def load_data(traindir, valdir, **kwargs):
    'generate the train and val dataloader, you can change this for your specific task\n\n    Args:\n        traindir (str): train dataset dir\n        valdir (str): validation dataset dir\n\n    Returns:\n        tuple: the train dataset and validation dataset\n    '
    train_transform = T.Compose([T.RandomCrop(512), T.RandomHorizontalFlip(), T.RandomVerticalFlip(), T.ToTensor(), T.Normalize()])
    val_transform = T.Compose([T.ToTensor(), T.Normalize()])
    dataset_train = ChangeDetectionDataset(traindir, extentions=kwargs['extensions'], transforms=train_transform)
    dataset_val = ChangeDetectionDataset(valdir, extentions=kwargs['extensions'], transforms=val_transform)
    return (dataset_train, dataset_val)
generate the train and val dataloader, you can change this for your specific task

Args:
    traindir (str): train dataset dir
    valdir (str): validation dataset dir

Returns:
    tuple: the train dataset and validation dataset
def load_data(traindir, valdir, **kwargs):
    'generate the train and val dataloader, you can change this for your specific task\n\n    Args:\n        traindir (str): train dataset dir\n        valdir (str): validation dataset dir\n\n    Returns:\n        tuple: the train dataset and validation dataset\n    '
    train_transform = T.Compose([T.RandomCrop(512), T.RandomHorizontalFlip(), T.RandomVerticalFlip(), T.ToTensor(), T.Normalize()])
    val_transform = T.Compose([T.ToTensor(), T.Normalize()])
    dataset_train = ChangeDetectionDataset(traindir, extentions=kwargs['extensions'], transforms=train_transform)
    dataset_val = ChangeDetectionDataset(valdir, extentions=kwargs['extensions'], transforms=val_transform)
    return (dataset_train, dataset_val)