[Fix] Fix several errors in docstring (#761)

* Fix docstring typos and change formats for api-doc to work

* Test on fixing noqa

* Fix noqa

* Fix noqa

mmdet3d/core/bbox/iou_calculators/iou3d_calculator.py

@@ -176,17 +176,17 @@ class AxisAlignedBboxOverlaps3D(object):
 
         Args:
             bboxes1 (Tensor): shape (B, m, 6) in <x1, y1, z1, x2, y2, z2>
-            format or empty.
+                format or empty.
             bboxes2 (Tensor): shape (B, n, 6) in <x1, y1, z1, x2, y2, z2>
-            format or empty.
+                format or empty.
                 B indicates the batch dim, in shape (B1, B2, ..., Bn).
-                If ``is_aligned `` is ``True``, then m and n must be equal.
+                If ``is_aligned`` is ``True``, then m and n must be equal.
             mode (str): "iou" (intersection over union) or "giou" (generalized
                 intersection over union).
             is_aligned (bool, optional): If True, then m and n must be equal.
                 Default False.
         Returns:
-            Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
+            Tensor: shape (m, n) if ``is_aligned`` is False else shape (m,)
         """
         assert bboxes1.size(-1) == bboxes2.size(-1) == 6
         return axis_aligned_bbox_overlaps_3d(bboxes1, bboxes2, mode,
@@ -204,17 +204,17 @@ def axis_aligned_bbox_overlaps_3d(bboxes1,
                                   is_aligned=False,
                                   eps=1e-6):
     """Calculate overlap between two set of axis aligned 3D bboxes. If
-    ``is_aligned `` is ``False``, then calculate the overlaps between each bbox
+    ``is_aligned`` is ``False``, then calculate the overlaps between each bbox
     of bboxes1 and bboxes2, otherwise the overlaps between each aligned pair of
     bboxes1 and bboxes2.
 
     Args:
         bboxes1 (Tensor): shape (B, m, 6) in <x1, y1, z1, x2, y2, z2>
-        format or empty.
+            format or empty.
         bboxes2 (Tensor): shape (B, n, 6) in <x1, y1, z1, x2, y2, z2>
-        format or empty.
+            format or empty.
             B indicates the batch dim, in shape (B1, B2, ..., Bn).
-            If ``is_aligned `` is ``True``, then m and n must be equal.
+            If ``is_aligned`` is ``True``, then m and n must be equal.
         mode (str): "iou" (intersection over union) or "giou" (generalized
             intersection over union).
         is_aligned (bool, optional): If True, then m and n must be equal.
@@ -223,7 +223,7 @@ def axis_aligned_bbox_overlaps_3d(bboxes1,
             stability. Default 1e-6.
 
     Returns:
-        Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
+        Tensor: shape (m, n) if ``is_aligned`` is False else shape (m,)
 
     Example:
         >>> bboxes1 = torch.FloatTensor([

mmdet3d/core/bbox/structures/base_box3d.py

@@ -199,7 +199,7 @@ class BaseInstance3DBoxes(object):
         """Convert self to ``dst`` mode.
 
         Args:
-            dst (:obj:`BoxMode`): The target Box mode.
+            dst (:obj:`Box3DMode`): The target Box mode.
             rt_mat (np.ndarray | torch.Tensor): The rotation and translation
                 matrix between different coordinates. Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
@@ -266,7 +266,7 @@ class BaseInstance3DBoxes(object):
             subject to Pytorch's indexing semantics.
 
         Returns:
-            :obj:`BaseInstances3DBoxes`: A new object of  \
+            :obj:`BaseInstance3DBoxes`: A new object of  \
                 :class:`BaseInstances3DBoxes` after indexing.
         """
         original_type = type(self)
@@ -293,10 +293,10 @@ class BaseInstance3DBoxes(object):
         """Concatenate a list of Boxes into a single Boxes.
 
         Args:
-            boxes_list (list[:obj:`BaseInstances3DBoxes`]): List of boxes.
+            boxes_list (list[:obj:`BaseInstance3DBoxes`]): List of boxes.
 
         Returns:
-            :obj:`BaseInstances3DBoxes`: The concatenated Boxes.
+            :obj:`BaseInstance3DBoxes`: The concatenated Boxes.
         """
         assert isinstance(boxes_list, (list, tuple))
         if len(boxes_list) == 0:
@@ -360,8 +360,8 @@ class BaseInstance3DBoxes(object):
             boxes2,  boxes1 and boxes2 should be in the same type.
 
