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       "name": "Data Loading (torch::data)",
       "headline": "Data Loading (torch::data)",
       "description": "PyTorch C++ data loading API \u2014 datasets, data loaders, samplers, and transforms.",
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       "articleBody": "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 \u003ctorch/torch.h\u003e // Load built-in dataset auto dataset = torch::data::datasets::MNIST(\"./data\") .map(torch::data::transforms::Normalize\u003c\u003e(0.1307, 0.3081)) .map(torch::data::transforms::Stack\u003c\u003e()); // 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\u0026 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",
       "author": {
         "@type": "Organization",
         "name": "PyTorch Contributors",
         "url": "https://pytorch.org"
       },
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       "datePublished": "2023-01-01T00:00:00Z",
       "dateModified": "2023-01-01T00:00:00Z"
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