How to train/eval simultaneously on multiple COCO-style datasets? about mmdetection HOT 4 CLOSED

def get_train_dataset(cfg):
    if isinstance(cfg.data.train['ann_file'], list) or isinstance(cfg.data.train['ann_file'], tuple):
        ann_files = cfg.data.train['ann_file']
        train_datasets = []
        for ann_file in ann_files:
            data_info = copy.deepcopy(cfg.data.train)
            data_info['ann_file'] = ann_file
            train_dset = obj_from_dict(data_info, datasets)
            train_datasets.append(train_dset)
        if len(train_datasets) > 1:
            train_dataset = ConcatDataset(train_datasets)
        else:
            train_dataset = train_datasets[0]
    else:
        train_dataset = obj_from_dict(cfg.data.train, datasets)
    return train_dataset

Traceback (most recent call last):
  File "tools/train.py", line 104, in <module>
    main()
  File "tools/train.py", line 100, in main
    logger=logger)
  File "/data/wanggu/mmdetection/mmdet/apis/train.py", line 59, in train_detector
    _non_dist_train(model, dataset, cfg, validate=validate)
  File "/data/wanggu/mmdetection/mmdet/apis/train.py", line 103, in _non_dist_train
    dist=False)
  File "/data/wanggu/mmdetection/mmdet/datasets/loader/build_loader.py", line 31, in build_dataloader
    sampler = GroupSampler(dataset, imgs_per_gpu)
  File "/data/wanggu/mmdetection/mmdet/datasets/loader/sampler.py", line 14, in __init__
    assert hasattr(dataset, 'flag')
AssertionError