=False, expand=False, center=None)[source]¶ Rotate the image by angle.
Parameters:  degrees (sequence or float or int) – 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).
 resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional) – An optional resampling filter. See http://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters If omitted, or if the image has mode “1” or “P”, it is set to PIL.Image.NEAREST.
 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 (2tuple, optional) – Optional center of rotation. Origin is the upper left corner. Default is the center of the image.

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 PIL Image randomly with a given probability.
Parameters: p (float) – probability of the image being flipped. Default value is 0.5

class
torchvision.transforms.
Resize
(size, interpolation=2)[source]¶ Resize the input PIL Image to the given size.
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)
 interpolation (int, optional) – Desired interpolation. Default is
PIL.Image.BILINEAR

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 PIL Image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default)
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
Transforms on torch.*Tensor¶

class
torchvision.transforms.
Normalize
(mean, std)[source]¶ Normalize a tensor image with mean and standard deviation. Given mean:
(M1,...,Mn)
and std:(S1,..,Sn)
forn
channels, this transform will normalize each channel of the inputtorch.*Tensor
i.e.input[channel] = (input[channel]  mean[channel]) / std[channel]
Parameters:  mean (sequence) – Sequence of means for each channel.
 std (sequence) – Sequence of standard deviations for each channel.
Conversion Transforms¶

class
torchvision.transforms.
ToPILImage
(mode=None)[source]¶ Convert a tensor or an ndarray to PIL Image.
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: 1. If the input has 3 channels, themode
is assumed to beRGB
. 2. If the input has 4 channels, themode
is assumed to beRGBA
. 3. If the input has 1 channel, themode
is determined by the data type (i,e,int
,float
,short
).
__call__
(pic)[source]¶ Parameters: pic (Tensor or numpy.ndarray) – Image to be converted to PIL Image. Returns: Image converted to PIL Image. Return type: PIL Image


class
torchvision.transforms.
ToTensor
[source]¶ Convert a
PIL Image
ornumpy.ndarray
to tensor.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].

__call__
(pic)[source]¶ Parameters: pic (PIL Image or numpy.ndarray) – Image to be converted to tensor. Returns: Converted image. Return type: Tensor

Generic Transforms¶
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 you can reuse the functional transform. For example, you can apply a functional transform to multiple images like this:
import torchvision.transforms.functional as TF
import random
def my_segmentation_transforms(image, segmentation):
if random.random() > 5:
angle = random.randint(30, 30)
image = TF.rotate(image, angle)
segmentation = TF.rotate(segmentation, angle)
# more transforms ...
return image, segmentation

torchvision.transforms.functional.
adjust_brightness
(img, brightness_factor)[source]¶ Adjust brightness of an Image.
Parameters:  img (PIL Image) – PIL Image to be adjusted.
 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

torchvision.transforms.functional.
adjust_contrast
(img, contrast_factor)[source]¶ Adjust contrast of an Image.
Parameters:  img (PIL Image) – PIL Image to be adjusted.
 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

torchvision.transforms.functional.
adjust_gamma
(img, gamma, gain=1)[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_out = 255 * gain * ((I_in / 255) ** gamma)See https://en.wikipedia.org/wiki/Gamma_correction for more details.
Parameters:

torchvision.transforms.functional.
adjust_hue
(img, hue_factor)[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 https://en.wikipedia.org/wiki/Hue for more details on Hue.
Parameters:  img (PIL Image) – PIL Image to be adjusted.
 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

torchvision.transforms.functional.
adjust_saturation
(img, saturation_factor)[source]¶ Adjust color saturation of an image.
Parameters:  img (PIL Image) – PIL Image to be adjusted.
 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

torchvision.transforms.functional.
affine
(img, angle, translate, scale, shear, resample=0, fillcolor=None)[source]¶ Apply affine transformation on the image keeping image center invariant
Parameters:  img (PIL Image) – PIL Image to be rotated.
 angle ({python:float, int}) – rotation angle in degrees between 180 and 180, clockwise direction.
 translate (list or tuple of python:integers) – horizontal and vertical translations (postrotation translation)
 scale (float) – overall scale
 shear (float) – shear angle value in degrees between 180 to 180, clockwise direction.
 resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional) – An optional resampling filter. See http://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters If omitted, or if the image has mode “1” or “P”, it is set to PIL.Image.NEAREST.
 fillcolor (int) – Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)

