ConcatDataset¶

class mmengine.dataset.ConcatDataset(datasets, lazy_init=False, ignore_keys=None)[source]¶

A wrapper of concatenated dataset.

Same as torch.utils.data.dataset.ConcatDataset and support lazy_init.

Note

ConcatDataset should not inherit from BaseDataset since get_subset and get_subset_ could produce ambiguous meaning sub-dataset which conflicts with original dataset. If you want to use a sub-dataset of ConcatDataset, you should set indices arguments for wrapped dataset which inherit from BaseDataset.

Parameters

datasets (Sequence[BaseDataset] or Sequence[dict]) – A list of datasets which will be concatenated.
lazy_init (bool, optional) – Whether to load annotation during instantiation. Defaults to False.
ignore_keys (List[str] or str) – Ignore the keys that can be unequal in dataset.metainfo. Defaults to None. New in version 0.3.0.

full_init()[source]¶: Loop to full_init each dataset.

get_data_info(idx)[source]¶

Get annotation by index.

Parameters: idx (int) – Global index of ConcatDataset.
Returns: The idx-th annotation of the datasets.
Return type: dict

get_subset(indices)[source]¶

Not supported in ConcatDataset for the ambiguous meaning of sub- dataset.

Parameters: indices (Union[List[int], int]) –
Return type: mmengine.dataset.base_dataset.BaseDataset

get_subset_(indices)[source]¶

Not supported in ConcatDataset for the ambiguous meaning of sub- dataset.

Parameters: indices (Union[List[int], int]) –
Return type: None

property metainfo: dict¶

Get the meta information of the first dataset in self.datasets.

Returns: Meta information of first dataset.
Return type: dict