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Class RandomSampler#

Inheritance Relationships#

Base Type#

Class Documentation#

class RandomSampler : public torch::data::samplers::Sampler<>#

A Sampler that returns random indices.

Public Functions

explicit RandomSampler(int64_t size, Dtype index_dtype = torch::kInt64)#

Constructs a RandomSampler with a size and dtype for the stored indices.

The constructor will eagerly allocate all required indices, which is the sequence 0 ... size - 1. index_dtype is the data type of the stored indices. You can change it to influence memory usage.

~RandomSampler() override#
virtual void reset(std::optional<size_t> new_size = std::nullopt) override#

Resets the RandomSampler to a new set of indices.

virtual std::optional<std::vector<size_t>> next(size_t batch_size) override#

Returns the next batch of indices.

virtual void save(serialize::OutputArchive &archive) const override#

Serializes the RandomSampler to the archive.

virtual void load(serialize::InputArchive &archive) override#

Deserializes the RandomSampler from the archive.

size_t index() const noexcept#

Returns the current index of the RandomSampler.