Transform Image Dataset (Experimental)

Transforms images within an image dataset

Documentation

Algorithms

Center Crop

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.

Width:

Desired output width of the crop

Height:

Desired output height of the crop

Grayscale

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

Number of output channels:

(1 or 3) number of channels desired for output image

Normalize

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]) for n channels, this transform will normalize each channel of the input torch.*Tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]

Standard deviation:

Sequence of standard deviations for each channel.

Mean:

Sequence of means for each channel.

Pad

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

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. Only number is supported for torch Tensor. Only int or str or tuple value is supported for PIL Image.

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. 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]

Padding size:

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.

Resize

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

Width:

Desired output width.

Interpolation:

Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Default is InterpolationMode.BILINEAR. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.BILINEAR and InterpolationMode.BICUBIC are supported. For backward compatibility integer values (e.g. PIL.Image.NEAREST) are still acceptable.

Height:

Desired output height.

To PIL Image

Convert a tensor or an ndarray to PIL Image. This transform does not support torchscript.

To Tensor

Convert a PIL Image or numpy.ndarray to tensor. This transform does not support torchscript.

Definition

Input ports

dataset dataset

Dataset

Output ports

dataset dataset

Dataset

Configuration

Fill (Fill)

(no description)

Height (Height)

(no description)

Interpolation (Interpolation)

(no description)

Mean (Mean)

(no description)

Number of output channels (Number of output channels)

(no description)

Padding mode (Padding mode)

(no description)

Padding size (Padding size)

(no description)

Standard deviation (Standard deviation)

(no description)

Width (Width)

(no description)

Algorithm (algorithm)

(no description)

Implementation

class node_transformdataset.TransformImageDataset