[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/datasets/lyft_dataset.py

@@ -129,7 +129,7 @@ class LyftDataset(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
@@ -137,7 +137,7 @@ class LyftDataset(Custom3DDataset):
                 - sweeps (list[dict]): infos of sweeps
                 - timestamp (float): sample timestamp
                 - img_filename (str, optional): image filename
-                - lidar2img (list[np.ndarray], optional): transformations \
+                - lidar2img (list[np.ndarray], optional): transformations
                     from lidar to different cameras
                 - ann_info (dict): annotation info
         """
@@ -190,7 +190,7 @@ class LyftDataset(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_names (list[str]): Class names of ground truths.
@@ -275,10 +275,11 @@ class LyftDataset(Custom3DDataset):
 
         Args:
             result_path (str): Path of the result file.
-            logger (logging.Logger | str | None): Logger used for printing
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            metric (str): Metric name used for evaluation. Default: 'bbox'.
-            result_name (str): Result name in the metric prefix.
+            metric (str, optional): Metric name used for evaluation.
+                Default: 'bbox'.
+            result_name (str, optional): Result name in the metric prefix.
                 Default: 'pts_bbox'.
 
         Returns:
@@ -312,18 +313,18 @@ class LyftDataset(Custom3DDataset):
 
         Args:
             results (list[dict]): Testing results of the dataset.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str): The prefix of json 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.
-            csv_savepath (str | None): The path for saving csv files.
+            csv_savepath (str): The path for saving csv files.
                 It includes the file path and the csv filename,
                 e.g., "a/b/filename.csv". If not specified,
                 the result will not be converted to csv file.
 
         Returns:
-            tuple: Returns (result_files, tmp_dir), where `result_files` is a \
-                dict containing the json filepaths, `tmp_dir` is the temporal \
-                directory created for saving json files when \
+            tuple: Returns (result_files, tmp_dir), where `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.
         """
         assert isinstance(results, list), 'results must be a list'
@@ -372,19 +373,22 @@ class LyftDataset(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: 'bbox'.
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str, optional): The prefix of json 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.
-            csv_savepath (str | None): The path for saving csv files.
+            csv_savepath (str, optional): The path for saving csv files.
                 It includes the file path and the csv filename,
                 e.g., "a/b/filename.csv". If not specified,
                 the result will not be converted to csv file.
-            show (bool): Whether to visualize.
+            result_names (list[str], optional): Result names in the
+                metric prefix. Default: ['pts_bbox'].
+            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.

mmdet3d/datasets/nuscenes_dataset.py

@@ -48,8 +48,9 @@ class NuScenesDataset(Custom3DDataset):
             Defaults to False.
         eval_version (bool, optional): Configuration version of evaluation.
             Defaults to  'detection_cvpr_2019'.
-        use_valid_flag (bool): Whether to use `use_valid_flag` key in the info
-            file as mask to filter gt_boxes and gt_names. Defaults to False.
+        use_valid_flag (bool, optional): Whether to use `use_valid_flag` key
+            in the info file as mask to filter gt_boxes and gt_names.
+            Defaults to False.
     """
     NameMapping = {
         'movable_object.barrier': 'barrier',
@@ -196,7 +197,7 @@ class NuScenesDataset(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.
@@ -204,7 +205,7 @@ class NuScenesDataset(Custom3DDataset):
                 - sweeps (list[dict]): Infos of sweeps.
                 - timestamp (float): Sample timestamp.
                 - img_filename (str, optional): Image filename.
-                - lidar2img (list[np.ndarray], optional): Transformations \
+                - lidar2img (list[np.ndarray], optional): Transformations
                     from lidar to different cameras.
                 - ann_info (dict): Annotation info.
         """
@@ -256,7 +257,7 @@ class NuScenesDataset(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_names (list[str]): Class names of ground truths.
@@ -374,10 +375,11 @@ class NuScenesDataset(Custom3DDataset):
 
         Args:
             result_path (str): Path of the result file.
-            logger (logging.Logger | str | None): Logger used for printing
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            metric (str): Metric name used for evaluation. Default: 'bbox'.
-            result_name (str): Result name in the metric prefix.
+            metric (str, optional): Metric name used for evaluation.
+                Default: 'bbox'.
+            result_name (str, optional): Result name in the metric prefix.
                 Default: 'pts_bbox'.
 
         Returns:
@@ -427,14 +429,14 @@ class NuScenesDataset(Custom3DDataset):
 
         Args:
             results (list[dict]): Testing results of the dataset.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str): The prefix of json 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: Returns (result_files, tmp_dir), where `result_files` is a \
-                dict containing the json filepaths, `tmp_dir` is the temporal \
-                directory created for saving json files when \
+            tuple: Returns (result_files, tmp_dir), where `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.
         """
         assert isinstance(results, list), 'results must be a list'
@@ -480,15 +482,16 @@ class NuScenesDataset(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: 'bbox'.
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str, optional): The prefix of json 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.
-            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.
@@ -624,7 +627,7 @@ def lidar_nusc_box_to_global(info,
         boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
         classes (list[str]): Mapped classes in the evaluation.
         eval_configs (object): Evaluation configuration object.
-        eval_version (str): Evaluation version.
+        eval_version (str, optional): Evaluation version.
             Default: 'detection_cvpr_2019'
 
     Returns:

mmdet3d/datasets/nuscenes_mono_dataset.py

@@ -44,8 +44,9 @@ class NuScenesMonoDataset(CocoDataset):
             - 'Camera': Box in camera coordinates.
         eval_version (str, optional): Configuration version of evaluation.
             Defaults to  'detection_cvpr_2019'.
-        use_valid_flag (bool): Whether to use `use_valid_flag` key in the info
-            file as mask to filter gt_boxes and gt_names. Defaults to False.
+        use_valid_flag (bool, optional): Whether to use `use_valid_flag` key
+            in the info file as mask to filter gt_boxes and gt_names.
+            Defaults to False.
         version (str, optional): Dataset version. Defaults to 'v1.0-trainval'.
     """
     CLASSES = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
@@ -140,8 +141,8 @@ class NuScenesMonoDataset(CocoDataset):
             ann_info (list[dict]): Annotation info of an image.
 
