[Refactor] Refactor code structure and docstrings (#803)

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning

mmdet3d/core/evaluation/kitti_utils/rotate_iou.py

@@ -291,7 +291,8 @@ def rotate_iou_kernel_eval(N,
                            dev_query_boxes,
                            dev_iou,
                            criterion=-1):
-    """Kernel of computing rotated iou.
+    """Kernel of computing rotated IoU. This function is for bev boxes in
+    camera coordinate system ONLY (the rotation is clockwise).
 
     Args:
         N (int): The number of boxes.
@@ -343,10 +344,14 @@ def rotate_iou_gpu_eval(boxes, query_boxes, criterion=-1, device_id=0):
     in one example with numba.cuda code). convert from [this project](
     https://github.com/hongzhenwang/RRPN-revise/tree/master/lib/rotation).
 
+    This function is for bev boxes in camera coordinate system ONLY
+    (the rotation is clockwise).
+
     Args:
         boxes (torch.Tensor): rbboxes. format: centers, dims,
             angles(clockwise when positive) with the shape of [N, 5].
-        query_boxes (float tensor: [K, 5]): rbboxes to compute iou with boxes.
+        query_boxes (torch.FloatTensor, shape=(K, 5)):
+            rbboxes to compute iou with boxes.
         device_id (int, optional): Defaults to 0. Device to use.
         criterion (int, optional): Indicate different type of iou.
             -1 indicate `area_inter / (area1 + area2 - area_inter)`,

mmdet3d/core/evaluation/lyft_eval.py

@@ -18,7 +18,7 @@ def load_lyft_gts(lyft, data_root, eval_split, logger=None):
         lyft (:obj:`LyftDataset`): Lyft class in the sdk.
         data_root (str): Root of data for reading splits.
         eval_split (str): Name of the split for evaluation.
-        logger (logging.Logger | str | None): Logger used for printing
+        logger (logging.Logger | str, optional): Logger used for printing
         related information during evaluation. Default: None.
 
     Returns:
@@ -96,7 +96,7 @@ def lyft_eval(lyft, data_root, res_path, eval_set, output_dir, logger=None):
         res_path (str): Path of result json file recording detections.
         eval_set (str): Name of the split for evaluation.
         output_dir (str): Output directory for output json files.
-        logger (logging.Logger | str | None): Logger used for printing
+        logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
 
     Returns:
@@ -202,9 +202,9 @@ def get_single_class_aps(gt, predictions, iou_thresholds):
 
     Args:
         gt (list[dict]): list of dictionaries in the format described above.
-        predictions (list[dict]): list of dictionaries in the format \
+        predictions (list[dict]): list of dictionaries in the format
             described below.
-        iou_thresholds (list[float]): IOU thresholds used to calculate \
+        iou_thresholds (list[float]): IOU thresholds used to calculate
             TP / FN
 
     Returns:

mmdet3d/core/evaluation/seg_eval.py

@@ -77,7 +77,7 @@ def seg_eval(gt_labels, seg_preds, label2cat, ignore_index, logger=None):
         seg_preds  (list[torch.Tensor]): Predictions.
         label2cat (dict): Map from label to category name.
         ignore_index (int): Index that will be ignored in evaluation.
-        logger (logging.Logger | str | None): The way to print the mAP
+        logger (logging.Logger | str, optional): The way to print the mAP
             summary. See `mmdet.utils.print_log()` for details. Default: None.
 
     Returns:

mmdet3d/core/evaluation/waymo_utils/prediction_kitti_to_waymo.py

@@ -114,7 +114,7 @@ class KITTI2Waymo(object):
                 instance_idx (int): Index of the instance to be converted.
 
             Returns:
-                :obj:`Object`: Predicted instance in waymo dataset \
+                :obj:`Object`: Predicted instance in waymo dataset
                     Object proto.
             """
             cls = kitti_result['name'][instance_idx]

mmdet3d/core/points/base_points.py

@@ -12,17 +12,17 @@ class BasePoints(object):
 
     Args:
         tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
-        points_dim (int): Number of the dimension of a point.
-            Each row is (x, y, z). Default to 3.
-        attribute_dims (dict): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+        points_dim (int, optional): Number of the dimension of a point.
+            Each row is (x, y, z). Defaults to 3.
+        attribute_dims (dict, optional): Dictionary to indicate the
+            meaning of extra dimension. Defaults to None.
 
     Attributes:
         tensor (torch.Tensor): Float matrix of N x points_dim.
         points_dim (int): Integer indicating the dimension of a point.
             Each row is (x, y, z, ...).
         attribute_dims (bool): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+            dimension. Defaults to None.
         rotation_axis (int): Default rotation axis for points rotation.
     """
 
@@ -47,7 +47,7 @@ class BasePoints(object):
 
     @property
     def coord(self):
-        """torch.Tensor: Coordinates of each point with size (N, 3)."""
+        """torch.Tensor: Coordinates of each point in shape (N, 3)."""
         return self.tensor[:, :3]
 
     @coord.setter
@@ -63,7 +63,8 @@ class BasePoints(object):
 
     @property
     def height(self):
-        """torch.Tensor: A vector with height of each point."""
+        """torch.Tensor:
+            A vector with height of each point in shape (N, 1), or None."""
         if self.attribute_dims is not None and \
                 'height' in self.attribute_dims.keys():
             return self.tensor[:, self.attribute_dims['height']]
@@ -93,7 +94,8 @@ class BasePoints(object):
 
     @property
     def color(self):
-        """torch.Tensor: A vector with color of each point."""
+        """torch.Tensor:
+            A vector with color of each point in shape (N, 3), or None."""
         if self.attribute_dims is not None and \
                 'color' in self.attribute_dims.keys():
             return self.tensor[:, self.attribute_dims['color']]
@@ -145,7 +147,7 @@ class BasePoints(object):
         Args:
             rotation (float | np.ndarray | torch.Tensor): Rotation matrix
                 or angle.
-            axis (int): Axis to rotate at. Defaults to None.
+            axis (int, optional): Axis to rotate at. Defaults to None.
         """
         if not isinstance(rotation, torch.Tensor):
             rotation = self.tensor.new_tensor(rotation)
@@ -169,7 +171,11 @@ class BasePoints(object):
 
     @abstractmethod
     def flip(self, bev_direction='horizontal'):
-        """Flip the points in BEV along given BEV direction."""
+        """Flip the points along given BEV direction.
+
+        Args:
+            bev_direction (str): Flip direction (horizontal or vertical).
+        """
         pass
 
     def translate(self, trans_vector):
@@ -206,7 +212,7 @@ class BasePoints(object):
             polygon, we try to reduce the burden for simpler cases.
 
         Returns:
-            torch.Tensor: A binary vector indicating whether each point is \
+            torch.Tensor: A binary vector indicating whether each point is
                 inside the reference range.
         """
         in_range_flags = ((self.tensor[:, 0] > point_range[0])
@@ -217,7 +223,11 @@ class BasePoints(object):
                           & (self.tensor[:, 2] < point_range[5]))
         return in_range_flags
 
-    @abstractmethod
+    @property
+    def bev(self):
+        """torch.Tensor: BEV of the points in shape (N, 2)."""
+        return self.tensor[:, [0, 1]]
+
     def in_range_bev(self, point_range):
         """Check whether the points are in the given range.
 
@@ -226,10 +236,14 @@ class BasePoints(object):
                 in order of (x_min, y_min, x_max, y_max).
 
