Class BaseDataElement

mmengine/structures/base_data_element.py:9–639

A base data interface that supports Tensor-like and dict-like operations. A typical data elements refer to predicted results or ground truth labels on a task, such as predicted bboxes, instance masks, semantic segmentation masks, etc. Because groundtruth labels and predicted results

class BaseDataElement:
    """A base data interface that supports Tensor-like and dict-like
    operations.

    A typical data elements refer to predicted results or ground truth labels
    on a task, such as predicted bboxes, instance masks, semantic
    segmentation masks, etc. Because groundtruth labels and predicted results
    often have similar properties (for example, the predicted bboxes and the
    groundtruth bboxes), MMEngine uses the same abstract data interface to
    encapsulate predicted results and groundtruth labels, and it is recommended
    to use different name conventions to distinguish them, such as using
    ``gt_instances`` and ``pred_instances`` to distinguish between labels and
    predicted results. Additionally, we distinguish data elements at instance
    level, pixel level, and label level. Each of these types has its own
    characteristics. Therefore, MMEngine defines the base class
    ``BaseDataElement``, and implement ``InstanceData``, ``PixelData``, and
    ``LabelData`` inheriting from ``BaseDataElement`` to represent different
    types of ground truth labels or predictions.

    Another common data element is sample data. A sample data consists of input
    data (such as an image) and its annotations and predictions. In general,
    an image can have multiple types of annotations and/or predictions at the
    same time (for example, both pixel-level semantic segmentation annotations
    and instance-level detection bboxes annotations). All labels and
    predictions of a training sample are often passed between Dataset, Model,
    Visualizer, and Evaluator components. In order to simplify the interface
    between components, we can treat them as a large data element and
    encapsulate them. Such data elements are generally called XXDataSample in
    the OpenMMLab. Therefore, Similar to `nn.Module`, the `BaseDataElement`
    allows `BaseDataElement` as its attribute. Such a class generally
    encapsulates all the data of a sample in the algorithm library, and its
    attributes generally are various types of data elements. For example,
    MMDetection is assigned by the BaseDataElement to encapsulate all the data
    elements of the sample labeling and prediction of a sample in the
    algorithm library.

    The attributes in ``BaseDataElement`` are divided into two parts,
    the ``metainfo`` and the ``data`` respectively.

        - ``metainfo``: Usually contains the
          information about the image such as filename,
          image_shape, pad_shape, etc. The attributes can be accessed or
          modified by dict-like or object-like operations, such as
          ``.`` (for data access and modification), ``in``, ``del``,
          ``pop(str)``, ``get(str)``, ``metainfo_keys()``,
          ``metainfo_values()``, ``metainfo_items()``, ``set_metainfo()`` (for
          set or change key-value pairs in metainfo).

        - ``data``: Annotations or model predictions are
          stored. The attributes can be accessed or modified by
          dict-like or object-like operations, such as
          ``.``, ``in``, ``del``, ``pop(str)``, ``get(str)``, ``keys()``,
          ``values()``, ``items()``. Users can also apply tensor-like
          methods to all :obj:`torch.Tensor` in the ``data_fields``,
          such as ``.cuda()``, ``.cpu()``, ``.numpy()``, ``.to()``,
          ``to_tensor()``, ``.detach()``.

    Args: