training/dataloader.py

#Module for creating data loaders for training and validation datasets.
from torch.utils.data import DataLoader, random_split
from dataset import XRayDataset

def get_dataloaders(
    csv_path,
    images_dir,
    batch_size=32,
    val_split=0.2
):
    full_dataset = XRayDataset(
        csv_path=csv_path,
        images_dir=images_dir,
        train=True
    )

    val_size = int(len(full_dataset) * val_split)
    train_size = len(full_dataset) - val_size

    train_ds, val_ds = random_split(
        full_dataset,
        [train_size, val_size]
    )

    # Disable augmentation for validation dataset so that we only apply normalization
    val_ds.dataset.transform = XRayDataset(
        csv_path,
        images_dir,
        train=False
    ).transform

    train_loader = DataLoader(
    train_ds,
    batch_size=batch_size,
    shuffle=True,
    num_workers=0
)

    val_loader = DataLoader(
    val_ds,
    batch_size=batch_size,
    shuffle=False,
    num_workers=0
)


    return train_loader, val_loader