Package 'torchvision' reference manual

Title:	Models, Datasets and Transformations for Images
Description:	Provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the 'torch' package and it's 'API' borrows heavily from 'PyTorch' vision package.
Authors:	Daniel Falbel [aut, cre], Christophe Regouby [ctb], RStudio [cph]
Maintainer:	Daniel Falbel <[email protected]>
License:	MIT + file LICENSE
Version:	0.6.0.9000
Built:	2024-11-16 06:06:29 UTC
Source:	https://github.com/mlverse/torchvision

Cifar datasets

Description

Downloads and prepares the CIFAR100 dataset.

Usage

cifar10_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

cifar100_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)
cifar10_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

cifar100_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

Arguments

`root`	(string): Root directory of dataset where directory `cifar-10-batches-bin` exists or will be saved to if download is set to TRUE.
`train`	(bool, optional): If TRUE, creates dataset from training set, otherwise creates from test set.
`transform`	(callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, `transform_random_crop()`
`target_transform`	(callable, optional): A function/transform that takes in the target and transforms it.
`download`	(bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

Draws bounding boxes on image.

Description

Draws bounding boxes on top of one image tensor

Usage

draw_bounding_boxes(
  image,
  boxes,
  labels = NULL,
  colors = NULL,
  fill = FALSE,
  width = 1,
  font = c("serif", "plain"),
  font_size = 10
)
draw_bounding_boxes(
  image,
  boxes,
  labels = NULL,
  colors = NULL,
  fill = FALSE,
  width = 1,
  font = c("serif", "plain"),
  font_size = 10
)

Arguments

`image`	: Tensor of shape (C x H x W) and dtype uint8.
`boxes`	: Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note that the boxes are absolute coordinates with respect to the image. In other words: `⁠0 = xmin < xmax < W⁠` and `⁠0 = ymin < ymax < H⁠`.
`labels`	: character vector containing the labels of bounding boxes.
`colors`	: character vector containing the colors of the boxes or single color for all boxes. The color can be represented as strings e.g. "red" or "#FF00FF". By default, viridis colors are generated for boxes.
`fill`	: If `TRUE` fills the bounding box with specified color.
`width`	: Width of text shift to the bounding box.
`font`	: NULL for the current font family, or a character vector of length 2 for Hershey vector fonts.
`font_size`	: The requested font size in points.

Value

torch_tensor of size (C, H, W) of dtype uint8: Image Tensor with bounding boxes plotted.

Examples

if (torch::torch_is_installed()) {
## Not run: 
image <- torch::torch_randint(170, 250, size = c(3, 360, 360))$to(torch::torch_uint8())
x <- torch::torch_randint(low = 1, high = 160, size = c(12,1))
y <- torch::torch_randint(low = 1, high = 260, size = c(12,1))
boxes <- torch::torch_cat(c(x, y, x + 20, y +  10), dim = 2)
bboxed <- draw_bounding_boxes(image, boxes, colors = "black", fill = TRUE)
tensor_image_browse(bboxed)

## End(Not run)
}
if (torch::torch_is_installed()) {
## Not run: 
image <- torch::torch_randint(170, 250, size = c(3, 360, 360))$to(torch::torch_uint8())
x <- torch::torch_randint(low = 1, high = 160, size = c(12,1))
y <- torch::torch_randint(low = 1, high = 260, size = c(12,1))
boxes <- torch::torch_cat(c(x, y, x + 20, y +  10), dim = 2)
bboxed <- draw_bounding_boxes(image, boxes, colors = "black", fill = TRUE)
tensor_image_browse(bboxed)

## End(Not run)
}

Draws Keypoints

Description

Draws Keypoints, an object describing a body part (like rightArm or leftShoulder), on given RGB tensor image.

Usage

draw_keypoints(
  image,
  keypoints,
  connectivity = NULL,
  colors = NULL,
  radius = 2,
  width = 3
)
draw_keypoints(
  image,
  keypoints,
  connectivity = NULL,
  colors = NULL,
  radius = 2,
  width = 3
)

Arguments

`image`	: Tensor of shape (3, H, W) and dtype uint8
`keypoints`	: Tensor of shape (N, K, 2) the K keypoints location for each of the N detected poses instance,
`connectivity`	: Vector of pair of keypoints to be connected (currently unavailable)
`colors`	: character vector containing the colors of the boxes or single color for all boxes. The color can be represented as strings e.g. "red" or "#FF00FF". By default, viridis colors are generated for keypoints
`radius`	: radius of the plotted keypoint.
`width`	: width of line connecting keypoints.

