mmpretrain.datasets.multi_label 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List

from mmpretrain.registry import DATASETS
from .base_dataset import BaseDataset

[文档]@DATASETS.register_module() class MultiLabelDataset(BaseDataset): """Multi-label Dataset. This dataset support annotation file in `OpenMMLab 2.0 style annotation format`. The annotation format is shown as follows. .. code-block:: none { "metainfo": { "classes":['A', 'B', 'C'....] }, "data_list": [ { "img_path": "test_img1.jpg", 'gt_label': [0, 1], }, { "img_path": "test_img2.jpg", 'gt_label': [2], }, ] .... } Args: ann_file (str): Annotation file path. metainfo (dict, optional): Meta information for dataset, such as class information. Defaults to None. data_root (str): The root directory for ``data_prefix`` and ``ann_file``. Defaults to ''. data_prefix (str | dict): Prefix for training data. Defaults to ''. filter_cfg (dict, optional): Config for filter data. Defaults to None. indices (int or Sequence[int], optional): Support using first few data in annotation file to facilitate training/testing on a smaller dataset. Defaults to None which means using all ``data_infos``. serialize_data (bool, optional): Whether to hold memory using serialized objects, when enabled, data loader workers can use shared RAM from master process instead of making a copy. Defaults to True. pipeline (list, optional): Processing pipeline. Defaults to []. test_mode (bool, optional): ``test_mode=True`` means in test phase. Defaults to False. lazy_init (bool, optional): Whether to load annotation during instantiation. In some cases, such as visualization, only the meta information of the dataset is needed, which is not necessary to load annotation file. ``Basedataset`` can skip load annotations to save time by set ``lazy_init=False``. Defaults to False. max_refetch (int, optional): If ``Basedataset.prepare_data`` get a None img. The maximum extra number of cycles to get a valid image. Defaults to 1000. classes (str | Sequence[str], optional): Specify names of classes. - If is string, it should be a file path, and the every line of the file is a name of a class. - If is a sequence of string, every item is a name of class. - If is None, use categories information in ``metainfo`` argument, annotation file or the class attribute ``METAINFO``. Defaults to None. """ def get_cat_ids(self, idx: int) -> List[int]: """Get category ids by index. Args: idx (int): Index of data. Returns: cat_ids (List[int]): Image categories of specified index. """ return self.get_data_info(idx)['gt_label']
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