Chapter 5: Data Handling with torch.utils.data with PyTorch
Abstract : PyTorch's torch.utils.data module provides essential tools for efficient and organized data handling, primarily through the Dataset and DataLoader classes. These abstractions streamline the process of loading, preprocessing, and feeding data into a model, especially for large or complex datasets. 1. torch.utils.data.Dataset : Purpose: This is an abstract class that represents a dataset. You typically create a custom dataset by subclassing Dataset and implementing two key methods: __len__(self) : Returns the total number of samples in the dataset. __getitem__(self, idx) : Retrieves a single sample and its corresponding label (or other target information) at the given index idx . This is where you would load data from disk, apply transformations, and prepare it for your model. Example: Python import torch from torch.utils.data import Dataset class CustomImageDatas...