Allows reordering elements and repeating them in arbitrary combinations.

This operation includes functionality of repeat, tile, and broadcast functions.

Usage

einops.repeat(x, expr, ..., .row_major = getOption("einops_row_major", FALSE))

repeat x

Arguments

x: tensor: array, matrix, or list of arrays of the same shape and type
expr: string: reduction pattern
...: either corresponding axes lengths or a single list of them.
.row_major: logical: whether to use row-major order for the output tensor. If TRUE, the operation is performed in row-major order, but the output will be in whatever order the parent framework uses (e.g. column-major for base::array()).

Value

tensor of the same type as input, with dimensions according to output pattern

Details

When composing axes, C-order enumeration is used (consecutive elements have different last axis). Find more examples in the vignettes.

Why can't the function be called as `repeat()`?

repeat is a reserved keyword in R that acts the same as while(TRUE), and has no way of being overridden. Hence, this function can only be called as einops.repeat() or using backticks as `repeat`().

Examples

if (requireNamespace("abind", quietly = TRUE)) {

set.seed(42)
# a grayscale image (of shape height x width)
image <- array(rnorm(30 * 40), dim = c(30, 40))

# change it to RGB format by repeating in each channel
output <- einops.repeat(image, 'h w -> h w c', c = 3)
# Visualize the output
as_image_tensor(output)

# repeat image 2 times along height (vertical axis)
output <- einops.repeat(image, 'h w -> (r h) w', r = 2)

# repeat image 2 times along height and 3 times along width
output <- einops.repeat(image, 'h w -> (h2 h) (w3 w)', h2 = 2, w3 = 3)

# convert each pixel to a small square 2x2, i.e. upsample an image by 2x
output <- einops.repeat(image, 'h w -> (h h2) (w w2)', h2 = 2, w2 = 2)

# 'pixelate' an image first by downsampling by 2x, then upsampling
downsampled <- reduce(image, '(h h2) (w w2) -> h w', 'mean', h2 = 2, w2 = 2)
output <- einops.repeat(downsampled, 'h w -> (h h2) (w w2)', h2 = 2, w2 = 2)
as_image_tensor(einops.repeat(output, 'h w -> h w 3'))

}