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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Callable, List, Sequence, Union | |
| from mmengine.dataset import BaseDataset, Compose | |
| from mmengine.dataset import ConcatDataset as MMENGINE_CONCATDATASET | |
| from mmocr.registry import DATASETS | |
| class ConcatDataset(MMENGINE_CONCATDATASET): | |
| """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``. | |
| Args: | |
| datasets (Sequence[BaseDataset] or Sequence[dict]): A list of datasets | |
| which will be concatenated. | |
| pipeline (list, optional): Processing pipeline to be applied to all | |
| of the concatenated datasets. Defaults to []. | |
| verify_meta (bool): Whether to verify the consistency of meta | |
| information of the concatenated datasets. Defaults to True. | |
| force_apply (bool): Whether to force apply pipeline to all datasets if | |
| any of them already has the pipeline configured. Defaults to False. | |
| lazy_init (bool, optional): Whether to load annotation during | |
| instantiation. Defaults to False. | |
| """ | |
| def __init__(self, | |
| datasets: Sequence[Union[BaseDataset, dict]], | |
| pipeline: List[Union[dict, Callable]] = [], | |
| verify_meta: bool = True, | |
| force_apply: bool = False, | |
| lazy_init: bool = False): | |
| self.datasets: List[BaseDataset] = [] | |
| # Compose dataset | |
| pipeline = Compose(pipeline) | |
| for i, dataset in enumerate(datasets): | |
| if isinstance(dataset, dict): | |
| self.datasets.append(DATASETS.build(dataset)) | |
| elif isinstance(dataset, BaseDataset): | |
| self.datasets.append(dataset) | |
| else: | |
| raise TypeError( | |
| 'elements in datasets sequence should be config or ' | |
| f'`BaseDataset` instance, but got {type(dataset)}') | |
| if len(pipeline.transforms) > 0: | |
| if len(self.datasets[-1].pipeline.transforms | |
| ) > 0 and not force_apply: | |
| raise ValueError( | |
| f'The pipeline of dataset {i} is not empty, ' | |
| 'please set `force_apply` to True.') | |
| self.datasets[-1].pipeline = pipeline | |
| self._metainfo = self.datasets[0].metainfo | |
| if verify_meta: | |
| # Only use metainfo of first dataset. | |
| for i, dataset in enumerate(self.datasets, 1): | |
| if self._metainfo != dataset.metainfo: | |
| raise ValueError( | |
| f'The meta information of the {i}-th dataset does not ' | |
| 'match meta information of the first dataset') | |
| self._fully_initialized = False | |
| if not lazy_init: | |
| self.full_init() | |
| self._metainfo.update(dict(cumulative_sizes=self.cumulative_sizes)) | |