         Returns:
-            torch.Tensor: Indicating whether each point is inside \
+            torch.Tensor: Indicating whether each point is inside
                 the reference range.
         """
-        pass
+        in_range_flags = ((self.bev[:, 0] > point_range[0])
+                          & (self.bev[:, 1] > point_range[1])
+                          & (self.bev[:, 1] < point_range[2])
+                          & (self.bev[:, 1] < point_range[3]))
+        return in_range_flags
 
     @abstractmethod
     def convert_to(self, dst, rt_mat=None):
@@ -237,14 +251,15 @@ class BasePoints(object):
 
         Args:
             dst (:obj:`CoordMode`): The target Box mode.
-            rt_mat (np.ndarray | torch.Tensor): The rotation and translation
-                matrix between different coordinates. Defaults to None.
+            rt_mat (np.ndarray | torch.Tensor, optional): The rotation and
+                translation matrix between different coordinates.
+                Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
                 usually comes along the change of sensors, e.g., from camera
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BasePoints`: The converted box of the same type \
+            :obj:`BasePoints`: The converted box of the same type
                 in the `dst` mode.
         """
         pass
@@ -276,7 +291,7 @@ class BasePoints(object):
             subject to Pytorch's indexing semantics.
 
         Returns:
-            :obj:`BasePoints`: A new object of  \
+            :obj:`BasePoints`: A new object of
                 :class:`BasePoints` after indexing.
         """
         original_type = type(self)
@@ -367,7 +382,7 @@ class BasePoints(object):
             device (str | :obj:`torch.device`): The name of the device.
 
         Returns:
-            :obj:`BasePoints`: A new boxes object on the \
+            :obj:`BasePoints`: A new boxes object on the
                 specific device.
         """
         original_type = type(self)
@@ -380,7 +395,7 @@ class BasePoints(object):
         """Clone the Points.
 
         Returns:
-            :obj:`BasePoints`: Box object with the same properties \
+            :obj:`BasePoints`: Box object with the same properties
                 as self.
         """
         original_type = type(self)
@@ -405,14 +420,14 @@ class BasePoints(object):
     def new_point(self, data):
         """Create a new point object with data.
 
-        The new point and its tensor has the similar properties \
+        The new point and its tensor has the similar properties
             as self and self.tensor, respectively.
 
         Args:
             data (torch.Tensor | numpy.array | list): Data to be copied.
 
         Returns:
-            :obj:`BasePoints`: A new point object with ``data``, \
+            :obj:`BasePoints`: A new point object with ``data``,
                 the object's other properties are similar to ``self``.
         """
         new_tensor = self.tensor.new_tensor(data) \

mmdet3d/core/points/cam_points.py

@@ -7,17 +7,17 @@ class CameraPoints(BasePoints):
 
     Args:
         tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
-        points_dim (int): Number of the dimension of a point.
-            Each row is (x, y, z). Default to 3.
-        attribute_dims (dict): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+        points_dim (int, optional): Number of the dimension of a point.
+            Each row is (x, y, z). Defaults to 3.
+        attribute_dims (dict, optional): Dictionary to indicate the
+            meaning of extra dimension. Defaults to None.
 
     Attributes:
         tensor (torch.Tensor): Float matrix of N x points_dim.
         points_dim (int): Integer indicating the dimension of a point.
             Each row is (x, y, z, ...).
         attribute_dims (bool): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+            dimension. Defaults to None.
         rotation_axis (int): Default rotation axis for points rotation.
     """
 
@@ -27,42 +27,35 @@ class CameraPoints(BasePoints):
         self.rotation_axis = 1
 
     def flip(self, bev_direction='horizontal'):
-        """Flip the boxes in BEV along given BEV direction."""
+        """Flip the points along given BEV direction.
+
+        Args:
+            bev_direction (str): Flip direction (horizontal or vertical).
+        """
         if bev_direction == 'horizontal':
             self.tensor[:, 0] = -self.tensor[:, 0]
         elif bev_direction == 'vertical':
             self.tensor[:, 2] = -self.tensor[:, 2]
 
-    def in_range_bev(self, point_range):
-        """Check whether the points are in the given range.
-
-        Args:
-            point_range (list | torch.Tensor): The range of point
-                in order of (x_min, y_min, x_max, y_max).
-
-        Returns:
-            torch.Tensor: Indicating whether each point is inside \
-                the reference range.
-        """
-        in_range_flags = ((self.tensor[:, 0] > point_range[0])
-                          & (self.tensor[:, 2] > point_range[1])
-                          & (self.tensor[:, 0] < point_range[2])
-                          & (self.tensor[:, 2] < point_range[3]))
-        return in_range_flags
+    @property
+    def bev(self):
+        """torch.Tensor: BEV of the points in shape (N, 2)."""
+        return self.tensor[:, [0, 2]]
 
     def convert_to(self, dst, rt_mat=None):
         """Convert self to ``dst`` mode.
 
         Args:
             dst (:obj:`CoordMode`): The target Point mode.
-            rt_mat (np.ndarray | torch.Tensor): The rotation and translation
-                matrix between different coordinates. Defaults to None.
+            rt_mat (np.ndarray | torch.Tensor, optional): The rotation and
+                translation matrix between different coordinates.
+                Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
                 usually comes along the change of sensors, e.g., from camera
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BasePoints`: The converted point of the same type \
+            :obj:`BasePoints`: The converted point of the same type
                 in the `dst` mode.
         """
         from mmdet3d.core.bbox import Coord3DMode

mmdet3d/core/points/depth_points.py

@@ -7,17 +7,17 @@ class DepthPoints(BasePoints):
 
     Args:
         tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
-        points_dim (int): Number of the dimension of a point.
-            Each row is (x, y, z). Default to 3.
-        attribute_dims (dict): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+        points_dim (int, optional): Number of the dimension of a point.
+            Each row is (x, y, z). Defaults to 3.
+        attribute_dims (dict, optional): Dictionary to indicate the
+            meaning of extra dimension. Defaults to None.
 
     Attributes:
         tensor (torch.Tensor): Float matrix of N x points_dim.
         points_dim (int): Integer indicating the dimension of a point.
             Each row is (x, y, z, ...).
         attribute_dims (bool): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+            dimension. Defaults to None.
         rotation_axis (int): Default rotation axis for points rotation.
     """
 
@@ -27,42 +27,30 @@ class DepthPoints(BasePoints):
         self.rotation_axis = 2
 
     def flip(self, bev_direction='horizontal'):
-        """Flip the boxes in BEV along given BEV direction."""
+        """Flip the points along given BEV direction.
+
+        Args:
+            bev_direction (str): Flip direction (horizontal or vertical).
+        """
         if bev_direction == 'horizontal':
             self.tensor[:, 0] = -self.tensor[:, 0]
         elif bev_direction == 'vertical':
             self.tensor[:, 1] = -self.tensor[:, 1]
 
-    def in_range_bev(self, point_range):
-        """Check whether the points are in the given range.
-
-        Args:
-            point_range (list | torch.Tensor): The range of point
-                in order of (x_min, y_min, x_max, y_max).
-
-        Returns:
-            torch.Tensor: Indicating whether each point is inside \
-                the reference range.
-        """
-        in_range_flags = ((self.tensor[:, 0] > point_range[0])
-                          & (self.tensor[:, 1] > point_range[1])
-                          & (self.tensor[:, 0] < point_range[2])
-                          & (self.tensor[:, 1] < point_range[3]))
-        return in_range_flags
-
     def convert_to(self, dst, rt_mat=None):
         """Convert self to ``dst`` mode.
 
