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 a dataset index everytime it's called.
.length returns the maximum number of samples that can be retrieved from that sampler.