Value

Image Tensor of dtype uint8 with keypoints drawn.

Examples

if (torch::torch_is_installed()) {
## Not run: 
image <- torch::torch_randint(190, 255, size = c(3, 360, 360))$to(torch::torch_uint8())
keypoints <- torch::torch_randint(low = 60, high = 300, size = c(4, 5, 2))
keypoint_image <- draw_keypoints(image, keypoints)
tensor_image_browse(keypoint_image)

## End(Not run)
}
if (torch::torch_is_installed()) {
## Not run: 
image <- torch::torch_randint(190, 255, size = c(3, 360, 360))$to(torch::torch_uint8())
keypoints <- torch::torch_randint(low = 60, high = 300, size = c(4, 5, 2))
keypoint_image <- draw_keypoints(image, keypoints)
tensor_image_browse(keypoint_image)

## End(Not run)
}

Draw segmentation masks

Description

Draw segmentation masks with their respective colors on top of a given RGB tensor image

Usage

draw_segmentation_masks(image, masks, alpha = 0.8, colors = NULL)
draw_segmentation_masks(image, masks, alpha = 0.8, colors = NULL)

Arguments

`image`	: torch_tensor of shape (3, H, W) and dtype uint8.
`masks`	: torch_tensor of shape (num_masks, H, W) or (H, W) and dtype bool.
`alpha`	: number between 0 and 1 denoting the transparency of the masks.
`colors`	: character vector containing the colors of the boxes or single color for all boxes. The color can be represented as strings e.g. "red" or "#FF00FF". By default, viridis colors are generated for masks

Value

torch_tensor of shape (3, H, W) and dtype uint8 of the image with segmentation masks drawn on top.

Examples

if (torch::torch_is_installed()) {
image <- torch::torch_randint(170, 250, size = c(3, 360, 360))$to(torch::torch_uint8())
mask <- torch::torch_tril(torch::torch_ones(c(360, 360)))$to(torch::torch_bool())
masked_image <- draw_segmentation_masks(image, mask, alpha = 0.2)
tensor_image_browse(masked_image)
}
if (torch::torch_is_installed()) {
image <- torch::torch_randint(170, 250, size = c(3, 360, 360))$to(torch::torch_uint8())
mask <- torch::torch_tril(torch::torch_ones(c(360, 360)))$to(torch::torch_bool())
masked_image <- draw_segmentation_masks(image, mask, alpha = 0.2)
tensor_image_browse(masked_image)
}

Create an image folder dataset

Description

A generic data loader for images stored in folders. See Details for more information.

Usage

image_folder_dataset(
  root,
  transform = NULL,
  target_transform = NULL,
  loader = NULL,
  is_valid_file = NULL
)
image_folder_dataset(
  root,
  transform = NULL,
  target_transform = NULL,
  loader = NULL,
  is_valid_file = NULL
)

Arguments

`root`	Root directory path.
`transform`	A function/transform that takes in an PIL image and returns a transformed version. E.g, `transform_random_crop()`.
`target_transform`	A function/transform that takes in the target and transforms it.
`loader`	A function to load an image given its path.
`is_valid_file`	A function that takes path of an Image file and check if the file is a valid file (used to check of corrupt files)

Details

This function assumes that the images for each class are contained in subdirectories of root. The names of these subdirectories are stored in the classes attribute of the returned object.

An example folder structure might look as follows:

root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png

Kuzushiji-MNIST

Description

Prepares the Kuzushiji-MNIST dataset and optionally downloads it.

Usage

kmnist_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)
kmnist_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

Arguments

`root`	(string): Root directory of dataset where `KMNIST/processed/training.pt` and `KMNIST/processed/test.pt` exist.
`train`	(bool, optional): If TRUE, creates dataset from `training.pt`, otherwise from `test.pt`.
`transform`	(callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, `transform_random_crop()`.
`target_transform`	(callable, optional): A function/transform that takes in the target and transforms it.
`download`	(bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

Load an Image using ImageMagick

Description

Load an image located at path using the {magick} package.

Usage

magick_loader(path)
magick_loader(path)

Arguments

path

path to the image to load from.