         Returns:
-            dict: A dict containing the following keys: bboxes, labels, \
-                gt_bboxes_3d, gt_labels_3d, attr_labels, centers2d, \
+            dict: A dict containing the following keys: bboxes, labels,
+                gt_bboxes_3d, gt_labels_3d, attr_labels, centers2d,
                 depths, bboxes_ignore, masks, seg_map
         """
         gt_bboxes = []
@@ -394,10 +395,11 @@ class NuScenesMonoDataset(CocoDataset):
 
         Args:
             result_path (str): Path of the result file.
-            logger (logging.Logger | str | None): Logger used for printing
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            metric (str): Metric name used for evaluation. Default: 'bbox'.
-            result_name (str): Result name in the metric prefix.
+            metric (str, optional): Metric name used for evaluation.
+                Default: 'bbox'.
+            result_name (str, optional): Result name in the metric prefix.
                 Default: 'img_bbox'.
 
         Returns:
@@ -448,13 +450,13 @@ class NuScenesMonoDataset(CocoDataset):
         Args:
             results (list[tuple | numpy.ndarray]): Testing results of the
                 dataset.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str): The prefix of json 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.
         """
         assert isinstance(results, list), 'results must be a list'
@@ -504,15 +506,18 @@ class NuScenesMonoDataset(CocoDataset):
 
         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: 'bbox'.
+            logger (logging.Logger | str, optional): Logger used for printing
                 related information during evaluation. Default: None.
-            jsonfile_prefix (str | None): The prefix of json files. It includes
+            jsonfile_prefix (str): The prefix of json 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.
-            show (bool): Whether to visualize.
+            result_names (list[str], optional): Result names in the
+                metric prefix. Default: ['img_bbox'].
+            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.
@@ -576,7 +581,7 @@ class NuScenesMonoDataset(CocoDataset):
         """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:
@@ -696,7 +701,7 @@ def cam_nusc_box_to_global(info,
         boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
         classes (list[str]): Mapped classes in the evaluation.
         eval_configs (object): Evaluation configuration object.
-        eval_version (str): Evaluation version.
+        eval_version (str, optional): Evaluation version.
             Default: 'detection_cvpr_2019'
 
     Returns:
@@ -736,7 +741,7 @@ def global_nusc_box_to_cam(info,
         boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
         classes (list[str]): Mapped classes in the evaluation.
         eval_configs (object): Evaluation configuration object.
-        eval_version (str): Evaluation version.
+        eval_version (str, optional): Evaluation version.
             Default: 'detection_cvpr_2019'
 
     Returns:
@@ -769,7 +774,7 @@ def nusc_box_to_cam_box3d(boxes):
         boxes (list[:obj:`NuScenesBox`]): List of predicted NuScenesBoxes.
 
     Returns:
-        tuple (:obj:`CameraInstance3DBoxes` | torch.Tensor | torch.Tensor): \
+        tuple (:obj:`CameraInstance3DBoxes` | torch.Tensor | torch.Tensor):
             Converted 3D bounding boxes, scores and labels.
     """
     locs = torch.Tensor([b.center for b in boxes]).view(-1, 3)

mmdet3d/datasets/pipelines/data_augment_utils.py

@@ -34,8 +34,8 @@ def box_collision_test(boxes, qboxes, clockwise=True):
     Args:
         boxes (np.ndarray): Corners of current boxes.
         qboxes (np.ndarray): Boxes to be avoid colliding.
-        clockwise (bool): Whether the corners are in clockwise order.
-            Default: True.
+        clockwise (bool, optional): Whether the corners are in
+            clockwise order. Default: True.
     """
     N = boxes.shape[0]
     K = qboxes.shape[0]
@@ -317,7 +317,7 @@ def box3d_transform_(boxes, loc_transform, rot_transform, valid_mask):
         boxes (np.ndarray): 3D boxes to be transformed.
         loc_transform (np.ndarray): Location transform to be applied.
         rot_transform (np.ndarray): Rotation transform to be applied.
-        valid_mask (np.ndarray | None): Mask to indicate which boxes are valid.
+        valid_mask (np.ndarray): Mask to indicate which boxes are valid.
     """
     num_box = boxes.shape[0]
     for i in range(num_box):
@@ -338,16 +338,17 @@ def noise_per_object_v3_(gt_boxes,
 
     Args:
         gt_boxes (np.ndarray): Ground truth boxes with shape (N, 7).
-        points (np.ndarray | None): Input point cloud with shape (M, 4).
-            Default: None.
-        valid_mask (np.ndarray | None): Mask to indicate which boxes are valid.
-            Default: None.
-        rotation_perturb (float): Rotation perturbation. Default: pi / 4.
-        center_noise_std (float): Center noise standard deviation.
+        points (np.ndarray, optional): Input point cloud with
+            shape (M, 4). Default: None.
+        valid_mask (np.ndarray, optional): Mask to indicate which
+            boxes are valid. Default: None.
+        rotation_perturb (float, optional): Rotation perturbation.
+            Default: pi / 4.
+        center_noise_std (float, optional): Center noise standard deviation.
             Default: 1.0.
-        global_random_rot_range (float): Global random rotation range.
-            Default: pi/4.
-        num_try (int): Number of try. Default: 100.
+        global_random_rot_range (float, optional): Global random rotation
+            range. Default: pi/4.
+        num_try (int, optional): Number of try. Default: 100.
     """
     num_boxes = gt_boxes.shape[0]
     if not isinstance(rotation_perturb, (list, tuple, np.ndarray)):

mmdet3d/datasets/pipelines/dbsampler.py

@@ -15,10 +15,10 @@ class BatchSampler:
 