         Args:
             dst (:obj:`CoordMode`): The target Point mode.
-            rt_mat (np.ndarray | torch.Tensor): The rotation and translation
-                matrix between different coordinates. Defaults to None.
+            rt_mat (np.ndarray | torch.Tensor, optional): The rotation and
+                translation matrix between different coordinates.
+                Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
                 usually comes along the change of sensors, e.g., from camera
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BasePoints`: The converted point of the same type \
+            :obj:`BasePoints`: The converted point of the same type
                 in the `dst` mode.
         """
         from mmdet3d.core.bbox import Coord3DMode

mmdet3d/core/points/lidar_points.py

@@ -7,17 +7,17 @@ class LiDARPoints(BasePoints):
 
     Args:
         tensor (torch.Tensor | np.ndarray | list): a N x points_dim matrix.
-        points_dim (int): Number of the dimension of a point.
-            Each row is (x, y, z). Default to 3.
-        attribute_dims (dict): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+        points_dim (int, optional): Number of the dimension of a point.
+            Each row is (x, y, z). Defaults to 3.
+        attribute_dims (dict, optional): Dictionary to indicate the
+            meaning of extra dimension. Defaults to None.
 
     Attributes:
         tensor (torch.Tensor): Float matrix of N x points_dim.
         points_dim (int): Integer indicating the dimension of a point.
             Each row is (x, y, z, ...).
         attribute_dims (bool): Dictionary to indicate the meaning of extra
-            dimension. Default to None.
+            dimension. Defaults to None.
         rotation_axis (int): Default rotation axis for points rotation.
     """
 
@@ -27,42 +27,30 @@ class LiDARPoints(BasePoints):
         self.rotation_axis = 2
 
     def flip(self, bev_direction='horizontal'):
-        """Flip the boxes in BEV along given BEV direction."""
+        """Flip the points along given BEV direction.
+
+        Args:
+            bev_direction (str): Flip direction (horizontal or vertical).
+        """
         if bev_direction == 'horizontal':
             self.tensor[:, 1] = -self.tensor[:, 1]
         elif bev_direction == 'vertical':
             self.tensor[:, 0] = -self.tensor[:, 0]
 
-    def in_range_bev(self, point_range):
-        """Check whether the points are in the given range.
-
-        Args:
-            point_range (list | torch.Tensor): The range of point
-                in order of (x_min, y_min, x_max, y_max).
-
-        Returns:
-            torch.Tensor: Indicating whether each point is inside \
-                the reference range.
-        """
-        in_range_flags = ((self.tensor[:, 0] > point_range[0])
-                          & (self.tensor[:, 1] > point_range[1])
-                          & (self.tensor[:, 0] < point_range[2])
-                          & (self.tensor[:, 1] < point_range[3]))
-        return in_range_flags
-
     def convert_to(self, dst, rt_mat=None):
         """Convert self to ``dst`` mode.
 
         Args:
             dst (:obj:`CoordMode`): The target Point mode.
-            rt_mat (np.ndarray | torch.Tensor): The rotation and translation
-                matrix between different coordinates. Defaults to None.
+            rt_mat (np.ndarray | torch.Tensor, optional): The rotation and
+                translation matrix between different coordinates.
+                Defaults to None.
                 The conversion from `src` coordinates to `dst` coordinates
                 usually comes along the change of sensors, e.g., from camera
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BasePoints`: The converted point of the same type \
+            :obj:`BasePoints`: The converted point of the same type
                 in the `dst` mode.
         """
         from mmdet3d.core.bbox import Coord3DMode

mmdet3d/core/post_processing/box3d_nms.py

@@ -15,13 +15,15 @@ def box3d_multiclass_nms(mlvl_bboxes,
                          mlvl_dir_scores=None,
                          mlvl_attr_scores=None,
                          mlvl_bboxes2d=None):
-    """Multi-class nms for 3D boxes.
+    """Multi-class NMS for 3D boxes. The IoU used for NMS is defined as the 2D
+    IoU between BEV boxes.
 
     Args:
         mlvl_bboxes (torch.Tensor): Multi-level boxes with shape (N, M).
             M is the dimensions of boxes.
         mlvl_bboxes_for_nms (torch.Tensor): Multi-level boxes with shape
             (N, 5) ([x1, y1, x2, y2, ry]). N is the number of boxes.
+            The coordinate system of the BEV boxes is counterclockwise.
         mlvl_scores (torch.Tensor): Multi-level boxes with shape
             (N, C + 1). N is the number of boxes. C is the number of classes.
         score_thr (float): Score thredhold to filter boxes with low
@@ -36,8 +38,8 @@ def box3d_multiclass_nms(mlvl_bboxes,
             boxes. Defaults to None.
 
     Returns:
-        tuple[torch.Tensor]: Return results after nms, including 3D \
-            bounding boxes, scores, labels, direction scores, attribute \
+        tuple[torch.Tensor]: Return results after nms, including 3D
+            bounding boxes, scores, labels, direction scores, attribute
             scores (optional) and 2D bounding boxes (optional).
     """
     # do multi class nms
@@ -128,13 +130,13 @@ def box3d_multiclass_nms(mlvl_bboxes,
 
 
 def aligned_3d_nms(boxes, scores, classes, thresh):
-    """3d nms for aligned boxes.
+    """3D NMS for aligned boxes.
 
     Args:
         boxes (torch.Tensor): Aligned box with shape [n, 6].
         scores (torch.Tensor): Scores of each box.
         classes (torch.Tensor): Class of each box.
-        thresh (float): Iou threshold for nms.
+        thresh (float): IoU threshold for nms.
 
     Returns:
         torch.Tensor: Indices of selected boxes.
@@ -188,8 +190,8 @@ def circle_nms(dets, thresh, post_max_size=83):
     Args:
         dets (torch.Tensor): Detection results with the shape of [N, 3].
         thresh (float): Value of threshold.
-        post_max_size (int): Max number of prediction to be kept. Defaults
-            to 83
+        post_max_size (int, optional): Max number of prediction to be kept.
+            Defaults to 83.
 
     Returns:
         torch.Tensor: Indexes of the detections to be kept.

mmdet3d/core/utils/array_converter.py

@@ -11,19 +11,20 @@ def array_converter(to_torch=True,
     """Wrapper function for data-type agnostic processing.
 
     First converts input arrays to PyTorch tensors or NumPy ndarrays
-    for middle calculation, then convert output to original data-type.
+    for middle calculation, then convert output to original data-type if
+    `recover=True`.
 
     Args:
-        to_torch (Bool): Whether convert to PyTorch tensors
+        to_torch (Bool, optional): Whether convert to PyTorch tensors
             for middle calculation. Defaults to True.
-        apply_to (tuple[str]): The arguments to which we apply data-type
-            conversion. Defaults to an empty tuple.
-        template_arg_name_ (str): Argument serving as the template (
+        apply_to (tuple[str], optional): The arguments to which we apply
+            data-type conversion. Defaults to an empty tuple.
+        template_arg_name_ (str, optional): Argument serving as the template (
             return arrays should have the same dtype and device
             as the template). Defaults to None. If None, we will use the
             first argument in `apply_to` as the template argument.
-        recover (Bool): Whether or not recover the wrapped function outputs
-            to the `template_arg_name_` type. Defaults to True.
+        recover (Bool, optional): Whether or not recover the wrapped function
+            outputs to the `template_arg_name_` type. Defaults to True.
 