MNIST dataset

Description

Prepares the MNIST dataset and optionally downloads it.

Usage

mnist_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)
mnist_dataset(
  root,
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

Arguments

`root`	(string): Root directory of dataset where `MNIST/processed/training.pt` and `MNIST/processed/test.pt` exist.
`train`	(bool, optional): If True, creates dataset from `training.pt`, otherwise from `test.pt`.
`transform`	(callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, `transform_random_crop()`.
`target_transform`	(callable, optional): A function/transform that takes in the target and transforms it.
`download`	(bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

AlexNet Model Architecture

Description

AlexNet model architecture from the One weird trick... paper.

Usage

model_alexnet(pretrained = FALSE, progress = TRUE, ...)
model_alexnet(pretrained = FALSE, progress = TRUE, ...)

Arguments

`pretrained`	(bool): If TRUE, returns a model pre-trained on ImageNet.
`progress`	(bool): If TRUE, displays a progress bar of the download to stderr.
`...`	other parameters passed to the model intializer. currently only `num_classes` is used.

Inception v3 model

Description

Architecture from Rethinking the Inception Architecture for Computer Vision The required minimum input size of the model is 75x75.

Usage

model_inception_v3(pretrained = FALSE, progress = TRUE, ...)
model_inception_v3(pretrained = FALSE, progress = TRUE, ...)

Arguments

pretrained

(bool): If TRUE, returns a model pre-trained on ImageNet

progress

(bool): If TRUE, displays a progress bar of the download to stderr

...

Used to pass keyword arguments to the Inception module:

aux_logits (bool): If TRUE, add an auxiliary branch that can improve training. Default: TRUE
transform_input (bool): If TRUE, preprocess the input according to the method with which it was trained on ImageNet. Default: FALSE

Note

Important: In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly.

Constructs a MobileNetV2 architecture from MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Description

Constructs a MobileNetV2 architecture from MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Usage

model_mobilenet_v2(pretrained = FALSE, progress = TRUE, ...)
model_mobilenet_v2(pretrained = FALSE, progress = TRUE, ...)

Arguments

`pretrained`	(bool): If TRUE, returns a model pre-trained on ImageNet.
`progress`	(bool): If TRUE, displays a progress bar of the download to stderr.
`...`	Other parameters passed to the model implementation.

ResNet implementation

Description

ResNet models implementation from Deep Residual Learning for Image Recognition and later related papers (see Functions)

Usage

model_resnet18(pretrained = FALSE, progress = TRUE, ...)

model_resnet34(pretrained = FALSE, progress = TRUE, ...)

model_resnet50(pretrained = FALSE, progress = TRUE, ...)

model_resnet101(pretrained = FALSE, progress = TRUE, ...)

model_resnet152(pretrained = FALSE, progress = TRUE, ...)

model_resnext50_32x4d(pretrained = FALSE, progress = TRUE, ...)

model_resnext101_32x8d(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet50_2(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet101_2(pretrained = FALSE, progress = TRUE, ...)
model_resnet18(pretrained = FALSE, progress = TRUE, ...)

model_resnet34(pretrained = FALSE, progress = TRUE, ...)

model_resnet50(pretrained = FALSE, progress = TRUE, ...)

model_resnet101(pretrained = FALSE, progress = TRUE, ...)

model_resnet152(pretrained = FALSE, progress = TRUE, ...)

model_resnext50_32x4d(pretrained = FALSE, progress = TRUE, ...)

model_resnext101_32x8d(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet50_2(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet101_2(pretrained = FALSE, progress = TRUE, ...)

Arguments

`pretrained`	(bool): If TRUE, returns a model pre-trained on ImageNet.
`progress`	(bool): If TRUE, displays a progress bar of the download to stderr.
`...`	Other parameters passed to the resnet model.

Functions

model_resnet18(): ResNet 18-layer model
model_resnet34(): ResNet 34-layer model
model_resnet50(): ResNet 50-layer model
model_resnet101(): ResNet 101-layer model
model_resnet152(): ResNet 152-layer model
model_resnext50_32x4d(): ResNeXt-50 32x4d model from "Aggregated Residual Transformation for Deep Neural Networks" with 32 groups having each a width of 4.
model_resnext101_32x8d(): ResNeXt-101 32x8d model from "Aggregated Residual Transformation for Deep Neural Networks" with 32 groups having each a width of 8.
model_wide_resnet50_2(): Wide ResNet-50-2 model from "Wide Residual Networks" with width per group of 128.
model_wide_resnet101_2(): Wide ResNet-101-2 model from "Wide Residual Networks" with width per group of 128.