     Args:
         sample_list (list[dict]): List of samples.
-        name (str | None): The category of samples. Default: None.
-        epoch (int | None): Sampling epoch. Default: None.
-        shuffle (bool): Whether to shuffle indices. Default: False.
-        drop_reminder (bool): Drop reminder. Default: False.
+        name (str, optional): The category of samples. Default: None.
+        epoch (int, optional): Sampling epoch. Default: None.
+        shuffle (bool, optional): Whether to shuffle indices. Default: False.
+        drop_reminder (bool, optional): Drop reminder. Default: False.
     """
 
     def __init__(self,
@@ -87,9 +87,9 @@ class DataBaseSampler(object):
         rate (float): Rate of actual sampled over maximum sampled number.
         prepare (dict): Name of preparation functions and the input value.
         sample_groups (dict): Sampled classes and numbers.
-        classes (list[str]): List of classes. Default: None.
-        points_loader(dict): Config of points loader. Default: dict(
-            type='LoadPointsFromFile', load_dim=4, use_dim=[0,1,2,3])
+        classes (list[str], optional): List of classes. Default: None.
+        points_loader(dict, optional): Config of points loader. Default:
+            dict(type='LoadPointsFromFile', load_dim=4, use_dim=[0,1,2,3])
     """
 
     def __init__(self,
@@ -198,9 +198,9 @@ class DataBaseSampler(object):
         Returns:
             dict: Dict of sampled 'pseudo ground truths'.
 
-                - gt_labels_3d (np.ndarray): ground truths labels \
+                - gt_labels_3d (np.ndarray): ground truths labels
                     of sampled objects.
-                - gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): \
+                - gt_bboxes_3d (:obj:`BaseInstance3DBoxes`):
                     sampled ground truth 3D bounding boxes
                 - points (np.ndarray): sampled points
                 - group_ids (np.ndarray): ids of sampled ground truths

mmdet3d/datasets/pipelines/formating.py

@@ -24,7 +24,7 @@ class DefaultFormatBundle(object):
     - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
     - gt_labels: (1)to tensor, (2)to DataContainer
     - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
-    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
+    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor,
                        (3)to DataContainer (stack=True)
     """
 
@@ -92,8 +92,8 @@ class Collect3D(object):
     The "img_meta" item is always populated.  The contents of the "img_meta"
     dictionary depends on "meta_keys". By default this includes:
 
-        - 'img_shape': shape of the image input to the network as a tuple \
-            (h, w, c).  Note that images may be zero padded on the \
+        - 'img_shape': shape of the image input to the network as a tuple
+            (h, w, c).  Note that images may be zero padded on the
             bottom/right if the batch tensor is larger than this shape.
         - 'scale_factor': a float indicating the preprocessing scale
         - 'flip': a boolean indicating if image flip transform was used
@@ -103,9 +103,9 @@ class Collect3D(object):
         - 'lidar2img': transform from lidar to image
         - 'depth2img': transform from depth to image
         - 'cam2img': transform from camera to image
-        - 'pcd_horizontal_flip': a boolean indicating if point cloud is \
+        - 'pcd_horizontal_flip': a boolean indicating if point cloud is
             flipped horizontally
-        - 'pcd_vertical_flip': a boolean indicating if point cloud is \
+        - 'pcd_vertical_flip': a boolean indicating if point cloud is
             flipped vertically
         - 'box_mode_3d': 3D box mode
         - 'box_type_3d': 3D box type
@@ -130,16 +130,15 @@ class Collect3D(object):
             'sample_idx', 'pcd_scale_factor', 'pcd_rotation', 'pts_filename')
     """
 
-    def __init__(self,
-                 keys,
-                 meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
-                            'depth2img', 'cam2img', 'pad_shape',
-                            'scale_factor', 'flip', 'pcd_horizontal_flip',
-                            'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d',
-                            'img_norm_cfg', 'pcd_trans', 'sample_idx',
-                            'pcd_scale_factor', 'pcd_rotation',
-                            'pcd_rotation_angle', 'pts_filename',
-                            'transformation_3d_flow')):
+    def __init__(
+        self,
+        keys,
+        meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
+                   'depth2img', 'cam2img', 'pad_shape', 'scale_factor', 'flip',
+                   'pcd_horizontal_flip', 'pcd_vertical_flip', 'box_mode_3d',
+                   'box_type_3d', 'img_norm_cfg', 'pcd_trans', 'sample_idx',
+                   'pcd_scale_factor', 'pcd_rotation', 'pcd_rotation_angle',
+                   'pts_filename', 'transformation_3d_flow')):
         self.keys = keys
         self.meta_keys = meta_keys
 

mmdet3d/datasets/pipelines/loading.py

@@ -14,9 +14,10 @@ class LoadMultiViewImageFromFiles(object):
     Expects results['img_filename'] to be a list of filenames.
 
     Args:
-        to_float32 (bool): Whether to convert the img to float32.
+        to_float32 (bool, optional): Whether to convert the img to float32.
             Defaults to False.
-        color_type (str): Color type of the file. Defaults to 'unchanged'.
+        color_type (str, optional): Color type of the file.
+            Defaults to 'unchanged'.
     """
 
     def __init__(self, to_float32=False, color_type='unchanged'):
@@ -30,7 +31,7 @@ class LoadMultiViewImageFromFiles(object):
             results (dict): Result dict containing multi-view image filenames.
 
         Returns:
-            dict: The result dict containing the multi-view image data. \
+            dict: The result dict containing the multi-view image data.
                 Added keys and values are described below.
 
                 - filename (str): Multi-view image filenames.
@@ -77,7 +78,7 @@ class LoadImageFromFileMono3D(LoadImageFromFile):
     detection, additional camera parameters need to be loaded.
 
     Args:
-        kwargs (dict): Arguments are the same as those in \
+        kwargs (dict): Arguments are the same as those in
             :class:`LoadImageFromFile`.
     """
 
@@ -102,17 +103,20 @@ class LoadPointsFromMultiSweeps(object):
     This is usually used for nuScenes dataset to utilize previous sweeps.
 