     Raises:
         ValueError: When template_arg_name_ is not among all args, or
@@ -254,9 +255,10 @@ class ArrayConverter:
             input_array (tuple | list | np.ndarray |
                 torch.Tensor | int | float ):
                 Input array. Defaults to None.
-            target_type (<class 'np.ndarray'> | <class 'torch.Tensor'>):
+            target_type (<class 'np.ndarray'> | <class 'torch.Tensor'>,
+                optional):
                 Type to which input array is converted. Defaults to None.
-            target_array (np.ndarray | torch.Tensor):
+            target_array (np.ndarray | torch.Tensor, optional):
                 Template array to which input array is converted.
                 Defaults to None.
 

mmdet3d/core/utils/gaussian.py

@@ -8,7 +8,7 @@ def gaussian_2d(shape, sigma=1):
 
     Args:
         shape (list[int]): Shape of the map.
-        sigma (float): Sigma to generate gaussian map.
+        sigma (float, optional): Sigma to generate gaussian map.
             Defaults to 1.
 
     Returns:
@@ -29,7 +29,7 @@ def draw_heatmap_gaussian(heatmap, center, radius, k=1):
         heatmap (torch.Tensor): Heatmap to be masked.
         center (torch.Tensor): Center coord of the heatmap.
         radius (int): Radius of gausian.
-        K (int): Multiple of masked_gaussian. Defaults to 1.
+        K (int, optional): Multiple of masked_gaussian. Defaults to 1.
 
     Returns:
         torch.Tensor: Masked heatmap.
@@ -59,7 +59,7 @@ def gaussian_radius(det_size, min_overlap=0.5):
 
     Args:
         det_size (tuple[torch.Tensor]): Size of the detection result.
-        min_overlap (float): Gaussian_overlap. Defaults to 0.5.
+        min_overlap (float, optional): Gaussian_overlap. Defaults to 0.5.
 
     Returns:
         torch.Tensor: Computed radius.

mmdet3d/core/visualizer/image_vis.py

@@ -18,7 +18,7 @@ def project_pts_on_img(points,
         raw_img (numpy.array): The numpy array of image.
         lidar2img_rt (numpy.array, shape=[4, 4]): The projection matrix
             according to the camera intrinsic parameters.
-        max_distance (float): the max distance of the points cloud.
+        max_distance (float, optional): the max distance of the points cloud.
             Default: 70.
         thickness (int, optional): The thickness of 2D points. Default: -1.
     """
@@ -69,7 +69,8 @@ def plot_rect3d_on_img(img,
         num_rects (int): Number of 3D rectangulars.
         rect_corners (numpy.array): Coordinates of the corners of 3D
             rectangulars. Should be in the shape of [num_rect, 8, 2].
-        color (tuple[int]): The color to draw bboxes. Default: (0, 255, 0).
+        color (tuple[int], optional): The color to draw bboxes.
+            Default: (0, 255, 0).
         thickness (int, optional): The thickness of bboxes. Default: 1.
     """
     line_indices = ((0, 1), (0, 3), (0, 4), (1, 2), (1, 5), (3, 2), (3, 7),
@@ -99,7 +100,8 @@ def draw_lidar_bbox3d_on_img(bboxes3d,
         lidar2img_rt (numpy.array, shape=[4, 4]): The projection matrix
             according to the camera intrinsic parameters.
         img_metas (dict): Useless here.
-        color (tuple[int]): The color to draw bboxes. Default: (0, 255, 0).
+        color (tuple[int], optional): The color to draw bboxes.
+            Default: (0, 255, 0).
         thickness (int, optional): The thickness of bboxes. Default: 1.
     """
     img = raw_img.copy()
@@ -136,7 +138,8 @@ def draw_depth_bbox3d_on_img(bboxes3d,
         raw_img (numpy.array): The numpy array of image.
         calibs (dict): Camera calibration information, Rt and K.
         img_metas (dict): Used in coordinates transformation.
-        color (tuple[int]): The color to draw bboxes. Default: (0, 255, 0).
+        color (tuple[int], optional): The color to draw bboxes.
+            Default: (0, 255, 0).
         thickness (int, optional): The thickness of bboxes. Default: 1.
     """
     from mmdet3d.core.bbox import points_cam2img
@@ -176,7 +179,8 @@ def draw_camera_bbox3d_on_img(bboxes3d,
         cam2img (dict): Camera intrinsic matrix,
             denoted as `K` in depth bbox coordinate system.
         img_metas (dict): Useless here.
-        color (tuple[int]): The color to draw bboxes. Default: (0, 255, 0).
+        color (tuple[int], optional): The color to draw bboxes.
+            Default: (0, 255, 0).
         thickness (int, optional): The thickness of bboxes. Default: 1.
     """
     from mmdet3d.core.bbox import points_cam2img

mmdet3d/core/visualizer/open3d_vis.py

@@ -22,12 +22,12 @@ def _draw_points(points,
         points (numpy.array | torch.tensor, shape=[N, 3+C]):
             points to visualize.
         vis (:obj:`open3d.visualization.Visualizer`): open3d visualizer.
-        points_size (int): the size of points to show on visualizer.
+        points_size (int, optional): the size of points to show on visualizer.
             Default: 2.
-        point_color (tuple[float]): the color of points.
+        point_color (tuple[float], optional): the color of points.
             Default: (0.5, 0.5, 0.5).
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points,
+            available mode ['xyz', 'xyzrgb']. Default: 'xyz'.
 
     Returns:
         tuple: points, color of each point.
@@ -69,19 +69,21 @@ def _draw_bboxes(bbox3d,
 
     Args:
         bbox3d (numpy.array | torch.tensor, shape=[M, 7]):
-            3d bbox (x, y, z, dx, dy, dz, yaw) to visualize.
+            3d bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
         vis (:obj:`open3d.visualization.Visualizer`): open3d visualizer.
         points_colors (numpy.array): color of each points.
-        pcd (:obj:`open3d.geometry.PointCloud`): point cloud. Default: None.
-        bbox_color (tuple[float]): the color of bbox. Default: (0, 1, 0).
-        points_in_box_color (tuple[float]):
+        pcd (:obj:`open3d.geometry.PointCloud`, optional): point cloud.
+            Default: None.
+        bbox_color (tuple[float], optional): the color of bbox.
+            Default: (0, 1, 0).
+        points_in_box_color (tuple[float], optional):
             the color of points inside bbox3d. Default: (1, 0, 0).
-        rot_axis (int): rotation axis of bbox. Default: 2.
-        center_mode (bool): indicate the center of bbox is bottom center
-            or gravity center. avaliable mode
+        rot_axis (int, optional): rotation axis of bbox. Default: 2.
+        center_mode (bool, optional): indicate the center of bbox is
+            bottom center or gravity center. avaliable mode
             ['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points,
+            avaliable mode ['xyz', 'xyzrgb']. Default: 'xyz'.
     """
     if isinstance(bbox3d, torch.Tensor):
         bbox3d = bbox3d.cpu().numpy()
@@ -135,23 +137,27 @@ def show_pts_boxes(points,
     Args:
         points (numpy.array | torch.tensor, shape=[N, 3+C]):
             points to visualize.
-        bbox3d (numpy.array | torch.tensor, shape=[M, 7]):
-            3d bbox (x, y, z, dx, dy, dz, yaw) to visualize. Default: None.
-        show (bool): whether to show the visualization results. Default: True.
-        save_path (str): path to save visualized results. Default: None.
-        points_size (int): the size of points to show on visualizer.
+        bbox3d (numpy.array | torch.tensor, shape=[M, 7], optional):
+            3D bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
+            Defaults to None.
+        show (bool, optional): whether to show the visualization results.
+            Default: True.
+        save_path (str, optional): path to save visualized results.
+            Default: None.
+        points_size (int, optional): the size of points to show on visualizer.
             Default: 2.
-        point_color (tuple[float]): the color of points.
+        point_color (tuple[float], optional): the color of points.
             Default: (0.5, 0.5, 0.5).
-        bbox_color (tuple[float]): the color of bbox. Default: (0, 1, 0).
-        points_in_box_color (tuple[float]):
+        bbox_color (tuple[float], optional): the color of bbox.
+            Default: (0, 1, 0).
+        points_in_box_color (tuple[float], optional):
             the color of points which are in bbox3d. Default: (1, 0, 0).
-        rot_axis (int): rotation axis of bbox. Default: 2.
-        center_mode (bool): indicate the center of bbox is bottom center
-            or gravity center. avaliable mode
+        rot_axis (int, optional): rotation axis of bbox. Default: 2.
+        center_mode (bool, optional): indicate the center of bbox is bottom
+            center or gravity center. avaliable mode
             ['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points, avaliable
+            mode ['xyz', 'xyzrgb']. Default: 'xyz'.
     """
     # TODO: support score and class info
     assert 0 <= rot_axis <= 2
@@ -196,21 +202,23 @@ def _draw_bboxes_ind(bbox3d,
 
