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       "name": "Samplers",
       "headline": "Samplers",
       "description": "Data samplers in PyTorch C++ \u2014 RandomSampler, SequentialSampler, DistributedRandomSampler, and StreamSampler.",
       "url": "/api/data/samplers.html",
       "articleBody": "Samplers# Samplers control the order in which samples are accessed from a dataset. They determine the indices that the DataLoader uses to fetch data. Sampler Base Class# template\u003ctypename BatchRequest = std::vector\u003csize_t\u003e\u003eclass Sampler# A Sampler is an object that yields an index with which to access a dataset. Subclassed by torch::data::samplers::RandomSampler, torch::data::samplers::SequentialSampler Public Types using BatchRequestType = BatchRequest# Public Functions virtual ~Sampler() = default# virtual void reset(std::optional\u003csize_t\u003e new_size) = 0# Resets the Sampler\u2019s internal state. Typically called before a new epoch. Optionally, accepts a new size when resetting the sampler. virtual std::optional\u003cBatchRequest\u003e next(size_t batch_size) = 0# Returns the next index if possible, or an empty optional if the sampler is exhausted for this epoch. virtual void save(serialize::OutputArchive \u0026archive) const = 0# Serializes the Sampler to the archive. virtual void load(serialize::InputArchive \u0026archive) = 0# Deserializes the Sampler from the archive. Sequential Sampler# Accesses samples in order from 0 to N-1. Use this for evaluation or when order matters. class SequentialSampler : public torch::data::samplers::Sampler\u003c\u003e# A Sampler that returns indices sequentially. Public Functions explicit SequentialSampler(size_t size)# Creates a SequentialSampler that will return indices in the range 0...size - 1. virtual void reset(std::optional\u003csize_t\u003e new_size = std::nullopt) override# Resets the SequentialSampler to zero. virtual std::optional\u003cstd::vector\u003csize_t\u003e\u003e next(size_t batch_size) override# Returns the next batch of indices. virtual void save(serialize::OutputArchive \u0026archive) const override# Serializes the SequentialSampler to the archive. virtual void load(serialize::InputArchive \u0026archive) override# Deserializes the SequentialSampler from the archive. size_t index() const noexcept# Returns the current index of the SequentialSampler. Random Sampler# Accesses samples in random order. Use this for training to ensure the model sees samples in different orders each epoch. class RandomSampler : public torch::data::samplers::Sampler\u003c\u003e# 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\u003csize_t\u003e new_size = std::nullopt) override# Resets the RandomSampler to a new set of indices. virtual std::optional\u003cstd::vector\u003csize_t\u003e\u003e next(size_t batch_size) override# Returns the next batch of indices. virtual void save(serialize::OutputArchive \u0026archive) const override# Serializes the RandomSampler to the archive. virtual void load(serialize::InputArchive \u0026archive) override# Deserializes the RandomSampler from the archive. size_t index() const noexcept# Returns the current index of the RandomSampler. Distributed Random Sampler# For distributed training, ensures each process gets a different subset of the data without overlap. class DistributedRandomSampler : public torch::data::samplers::DistributedSampler\u003c\u003e# Select samples randomly. The sampling order is shuffled at each reset() call. Public Functions DistributedRandomSampler(size_t size, size_t num_replicas = 1, size_t rank = 0, bool allow_duplicates = true)# virtual void reset(std::optional\u003csize_t\u003e new_size = std::nullopt) override# Resets the DistributedRandomSampler to a new set of indices. virtual std::optional\u003cstd::vector\u003csize_t\u003e\u003e next(size_t batch_size) override# Returns the next batch of indices. virtual void save(serialize::OutputArchive \u0026archive) const override# Serializes the DistributedRandomSampler to the archive. virtual void load(serialize::InputArchive \u0026archive) override# Deserializes the DistributedRandomSampler from the archive. size_t index() const noexcept# Returns the current index of the DistributedRandomSampler. Distributed Sampler (Base)# template\u003ctypename BatchRequest = std::vector\u003csize_t\u003e\u003eclass DistributedSampler : public torch::data::samplers::Sampler\u003cstd::vector\u003csize_t\u003e\u003e# A Sampler that selects a subset of indices to sample from and defines a sampling behavior. In a distributed setting, this selects a subset of the indices depending on the provided num_replicas and rank parameters. The Sampler performs a rounding operation based on the allow_duplicates parameter to decide the local sample count. Subclassed by torch::data::samplers::DistributedRandomSampler, torch::data::samplers::DistributedSequentialSampler Public Functions inline DistributedSampler(size_t size, size_t num_replicas = 1, size_t rank = 0, bool allow_duplicates = true)# inline void set_epoch(size_t epoch)# Set the epoch for the current enumeration. This can be used to alter the sample selection and shuffling behavior. inline size_t epoch() const# Distributed Sequential Sampler# class DistributedSequentialSampler : public torch::data::samplers::DistributedSampler\u003c\u003e# Select samples sequentially. Public Functions DistributedSequentialSampler(size_t size, size_t num_replicas = 1, size_t rank = 0, bool allow_duplicates = true)# virtual void reset(std::optional\u003csize_t\u003e new_size = std::nullopt) override# Resets the DistributedSequentialSampler to a new set of indices. virtual std::optional\u003cstd::vector\u003csize_t\u003e\u003e next(size_t batch_size) override# Returns the next batch of indices. virtual void save(serialize::OutputArchive \u0026archive) const override# Serializes the DistributedSequentialSampler to the archive. virtual void load(serialize::InputArchive \u0026archive) override# Deserializes the DistributedSequentialSampler from the archive. size_t index() const noexcept# Returns the current index of the DistributedSequentialSampler. Stream Sampler# class StreamSampler : public torch::data::samplers::Sampler\u003cBatchSize\u003e# A sampler for (potentially infinite) streams of data. The major feature of the StreamSampler is that it does not return particular indices, but instead only the number of elements to fetch from the dataset. The dataset has to decide how to produce those elements. Public Functions explicit StreamSampler(size_t epoch_size)# Constructs the StreamSampler with the number of individual examples that should be fetched until the sampler is exhausted. virtual void reset(std::optional\u003csize_t\u003e new_size = std::nullopt) override# Resets the internal state of the sampler. virtual std::optional\u003cBatchSize\u003e next(size_t batch_size) override# Returns a BatchSize object with the number of elements to fetch in the next batch. This number is the minimum of the supplied batch_size and the difference between the epoch_size and the current index. If the epoch_size has been reached, returns an empty optional. virtual void save(serialize::OutputArchive \u0026archive) const override# Serializes the StreamSampler to the archive. virtual void load(serialize::InputArchive \u0026archive) override# Deserializes the StreamSampler from the archive.",
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         "@type": "Organization",
         "name": "PyTorch Contributors",
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       "datePublished": "2023-01-01T00:00:00Z",
       "dateModified": "2023-01-01T00:00:00Z"
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