Data Loading and Preprocessing: tf.data to torch.utils.data
Effective data management is a foundational aspect of any machine learning project. As a TensorFlow developer, you are likely familiar with tf.data for creating input pipelines. This chapter focuses on how PyTorch approaches data loading and preprocessing, helping you adapt your existing skills.
We will examine PyTorch's torch.utils.data module, which provides the main tools for this purpose. You will learn to define custom data sources using the Dataset class and efficiently iterate over your data in batches using DataLoader. We will also cover torchvision.transforms for applying common preprocessing and augmentation techniques. By the end of this chapter, you will be able to construct flexible and performant data pipelines for your PyTorch models, drawing comparisons to your TensorFlow experience.
Sections
3.1 Data Structures: tf.data.Dataset and torch.utils.data.Dataset
3.2 Batching and Iteration: TensorFlow DataLoaders and PyTorch DataLoaders
3.3 Data Augmentation: TensorFlow Methods and torchvision.transforms
3.4 Implementing Custom Datasets in PyTorch
3.5 Preprocessing Data with PyTorch Transforms
3.6 Building Efficient Data Pipelines in PyTorch
3.7 Hands-on Practical: Creating Custom Datasets and DataLoaders
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