     Args:
-        sweeps_num (int): Number of sweeps. Defaults to 10.
-        load_dim (int): Dimension number of the loaded points. Defaults to 5.
-        use_dim (list[int]): Which dimension to use. Defaults to [0, 1, 2, 4].
-        file_client_args (dict): Config dict of file clients, refer to
+        sweeps_num (int, optional): Number of sweeps. Defaults to 10.
+        load_dim (int, optional): Dimension number of the loaded points.
+            Defaults to 5.
+        use_dim (list[int], optional): Which dimension to use.
+            Defaults to [0, 1, 2, 4].
+        file_client_args (dict, optional): Config dict of file clients,
+            refer to
             https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
             for more details. Defaults to dict(backend='disk').
-        pad_empty_sweeps (bool): Whether to repeat keyframe when
+        pad_empty_sweeps (bool, optional): Whether to repeat keyframe when
             sweeps is empty. Defaults to False.
-        remove_close (bool): Whether to remove close points.
+        remove_close (bool, optional): Whether to remove close points.
             Defaults to False.
-        test_mode (bool): If test_model=True used for testing, it will not
+        test_mode (bool, optional): If `test_mode=True`, it will not
             randomly sample sweeps but select the nearest N frames.
             Defaults to False.
     """
@@ -161,7 +165,7 @@ class LoadPointsFromMultiSweeps(object):
 
         Args:
             points (np.ndarray | :obj:`BasePoints`): Sweep points.
-            radius (float): Radius below which points are removed.
+            radius (float, optional): Radius below which points are removed.
                 Defaults to 1.0.
 
         Returns:
@@ -182,14 +186,14 @@ class LoadPointsFromMultiSweeps(object):
         """Call function to load multi-sweep point clouds from files.
 
         Args:
-            results (dict): Result dict containing multi-sweep point cloud \
+            results (dict): Result dict containing multi-sweep point cloud
                 filenames.
 
         Returns:
-            dict: The result dict containing the multi-sweep points data. \
+            dict: The result dict containing the multi-sweep points data.
                 Added key and value are described below.
 
-                - points (np.ndarray | :obj:`BasePoints`): Multi-sweep point \
+                - points (np.ndarray | :obj:`BasePoints`): Multi-sweep point
                     cloud arrays.
         """
         points = results['points']
@@ -243,8 +247,8 @@ class PointSegClassMapping(object):
 
     Args:
         valid_cat_ids (tuple[int]): A tuple of valid category.
-        max_cat_id (int): The max possible cat_id in input segmentation mask.
-            Defaults to 40.
+        max_cat_id (int, optional): The max possible cat_id in input
+            segmentation mask. Defaults to 40.
     """
 
     def __init__(self, valid_cat_ids, max_cat_id=40):
@@ -268,7 +272,7 @@ class PointSegClassMapping(object):
             results (dict): Result dict containing point semantic masks.
 
         Returns:
-            dict: The result dict containing the mapped category ids. \
+            dict: The result dict containing the mapped category ids.
                 Updated key and value are described below.
 
                 - pts_semantic_mask (np.ndarray): Mapped semantic masks.
@@ -307,7 +311,7 @@ class NormalizePointsColor(object):
             results (dict): Result dict containing point clouds data.
 
         Returns:
-            dict: The result dict containing the normalized points. \
+            dict: The result dict containing the normalized points.
                 Updated key and value are described below.
 
                 - points (:obj:`BasePoints`): Points after color normalization.
@@ -342,14 +346,17 @@ class LoadPointsFromFile(object):
             - 'LIDAR': Points in LiDAR coordinates.
             - 'DEPTH': Points in depth coordinates, usually for indoor dataset.
             - 'CAMERA': Points in camera coordinates.
-        load_dim (int): The dimension of the loaded points.
+        load_dim (int, optional): The dimension of the loaded points.
             Defaults to 6.
-        use_dim (list[int]): Which dimensions of the points to be used.
+        use_dim (list[int], optional): Which dimensions of the points to use.
             Defaults to [0, 1, 2]. For KITTI dataset, set use_dim=4
             or use_dim=[0, 1, 2, 3] to use the intensity dimension.
-        shift_height (bool): Whether to use shifted height. Defaults to False.
-        use_color (bool): Whether to use color features. Defaults to False.
-        file_client_args (dict): Config dict of file clients, refer to
+        shift_height (bool, optional): Whether to use shifted height.
+            Defaults to False.
+        use_color (bool, optional): Whether to use color features.
+            Defaults to False.
+        file_client_args (dict, optional): Config dict of file clients,
+            refer to
             https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
             for more details. Defaults to dict(backend='disk').
     """
@@ -405,7 +412,7 @@ class LoadPointsFromFile(object):
             results (dict): Result dict containing point clouds data.
 
         Returns:
-            dict: The result dict containing the point clouds data. \
+            dict: The result dict containing the point clouds data.
                 Added key and value are described below.
 
                 - points (:obj:`BasePoints`): Point clouds data.

mmdet3d/datasets/pipelines/test_time_aug.py

@@ -16,18 +16,19 @@ class MultiScaleFlipAug3D(object):
         img_scale (tuple | list[tuple]: Images scales for resizing.
         pts_scale_ratio (float | list[float]): Points scale ratios for
             resizing.
-        flip (bool): Whether apply flip augmentation. Defaults to False.
-        flip_direction (str | list[str]): Flip augmentation directions
-            for images, options are "horizontal" and "vertical".
+        flip (bool, optional): Whether apply flip augmentation.
+            Defaults to False.
+        flip_direction (str | list[str], optional): Flip augmentation
+            directions for images, options are "horizontal" and "vertical".
             If flip_direction is list, multiple flip augmentations will
             be applied. It has no effect when ``flip == False``.
             Defaults to "horizontal".
-        pcd_horizontal_flip (bool): Whether apply horizontal flip augmentation
-            to point cloud. Defaults to True. Note that it works only when
-            'flip' is turned on.
-        pcd_vertical_flip (bool): Whether apply vertical flip augmentation
-            to point cloud. Defaults to True. Note that it works only when
-            'flip' is turned on.
+        pcd_horizontal_flip (bool, optional): Whether apply horizontal
+            flip augmentation to point cloud. Defaults to True.
+            Note that it works only when 'flip' is turned on.
+        pcd_vertical_flip (bool, optional): Whether apply vertical flip
+            augmentation to point cloud. Defaults to True.
+            Note that it works only when 'flip' is turned on.
     """
 
     def __init__(self,
@@ -70,7 +71,7 @@ class MultiScaleFlipAug3D(object):
             results (dict): Result dict contains the data to augment.
 