VGG implementation

Description

VGG models implementations based on Very Deep Convolutional Networks For Large-Scale Image Recognition

Usage

model_vgg11(pretrained = FALSE, progress = TRUE, ...)

model_vgg11_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg13(pretrained = FALSE, progress = TRUE, ...)

model_vgg13_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg16(pretrained = FALSE, progress = TRUE, ...)

model_vgg16_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg19(pretrained = FALSE, progress = TRUE, ...)

model_vgg19_bn(pretrained = FALSE, progress = TRUE, ...)
model_vgg11(pretrained = FALSE, progress = TRUE, ...)

model_vgg11_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg13(pretrained = FALSE, progress = TRUE, ...)

model_vgg13_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg16(pretrained = FALSE, progress = TRUE, ...)

model_vgg16_bn(pretrained = FALSE, progress = TRUE, ...)

model_vgg19(pretrained = FALSE, progress = TRUE, ...)

model_vgg19_bn(pretrained = FALSE, progress = TRUE, ...)

Arguments

`pretrained`	(bool): If TRUE, returns a model pre-trained on ImageNet
`progress`	(bool): If TRUE, displays a progress bar of the download to stderr
`...`	other parameters passed to the VGG model implementation.

Functions

model_vgg11(): VGG 11-layer model (configuration "A")
model_vgg11_bn(): VGG 11-layer model (configuration "A") with batch normalization
model_vgg13(): VGG 13-layer model (configuration "B")
model_vgg13_bn(): VGG 13-layer model (configuration "B") with batch normalization
model_vgg16(): VGG 13-layer model (configuration "D")
model_vgg16_bn(): VGG 13-layer model (configuration "D") with batch normalization
model_vgg19(): VGG 19-layer model (configuration "E")
model_vgg19_bn(): VGG 19-layer model (configuration "E") with batch normalization

Display image tensor

Description

Display image tensor into browser

Usage

tensor_image_browse(image, browser = getOption("browser"))
tensor_image_browse(image, browser = getOption("browser"))

Arguments

`image`	`torch_tensor()` of shape (1, W, H) for grayscale image or (3, W, H) for color image to display
`browser`	argument passed to browseURL

Display image tensor

Description

Display image tensor onto the X11 device

Usage

tensor_image_display(image, animate = TRUE)
tensor_image_display(image, animate = TRUE)

Arguments

`image`	`torch_tensor()` of shape (1, W, H) for grayscale image or (3, W, H) for color image to display
`animate`	support animations in the X11 display

Tiny ImageNet dataset

Description

Prepares the Tiny ImageNet dataset and optionally downloads it.

Usage

tiny_imagenet_dataset(root, split = "train", download = FALSE, ...)
tiny_imagenet_dataset(root, split = "train", download = FALSE, ...)

Arguments

`root`	directory path to download the dataset.
`split`	dataset split, `train`, `validation` or `test`.
`download`	whether to download or not the dataset.
`...`	other arguments passed to `image_folder_dataset()`.

Adjust the brightness of an image

Description

Adjust the brightness of an image

Usage

transform_adjust_brightness(img, brightness_factor)
transform_adjust_brightness(img, brightness_factor)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Adjust the contrast of an image

Description

Adjust the contrast of an image

Usage

transform_adjust_contrast(img, contrast_factor)
transform_adjust_contrast(img, contrast_factor)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Adjust the gamma of an RGB image

Description

Also known as Power Law Transform. Intensities in RGB mode are adjusted based on the following equation:

$I_{\mbox{out}} = 255 \times \mbox{gain} \times \left (\frac{I_{\mbox{in}}}{255}\right)^{\gamma}$

Usage

transform_adjust_gamma(img, gamma, gain = 1)
transform_adjust_gamma(img, gamma, gain = 1)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Details

See Gamma Correction for more details.

Adjust the hue of an image

Description

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.

Usage

transform_adjust_hue(img, hue_factor)
transform_adjust_hue(img, hue_factor)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Details

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.

