as_lazy_tensor {mlr3torch}

Convert to Lazy Tensor

Description

Convert a object to a lazy_tensor.

Usage

as_lazy_tensor(x, ...)

## S3 method for class 'dataset'
as_lazy_tensor(x, dataset_shapes = NULL, ids = NULL, ...)

Arguments

x

(any)
Object to convert to a lazy_tensor

...

(any)
Additional arguments passed to the method.

dataset_shapes

(named list() of (integer() or NULL))
The shapes of the output. Names are the elements of the list returned by the dataset. If the shape is not NULL (unknown, e.g. for images of different sizes) the first dimension must be NA to indicate the batch dimension.

ids

(integer())
Which ids to include in the lazy tensor.

Examples

iris_ds = dataset("iris",
  initialize = function() {
    self$iris = iris[, -5]
  },
  .getbatch = function(i) {
    list(x = torch_tensor(as.matrix(self$iris[i, ])))
  },
  .length = function() nrow(self$iris)
)()
# no need to specify the dataset shapes as they can be inferred from the .getbatch method
# only first 5 observations
as_lazy_tensor(iris_ds, ids = 1:5)
# all observations
head(as_lazy_tensor(iris_ds))

iris_ds2 = dataset("iris",
  initialize = function() self$iris = iris[, -5],
  .getitem = function(i) list(x = torch_tensor(as.numeric(self$iris[i, ]))),
  .length = function() nrow(self$iris)
)()
# if .getitem is implemented we cannot infer the shapes as they might vary,
# so we have to annotate them explicitly
as_lazy_tensor(iris_ds2, dataset_shapes = list(x = c(NA, 4L)))[1:5]

# Convert a matrix
lt = as_lazy_tensor(matrix(rnorm(100), nrow = 20))
materialize(lt[1:5], rbind = TRUE)