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 

def crnn_recognition(cropped_image, model):
    converter = utils.strLabelConverter(alphabet)

    image = cropped_image.convert('L')

    ##
    # w = int(image.size[0] / (280 * 1.0 / 160))
    transformer = dataset.resizeNormalize((280, 32))
    image = transformer(image)
    # if torch.cuda.is_available():
    #     image = image.cuda()
    image = image.view(1, *image.size())
    image = Variable(image)

    model.eval()
    preds = model(image)

    _, preds = preds.max(2)
    preds = preds.transpose(1, 0).contiguous().view(-1)

    preds_size = Variable(torch.IntTensor([preds.size(0)]))
    sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
    print('results: {0}'.format(sim_pred))
    return sim_pred