         Returns:
-            dict: The result dict contains the data that is augmented with \
+            dict: The result dict contains the data that is augmented with
                 different scales and flips.
         """
         aug_data = []

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -22,7 +22,7 @@ class RandomDropPointsColor(object):
     util/transform.py#L223>`_ for more details.
 
     Args:
-        drop_ratio (float): The probability of dropping point colors.
+        drop_ratio (float, optional): The probability of dropping point colors.
             Defaults to 0.2.
     """
 
@@ -38,7 +38,7 @@ class RandomDropPointsColor(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after color dropping, \
+            dict: Results after color dropping,
                 'points' key is updated in the result dict.
         """
         points = input_dict['points']
@@ -105,10 +105,11 @@ class RandomFlip3D(RandomFlip):
 
         Args:
             input_dict (dict): Result dict from loading pipeline.
-            direction (str): Flip direction. Default: horizontal.
+            direction (str, optional): Flip direction.
+                Default: 'horizontal'.
 
         Returns:
-            dict: Flipped results, 'points', 'bbox3d_fields' keys are \
+            dict: Flipped results, 'points', 'bbox3d_fields' keys are
                 updated in the result dict.
         """
         assert direction in ['horizontal', 'vertical']
@@ -137,15 +138,15 @@ class RandomFlip3D(RandomFlip):
             input_dict['cam2img'][0][2] = w - input_dict['cam2img'][0][2]
 
     def __call__(self, input_dict):
-        """Call function to flip points, values in the ``bbox3d_fields`` and \
+        """Call function to flip points, values in the ``bbox3d_fields`` and
         also flip 2D image and its annotations.
 
         Args:
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Flipped results, 'flip', 'flip_direction', \
-                'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added \
+            dict: Flipped results, 'flip', 'flip_direction',
+                'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added
                 into result dict.
         """
         # filp 2D image and its annotations
@@ -187,20 +188,20 @@ class RandomFlip3D(RandomFlip):
 class RandomJitterPoints(object):
     """Randomly jitter point coordinates.
 
-    Different from the global translation in ``GlobalRotScaleTrans``, here we \
+    Different from the global translation in ``GlobalRotScaleTrans``, here we
         apply different noises to each point in a scene.
 
     Args:
         jitter_std (list[float]): The standard deviation of jittering noise.
-            This applies random noise to all points in a 3D scene, which is \
-            sampled from a gaussian distribution whose standard deviation is \
+            This applies random noise to all points in a 3D scene, which is
+            sampled from a gaussian distribution whose standard deviation is
             set by ``jitter_std``. Defaults to [0.01, 0.01, 0.01]
-        clip_range (list[float] | None): Clip the randomly generated jitter \
+        clip_range (list[float]): Clip the randomly generated jitter
             noise into this range. If None is given, don't perform clipping.
             Defaults to [-0.05, 0.05]
 
     Note:
-        This transform should only be used in point cloud segmentation tasks \
+        This transform should only be used in point cloud segmentation tasks
             because we don't transform ground-truth bboxes accordingly.
         For similar transform in detection task, please refer to `ObjectNoise`.
     """
@@ -229,7 +230,7 @@ class RandomJitterPoints(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after adding noise to each point, \
+            dict: Results after adding noise to each point,
                 'points' key is updated in the result dict.
         """
         points = input_dict['points']
@@ -291,8 +292,8 @@ class ObjectSample(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after object sampling augmentation, \
-                'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated \
+            dict: Results after object sampling augmentation,
+                'points', 'gt_bboxes_3d', 'gt_labels_3d' keys are updated
                 in the result dict.
         """
         gt_bboxes_3d = input_dict['gt_bboxes_3d']
@@ -388,7 +389,7 @@ class ObjectNoise(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after adding noise to each object, \
+            dict: Results after adding noise to each object,
                 'points', 'gt_bboxes_3d' keys are updated in the result dict.
         """
         gt_bboxes_3d = input_dict['gt_bboxes_3d']
@@ -428,10 +429,10 @@ class GlobalAlignment(object):
         rotation_axis (int): Rotation axis for points and bboxes rotation.
 
     Note:
-        We do not record the applied rotation and translation as in \
-            GlobalRotScaleTrans. Because usually, we do not need to reverse \
+        We do not record the applied rotation and translation as in
+            GlobalRotScaleTrans. Because usually, we do not need to reverse
             the alignment step.
-        For example, ScanNet 3D detection task uses aligned ground-truth \
+        For example, ScanNet 3D detection task uses aligned ground-truth
             bounding boxes for evaluation.
     """
 
@@ -483,7 +484,7 @@ class GlobalAlignment(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after global alignment, 'points' and keys in \
+            dict: Results after global alignment, 'points' and keys in
                 input_dict['bbox3d_fields'] are updated in the result dict.
         """
         assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
@@ -512,15 +513,15 @@ class GlobalRotScaleTrans(object):
     """Apply global rotation, scaling and translation to a 3D scene.
 