Adjust the color saturation of an image

Description

Adjust the color saturation of an image

Usage

transform_adjust_saturation(img, saturation_factor)
transform_adjust_saturation(img, saturation_factor)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Apply affine transformation on an image keeping image center invariant

Description

Apply affine transformation on an image keeping image center invariant

Usage

transform_affine(
  img,
  angle,
  translate,
  scale,
  shear,
  resample = 0,
  fillcolor = NULL
)
transform_affine(
  img,
  angle,
  translate,
  scale,
  shear,
  resample = 0,
  fillcolor = NULL
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`angle`	(float or int): rotation angle value in degrees, counter-clockwise.
`translate`	(sequence of int) – horizontal and vertical translations (post-rotation 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.
`resample`	(int, optional): An optional resampling filter. See interpolation modes.
`fillcolor`	(tuple or int): Optional fill color (Tuple for RGB Image and int for grayscale) for the area outside the transform in the output image (Pillow>=5.0.0). This option is not supported for Tensor input. Fill value for the area outside the transform in the output image is always 0.

Crops the given image at the center

Description

The image can be a Magick Image or a torch Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions.

Usage

transform_center_crop(img, size)
transform_center_crop(img, size)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size of the crop. If size is an int instead of sequence like c(h, w), a square crop (size, size) is made. If provided a tuple or list of length 1, it will be interpreted as `c(size, size)`.

Randomly change the brightness, contrast and saturation of an image

Description

Randomly change the brightness, contrast and saturation of an image

Usage

transform_color_jitter(
  img,
  brightness = 0,
  contrast = 0,
  saturation = 0,
  hue = 0
)
transform_color_jitter(
  img,
  brightness = 0,
  contrast = 0,
  saturation = 0,
  hue = 0
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`brightness`	(float or tuple of 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 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 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 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.

Convert a tensor image to the given `dtype` and scale the values accordingly

Description

Convert a tensor image to the given dtype and scale the values accordingly

Usage

transform_convert_image_dtype(img, dtype = torch::torch_float())
transform_convert_image_dtype(img, dtype = torch::torch_float())

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`dtype`	(torch.dtype): 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.

Crop the given image at specified location and output size

Description

Crop the given image at specified location and output size

Usage

transform_crop(img, top, left, height, width)
transform_crop(img, top, left, height, width)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Crop image into four corners and a central crop

Description

Crop the given image into four corners and the central crop. This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns.

Usage

transform_five_crop(img, size)
transform_five_crop(img, size)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size. If size is a sequence like c(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).

Convert image to grayscale

Description

Convert image to grayscale

Usage

transform_grayscale(img, num_output_channels)
transform_grayscale(img, num_output_channels)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`num_output_channels`	(int): (1 or 3) number of channels desired for output image

Horizontally flip a PIL Image or Tensor

Description

Horizontally flip a PIL Image or Tensor

Usage

transform_hflip(img)
transform_hflip(img)

Arguments

img

A magick-image, array or torch_tensor.

Transform a tensor image with a square transformation matrix and a mean_vector computed offline

Description

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.

Usage

transform_linear_transformation(img, transformation_matrix, mean_vector)
transform_linear_transformation(img, transformation_matrix, mean_vector)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`transformation_matrix`	(Tensor): tensor `⁠[D x D]⁠`, D = C x H x W.
`mean_vector`	(Tensor): tensor D, D = C x H x W.

Applications

whitening transformation: Suppose X is a column vector zero-centered 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.

Normalize a tensor image with mean and standard deviation

Description

Given mean: ⁠(mean[1],...,mean[n])⁠ and std: ⁠(std[1],..,std[n])⁠ for n channels, this transform will normalize each channel of the input torch_tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]

Usage

transform_normalize(img, mean, std, inplace = FALSE)
transform_normalize(img, mean, std, inplace = FALSE)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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 in-place.

Note

This transform acts out of place, i.e., it does not mutate the input tensor.

Pad the given image on all sides with the given "pad" value

Description

The image can be a Magick Image or a torch Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions.

