def __init_

def init()

in training/data.py [0:0]
69 lines of code
17 McCabe index (conditional complexity)

    def __init__(self, **kwargs):
        if 'questions_h5' not in kwargs:
            raise ValueError('Must give questions_h5')
        if 'data_json' not in kwargs:
            raise ValueError('Must give data_json')
        if 'vocab' not in kwargs:
            raise ValueError('Must give vocab')
        if 'input_type' not in kwargs:
            raise ValueError('Must give input_type')
        if 'split' not in kwargs:
            raise ValueError('Must give split')
        if 'gpu_id' not in kwargs:
            raise ValueError('Must give gpu_id')

        questions_h5_path = kwargs.pop('questions_h5')
        data_json = kwargs.pop('data_json')
        input_type = kwargs.pop('input_type')

        split = kwargs.pop('split')
        vocab = kwargs.pop('vocab')

        gpu_id = kwargs.pop('gpu_id')

        if 'max_threads_per_gpu' in kwargs:
            max_threads_per_gpu = kwargs.pop('max_threads_per_gpu')
        else:
            max_threads_per_gpu = 10

        if 'to_cache' in kwargs:
            to_cache = kwargs.pop('to_cache')
        else:
            to_cache = False

        if 'target_obj_conn_map_dir' in kwargs:
            target_obj_conn_map_dir = kwargs.pop('target_obj_conn_map_dir')
        else:
            target_obj_conn_map_dir = False

        if 'map_resolution' in kwargs:
            map_resolution = kwargs.pop('map_resolution')
        else:
            map_resolution = 1000

        if 'image' in input_type or 'cnn' in input_type:
            kwargs['collate_fn'] = eqaCollateCnn
        elif 'lstm' in input_type:
            kwargs['collate_fn'] = eqaCollateSeq2seq

        if 'overfit' in kwargs:
            overfit = kwargs.pop('overfit')
        else:
            overfit = False

        if 'max_controller_actions' in kwargs:
            max_controller_actions = kwargs.pop('max_controller_actions')
        else:
            max_controller_actions = 5

        if 'max_actions' in kwargs:
            max_actions = kwargs.pop('max_actions')
        else:
            max_actions = None 

        print('Reading questions from ', questions_h5_path)
        with h5py.File(questions_h5_path, 'r') as questions_h5:
            self.dataset = EqaDataset(
                questions_h5,
                vocab,
                num_frames=kwargs.pop('num_frames'),
                data_json=data_json,
                split=split,
                gpu_id=gpu_id,
                input_type=input_type,
                max_threads_per_gpu=max_threads_per_gpu,
                to_cache=to_cache,
                target_obj_conn_map_dir=target_obj_conn_map_dir,
                map_resolution=map_resolution,
                overfit=overfit,
                max_controller_actions=max_controller_actions,
                max_actions=max_actions)

        super(EqaDataLoader, self).__init__(self.dataset, **kwargs)
def __init__()

def init()