     Args:
         bbox3d (numpy.array | torch.tensor, shape=[M, 7]):
-            3d bbox (x, y, z, dx, dy, dz, yaw) to visualize.
+            3d bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
         vis (:obj:`open3d.visualization.Visualizer`): open3d visualizer.
         indices (numpy.array | torch.tensor, shape=[N, M]):
             indicate which bbox3d that each point lies in.
         points_colors (numpy.array): color of each points.
-        pcd (:obj:`open3d.geometry.PointCloud`): point cloud. Default: None.
-        bbox_color (tuple[float]): the color of bbox. Default: (0, 1, 0).
-        points_in_box_color (tuple[float]):
+        pcd (:obj:`open3d.geometry.PointCloud`, optional): point cloud.
+            Default: None.
+        bbox_color (tuple[float], optional): the color of bbox.
+            Default: (0, 1, 0).
+        points_in_box_color (tuple[float], optional):
             the color of points which are in bbox3d. Default: (1, 0, 0).
-        rot_axis (int): rotation axis of bbox. Default: 2.
-        center_mode (bool): indicate the center of bbox is bottom center
-            or gravity center. avaliable mode
+        rot_axis (int, optional): rotation axis of bbox. Default: 2.
+        center_mode (bool, optional): indicate the center of bbox is
+            bottom center or gravity center. avaliable mode
             ['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points,
+            avaliable mode ['xyz', 'xyzrgb']. Default: 'xyz'.
     """
     if isinstance(bbox3d, torch.Tensor):
         bbox3d = bbox3d.cpu().numpy()
@@ -270,24 +278,28 @@ def show_pts_index_boxes(points,
         points (numpy.array | torch.tensor, shape=[N, 3+C]):
             points to visualize.
         bbox3d (numpy.array | torch.tensor, shape=[M, 7]):
-            3d bbox (x, y, z, dx, dy, dz, yaw) to visualize. Default: None.
-        show (bool): whether to show the visualization results. Default: True.
-        indices (numpy.array | torch.tensor, shape=[N, M]):
+            3D bbox (x, y, z, x_size, y_size, z_size, yaw) to visualize.
+            Defaults to None.
+        show (bool, optional): whether to show the visualization results.
+            Default: True.
+        indices (numpy.array | torch.tensor, shape=[N, M], optional):
             indicate which bbox3d that each point lies in. Default: None.
-        save_path (str): path to save visualized results. Default: None.
-        points_size (int): the size of points to show on visualizer.
+        save_path (str, optional): path to save visualized results.
+            Default: None.
+        points_size (int, optional): the size of points to show on visualizer.
             Default: 2.
-        point_color (tuple[float]): the color of points.
+        point_color (tuple[float], optional): the color of points.
             Default: (0.5, 0.5, 0.5).
-        bbox_color (tuple[float]): the color of bbox. Default: (0, 1, 0).
-        points_in_box_color (tuple[float]):
+        bbox_color (tuple[float], optional): the color of bbox.
+            Default: (0, 1, 0).
+        points_in_box_color (tuple[float], optional):
             the color of points which are in bbox3d. Default: (1, 0, 0).
-        rot_axis (int): rotation axis of bbox. Default: 2.
-        center_mode (bool): indicate the center of bbox is bottom center
-            or gravity center. avaliable mode
+        rot_axis (int, optional): rotation axis of bbox. Default: 2.
+        center_mode (bool, optional): indicate the center of bbox is
+            bottom center or gravity center. avaliable mode
             ['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points,
+            avaliable mode ['xyz', 'xyzrgb']. Default: 'xyz'.
     """
     # TODO: support score and class info
     assert 0 <= rot_axis <= 2
@@ -324,24 +336,27 @@ class Visualizer(object):
         points (numpy.array, shape=[N, 3+C]): Points to visualize. The Points
             cloud is in mode of Coord3DMode.DEPTH (please refer to
             core.structures.coord_3d_mode).
-        bbox3d (numpy.array, shape=[M, 7]): 3d bbox (x, y, z, dx, dy, dz, yaw)
-            to visualize. The 3d bbox is in mode of Box3DMode.DEPTH with
+        bbox3d (numpy.array, shape=[M, 7], optional): 3D bbox
+            (x, y, z, x_size, y_size, z_size, yaw) to visualize.
+            The 3D bbox is in mode of Box3DMode.DEPTH with
             gravity_center (please refer to core.structures.box_3d_mode).
             Default: None.
-        save_path (str): path to save visualized results. Default: None.
-        points_size (int): the size of points to show on visualizer.
+        save_path (str, optional): path to save visualized results.
+            Default: None.
+        points_size (int, optional): the size of points to show on visualizer.
             Default: 2.
-        point_color (tuple[float]): the color of points.
+        point_color (tuple[float], optional): the color of points.
             Default: (0.5, 0.5, 0.5).
-        bbox_color (tuple[float]): the color of bbox. Default: (0, 1, 0).
-        points_in_box_color (tuple[float]):
+        bbox_color (tuple[float], optional): the color of bbox.
+            Default: (0, 1, 0).
+        points_in_box_color (tuple[float], optional):
             the color of points which are in bbox3d. Default: (1, 0, 0).
-        rot_axis (int): rotation axis of bbox. Default: 2.
-        center_mode (bool): indicate the center of bbox is bottom center
-            or gravity center. avaliable mode
+        rot_axis (int, optional): rotation axis of bbox. Default: 2.
+        center_mode (bool, optional): indicate the center of bbox is
+            bottom center or gravity center. avaliable mode
             ['lidar_bottom', 'camera_bottom']. Default: 'lidar_bottom'.
-        mode (str):  indicate type of the input points, avaliable mode
-            ['xyz', 'xyzrgb']. Default: 'xyz'.
+        mode (str, optional):  indicate type of the input points,
+            avaliable mode ['xyz', 'xyzrgb']. Default: 'xyz'.
     """
 
     def __init__(self,
@@ -390,9 +405,10 @@ class Visualizer(object):
 
         Args:
             bbox3d (numpy.array, shape=[M, 7]):
-                3D bbox (x, y, z, dx, dy, dz, yaw) to be visualized.
-                The 3d bbox is in mode of Box3DMode.DEPTH with
-                gravity_center (please refer to core.structures.box_3d_mode).
+                3D bbox (x, y, z, x_size, y_size, z_size, yaw)
+                to be visualized. The 3d bbox is in mode of
+                Box3DMode.DEPTH with gravity_center (please refer to
+                core.structures.box_3d_mode).
             bbox_color (tuple[float]): the color of bbox. Defaule: None.
             points_in_box_color (tuple[float]): the color of points which
                 are in bbox3d. Defaule: None.
@@ -431,7 +447,7 @@ class Visualizer(object):
         """Visualize the points cloud.
 
