sampler function

Creates a new Sampler

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

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

Arguments

• 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.

Details

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.