sampler: Creates a new Sampler

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Creates a new Sampler

Description

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()
)

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 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.

torch documentation built on Nov. 5, 2025, 7:02 p.m.