     Args:
-        rot_range (list[float]): Range of rotation angle.
+        rot_range (list[float], optional): Range of rotation angle.
             Defaults to [-0.78539816, 0.78539816] (close to [-pi/4, pi/4]).
-        scale_ratio_range (list[float]): Range of scale ratio.
+        scale_ratio_range (list[float], optional): Range of scale ratio.
             Defaults to [0.95, 1.05].
-        translation_std (list[float]): The standard deviation of translation
-            noise. This applies random translation to a scene by a noise, which
+        translation_std (list[float], optional): The standard deviation of
+            translation noise applied to a scene, which
             is sampled from a gaussian distribution whose standard deviation
             is set by ``translation_std``. Defaults to [0, 0, 0]
-        shift_height (bool): Whether to shift height.
+        shift_height (bool, optional): Whether to shift height.
             (the fourth dimension of indoor points) when scaling.
             Defaults to False.
     """
@@ -559,8 +560,8 @@ class GlobalRotScaleTrans(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after translation, 'points', 'pcd_trans' \
-                and keys in input_dict['bbox3d_fields'] are updated \
+            dict: Results after translation, 'points', 'pcd_trans'
+                and keys in input_dict['bbox3d_fields'] are updated
                 in the result dict.
         """
         translation_std = np.array(self.translation_std, dtype=np.float32)
@@ -578,8 +579,8 @@ class GlobalRotScaleTrans(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after rotation, 'points', 'pcd_rotation' \
-                and keys in input_dict['bbox3d_fields'] are updated \
+            dict: Results after rotation, 'points', 'pcd_rotation'
+                and keys in input_dict['bbox3d_fields'] are updated
                 in the result dict.
         """
         rotation = self.rot_range
@@ -608,7 +609,7 @@ class GlobalRotScaleTrans(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after scaling, 'points'and keys in \
+            dict: Results after scaling, 'points'and keys in
                 input_dict['bbox3d_fields'] are updated in the result dict.
         """
         scale = input_dict['pcd_scale_factor']
@@ -630,7 +631,7 @@ class GlobalRotScaleTrans(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after scaling, 'pcd_scale_factor' are updated \
+            dict: Results after scaling, 'pcd_scale_factor' are updated
                 in the result dict.
         """
         scale_factor = np.random.uniform(self.scale_ratio_range[0],
@@ -638,7 +639,7 @@ class GlobalRotScaleTrans(object):
         input_dict['pcd_scale_factor'] = scale_factor
 
     def __call__(self, input_dict):
-        """Private function to rotate, scale and translate bounding boxes and \
+        """Private function to rotate, scale and translate bounding boxes and
         points.
 
         Args:
@@ -646,7 +647,7 @@ class GlobalRotScaleTrans(object):
 
         Returns:
             dict: Results after scaling, 'points', 'pcd_rotation',
-                'pcd_scale_factor', 'pcd_trans' and keys in \
+                'pcd_scale_factor', 'pcd_trans' and keys in
                 input_dict['bbox3d_fields'] are updated in the result dict.
         """
         if 'transformation_3d_flow' not in input_dict:
@@ -684,7 +685,7 @@ class PointShuffle(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after filtering, 'points', 'pts_instance_mask' \
+            dict: Results after filtering, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         idx = input_dict['points'].shuffle()
@@ -723,7 +724,7 @@ class ObjectRangeFilter(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
+            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
                 keys are updated in the result dict.
         """
         # Check points instance type and initialise bev_range
@@ -775,7 +776,7 @@ class PointsRangeFilter(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after filtering, 'points', 'pts_instance_mask' \
+            dict: Results after filtering, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         points = input_dict['points']
@@ -821,7 +822,7 @@ class ObjectNameFilter(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d' \
+            dict: Results after filtering, 'gt_bboxes_3d', 'gt_labels_3d'
                 keys are updated in the result dict.
         """
         gt_labels_3d = input_dict['gt_labels_3d']
@@ -913,7 +914,7 @@ class PointSample(object):
         Args:
             input_dict (dict): Result dict from loading pipeline.
         Returns:
-            dict: Results after sampling, 'points', 'pts_instance_mask' \
+            dict: Results after sampling, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         points = results['points']
@@ -994,10 +995,10 @@ class IndoorPatchPointSample(object):
             additional features. Defaults to False.
         num_try (int, optional): Number of times to try if the patch selected
             is invalid. Defaults to 10.
-        enlarge_size (float | None, optional): Enlarge the sampled patch to
+        enlarge_size (float, optional): Enlarge the sampled patch to
             [-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
             an augmentation. If None, set it as 0. Defaults to 0.2.
-        min_unique_num (int | None, optional): Minimum number of unique points
+        min_unique_num (int, optional): Minimum number of unique points
             the sampled patch should contain. If None, use PointNet++'s method
             to judge uniqueness. Defaults to None.
         eps (float, optional): A value added to patch boundary to guarantee
@@ -1038,7 +1039,7 @@ class IndoorPatchPointSample(object):
                           attribute_dims, point_type):
         """Generating model input.
 
-        Generate input by subtracting patch center and adding additional \
+        Generate input by subtracting patch center and adding additional
             features. Currently support colors and normalized xyz as features.
 
         Args:
@@ -1182,7 +1183,7 @@ class IndoorPatchPointSample(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after sampling, 'points', 'pts_instance_mask' \
+            dict: Results after sampling, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         points = results['points']
@@ -1242,7 +1243,7 @@ class BackgroundPointsFilter(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after filtering, 'points', 'pts_instance_mask' \
+            dict: Results after filtering, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         points = input_dict['points']
@@ -1340,7 +1341,7 @@ class VoxelBasedPointSampler(object):
             input_dict (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Results after sampling, 'points', 'pts_instance_mask' \
+            dict: Results after sampling, 'points', 'pts_instance_mask'
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
         points = results['points']

mmdet3d/datasets/scannet_dataset.py

@@ -78,13 +78,13 @@ class ScanNetDataset(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.
                 - file_name (str): Filename of point clouds.
-                - img_prefix (str | None, optional): Prefix of image files.
+                - img_prefix (str, optional): Prefix of image files.
                 - img_info (dict, optional): Image info.
                 - ann_info (dict): Annotation info.
         """
@@ -129,12 +129,12 @@ class ScanNetDataset(Custom3DDataset):
         Returns:
             dict: annotation information consists of the following keys:
 
-                - gt_bboxes_3d (:obj:`DepthInstance3DBoxes`): \
+                - gt_bboxes_3d (:obj:`DepthInstance3DBoxes`):
                     3D ground truth bboxes
                 - gt_labels_3d (np.ndarray): Labels of ground truths.
                 - pts_instance_mask_path (str): Path of instance masks.
                 - pts_semantic_mask_path (str): Path of semantic masks.
-                - axis_align_matrix (np.ndarray): Transformation matrix for \
+                - axis_align_matrix (np.ndarray): Transformation matrix for
                     global scene alignment.
         """
         # Use index to get the annos, thus the evalhook could also use this api
@@ -172,7 +172,7 @@ class ScanNetDataset(Custom3DDataset):
     def prepare_test_data(self, index):
         """Prepare data for testing.
 
