Lecture 08 on PyTorch: Using PyTorch DataLoader

In lecture 08 of the PyTorch series, we will be focusing on how to use PyTorch DataLoader to efficiently load and preprocess data for training deep learning models. PyTorch DataLoader is a utility that helps in loading and batching data for training neural networks. By using DataLoader, you can efficiently handle large datasets, apply data augmentation, shuffle the data, and create batches for training.

To get started, make sure you have PyTorch installed on your system. If you don’t have it installed, you can install it using pip:

pip install torch torchvision

Once you have PyTorch installed, let’s start by importing the necessary libraries:

import torch
import torchvision
from torch.utils.data import Dataset, DataLoader

Next, let’s create a custom dataset to work with. For this tutorial, we will use the CIFAR-10 dataset, which is a popular dataset for image classification tasks. To create a custom dataset, you need to subclass the Dataset class from PyTorch and implement the __getitem__ and __len__ methods. The __getitem__ method should return a sample from the dataset at the given index, and the __len__ method should return the total number of samples in the dataset.

class CustomDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = targets
        self.transform = transform

    def __getitem__(self, index):
        img, target = self.data[index], self.targets[index]

        if self.transform:
            img = self.transform(img)

        return img, target

    def __len__(self):
        return len(self.data)

Now, we need to load the CIFAR-10 dataset and create train and test datasets using the CustomDataset class. We also need to create DataLoader objects for both the train and test datasets. DataLoader takes the dataset object, batch size, shuffle, and other arguments as input.

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

train_data = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_data = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

Now that we have set up our dataset and DataLoader objects, we can iterate over the train_loader to get batches of data for training our neural network. Each batch will contain the input data and corresponding labels. Here’s an example of how to iterate over the DataLoader object:

for inputs, labels in train_loader:
    # Forward pass
    outputs = model(inputs)

    # Calculate loss
    loss = criterion(outputs, labels)

    # Backward pass and update weights
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

In the above code snippet, model represents your neural network model, criterion is the loss function, and optimizer is the optimization algorithm you are using to update the model parameters. By using DataLoader, you can easily iterate over batches of data, apply data augmentation, shuffle the data, and train your deep learning models efficiently.

In conclusion, PyTorch DataLoader is a powerful utility that simplifies the process of loading and preprocessing data for training deep learning models. By following the steps outlined in this tutorial, you can create custom datasets, use DataLoader to load and batch the data, and train your neural networks with ease. I hope this tutorial has been helpful in understanding how to use PyTorch DataLoader effectively. Happy coding!