#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <torch/data/samplers/base.h>
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#include <torch/types.h>
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#include <cstddef>
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#include <vector>
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namespace torch {
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namespace serialize {
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class OutputArchive;
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class InputArchive;
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} // namespace serialize
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} // namespace torch
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namespace torch {
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namespace data {
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namespace samplers {
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/// A `Sampler` that returns random indices.
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class TORCH_API RandomSampler : public Sampler<> {
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public:
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/// Constructs a `RandomSampler` with a size and dtype for the stored indices.
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///
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/// The constructor will eagerly allocate all required indices, which is the
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/// sequence `0 ... size - 1`. `index_dtype` is the data type of the stored
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/// indices. You can change it to influence memory usage.
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explicit RandomSampler(
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int64_t size,
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Dtype index_dtype = torch::kInt64);
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~RandomSampler() override;
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/// Resets the `RandomSampler` to a new set of indices.
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void reset(optional<size_t> new_size = nullopt) override;
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/// Returns the next batch of indices.
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optional<std::vector<size_t>> next(size_t batch_size) override;
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/// Serializes the `RandomSampler` to the `archive`.
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void save(serialize::OutputArchive& archive) const override;
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/// Deserializes the `RandomSampler` from the `archive`.
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void load(serialize::InputArchive& archive) override;
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/// Returns the current index of the `RandomSampler`.
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size_t index() const noexcept;
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private:
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Tensor indices_;
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int64_t index_ = 0;
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};
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} // namespace samplers
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} // namespace data
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} // namespace torch
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