Reader-friendly smart element reordering for multidimensional tensors.

This operation includes functionality of transpose (axes permutation), reshape (view), squeeze, unsqueeze, stack, concatenate and other operations.

Usage

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

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

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.

Examples

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

# suppose we have a set of 32 images in "h w c" format (height-width-channel)
images <- lapply(1:32, function(i) {
    as_image_tensor(array(rnorm(30*40*3), dim = c(30, 40, 3)))
})

# stacked and reordered axes to "b c h w" format
y <- rearrange(images, 'b h w c -> b c h w')

# concatenate images along height (vertical axis), 960 = 32 * 30
y <- rearrange(images, 'b h w c -> (b h) w c')

# concatenated images along horizontal axis, 1280 = 32 * 40
y <- rearrange(images, 'b h w c -> h (b w) c')

# flattened each image into a vector, 3600 = 30 * 40 * 3
y <- rearrange(images, 'b h w c -> b (c h w)')

# split each image into 4 smaller quadrants, 128 = 32 * 2 * 2
y <- rearrange(
    images, 'b (h1 h) (w1 w) c -> (b h1 w1) h w c', h1 = 2, w1 = 2
)

# space-to-depth operation
y <- rearrange(
    images, 'b (h h1) (w w1) c -> b h w (c h1 w1)', h1 = 2, w1 = 2
)

}