Usage

transform_pad(img, padding, fill = 0, padding_mode = "constant")
transform_pad(img, padding, fill = 0, padding_mode = "constant")

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`padding`	(int or tuple or list): 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, right, top and bottom borders respectively.
`fill`	(int 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 int value is supported for Tensors.
`padding_mode`	Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. Mode symmetric is not yet supported for Tensor inputs. 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]⁠`

Perspective transformation of an image

Description

Perspective transformation of an image

Usage

transform_perspective(
  img,
  startpoints,
  endpoints,
  interpolation = 2,
  fill = NULL
)
transform_perspective(
  img,
  startpoints,
  endpoints,
  interpolation = 2,
  fill = NULL
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`startpoints`	(list of list of ints): List containing four lists of two integers corresponding to four corners `⁠[top-left, top-right, bottom-right, bottom-left]⁠` of the original image.
`endpoints`	(list of list of ints): List containing four lists of two integers corresponding to four corners `⁠[top-left, top-right, bottom-right, bottom-left]⁠` of the transformed image.
`interpolation`	(int, optional) Desired interpolation. An integer `0 = nearest`, `2 = bilinear`, and `3 = bicubic` or a name from `magick::filter_types()`.
`fill`	(int 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 int value is supported for Tensors.

Random affine transformation of the image keeping center invariant

Description

Random affine transformation of the image keeping center invariant

Usage

transform_random_affine(
  img,
  degrees,
  translate = NULL,
  scale = NULL,
  shear = NULL,
  resample = 0,
  fillcolor = 0
)
transform_random_affine(
  img,
  degrees,
  translate = NULL,
  scale = NULL,
  shear = NULL,
  resample = 0,
  fillcolor = 0
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`degrees`	(sequence or float or int): Range of degrees to select from. If degrees is a number instead of sequence like c(min, max), the range of degrees will be (-degrees, +degrees).
`translate`	(tuple, optional): tuple of maximum absolute fraction for horizontal and vertical translations. For example `translate=c(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 c(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 shear is a number, a shear parallel to the x axis in the range (-shear, +shear) will be applied. Else if shear is a tuple or list of 2 values a shear parallel to the x axis in the range `⁠(shear[1], shear[2])⁠` will be applied. Else if shear is a tuple or list of 4 values, a x-axis shear in `⁠(shear[1], shear[2])⁠` and y-axis shear in `⁠(shear[3], shear[4])⁠` will be applied. Will not apply shear by default.
`resample`	(int, optional): An optional resampling filter. See interpolation modes.
`fillcolor`	(tuple or int): Optional fill color (Tuple for RGB Image and int for grayscale) for the area outside the transform in the output image (Pillow>=5.0.0). This option is not supported for Tensor input. Fill value for the area outside the transform in the output image is always 0.

Apply a list of transformations randomly with a given probability

Description

Apply a list of transformations randomly with a given probability

Usage

transform_random_apply(img, transforms, p = 0.5)
transform_random_apply(img, transforms, p = 0.5)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`transforms`	(list or tuple): list of transformations.
`p`	(float): probability.

Apply single transformation randomly picked from a list

Description

Apply single transformation randomly picked from a list

Usage

transform_random_choice(img, transforms)
transform_random_choice(img, transforms)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`transforms`	(list or tuple): list of transformations.

Crop the given image at a random location

Description

The image can be a Magick Image or a Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions.

Usage

transform_random_crop(
  img,
  size,
  padding = NULL,
  pad_if_needed = FALSE,
  fill = 0,
  padding_mode = "constant"
)
transform_random_crop(
  img,
  size,
  padding = NULL,
  pad_if_needed = FALSE,
  fill = 0,
  padding_mode = "constant"
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size. If size is a sequence like c(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).
`padding`	(int or tuple or list): 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, right, top and bottom borders respectively.
`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`	(int 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 int value is supported for Tensors.
`padding_mode`	Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. Mode symmetric is not yet supported for Tensor inputs. 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]⁠`

Randomly selects a rectangular region in an image and erases its pixel values

Description

'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/pdf/1708.04896

Usage

transform_random_erasing(
  img,
  p = 0.5,
  scale = c(0.02, 0.33),
  ratio = c(0.3, 3.3),
  value = 0,
  inplace = FALSE
)
transform_random_erasing(
  img,
  p = 0.5,
  scale = c(0.02, 0.33),
  ratio = c(0.3, 3.3),
  value = 0,
  inplace = FALSE
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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.

Randomly convert image to grayscale with a given probability

Description

Convert image to grayscale with a probability of p.

