[FIX] delete incorrect substitution

abl/data/structures/base_data_element.py

@@ -7,7 +7,7 @@ import torch
 
 
 # Modified from
-# https://github.com/open-mmlab/mmengine/blob/main/mmengine/data.structures/base_data_element.py
+# https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py
 class BaseDataElement:
     """A base data interface that supports Tensor-like and dict-like
     operations.
@@ -74,7 +74,7 @@ class BaseDataElement:
 
     Examples:
         >>> import torch
-        >>> from mmengine.data.structures import BaseDataElement
+        >>> from mmengine.structures import BaseDataElement
         >>> gt_instances = BaseDataElement()
         >>> bboxes = torch.rand((5, 4))
         >>> scores = torch.rand((5,))

abl/data/structures/list_data.py

@@ -15,16 +15,16 @@ IndexType = Union[str, slice, int, list, LongTypeTensor, BoolTypeTensor, np.ndar
 
 
 # Modified from
-# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_data.structures/instance_data.py # noqa
+# 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/data.structures/instances.py
+    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 data.structures such as `torch.Tensor`, `numpy.ndarray`, `list`, `str` and `tuple`.
+    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`.
 
     Examples:
         >>> from abl.data.structures import ListData

examples/hwf/hwf.ipynb

@@ -55,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Both `train_data` and `test_data` have the same data.structures: tuples with three components: X (list where each element is a list of images), gt_pseudo_label (list where each element is a list of symbols, i.e., pseudo-labels) and Y (list where each element is the computed result). The length and data.structures of datasets are illustrated as follows.\n",
+    "Both `train_data` and `test_data` have the same structures: tuples with three components: X (list where each element is a list of images), gt_pseudo_label (list where each element is a list of symbols, i.e., pseudo-labels) and Y (list where each element is the computed result). The length and structures of datasets are illustrated as follows.\n",
     "\n",
     "Note: ``gt_pseudo_label`` is only used to evaluate the performance of the learning part but not to train the model."
    ]

examples/zoo/zoo.ipynb

@@ -56,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`train_data` and `test_data` share identical data.structures: tuples with three components: X (list where each element is a list of two images), gt_pseudo_label (list where each element is a list of two digits, i.e., pseudo-labels) and Y (list where each element is the sum of the two digits). The length and data.structures of datasets are illustrated as follows.\n",
+    "`train_data` and `test_data` share identical structures: tuples with three components: X (list where each element is a list of two images), gt_pseudo_label (list where each element is a list of two digits, i.e., pseudo-labels) and Y (list where each element is the sum of the two digits). The length and structures of datasets are illustrated as follows.\n",
     "\n",
     "Note: ``gt_pseudo_label`` is only used to evaluate the performance of the learning part but not to train the model."
    ]
@@ -356,7 +356,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.18"
   },
   "orig_nbformat": 4,
   "vscode": {