What are the benefits of using class when defining a dataloader?

1

I have the following dataloader below:

def load_dataset(size_batch, size):
    data_path = "/home/bledc/dataset/test_set/crops_BSD"
    transformations = transforms.Compose([
        transforms.Grayscale(num_output_channels=1),
        transforms.ToTensor()
        ])

    train_dataset = datasets.ImageFolder(
        root=data_path,
        transform=transformations
    )
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=size_batch,
        shuffle=True,
        num_workers=0,
        drop_last=True
    )
    return train_loader

I iterate through it in my training loop with the following:

data_loader = load_dataset(batch_size, width)
for data in data_loader:
        model.zero_grad()
        optimizer.zero_grad()
        img, _ = data
        img = img.to(device)

Can someone explain to me what the benefit would be of writing the load_dataset() function into a class? The reason for this is that I have been using the above template for dataloading from code I found online but it seems that most codebases use class LoaderName(Dataset) followed by definiting initial conditions and super().

Thank you.

2

load_dataset would be a function, which returns the DataLoader not a class.
I would claim it depends on your coding style and wrapping the DataLoader creation into a function might allow you to move it into another file more easily.

This class definition would be used to create a custom Dataset as described here. In your example you are using ImageFolder so wouldn’t need the custom Dataset class.

3

Thanks for clearing this up @ptrblck . As always your help on the forum is greatly appreciated.