Class InstanceData

mmengine/structures/instance_data.py:34–311

Data structure for instance-level annotations or predictions. Subclass of :class:`BaseDataElement`. All value in `data_fields` should have the same length. This design refer to https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/instances.py # noqa E501

# Modified from
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa
class InstanceData(BaseDataElement):
    """Data structure for instance-level annotations or predictions.

    Subclass of :class:`BaseDataElement`. All value in `data_fields`
    should have the same length. This design refer to
    https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/instances.py # noqa E501
    InstanceData also support extra functions: ``index``, ``slice`` and ``cat`` for data field. The type of value
    in data field can be base data structure such as `torch.Tensor`, `numpy.ndarray`, `list`, `str`, `tuple`,
    and can be customized data structure that has ``__len__``, ``__getitem__`` and ``cat`` attributes.

    Examples:
        >>> # custom data structure
        >>> class TmpObject:
        ...     def __init__(self, tmp) -> None:
        ...         assert isinstance(tmp, list)
        ...         self.tmp = tmp
        ...     def __len__(self):
        ...         return len(self.tmp)
        ...     def __getitem__(self, item):
        ...         if isinstance(item, int):
        ...             if item >= len(self) or item < -len(self):  # type:ignore
        ...                 raise IndexError(f'Index {item} out of range!')
        ...             else:
        ...                 # keep the dimension
        ...                 item = slice(item, None, len(self))
        ...         return TmpObject(self.tmp[item])
        ...     @staticmethod
        ...     def cat(tmp_objs):
        ...         assert all(isinstance(results, TmpObject) for results in tmp_objs)
        ...         if len(tmp_objs) == 1:
        ...             return tmp_objs[0]
        ...         tmp_list = [tmp_obj.tmp for tmp_obj in tmp_objs]
        ...         tmp_list = list(itertools.chain(*tmp_list))
        ...         new_data = TmpObject(tmp_list)
        ...         return new_data
        ...     def __repr__(self):
        ...         return str(self.tmp)
        >>> from mmengine.structures import InstanceData
        >>> import numpy as np
        >>> import torch
        >>> img_meta = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3))
        >>> instance_data = InstanceData(metainfo=img_meta)
        >>> 'img_shape' in instance_data
        True
        >>> instance_data.det_labels = torch.LongTensor([2, 3])
        >>> instance_data["det_scores"] = torch.Tensor([0.8, 0.7])
        >>> instance_data.bboxes = torch.rand((2, 4))
        >>> instance_data.polygons = TmpObject([[1, 2, 3, 4], [5, 6, 7, 8]])
        >>> len(instance_data)
        2
        >>> print(instance_data)
        <InstanceData(
            META INFORMATION
            img_shape: (800, 1196, 3)
            pad_shape: (800, 1216, 3)
            DATA FIELDS
            det_labels: tensor([2, 3])
            det_scores: tensor([0.8000, 0.7000])