         Args:
-            boxes1 (:obj:`BaseInstanceBoxes`): Boxes 1 contain N boxes.
-            boxes2 (:obj:`BaseInstanceBoxes`): Boxes 2 contain M boxes.
+            boxes1 (:obj:`BaseInstance3DBoxes`): Boxes 1 contain N boxes.
+            boxes2 (:obj:`BaseInstance3DBoxes`): Boxes 2 contain M boxes.
             mode (str, optional): Mode of iou calculation. Defaults to 'iou'.
 
         Returns:
@@ -392,8 +392,8 @@ class BaseInstance3DBoxes(object):
             ``boxes2``, ``boxes1`` and ``boxes2`` should be in the same type.
 
         Args:
-            boxes1 (:obj:`BaseInstanceBoxes`): Boxes 1 contain N boxes.
-            boxes2 (:obj:`BaseInstanceBoxes`): Boxes 2 contain M boxes.
+            boxes1 (:obj:`BaseInstance3DBoxes`): Boxes 1 contain N boxes.
+            boxes2 (:obj:`BaseInstance3DBoxes`): Boxes 2 contain M boxes.
             mode (str, optional): Mode of iou calculation. Defaults to 'iou'.
 
         Returns:

mmdet3d/core/bbox/structures/box_3d_mode.py

@@ -67,8 +67,8 @@ class Box3DMode(IntEnum):
             box (tuple | list | np.ndarray |
                 torch.Tensor | BaseInstance3DBoxes):
                 Can be a k-tuple, k-list or an Nxk array/tensor, where k = 7.
-            src (:obj:`BoxMode`): The src Box mode.
-            dst (:obj:`BoxMode`): The target Box mode.
+            src (:obj:`Box3DMode`): The src Box mode.
+            dst (:obj:`Box3DMode`): The target Box mode.
             rt_mat (np.ndarray | torch.Tensor): The rotation and translation
                 matrix between different coordinates. Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
@@ -87,7 +87,7 @@ class Box3DMode(IntEnum):
         single_box = isinstance(box, (list, tuple))
         if single_box:
             assert len(box) >= 7, (
-                'BoxMode.convert takes either a k-tuple/list or '
+                'Box3DMode.convert takes either a k-tuple/list or '
                 'an Nxk array/tensor, where k >= 7')
             arr = torch.tensor(box)[None, :]
         else:

mmdet3d/core/bbox/structures/cam_box3d.py

@@ -307,7 +307,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
         """Convert self to ``dst`` mode.
 
         Args:
-            dst (:obj:`BoxMode`): The target Box mode.
+            dst (:obj:`Box3DMode`): The target Box mode.
             rt_mat (np.ndarray | torch.Tensor): The rotation and translation
                 matrix between different coordinates. Defaults to None.
                 The conversion from ``src`` coordinates to ``dst`` coordinates

mmdet3d/core/bbox/structures/depth_box3d.py

@@ -233,7 +233,7 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
         """Convert self to ``dst`` mode.
 
         Args:
-            dst (:obj:`BoxMode`): The target Box mode.
+            dst (:obj:`Box3DMode`): The target Box mode.
             rt_mat (np.ndarray | torch.Tensor): The rotation and translation
                 matrix between different coordinates. Defaults to None.
                 The conversion from ``src`` coordinates to ``dst`` coordinates

mmdet3d/core/bbox/structures/lidar_box3d.py

@@ -224,7 +224,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
         """Convert self to ``dst`` mode.
 
         Args:
-            dst (:obj:`BoxMode`): the target Box mode
+            dst (:obj:`Box3DMode`): the target Box mode
             rt_mat (np.ndarray | torch.Tensor): The rotation and translation
                 matrix between different coordinates. Defaults to None.
                 The conversion from ``src`` coordinates to ``dst`` coordinates

mmdet3d/datasets/lyft_dataset.py

@@ -23,7 +23,7 @@ class LyftDataset(Custom3DDataset):
     This class serves as the API for experiments on the Lyft Dataset.
 
     Please refer to
-    `<https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/data>`_  # noqa
+    `<https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/data>`_
     for data downloading.
 