Usage

transform_random_grayscale(img, p = 0.1)
transform_random_grayscale(img, p = 0.1)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`p`	(float): probability that image should be converted to grayscale (default 0.1).

Horizontally flip an image randomly with a given probability

Description

Horizontally flip an image randomly with a given probability. The image can be a Magick Image or a torch Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions

Usage

transform_random_horizontal_flip(img, p = 0.5)
transform_random_horizontal_flip(img, p = 0.5)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`p`	(float): probability of the image being flipped. Default value is 0.5

Apply a list of transformations in a random order

Description

Apply a list of transformations in a random order

Usage

transform_random_order(img, transforms)
transform_random_order(img, transforms)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`transforms`	(list or tuple): list of transformations.

Random perspective transformation of an image with a given probability

Description

Performs a random perspective transformation of the given image with a given probability

Usage

transform_random_perspective(
  img,
  distortion_scale = 0.5,
  p = 0.5,
  interpolation = 2,
  fill = 0
)
transform_random_perspective(
  img,
  distortion_scale = 0.5,
  p = 0.5,
  interpolation = 2,
  fill = 0
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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`	(int, optional) Desired interpolation. An integer `0 = nearest`, `2 = bilinear`, and `3 = bicubic` or a name from `magick::filter_types()`.
`fill`	(int 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 int value is supported for Tensors.

Crop image to random size and aspect ratio

Description

Crop the given image to a random size and aspect ratio. The image can be a Magick Image or a Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions

Usage

transform_random_resized_crop(
  img,
  size,
  scale = c(0.08, 1),
  ratio = c(3/4, 4/3),
  interpolation = 2
)
transform_random_resized_crop(
  img,
  size,
  scale = c(0.08, 1),
  ratio = c(3/4, 4/3),
  interpolation = 2
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size. If size is a sequence like c(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).
`scale`	(tuple of float): range of size of the origin size cropped
`ratio`	(tuple of float): range of aspect ratio of the origin aspect ratio cropped.
`interpolation`	(int, optional) Desired interpolation. An integer `0 = nearest`, `2 = bilinear`, and `3 = bicubic` or a name from `magick::filter_types()`.

Details

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.

Rotate the image by angle

Description

Rotate the image by angle

Usage

transform_random_rotation(
  img,
  degrees,
  resample = 0,
  expand = FALSE,
  center = NULL,
  fill = NULL
)
transform_random_rotation(
  img,
  degrees,
  resample = 0,
  expand = FALSE,
  center = NULL,
  fill = NULL
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`degrees`	(sequence or float or int): Range of degrees to select from. If degrees is a number instead of sequence like c(min, max), the range of degrees will be (-degrees, +degrees).
`resample`	(int, optional): An optional resampling filter. See interpolation modes.
`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`	(list or tuple, optional): Optional center of rotation, c(x, y). Origin is the upper left corner. Default is the center of the image.
`fill`	(n-tuple or int or float): Pixel fill value for area outside the rotated image. If int or float, the value is used for all bands respectively. Defaults to 0 for all bands. This option is only available for Pillow>=5.2.0. This option is not supported for Tensor input. Fill value for the area outside the transform in the output image is always 0.

Vertically flip an image randomly with a given probability

Description

The image can be a PIL Image or a torch Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions

Usage

transform_random_vertical_flip(img, p = 0.5)
transform_random_vertical_flip(img, p = 0.5)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`p`	(float): probability of the image being flipped. Default value is 0.5

Resize the input image to the given size

Description

The image can be a Magic Image or a torch Tensor, in which case it is expected to have ⁠[..., H, W]⁠ shape, where ... means an arbitrary number of leading dimensions

Usage

transform_resize(img, size, interpolation = 2)
transform_resize(img, size, interpolation = 2)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size. If size is a sequence like c(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. An integer `0 = nearest`, `2 = bilinear`, and `3 = bicubic` or a name from `magick::filter_types()`.

Crop an image and resize it to a desired size

Description

Crop an image and resize it to a desired size

Usage

transform_resized_crop(img, top, left, height, width, size, interpolation = 2)
transform_resized_crop(img, top, left, height, width, size, interpolation = 2)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`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. If size is a sequence like c(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. An integer `0 = nearest`, `2 = bilinear`, and `3 = bicubic` or a name from `magick::filter_types()`.

