Data Loading (torch::data)

The torch::data namespace provides utilities for loading and processing datasets during training. It includes dataset abstractions, data loaders for batching and shuffling, samplers for controlling data access patterns, and transforms for data augmentation.

When to use torch::data:

• When loading training data in batches

• When you need parallel data loading with multiple workers

• When implementing custom datasets or transforms

Components overview:

• Dataset: Defines how to access individual samples (implement get() and size())

• DataLoader: Batches samples and optionally shuffles/parallelizes loading

• Sampler: Controls the order in which samples are accessed

• Transform: Applies preprocessing (normalization, augmentation) to samples

Basic usage:

#include <torch/torch.h>

// Load built-in dataset
auto dataset = torch::data::datasets::MNIST("./data")
    .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
    .map(torch::data::transforms::Stack<>());

// Create data loader with batching and shuffling
auto data_loader = torch::data::make_data_loader(
    std::move(dataset),
    torch::data::DataLoaderOptions().batch_size(64).workers(4));

// Iterate over batches
for (auto& batch : *data_loader) {
    auto images = batch.data;   // Shape: [64, 1, 28, 28]
    auto labels = batch.target; // Shape: [64]
}

Header Files

• torch/csrc/api/include/torch/data.h - Main data header

• torch/csrc/api/include/torch/data/dataloader.h - DataLoader

• torch/csrc/api/include/torch/data/datasets.h - Dataset classes

• torch/csrc/api/include/torch/data/samplers.h - Samplers

Module Categories

• Datasets
• DataLoader
• Samplers
• Transforms