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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path as osp | |
| from typing import List, Optional | |
| from mmengine.dataset import BaseDataset | |
| from mmengine.fileio import load | |
| from mmengine.utils import is_abs | |
| from ..registry import DATASETS | |
| class BaseDetDataset(BaseDataset): | |
| """Base dataset for detection. | |
| Args: | |
| proposal_file (str, optional): Proposals file path. Defaults to None. | |
| file_client_args (dict): Arguments to instantiate the | |
| corresponding backend in mmdet <= 3.0.0rc6. Defaults to None. | |
| backend_args (dict, optional): Arguments to instantiate the | |
| corresponding backend. Defaults to None. | |
| """ | |
| def __init__(self, | |
| *args, | |
| seg_map_suffix: str = '.png', | |
| proposal_file: Optional[str] = None, | |
| file_client_args: dict = None, | |
| backend_args: dict = None, | |
| **kwargs) -> None: | |
| self.seg_map_suffix = seg_map_suffix | |
| self.proposal_file = proposal_file | |
| self.backend_args = backend_args | |
| if file_client_args is not None: | |
| raise RuntimeError( | |
| 'The `file_client_args` is deprecated, ' | |
| 'please use `backend_args` instead, please refer to' | |
| 'https://github.com/open-mmlab/mmdetection/blob/main/configs/_base_/datasets/coco_detection.py' # noqa: E501 | |
| ) | |
| super().__init__(*args, **kwargs) | |
| def full_init(self) -> None: | |
| """Load annotation file and set ``BaseDataset._fully_initialized`` to | |
| True. | |
| If ``lazy_init=False``, ``full_init`` will be called during the | |
| instantiation and ``self._fully_initialized`` will be set to True. If | |
| ``obj._fully_initialized=False``, the class method decorated by | |
| ``force_full_init`` will call ``full_init`` automatically. | |
| Several steps to initialize annotation: | |
| - load_data_list: Load annotations from annotation file. | |
| - load_proposals: Load proposals from proposal file, if | |
| `self.proposal_file` is not None. | |
| - filter data information: Filter annotations according to | |
| filter_cfg. | |
| - slice_data: Slice dataset according to ``self._indices`` | |
| - serialize_data: Serialize ``self.data_list`` if | |
| ``self.serialize_data`` is True. | |
| """ | |
| if self._fully_initialized: | |
| return | |
| # load data information | |
| self.data_list = self.load_data_list() | |
| # get proposals from file | |
| if self.proposal_file is not None: | |
| self.load_proposals() | |
| # filter illegal data, such as data that has no annotations. | |
| self.data_list = self.filter_data() | |
| # Get subset data according to indices. | |
| if self._indices is not None: | |
| self.data_list = self._get_unserialized_subset(self._indices) | |
| # serialize data_list | |
| if self.serialize_data: | |
| self.data_bytes, self.data_address = self._serialize_data() | |
| self._fully_initialized = True | |
| def load_proposals(self) -> None: | |
| """Load proposals from proposals file. | |
| The `proposals_list` should be a dict[img_path: proposals] | |
| with the same length as `data_list`. And the `proposals` should be | |
| a `dict` or :obj:`InstanceData` usually contains following keys. | |
| - bboxes (np.ndarry): Has a shape (num_instances, 4), | |
| the last dimension 4 arrange as (x1, y1, x2, y2). | |
| - scores (np.ndarry): Classification scores, has a shape | |
| (num_instance, ). | |
| """ | |
| # TODO: Add Unit Test after fully support Dump-Proposal Metric | |
| if not is_abs(self.proposal_file): | |
| self.proposal_file = osp.join(self.data_root, self.proposal_file) | |
| proposals_list = load( | |
| self.proposal_file, backend_args=self.backend_args) | |
| assert len(self.data_list) == len(proposals_list) | |
| for data_info in self.data_list: | |
| img_path = data_info['img_path'] | |
| # `file_name` is the key to obtain the proposals from the | |
| # `proposals_list`. | |
| file_name = osp.join( | |
| osp.split(osp.split(img_path)[0])[-1], | |
| osp.split(img_path)[-1]) | |
| proposals = proposals_list[file_name] | |
| data_info['proposals'] = proposals | |
| def get_cat_ids(self, idx: int) -> List[int]: | |
| """Get COCO category ids by index. | |
| Args: | |
| idx (int): Index of data. | |
| Returns: | |
| List[int]: All categories in the image of specified index. | |
| """ | |
| instances = self.get_data_info(idx)['instances'] | |
| return [instance['bbox_label'] for instance in instances] | |