def data_loader():
    # train
    train_dataset = dataset.lmdbDataset(root=args.trainroot)
    assert train_dataset
    if not params.random_sample:
        sampler = dataset.randomSequentialSampler(train_dataset, params.batchSize)
    else:
        sampler = None
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \
            shuffle=True, sampler=sampler, num_workers=int(params.workers), \
            collate_fn=dataset.alignCollate(imgH=params.imgH, imgW=params.imgW, keep_ratio=params.keep_ratio))
    
    # val
    val_dataset = dataset.lmdbDataset(root=args.valroot, transform=dataset.resizeNormalize((params.imgW, params.imgH)))
    assert val_dataset
    val_loader = torch.utils.data.DataLoader(val_dataset, shuffle=True, batch_size=params.batchSize, num_workers=int(params.workers))
    
    return train_loader, val_loader