-        We should take axis_align_matrix from self.data_infos since we need \
+        We should take axis_align_matrix from self.data_infos since we need
             to align point clouds.
 
         Args:
@@ -272,7 +272,7 @@ class ScanNetSegDataset(Custom3DSegDataset):
             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).
             Defaults to None.
         scene_idxs (np.ndarray | str, optional): Precomputed index to load
@@ -424,7 +424,7 @@ class ScanNetSegDataset(Custom3DSegDataset):
 
         Args:
             outputs (list[dict]): Testing results of the dataset.
-            txtfile_prefix (str | None): The prefix of saved files. It includes
+            txtfile_prefix (str): The prefix of saved 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.
 

mmdet3d/datasets/sunrgbd_dataset.py

@@ -74,13 +74,13 @@ class SUNRGBDDataset(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, optional): Filename of point clouds.
                 - file_name (str, optional): Filename of point clouds.
-                - img_prefix (str | None, optional): Prefix of image files.
+                - img_prefix (str, optional): Prefix of image files.
                 - img_info (dict, optional): Image info.
                 - calib (dict, optional): Camera calibration info.
                 - ann_info (dict): Annotation info.
@@ -125,7 +125,7 @@ class SUNRGBDDataset(Custom3DDataset):
         Returns:
             dict: annotation information consists of the following keys:
 
-                - gt_bboxes_3d (:obj:`DepthInstance3DBoxes`): \
+                - gt_bboxes_3d (:obj:`DepthInstance3DBoxes`):
                     3D ground truth bboxes
                 - gt_labels_3d (np.ndarray): Labels of ground truths.
                 - pts_instance_mask_path (str): Path of instance masks.
@@ -239,12 +239,15 @@ class SUNRGBDDataset(Custom3DDataset):
 
         Args:
             results (list[dict]): List of results.
-            metric (str | list[str]): Metrics to be evaluated.
-            iou_thr (list[float]): AP IoU thresholds.
-            iou_thr_2d (list[float]): AP IoU thresholds for 2d evaluation.
-            show (bool): Whether to visualize.
+            metric (str | list[str], optional): Metrics to be evaluated.
+                Default: None.
+            iou_thr (list[float], optional): AP IoU thresholds for 3D
+                evaluation. Default: (0.25, 0.5).
+            iou_thr_2d (list[float], optional): AP IoU thresholds for 2D
+                evaluation. Default: (0.5, ).
+            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.

mmdet3d/datasets/utils.py

@@ -25,7 +25,7 @@ def is_loading_function(transform):
         transform (dict | :obj:`Pipeline`): A transform config or a function.
 
     Returns:
-        bool | None: Whether it is a loading function. None means can't judge.
+        bool: Whether it is a loading function. None means can't judge.
             When transform is `MultiScaleFlipAug3D`, we return None.
     """
     # TODO: use more elegant way to distinguish loading modules
@@ -92,7 +92,7 @@ def get_loading_pipeline(pipeline):
         ...    dict(type='Collect3D',
         ...         keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
         ...    ]
-        >>> assert expected_pipelines ==\
+        >>> assert expected_pipelines == \
         ...        get_loading_pipeline(pipelines)
     """
     loading_pipeline = []
@@ -126,7 +126,7 @@ def extract_result_dict(results, key):
         key (str): Key of the desired data.
 
     Returns:
-        np.ndarray | torch.Tensor | None: Data term.
+        np.ndarray | torch.Tensor: Data term.
     """
     if key not in results.keys():
         return None

mmdet3d/datasets/waymo_dataset.py

@@ -46,8 +46,9 @@ class WaymoDataset(KittiDataset):
             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: [-85, -85, -5, 85, 85, 5].
+        pcd_limit_range (list(float), optional): The range of point cloud used
+            to filter invalid predicted boxes.
+            Default: [-85, -85, -5, 85, 85, 5].
     """
 
     CLASSES = ('Car', 'Cyclist', 'Pedestrian')
@@ -100,7 +101,7 @@ class WaymoDataset(KittiDataset):
 
                 - 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 from
                     lidar to different cameras
@@ -140,15 +141,15 @@ class WaymoDataset(KittiDataset):
 
         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.
-            data_format (str | None): Output data format. Default: 'waymo'.
-                Another supported choice is 'kitti'.
+            data_format (str, optional): Output data format.
+                Default: 'waymo'. Another supported choice is 'kitti'.
 
         Returns:
             tuple: (result_files, tmp_dir), result_files is a dict containing
@@ -226,18 +227,18 @@ class WaymoDataset(KittiDataset):
 
         Args:
             results (list[dict]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
+            metric (str | list[str], optional): Metrics to be evaluated.
                 Default: 'waymo'. Another supported metric is 'kitti'.
-            logger (logging.Logger | str | None): Logger used for printing
+            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.
@@ -364,8 +365,8 @@ class WaymoDataset(KittiDataset):
             net_outputs (List[np.ndarray]): list of array storing the
                 bbox and score
             class_nanes (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 dict have the kitti 3d format

mmdet3d/models/backbones/pointnet2_sa_msg.py

@@ -134,7 +134,7 @@ class PointNet2SAMSG(BasePointNet):
                 - sa_xyz (torch.Tensor): The coordinates of sa features.
                 - sa_features (torch.Tensor): The features from the
                     last Set Aggregation Layers.
-                - sa_indices (torch.Tensor): Indices of the \
+                - sa_indices (torch.Tensor): Indices of the
                     input points.
         """
         xyz, features = self._split_point_feats(points)

mmdet3d/models/backbones/pointnet2_sa_ssg.py

@@ -97,11 +97,11 @@ class PointNet2SASSG(BasePointNet):
         Returns:
             dict[str, list[torch.Tensor]]: Outputs after SA and FP modules.
 