         Args:
-            save_path (str): path to save image. Default: None.
+            save_path (str, optional): path to save image. Default: None.
         """
 
         self.o3d_visualizer.run()

mmdet3d/core/visualizer/show_result.py

@@ -35,7 +35,7 @@ def _write_oriented_bbox(scene_bbox, out_filename):
 
     Args:
         scene_bbox(list[ndarray] or ndarray): xyz pos of center and
-            3 lengths (dx,dy,dz) and heading angle around Z axis.
+            3 lengths (x_size, y_size, z_size) and heading angle around Z axis.
             Y forward, X right, Z upward. heading angle of positive X is 0,
             heading angle of positive Y is 90 degrees.
         out_filename(str): Filename.
@@ -87,8 +87,10 @@ def show_result(points,
         pred_bboxes (np.ndarray): Predicted boxes.
         out_dir (str): Path of output directory
         filename (str): Filename of the current frame.
-        show (bool): Visualize the results online. Defaults to False.
-        snapshot (bool): Whether to save the online results. Defaults to False.
+        show (bool, optional): Visualize the results online.
+            Defaults to False.
+        snapshot (bool, optional): Whether to save the online results.
+            Defaults to False.
     """
     result_path = osp.join(out_dir, filename)
     mmcv.mkdir_or_exist(result_path)
@@ -141,10 +143,10 @@ def show_seg_result(points,
         out_dir (str): Path of output directory
         filename (str): Filename of the current frame.
         palette (np.ndarray): Mapping between class labels and colors.
-        ignore_index (int, optional): The label index to be ignored, e.g. \
+        ignore_index (int, optional): The label index to be ignored, e.g.
             unannotated points. Defaults to None.
         show (bool, optional): Visualize the results online. Defaults to False.
-        snapshot (bool, optional): Whether to save the online results. \
+        snapshot (bool, optional): Whether to save the online results.
             Defaults to False.
     """
     # we need 3D coordinates to visualize segmentation mask
@@ -219,14 +221,16 @@ def show_multi_modality_result(img,
             according to the camera intrinsic parameters.
         out_dir (str): Path of output directory.
         filename (str): Filename of the current frame.
-        box_mode (str): Coordinate system the boxes are in. Should be one of
-           'depth', 'lidar' and 'camera'. Defaults to 'lidar'.
-        img_metas (dict): Used in projecting depth bbox.
-        show (bool): Visualize the results online. Defaults to False.
-        gt_bbox_color (str or tuple(int)): Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (255, 102, 61)
-        pred_bbox_color (str or tuple(int)): Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (72, 101, 241)
+        box_mode (str, optional): Coordinate system the boxes are in.
+            Should be one of 'depth', 'lidar' and 'camera'.
+            Defaults to 'lidar'.
+        img_metas (dict, optional): Used in projecting depth bbox.
+            Defaults to None.
+        show (bool, optional): Visualize the results online. Defaults to False.
+        gt_bbox_color (str or tuple(int), optional): Color of bbox lines.
+           The tuple of color should be in BGR order. Default: (255, 102, 61).
+        pred_bbox_color (str or tuple(int), optional): Color of bbox lines.
+           The tuple of color should be in BGR order. Default: (72, 101, 241).
     """
     if box_mode == 'depth':
         draw_bbox = draw_depth_bbox3d_on_img

mmdet3d/core/voxel/voxel_generator.py

@@ -82,18 +82,18 @@ def points_to_voxel(points,
     """convert kitti points(N, >=3) to voxels.
 
     Args:
-        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
+        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and
             points[:, 3:] contain other information such as reflectivity.
         voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size
-        coors_range (list[float | tuple[float] | ndarray]): Voxel range. \
+        coors_range (list[float | tuple[float] | ndarray]): Voxel range.
             format: xyzxyz, minmax
         max_points (int): Indicate maximum points contained in a voxel.
-        reverse_index (bool): Whether return reversed coordinates. \
-            if points has xyz format and reverse_index is True, output \
-            coordinates will be zyx format, but points in features always \
+        reverse_index (bool): Whether return reversed coordinates.
+            if points has xyz format and reverse_index is True, output
+            coordinates will be zyx format, but points in features always
             xyz format.
-        max_voxels (int): Maximum number of voxels this function creates. \
-            For second, 20000 is a good choice. Points should be shuffled for \
+        max_voxels (int): Maximum number of voxels this function creates.
+            For second, 20000 is a good choice. Points should be shuffled for
             randomness before this function because max_voxels drops points.
 
     Returns:
@@ -147,20 +147,20 @@ def _points_to_voxel_reverse_kernel(points,
     """convert kitti points(N, >=3) to voxels.
 
     Args:
-        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
+        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and
             points[:, 3:] contain other information such as reflectivity.
-        voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size \
-        coors_range (list[float | tuple[float] | ndarray]): Range of voxels. \
+        voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size
+        coors_range (list[float | tuple[float] | ndarray]): Range of voxels.
             format: xyzxyz, minmax
         num_points_per_voxel (int): Number of points per voxel.
-        coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W), \
-            which has the same shape as the complete voxel map. It indicates \
+        coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W),
+            which has the same shape as the complete voxel map. It indicates
             the index of each corresponding voxel.
         voxels (np.ndarray): Created empty voxels.
         coors (np.ndarray): Created coordinates of each voxel.
         max_points (int): Indicate maximum points contained in a voxel.
-        max_voxels (int): Maximum number of voxels this function create. \
-            for second, 20000 is a good choice. Points should be shuffled for \
+        max_voxels (int): Maximum number of voxels this function create.
+            for second, 20000 is a good choice. Points should be shuffled for
             randomness before this function because max_voxels drops points.
 
     Returns:
@@ -221,20 +221,20 @@ def _points_to_voxel_kernel(points,
     """convert kitti points(N, >=3) to voxels.
 
     Args:
-        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \
+        points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and
             points[:, 3:] contain other information such as reflectivity.
         voxel_size (list, tuple, np.ndarray): [3] xyz, indicate voxel size.
-        coors_range (list[float | tuple[float] | ndarray]): Range of voxels. \
+        coors_range (list[float | tuple[float] | ndarray]): Range of voxels.
             format: xyzxyz, minmax
         num_points_per_voxel (int): Number of points per voxel.
-        coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W), \
-            which has the same shape as the complete voxel map. It indicates \
+        coor_to_voxel_idx (np.ndarray): A voxel grid of shape (D, H, W),
+            which has the same shape as the complete voxel map. It indicates
             the index of each corresponding voxel.
         voxels (np.ndarray): Created empty voxels.
         coors (np.ndarray): Created coordinates of each voxel.
         max_points (int): Indicate maximum points contained in a voxel.
-        max_voxels (int): Maximum number of voxels this function create. \
-            for second, 20000 is a good choice. Points should be shuffled for \
+        max_voxels (int): Maximum number of voxels this function create.
+            for second, 20000 is a good choice. Points should be shuffled for
             randomness before this function because max_voxels drops points.
 
     Returns:

mmdet3d/datasets/custom_3d.py

@@ -88,7 +88,7 @@ class Custom3DDataset(Dataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Data information that will be passed to the data \
+            dict: Data information that will be passed to the data
                 preprocessing pipelines. It includes the following keys:
 
                 - sample_idx (str): Sample index.
@@ -177,7 +177,7 @@ class Custom3DDataset(Dataset):
         """Get class names of current dataset.
 
