[DOC] remove BaseDataElement

abl/data/structures/list_data.py

@@ -18,13 +18,43 @@ IndexType = Union[str, slice, int, list, LongTypeTensor, BoolTypeTensor, np.ndar
 # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa
 class ListData(BaseDataElement):
     """
-    Data structure for example-level data.
-
-    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
-
-    ListData supports `index` and `slice` for data field. The type of value in data field can be either `None` or `list` of base data structures such as `torch.Tensor`, `numpy.ndarray`, `list`, `str` and `tuple`.
+    Abstract Data Interface used throughout the ABL-Package.
+
+    `ListData` is the underlying data structure used in the ABL-Package,
+    designed to manage diverse forms of data dynamically generated throughout the
+    Abductive Learning (ABL) framework. This includes handling raw data, predicted
+    pseudo-labels, abduced pseudo-labels, pseudo-label indices, etc.
+
+    As a fundamental data structure in ABL, `ListData` is essential for the smooth
+    transfer and manipulation of data across various components of the ABL framework,
+    such as prediction, abductive reasoning, and training phases. It provides a
+    unified data format across these stages, ensuring compatibility and flexibility
+    in handling diverse data forms in the ABL framework.
+
+    The attributes in ``ListData`` are divided into two parts,
+    the ``metainfo`` and the ``data`` respectively.
+
+        - ``metainfo``: Usually used to store basic information about data examples,
+          such as symbol number, image size, 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``: raw data, labels, predictions, and abduced results 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()``.
+
+    ListData supports `index` and `slice` for data field. The type of value in
+    data field can be either `None` or `list` of base data structures such as
+    `torch.Tensor`, `numpy.ndarray`, `list`, `str` and `tuple`.
+
+    This design is inspired by and extends the functionalities of the `BaseDataElement`
+    class implemented in MMEngine.
+    https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py # noqa E501
 
     Examples:
         >>> from abl.data.structures import ListData

docs/Intro/Basics.rst

@@ -22,8 +22,7 @@ AI: data, models, and knowledge.
 .. image:: ../img/ABL-Package.png
 
 **Data** part manages the storage, operation, and evaluation of data.
-It first features class ``ListData`` (derived from base class
-``BaseDataElement``), which defines the data structures used in
+It first features class ``ListData``, which defines the data structures used in
 Abductive Learning, and comprises common data operations like insertion,
 deletion, retrieval, slicing, etc. Additionally, a series of Evaluation
 Metrics, including class ``SymbolAccuracy`` and ``ReasoningMetric`` (both

docs/Intro/Datasets.rst

@@ -53,11 +53,11 @@ As an illustration, in the MNIST Addition example, the data used for training ar
 Data Structure
 --------------
 
-Besides the user-provided dataset, various forms of data are utilized and dynamicly generate throughout the training and testing process of Abductive Learning framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, and so on. To manage this diversity and ensure a stable, versatile interface, ABL-Package employs `abstract data interfaces <../API/abl.data.html>`_ to encapsulate different forms of data that will be used in the total learning process.
+Besides the user-provided dataset, various forms of data are utilized and dynamicly generated throughout the training and testing process of Abductive Learning framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, and so on. To manage this diversity and ensure a stable, versatile interface, ABL-Package employs `abstract data interfaces <../API/abl.data.html#data-structure>`_ to encapsulate different forms of data that will be used in the total learning process.
 
-``BaseDataElement`` is the base class for all abstract data interfaces. Inherited from ``BaseDataElement``, ``ListData`` is the most commonly used abstract data interface in ABL-Package. As the fundamental data structure, ``ListData`` implements commonly used data manipulation methods and is responsible for transferring data between various components of ABL, ensuring that stages such as prediction, training, and abductive reasoning can utilize ``ListData`` as a unified input format. 
+``ListData`` is the underlying abstract data interface utilized in ABL-Package. As the fundamental data structure, ``ListData`` implements commonly used data manipulation methods and is responsible for transferring data between various components of ABL, ensuring that stages such as prediction, abductive reasoning, and training can utilize ``ListData`` as a unified input format. 
 
-Before proceeding to other stages, user-provided datasets are firstly converted into ``ListData``. For flexibility, ABL-Package also allows user to directly supply data in ``ListData`` format, which similarly requires the inclusion of three attributes: ``X``, ``gt_pseudo_label``, and ``Y``. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/abl.data.html>`_.
+Before proceeding to other stages, user-provided datasets are firstly converted into ``ListData``. For flexibility, ABL-Package also allows user to directly supply data in ``ListData`` format, which similarly requires the inclusion of three attributes: ``X``, ``gt_pseudo_label``, and ``Y``. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/abl.data.html#data-structure>`_.
 
 .. code-block:: python