How do I create a custom DataLoader in PyTorch?

How do I create a custom DataLoader in PyTorch?

To create a custom DataLoader in PyTorch, you need to subclass the torch.utils.data.Dataset class and implement the __len__ and __getitem__ methods.

Can you show me an example of a custom Dataset?

Sure! Here's a simple example:

import torch
from torch.utils.data import Dataset

class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data

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

    def __getitem__(self, idx):
        return self.data[idx]

What about the DataLoader itself?

Once you have your custom Dataset, you can create a DataLoader like this:

from torch.utils.data import DataLoader

dataset = MyDataset(data)
loader = DataLoader(dataset, batch_size=4, shuffle=True)

How can I use this DataLoader in my training loop?

You can iterate over the DataLoader in your training loop like this:

for batch in loader:
    # Your training code here