Convert RGB Image Tensor to Grayscale

Description

For RGB to Grayscale conversion, ITU-R 601-2 luma transform is performed which is L = R * 0.2989 + G * 0.5870 + B * 0.1140

Usage

transform_rgb_to_grayscale(img)
transform_rgb_to_grayscale(img)

Arguments

img

A magick-image, array or torch_tensor.

Angular rotation of an image

Description

Angular rotation of an image

Usage

transform_rotate(
  img,
  angle,
  resample = 0,
  expand = FALSE,
  center = NULL,
  fill = NULL
)
transform_rotate(
  img,
  angle,
  resample = 0,
  expand = FALSE,
  center = NULL,
  fill = NULL
)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`angle`	(float or int): rotation angle value in degrees, counter-clockwise.
`resample`	(int, optional): An optional resampling filter. See interpolation modes.
`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`	(list or tuple, optional): Optional center of rotation, c(x, y). Origin is the upper left corner. Default is the center of the image.
`fill`	(n-tuple or int or float): Pixel fill value for area outside the rotated image. If int or float, the value is used for all bands respectively. Defaults to 0 for all bands. This option is only available for Pillow>=5.2.0. This option is not supported for Tensor input. Fill value for the area outside the transform in the output image is always 0.

Crop an image and the flipped image each into four corners and a central crop

Description

Crop the given image into four corners and the central crop, plus the flipped version of these (horizontal flipping is used by default). This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns.

Usage

transform_ten_crop(img, size, vertical_flip = FALSE)
transform_ten_crop(img, size, vertical_flip = FALSE)

Arguments

`img`	A `magick-image`, `array` or `torch_tensor`.
`size`	(sequence or int): Desired output size. If size is a sequence like c(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).
`vertical_flip`	(bool): Use vertical flipping instead of horizontal

Convert an image to a tensor

Description

Converts a Magick Image or array (H x W x C) in the range ⁠[0, 255]⁠ to a torch_tensor of shape (C x H x W) in the range ⁠[0.0, 1.0]⁠. In the other cases, tensors are returned without scaling.

Usage

transform_to_tensor(img)
transform_to_tensor(img)

Arguments

img

A magick-image, array or torch_tensor.

Note

Because the input image is scaled to ⁠[0.0, 1.0]⁠, this transformation should not be used when transforming target image masks.

Vertically flip a PIL Image or Tensor

Description

Vertically flip a PIL Image or Tensor

Usage

transform_vflip(img)
transform_vflip(img)

Arguments

img

A magick-image, array or torch_tensor.

A simplified version of torchvision.utils.make_grid

Description

Arranges a batch of (image) tensors in a grid, with optional padding between images. Expects a 4d mini-batch tensor of shape (B x C x H x W).

Usage

vision_make_grid(
  tensor,
  scale = TRUE,
  num_rows = 8,
  padding = 2,
  pad_value = 0
)
vision_make_grid(
  tensor,
  scale = TRUE,
  num_rows = 8,
  padding = 2,
  pad_value = 0
)

Arguments

`tensor`	tensor to arrange in grid.
`scale`	whether to normalize (min-max-scale) the input tensor.
`num_rows`	number of rows making up the grid (default 8).
`padding`	amount of padding between batch images (default 2).
`pad_value`	pixel value to use for padding.

Package 'torchvision'

Help Index

Base loader

Description

Usage

Arguments

Cifar datasets

Description

Usage

Arguments

Draws bounding boxes on image.

Description

Usage

Arguments

Value

See Also

Examples

Draws Keypoints

Description

Usage

Arguments

Value

See Also

Examples

Draw segmentation masks

Description

Usage

Arguments

Value

See Also

Examples

Create an image folder dataset

Description

Usage

Arguments

Details

Kuzushiji-MNIST

Description

Usage

Arguments

Load an Image using ImageMagick

Description

Usage

Arguments

MNIST dataset

Description

Usage

Arguments

AlexNet Model Architecture

Description

Usage

Arguments

See Also

Inception v3 model

Description

Usage

Arguments

Note

See Also

Constructs a MobileNetV2 architecture from MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Description

Usage

Arguments

See Also

ResNet implementation

Description

Usage

Arguments

Functions

See Also

VGG implementation

Description

Usage

Arguments

Functions

See Also

Display image tensor

Description

Usage

Arguments

Convert a tensor image to the given `dtype` and scale the values accordingly