PyTorch dataset loaders

Pytorch includes data loaders for several datasets to help you get started. The torch.dataloader is the class used for loading datasets. The following is a list of the included torch datasets and a brief description:

Here is a typical example of how we load one of these datasets into PyTorch:

CIFAR10 is a torch.utils.dataset object. Here, we are passing it four arguments. We specify a root directory relative to where the code is running, a Boolean, train, indicating if we want the test or training set loaded, a Boolean that, if set to True, will check to see if the dataset has previously been downloaded and if not download it, and a callable transform. In this case, the transform we select is ToTensor(). This is an inbuilt class of torchvision.transforms that makes the class return a tensor. We will discuss transforms in more detail later in the chapter.

The contents of the dataset can be retrieved by a simple index lookup. We can also check the length of the entire dataset with the len function. We can also loop through the dataset in order. The following code demonstrates this: