Creates a new Sampler — sampler • torch

Samplers can be used with dataloader() when creating batches from a torch dataset().

Usage

sampler(
  name = NULL,
  inherit = Sampler,
  ...,
  private = NULL,
  active = NULL,
  parent_env = parent.frame()
)

name: (optional) name of the sampler
inherit: (optional) you can inherit from other samplers to re-use some methods.
...: Pass any number of fields or methods. You should at least define the initialize and step methods. See the examples section.
private: (optional) a list of private methods for the sampler
active: (optional) a list of active methods for the sampler.
parent_env: used to capture the right environment to define the class. The default is fine for most situations.

A sampler must implement the .iter and .length() methods.

initialize takes in a data_source. In general this is a dataset().
.iter returns a function that returns an integer vector or coro::exhausted(). For a sampler, the integer vector should have length 1 (the value is one data index). For a batch_sampler, the integer vector should have length equal to batch size (the values are indices in the batch).
.length returns the maximum number of times that .iter() can be called, before it returns coro::exhausted(). For a sampler, this the number of samples. For a batch_sampler, this is the number of batches.