     Args:
@@ -49,7 +49,7 @@ class LyftDataset(Custom3DDataset):
             Defaults to True.
         test_mode (bool, optional): Whether the dataset is in test mode.
             Defaults to False.
-    """
+    """  # noqa: E501
     NameMapping = {
         'bicycle': 'bicycle',
         'bus': 'bus',

mmdet3d/models/dense_heads/free_anchor3d_head.py

@@ -14,7 +14,7 @@ class FreeAnchor3DHead(Anchor3DHead):
 
     Note:
         This implementation is directly modified from the `mmdet implementation
-        <https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/free_anchor_retina_head.py>`_  # noqa
+        <https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/free_anchor_retina_head.py>`_.
         We find it also works on 3D detection with minor modification, i.e.,
         different hyper-parameters and a additional direction classifier.
 
@@ -25,7 +25,7 @@ class FreeAnchor3DHead(Anchor3DHead):
         gamma (float): Gamma parameter in focal loss.
         alpha (float): Alpha parameter in focal loss.
         kwargs (dict): Other arguments are the same as those in :class:`Anchor3DHead`.
-    """
+    """  # noqa: E501
 
     def __init__(self,
                  pre_anchor_topk=50,

mmdet3d/models/detectors/fcos_mono3d.py

@@ -4,11 +4,11 @@ from .single_stage_mono3d import SingleStageMono3DDetector
 
 @DETECTORS.register_module()
 class FCOSMono3D(SingleStageMono3DDetector):
-    r"""FCOS3D <https://arxiv.org/abs/2104.10956>`_ for monocular 3D object detection.
+    r"""`FCOS3D <https://arxiv.org/abs/2104.10956>`_ for monocular 3D object detection.
 
     Currently please refer to our entry on the
-    `leaderboard <https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera>` # noqa
-    """
+    `leaderboard <https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera>`_.
+    """  # noqa: E501
 
     def __init__(self,
                  backbone,

mmdet3d/models/detectors/imvotenet.py

@@ -12,7 +12,7 @@ from .base import Base3DDetector
 def sample_valid_seeds(mask, num_sampled_seed=1024):
     r"""Randomly sample seeds from all imvotes.
 
-    Modified from `<https://github.com/facebookresearch/imvotenet/blob/a8856345146bacf29a57266a2f0b874406fd8823/models/imvotenet.py#L26>`_  # noqa
+    Modified from `<https://github.com/facebookresearch/imvotenet/blob/a8856345146bacf29a57266a2f0b874406fd8823/models/imvotenet.py#L26>`_
 
     Args:
         mask (torch.Tensor): Bool tensor in shape (
@@ -22,7 +22,7 @@ def sample_valid_seeds(mask, num_sampled_seed=1024):
 
     Returns:
         torch.Tensor: Indices with shape (num_sampled_seed).
-    """
+    """  # noqa: E501
     device = mask.device
     batch_size = mask.shape[0]
     sample_inds = mask.new_zeros((batch_size, num_sampled_seed),
@@ -600,10 +600,8 @@ class ImVoteNet(Base3DDetector):
                              img_metas,
                              proposals=None,
                              rescale=False):
-        """Test without augmentation, image network pretrain. May refer to
-        https://github.com/open-
-        mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py  #
-        noqa.
+        r"""Test without augmentation, image network pretrain. May refer to
+        `<https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py>`_.
 
         Args:
             img (torch.Tensor): Should have a shape NxCxHxW, which contains
@@ -616,7 +614,7 @@ class ImVoteNet(Base3DDetector):
 
         Returns:
             list[list[torch.Tensor]]: Predicted 2d boxes.
-        """
+        """  # noqa: E501
         assert self.with_img_bbox, 'Img bbox head must be implemented.'
         assert self.with_img_backbone, 'Img backbone must be implemented.'
         assert self.with_img_rpn, 'Img rpn must be implemented.'
@@ -701,10 +699,8 @@ class ImVoteNet(Base3DDetector):
         return bbox_results
 
     def aug_test_img_only(self, img, img_metas, rescale=False):
-        """Test function with augmentation, image network pretrain. May refer
-        to https://github.com/open-
-        mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py  #
-        noqa.
+        r"""Test function with augmentation, image network pretrain. May refer
+        to `<https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py>`_.
 
         Args:
             img (list[list[torch.Tensor]], optional): the outer
@@ -722,7 +718,7 @@ class ImVoteNet(Base3DDetector):
 
         Returns:
             list[list[torch.Tensor]]: Predicted 2d boxes.
-        """
+        """  # noqa: E501
         assert self.with_img_bbox, 'Img bbox head must be implemented.'
         assert self.with_img_backbone, 'Img backbone must be implemented.'
         assert self.with_img_rpn, 'Img rpn must be implemented.'