def load_mono_data()

in src/data/loader.py [0:0]

• 33 lines of code
• 22 McCabe index (conditional complexity)

def load_mono_data(params, data):
    """
    Load monolingual data.
    """
    data['mono'] = {}
    data['mono_stream'] = {}

    for lang in params.mono_dataset.keys():

        logger.info('============ Monolingual data (%s)' % lang)

        assert lang in params.langs and lang not in data['mono']
        data['mono'][lang] = {}
        data['mono_stream'][lang] = {}

        for splt in ['train', 'valid', 'test']:

            # no need to load training data for evaluation
            if splt == 'train' and params.eval_only:
                continue

            # load data / update dictionary parameters / update data
            mono_data = load_binarized(params.mono_dataset[lang][splt], params)
            set_dico_parameters(params, data, mono_data['dico'])

            # create stream dataset
            bs = params.batch_size if splt == 'train' else 1
            data['mono_stream'][lang][splt] = StreamDataset(mono_data['sentences'], mono_data['positions'], bs, params)

            # if there are several processes on the same machine, we can split the dataset
            if splt == 'train' and params.split_data and 1 < params.n_gpu_per_node <= data['mono_stream'][lang][splt].n_batches:
                n_batches = data['mono_stream'][lang][splt].n_batches // params.n_gpu_per_node
                a = n_batches * params.local_rank
                b = n_batches * params.local_rank + n_batches
                data['mono_stream'][lang][splt].select_data(a, b)

            # for denoising auto-encoding and online back-translation, we need a non-stream (batched) dataset
            if lang in params.ae_steps or lang in params.bt_src_langs:

                # create batched dataset
                dataset = Dataset(mono_data['sentences'], mono_data['positions'], params)

                # remove empty and too long sentences
                if splt == 'train':
                    dataset.remove_empty_sentences()
                    dataset.remove_long_sentences(params.max_len)

                # if there are several processes on the same machine, we can split the dataset
                if splt == 'train' and params.n_gpu_per_node > 1 and params.split_data:
                    n_sent = len(dataset) // params.n_gpu_per_node
                    a = n_sent * params.local_rank
                    b = n_sent * params.local_rank + n_sent
                    dataset.select_data(a, b)

                data['mono'][lang][splt] = dataset

            logger.info("")

    logger.info("")