How to use the mmcv.runner.obj_from_dict function in mmcv

def main():
    args = parse_args()

    cfg = mmcv.Config.fromfile(args.config)

    dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))

    output_list = []
    for out in args.outputs:
        output_list.append(mmcv.load(out))

    if args.score_weights:
        weights = np.array(args.score_weights) / sum(args.score_weights)
    else:
        weights = [1. / len(output_list) for _ in output_list]

    def merge_scores(idx):
        def merge_part(arrs, index, weights):
            if arrs[0][index] is not None:
                return np.sum([a[index] * w for a, w in zip(arrs, weights)],
                              axis=0)
            else:

def __init__(self, dataset, interval=1):
        if isinstance(dataset, Dataset):
            self.dataset = dataset
        elif isinstance(dataset, dict):
            self.dataset = obj_from_dict(dataset, datasets,
                                         {'test_mode': True})
        else:
            raise TypeError(
                'dataset must be a Dataset object or a dict, not {}'.format(
                    type(dataset)))
        self.interval = interval

def main():
    parser = ArgumentParser(description='VOC Evaluation')
    parser.add_argument('result', help='result file path')
    parser.add_argument('config', help='config file path')
    parser.add_argument(
        '--iou-thr',
        type=float,
        default=0.5,
        help='IoU threshold for evaluation')
    args = parser.parse_args()
    cfg = mmcv.Config.fromfile(args.config)
    test_dataset = mmcv.runner.obj_from_dict(cfg.data.test, datasets)
    voc_eval(args.result, test_dataset, args.iou_thr)

def build_assigner(cfg, **kwargs):
    if isinstance(cfg, assigners.BaseAssigner):
        return cfg
    elif isinstance(cfg, dict):
        return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs)
    else:
        raise TypeError('Invalid type {} for building a sampler'.format(
            type(cfg)))

def main():
    parser = ArgumentParser(description='VOC Evaluation')
    parser.add_argument('result', help='result file path')
    parser.add_argument('config', help='config file path')
    parser.add_argument(
        '--iou-thr',
        type=float,
        default=0.5,
        help='IoU threshold for evaluation')
    args = parser.parse_args()
    cfg = mmcv.Config.fromfile(args.config)
    test_dataset = mmcv.runner.obj_from_dict(cfg.data.test, datasets)
    voc_eval(args.result, test_dataset, args.iou_thr)

def build_optimizer(model, optimizer_cfg):
    """Build optimizer from configs.
    """
    if hasattr(model, 'module'):
        model = model.module

    optimizer_cfg = optimizer_cfg.copy()
    paramwise_options = optimizer_cfg.pop('paramwise_options', None)
    assert paramwise_options is None
    return obj_from_dict(optimizer_cfg, torch.optim,
                         dict(params=model.parameters()))

def __init__(self, dataset, interval=1):
        if isinstance(dataset, Dataset):
            self.dataset = dataset
        elif isinstance(dataset, dict):
            self.dataset = obj_from_dict(dataset, datasets,
                                         {'test_mode': True})
        else:
            raise TypeError(
                'dataset must be a Dataset object or a dict, not {}'.format(
                    type(dataset)))
        self.interval = interval
        self.lock_dir = None

torch.optim.Optimizer: The initialized optimizer.

    Example:
        >>> model = torch.nn.modules.Conv1d(1, 1, 1)
        >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
        >>>                      weight_decay=0.0001)
        >>> optimizer = build_optimizer(model, optimizer_cfg)
    """
    if hasattr(model, 'module'):
        model = model.module

    optimizer_cfg = optimizer_cfg.copy()
    paramwise_options = optimizer_cfg.pop('paramwise_options', None)
    # if no paramwise option is specified, just use the global setting
    if paramwise_options is None:
        return obj_from_dict(optimizer_cfg, torch.optim,
                             dict(params=model.parameters()))
    else:
        assert isinstance(paramwise_options, dict)
        # get base lr and weight decay
        base_lr = optimizer_cfg['lr']
        base_wd = optimizer_cfg.get('weight_decay', None)
        # weight_decay must be explicitly specified if mult is specified
        if ('bias_decay_mult' in paramwise_options
                or 'norm_decay_mult' in paramwise_options):
            assert base_wd is not None
        # get param-wise options
        bias_lr_mult = paramwise_options.get('bias_lr_mult', 1.)
        bias_decay_mult = paramwise_options.get('bias_decay_mult', 1.)
        norm_decay_mult = paramwise_options.get('norm_decay_mult', 1.)
        # set param-wise lr and weight decay
        params = []