         Args:
-            classes (Sequence[str] | str | None): If classes is None, use
+            classes (Sequence[str] | str): If classes is None, use
                 default CLASSES defined by builtin dataset. If classes is a
                 string, take it as a file name. The file contains the name of
                 classes where each line contains one class name. If classes is
@@ -207,13 +207,13 @@ class Custom3DDataset(Dataset):
 
         Args:
             outputs (list[dict]): Testing results of the dataset.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str): The prefix of pkl files. It includes
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
 
         Returns:
-            tuple: (outputs, tmp_dir), outputs is the detection results, \
-                tmp_dir is the temporal directory created for saving json \
+            tuple: (outputs, tmp_dir), outputs is the detection results,
+                tmp_dir is the temporal directory created for saving json
                 files when ``jsonfile_prefix`` is not specified.
         """
         if pklfile_prefix is None:
@@ -237,11 +237,14 @@ class Custom3DDataset(Dataset):
 
         Args:
             results (list[dict]): List of results.
-            metric (str | list[str]): Metrics to be evaluated.
-            iou_thr (list[float]): AP IoU thresholds.
-            show (bool): Whether to visualize.
+            metric (str | list[str], optional): Metrics to be evaluated.
+                Defaults to None.
+            iou_thr (list[float]): AP IoU thresholds. Defaults to (0.25, 0.5).
+            logger (logging.Logger | str, optional): Logger used for printing
+                related information during evaluation. Defaults to None.
+            show (bool, optional): Whether to visualize.
                 Default: False.
-            out_dir (str): Path to save the visualization results.
+            out_dir (str, optional): Path to save the visualization results.
                 Default: None.
             pipeline (list[dict], optional): raw data loading for showing.
                 Default: None.
@@ -281,7 +284,7 @@ class Custom3DDataset(Dataset):
         """Get data loading pipeline in self.show/evaluate function.
 
         Args:
-            pipeline (list[dict] | None): Input pipeline. If None is given, \
+            pipeline (list[dict]): Input pipeline. If None is given,
                 get from self.pipeline.
         """
         if pipeline is None:

mmdet3d/datasets/custom_3d_seg.py

@@ -32,7 +32,7 @@ class Custom3DSegDataset(Dataset):
             as input. Defaults to None.
         test_mode (bool, optional): Whether the dataset is in test mode.
             Defaults to False.
-        ignore_index (int, optional): The label index to be ignored, e.g. \
+        ignore_index (int, optional): The label index to be ignored, e.g.
             unannotated points. If None is given, set to len(self.CLASSES) to
             be consistent with PointSegClassMapping function in pipeline.
             Defaults to None.
@@ -102,7 +102,7 @@ class Custom3DSegDataset(Dataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Data information that will be passed to the data \
+            dict: Data information that will be passed to the data
                 preprocessing pipelines. It includes the following keys:
 
                 - sample_idx (str): Sample index.
@@ -179,13 +179,13 @@ class Custom3DSegDataset(Dataset):
         This function is taken from MMSegmentation.
 
         Args:
-            classes (Sequence[str] | str | None): If classes is None, use
+            classes (Sequence[str] | str): If classes is None, use
                 default CLASSES defined by builtin dataset. If classes is a
                 string, take it as a file name. The file contains the name of
                 classes where each line contains one class name. If classes is
                 a tuple or list, override the CLASSES defined by the dataset.
                 Defaults to None.
-            palette (Sequence[Sequence[int]]] | np.ndarray | None):
+            palette (Sequence[Sequence[int]]] | np.ndarray):
                 The palette of segmentation map. If None is given, random
                 palette will be generated. Defaults to None.
         """
@@ -276,13 +276,13 @@ class Custom3DSegDataset(Dataset):
 
         Args:
             outputs (list[dict]): Testing results of the dataset.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str): The prefix of pkl files. It includes
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
 
         Returns:
-            tuple: (outputs, tmp_dir), outputs is the detection results, \
-                tmp_dir is the temporal directory created for saving json \
+            tuple: (outputs, tmp_dir), outputs is the detection results,
+                tmp_dir is the temporal directory created for saving json
                 files when ``jsonfile_prefix`` is not specified.
         """
         if pklfile_prefix is None:
@@ -306,7 +306,7 @@ class Custom3DSegDataset(Dataset):
         Args:
             results (list[dict]): List of results.
             metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | None | str): Logger used for printing
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Defaults to None.
             show (bool, optional): Whether to visualize.
                 Defaults to False.
@@ -364,7 +364,7 @@ class Custom3DSegDataset(Dataset):
         """Get data loading pipeline in self.show/evaluate function.
 
         Args:
-            pipeline (list[dict] | None): Input pipeline. If None is given, \
+            pipeline (list[dict]): Input pipeline. If None is given,
                 get from self.pipeline.
         """
         if pipeline is None:

mmdet3d/datasets/kitti2d_dataset.py

@@ -206,7 +206,8 @@ class Kitti2DDataset(CustomDataset):
         Args:
             outputs (list[np.ndarray]): List of arrays storing the inferenced
                 bounding boxes and scores.
-            out (str | None): The prefix of output file. Default: None.
+            out (str, optional): The prefix of output file.
+                Default: None.
 
         Returns:
             list[dict]: A list of dictionaries with the kitti 2D format.
@@ -222,7 +223,7 @@ class Kitti2DDataset(CustomDataset):
 
         Args:
             result_files (str): Path of result files.
-            eval_types (str): Types of evaluation. Default: None.
+            eval_types (str, optional): Types of evaluation. Default: None.
                 KITTI dataset only support 'bbox' evaluation type.
 
         Returns:

mmdet3d/datasets/kitti_dataset.py

@@ -47,8 +47,9 @@ class KittiDataset(Custom3DDataset):
             Defaults to True.
         test_mode (bool, optional): Whether the dataset is in test mode.
             Defaults to False.
-        pcd_limit_range (list): The range of point cloud used to filter
-            invalid predicted boxes. Default: [0, -40, -3, 70.4, 40, 0.0].
+        pcd_limit_range (list, optional): The range of point cloud used to
+            filter invalid predicted boxes.
+            Default: [0, -40, -3, 70.4, 40, 0.0].
     """
     CLASSES = ('car', 'pedestrian', 'cyclist')
 
@@ -100,14 +101,14 @@ class KittiDataset(Custom3DDataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Data information that will be passed to the data \
+            dict: Data information that will be passed to the data
                 preprocessing pipelines. It includes the following keys:
 
                 - sample_idx (str): Sample index.
                 - pts_filename (str): Filename of point clouds.
-                - img_prefix (str | None): Prefix of image files.
+                - img_prefix (str): Prefix of image files.
                 - img_info (dict): Image info.
-                - lidar2img (list[np.ndarray], optional): Transformations \
+                - lidar2img (list[np.ndarray], optional): Transformations
                     from lidar to different cameras.
                 - ann_info (dict): Annotation info.
         """
@@ -145,7 +146,7 @@ class KittiDataset(Custom3DDataset):
         Returns:
             dict: annotation information consists of the following keys:
 
-                - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): \
+                - gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`):
                     3D ground truth bboxes.
                 - gt_labels_3d (np.ndarray): Labels of ground truths.
                 - gt_bboxes (np.ndarray): 2D ground truth bboxes.
@@ -248,17 +249,17 @@ class KittiDataset(Custom3DDataset):
 
         Args:
             outputs (list[dict]): Testing results of the dataset.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str): The prefix of pkl files. It includes
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
-            submission_prefix (str | None): The prefix of submitted files. It
+            submission_prefix (str): The prefix of submitted files. It
                 includes the file path and the prefix of filename, e.g.,
                 "a/b/prefix". If not specified, a temp file will be created.
                 Default: None.
 