torchvision.transforms.functional.
crop
(img, i, j, h, w)[source]¶ Crop the given PIL Image.
Parameters:  img (PIL Image) – Image to be cropped.
 i – Upper pixel coordinate.
 j – Left pixel coordinate.
 h – Height of the cropped image.
 w – Width of the cropped image.
Returns: Cropped image.
Return type: PIL Image

torchvision.transforms.functional.
five_crop
(img, size)[source]¶ Crop the given PIL Image into four corners and the central crop.
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: 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. Returns:  tuple (tl, tr, bl, br, center) corresponding top left,
 top right, bottom left, bottom right and center crop.
Return type: tuple

torchvision.transforms.functional.
hflip
(img)[source]¶ Horizontally flip the given PIL Image.
Parameters: img (PIL Image) – Image to be flipped. Returns: Horizontall flipped image. Return type: PIL Image

torchvision.transforms.functional.
normalize
(tensor, mean, std)[source]¶ Normalize a tensor image with mean and standard deviation.
See
Normalize
for more details.Parameters:  tensor (Tensor) – Tensor image of size (C, H, W) to be normalized.
 mean (sequence) – Sequence of means for each channel.
 std (sequence) – Sequence of standard deviations for each channely.
Returns: Normalized Tensor image.
Return type:

torchvision.transforms.functional.
pad
(img, padding, fill=0, padding_mode='constant')[source]¶ Pad the given PIL Image on all sides with speficified padding mode and fill value.
Parameters:  img (PIL Image) – Image to be padded.
 padding (int or tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
 fill – 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
 padding_mode –
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 on the edge of the image reflect: pads with reflection of image (without repeating the last value on the edge)
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)
 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

torchvision.transforms.functional.
resize
(img, size, interpolation=2)[source]¶ Resize the input PIL Image to the given size.
Parameters:  img (PIL Image) – 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 maintaing the aspect ratio. i.e, if height > width, then image will be rescaled to (size * height / width, size)
 interpolation (int, optional) – Desired interpolation. Default is
PIL.Image.BILINEAR
Returns: Resized image.
Return type: PIL Image

torchvision.transforms.functional.
resized_crop
(img, i, j, h, w, size, interpolation=2)[source]¶ Crop the given PIL Image and resize it to desired size.
Notably used in RandomResizedCrop.
Parameters:  img (PIL Image) – Image to be cropped.
 i – Upper pixel coordinate.
 j – Left pixel coordinate.
 h – Height of the cropped image.
 w – Width of the cropped image.
 size (sequence or int) – Desired output size. Same semantics as
scale
.  interpolation (int, optional) – Desired interpolation. Default is
PIL.Image.BILINEAR
.
Returns: Cropped image.
Return type: PIL Image

torchvision.transforms.functional.
rotate
(img, angle, resample=False, expand=False, center=None)[source]¶ Rotate the image by angle.
Parameters:  img (PIL Image) – PIL Image to be rotated.
 angle ({python:float, int}) – In degrees degrees counter clockwise order.
 resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional) – An optional resampling filter. See http://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters If omitted, or if the image has mode “1” or “P”, it is set to PIL.Image.NEAREST.
 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 (2tuple, optional) – Optional center of rotation. Origin is the upper left corner. Default is the center of the image.

torchvision.transforms.functional.
ten_crop
(img, size, vertical_flip=False)[source]¶  Crop the given PIL Image into four corners and the central crop plus the
 flipped version of these (horizontal flipping is used by default).
Note
This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets yourDataset
returns. Args:
 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.
vertical_flip (bool): Use vertical flipping instead of horizontal
 Returns:
 tuple: 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.

torchvision.transforms.functional.
to_grayscale
(img, num_output_channels=1)[source]¶ Convert image to grayscale version of image.
Parameters: img (PIL Image) – Image to be converted to grayscale. 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

torchvision.transforms.functional.
to_pil_image
(pic, mode=None)[source]¶ Convert a tensor or an ndarray to PIL Image.
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

torchvision.transforms.functional.
to_tensor
(pic)[source]¶ Convert a
PIL Image
ornumpy.ndarray
to tensor.See
ToTensor
for more details.Parameters: pic (PIL Image or numpy.ndarray) – Image to be converted to tensor. Returns: Converted image. Return type: Tensor