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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List
from mmcls.registry import DATASETS
from .base_dataset import BaseDataset
@DATASETS.register_module()
class MultiLabelDataset(BaseDataset):
"""Multi-label Dataset.
This dataset support annotation file in `OpenMMLab 2.0 style annotation
format`.
.. _OpenMMLab 2.0 style annotation format:
https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/basedataset.md
The annotation format is shown as follows.
.. code-block:: none
{
"metainfo":
{
"classes":['A', 'B', 'C'....]
},
"data_list":
[
{
"img_path": "test_img1.jpg",
'img_label': [0, 1],
},
{
"img_path": "test_img2.jpg",
'img_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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