         Returns:
-            tuple: (result_files, tmp_dir), result_files is a dict containing \
-                the json filepaths, tmp_dir is the temporal directory created \
+            tuple: (result_files, tmp_dir), result_files is a dict containing
+                the json filepaths, tmp_dir is the temporal directory created
                 for saving json files when jsonfile_prefix is not specified.
         """
         if pklfile_prefix is None:
@@ -308,17 +309,18 @@ class KittiDataset(Custom3DDataset):
 
         Args:
             results (list[dict]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | str | None): Logger used for printing
+            metric (str | list[str], optional): Metrics to be evaluated.
+                Default: None.
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str, optional): The prefix of pkl files, including
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
-            submission_prefix (str | None): The prefix of submission datas.
+            submission_prefix (str, optional): The prefix of submission datas.
                 If not specified, the submission data will not be generated.
-            show (bool): Whether to visualize.
+            show (bool, optional): Whether to visualize.
                 Default: False.
-            out_dir (str): Path to save the visualization results.
+            out_dir (str, optional): Path to save the visualization results.
                 Default: None.
             pipeline (list[dict], optional): raw data loading for showing.
                 Default: None.
@@ -371,11 +373,11 @@ class KittiDataset(Custom3DDataset):
         submission.
 
         Args:
-            net_outputs (list[np.ndarray]): List of array storing the \
+            net_outputs (list[np.ndarray]): List of array storing the
                 inferenced bounding boxes and scores.
             class_names (list[String]): A list of class names.
-            pklfile_prefix (str | None): The prefix of pkl file.
-            submission_prefix (str | None): The prefix of submission file.
+            pklfile_prefix (str): The prefix of pkl file.
+            submission_prefix (str): The prefix of submission file.
 
         Returns:
             list[dict]: A list of dictionaries with the kitti format.
@@ -486,11 +488,11 @@ class KittiDataset(Custom3DDataset):
         submission.
 
         Args:
-            net_outputs (list[np.ndarray]): List of array storing the \
+            net_outputs (list[np.ndarray]): List of array storing the
                 inferenced bounding boxes and scores.
             class_names (list[String]): A list of class names.
-            pklfile_prefix (str | None): The prefix of pkl file.
-            submission_prefix (str | None): The prefix of submission file.
+            pklfile_prefix (str): The prefix of pkl file.
+            submission_prefix (str): The prefix of submission file.
 
         Returns:
             list[dict]: A list of dictionaries have the kitti format
@@ -604,9 +606,9 @@ class KittiDataset(Custom3DDataset):
             dict: Valid predicted boxes.
 
                 - bbox (np.ndarray): 2D bounding boxes.
-                - box3d_camera (np.ndarray): 3D bounding boxes in \
+                - box3d_camera (np.ndarray): 3D bounding boxes in
                     camera coordinate.
-                - box3d_lidar (np.ndarray): 3D bounding boxes in \
+                - box3d_lidar (np.ndarray): 3D bounding boxes in
                     LiDAR coordinate.
                 - scores (np.ndarray): Scores of boxes.
                 - label_preds (np.ndarray): Class label predictions.

mmdet3d/datasets/kitti_mono_dataset.py

@@ -57,8 +57,8 @@ class KittiMonoDataset(NuScenesMonoDataset):
             with_mask (bool): Whether to parse mask annotations.
 
         Returns:
-            dict: A dict containing the following keys: bboxes, bboxes_ignore,\
-                labels, masks, seg_map. "masks" are raw annotations and not \
+            dict: A dict containing the following keys: bboxes, bboxes_ignore,
+                labels, masks, seg_map. "masks" are raw annotations and not
                 decoded into binary masks.
         """
         gt_bboxes = []
@@ -147,17 +147,17 @@ class KittiMonoDataset(NuScenesMonoDataset):
 
         Args:
             outputs (list[dict]): Testing results of the dataset.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str): The prefix of pkl files. It includes
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
-            submission_prefix (str | None): The prefix of submitted files. It
+            submission_prefix (str): The prefix of submitted files. It
                 includes the file path and the prefix of filename, e.g.,
                 "a/b/prefix". If not specified, a temp file will be created.
                 Default: None.
 
         Returns:
-            tuple: (result_files, tmp_dir), result_files is a dict containing \
-                the json filepaths, tmp_dir is the temporal directory created \
+            tuple: (result_files, tmp_dir), result_files is a dict containing
+                the json filepaths, tmp_dir is the temporal directory created
                 for saving json files when jsonfile_prefix is not specified.
         """
         if pklfile_prefix is None:
@@ -207,17 +207,18 @@ class KittiMonoDataset(NuScenesMonoDataset):
 
         Args:
             results (list[dict]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | str | None): Logger used for printing
+            metric (str | list[str], optional): Metrics to be evaluated.
+                Defaults to None.
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            pklfile_prefix (str | None): The prefix of pkl files. It includes
+            pklfile_prefix (str, optional): The prefix of pkl files, including
                 the file path and the prefix of filename, e.g., "a/b/prefix".
                 If not specified, a temp file will be created. Default: None.
-            submission_prefix (str | None): The prefix of submission datas.
+            submission_prefix (str, optional): The prefix of submission datas.
                 If not specified, the submission data will not be generated.
-            show (bool): Whether to visualize.
+            show (bool, optional): Whether to visualize.
                 Default: False.
-            out_dir (str): Path to save the visualization results.
+            out_dir (str, optional): Path to save the visualization results.
                 Default: None.
 
         Returns:
@@ -268,11 +269,11 @@ class KittiMonoDataset(NuScenesMonoDataset):
         submission.
 
         Args:
-            net_outputs (list[np.ndarray]): List of array storing the \
+            net_outputs (list[np.ndarray]): List of array storing the
                 inferenced bounding boxes and scores.
             class_names (list[String]): A list of class names.
-            pklfile_prefix (str | None): The prefix of pkl file.
-            submission_prefix (str | None): The prefix of submission file.
+            pklfile_prefix (str): The prefix of pkl file.
+            submission_prefix (str): The prefix of submission file.
 
         Returns:
             list[dict]: A list of dictionaries with the kitti format.
@@ -383,11 +384,11 @@ class KittiMonoDataset(NuScenesMonoDataset):
         submission.
 
         Args:
-            net_outputs (list[np.ndarray]): List of array storing the \
+            net_outputs (list[np.ndarray]): List of array storing the
                 inferenced bounding boxes and scores.
             class_names (list[String]): A list of class names.
-            pklfile_prefix (str | None): The prefix of pkl file.
-            submission_prefix (str | None): The prefix of submission file.
+            pklfile_prefix (str): The prefix of pkl file.
+            submission_prefix (str): The prefix of submission file.
 
         Returns:
             list[dict]: A list of dictionaries have the kitti format
@@ -498,7 +499,7 @@ class KittiMonoDataset(NuScenesMonoDataset):
         Returns:
             dict: Valid predicted boxes.
                 - bbox (np.ndarray): 2D bounding boxes.
-                - box3d_camera (np.ndarray): 3D bounding boxes in \
+                - box3d_camera (np.ndarray): 3D bounding boxes in
                     camera coordinate.
                 - scores (np.ndarray): Scores of boxes.
                 - label_preds (np.ndarray): Class label predictions.