assert_positive_extent: Assert that a Computed Output Size is Positive

In mlr3torch: Deep Learning with 'mlr3'

Assert that a Computed Output Size is Positive

Description

Rejects a computed output size that no tensor can have, i.e. one that is not a positive number. Operators such as convolutions and pooling compute their spatial output sizes from kernel_size, stride, padding and dilation, a combination of which can produce a size of zero or less. Unknown (NA) sizes are accepted, since nothing can be said about them.

Usage

assert_positive_extent(extent, shape_in, id)

Arguments

extent	(numeric()) The sizes an operator computed for the dimensions it changes, e.g. the spatial dimensions of a convolution.
shape_in	(integer()) The input shape, used for the error message.
id	(character(1) | NULL) The id of the PipeOp, which the error message names. NULL when the assertion is not made for a PipeOp.

Value

The extent, invisibly.

See Also

Other Shape Assertions: assert_dim_in_range(), assert_known_dims(), assert_ndim(), assert_not_batch_dim(), assert_same_batch_size(), assert_same_ndim(), assert_shape(), assert_shapes()

Examples

assert_positive_extent(c(30, NA), c(NA, 3, 32, 32), id = "nn_conv2d")
try(assert_positive_extent(c(0, 4), c(NA, 3, 32, 32), id = "nn_conv2d"))

mlr3torch documentation built on Oct. 30, 2026, 5:10 p.m.