-                - fp_xyz (list[torch.Tensor]): The coordinates of \
+                - fp_xyz (list[torch.Tensor]): The coordinates of
                     each fp features.
-                - fp_features (list[torch.Tensor]): The features \
+                - fp_features (list[torch.Tensor]): The features
                     from each Feature Propagate Layers.
-                - fp_indices (list[torch.Tensor]): Indices of the \
+                - fp_indices (list[torch.Tensor]): Indices of the
                     input points.
         """
         xyz, features = self._split_point_feats(points)

mmdet3d/models/decode_heads/decode_head.py

@@ -13,17 +13,18 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
     Args:
         channels (int): Channels after modules, before conv_seg.
         num_classes (int): Number of classes.
-        dropout_ratio (float): Ratio of dropout layer. Default: 0.5.
-        conv_cfg (dict|None): Config of conv layers.
+        dropout_ratio (float, optional): Ratio of dropout layer. Default: 0.5.
+        conv_cfg (dict, optional): Config of conv layers.
             Default: dict(type='Conv1d').
-        norm_cfg (dict|None): Config of norm layers.
+        norm_cfg (dict, optional): Config of norm layers.
             Default: dict(type='BN1d').
-        act_cfg (dict): Config of activation layers.
+        act_cfg (dict, optional): Config of activation layers.
             Default: dict(type='ReLU').
-        loss_decode (dict): Config of decode loss.
+        loss_decode (dict, optional): Config of decode loss.
             Default: dict(type='CrossEntropyLoss').
-        ignore_index (int | None): The label index to be ignored. When using
-            masked BCE loss, ignore_index should be set to None. Default: 255.
+        ignore_index (int, optional): The label index to be ignored.
+            When using masked BCE loss, ignore_index should be set to None.
+            Default: 255.
     """
 
     def __init__(self,
@@ -110,9 +111,9 @@ class Base3DDecodeHead(BaseModule, metaclass=ABCMeta):
         """Compute semantic segmentation loss.
 
         Args:
-            seg_logit (torch.Tensor): Predicted per-point segmentation logits \
+            seg_logit (torch.Tensor): Predicted per-point segmentation logits
                 of shape [B, num_classes, N].
-            seg_label (torch.Tensor): Ground-truth segmentation label of \
+            seg_label (torch.Tensor): Ground-truth segmentation label of
                 shape [B, N].
         """
         loss = dict()

mmdet3d/models/decode_heads/paconv_head.py

@@ -14,7 +14,7 @@ class PAConvHead(PointNet2Head):
 
     Args:
         fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
-        fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
+        fp_norm_cfg (dict): Config of norm layers used in FP modules.
             Default: dict(type='BN2d').
     """
 

mmdet3d/models/decode_heads/pointnet2_head.py

@@ -16,7 +16,7 @@ class PointNet2Head(Base3DDecodeHead):
 
     Args:
         fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
-        fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
+        fp_norm_cfg (dict): Config of norm layers used in FP modules.
             Default: dict(type='BN2d').
     """
 

mmdet3d/models/dense_heads/anchor3d_head.py

@@ -145,7 +145,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
             x (torch.Tensor): Input features.
 
         Returns:
-            tuple[torch.Tensor]: Contain score of each class, bbox \
+            tuple[torch.Tensor]: Contain score of each class, bbox
                 regression and direction classification predictions.
         """
         cls_score = self.conv_cls(x)
@@ -163,7 +163,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
                 features produced by FPN.
 
         Returns:
-            tuple[list[torch.Tensor]]: Multi-level class score, bbox \
+            tuple[list[torch.Tensor]]: Multi-level class score, bbox
                 and direction predictions.
         """
         return multi_apply(self.forward_single, feats)
@@ -177,7 +177,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
             device (str): device of current module.
 
         Returns:
-            list[list[torch.Tensor]]: Anchors of each image, valid flags \
+            list[list[torch.Tensor]]: Anchors of each image, valid flags
                 of each image.
         """
         num_imgs = len(input_metas)
@@ -207,7 +207,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
             num_total_samples (int): The number of valid samples.
 
         Returns:
-            tuple[torch.Tensor]: Losses of class, bbox \
+            tuple[torch.Tensor]: Losses of class, bbox
                 and direction, respectively.
         """
         # classification loss
@@ -285,7 +285,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
                 the 7th dimension is rotation dimension.
 
         Returns:
-            tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th \
+            tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th
                 dimensions are changed.
         """
         rad_pred_encoding = torch.sin(boxes1[..., 6:7]) * torch.cos(
@@ -318,16 +318,16 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
                 of each sample.
             gt_labels (list[torch.Tensor]): Gt labels of each sample.
             input_metas (list[dict]): Contain pcd and img's meta info.
-            gt_bboxes_ignore (None | list[torch.Tensor]): Specify
-                which bounding.
+            gt_bboxes_ignore (list[torch.Tensor]): Specify
+                which bounding boxes to ignore.
 
         Returns:
-            dict[str, list[torch.Tensor]]: Classification, bbox, and \
+            dict[str, list[torch.Tensor]]: Classification, bbox, and
                 direction losses of each level.
 
                 - loss_cls (list[torch.Tensor]): Classification losses.
                 - loss_bbox (list[torch.Tensor]): Box regression losses.
-                - loss_dir (list[torch.Tensor]): Direction classification \
+                - loss_dir (list[torch.Tensor]): Direction classification
                     losses.
         """
         featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
@@ -385,7 +385,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
             dir_cls_preds (list[torch.Tensor]): Multi-level direction
                 class predictions.
             input_metas (list[dict]): Contain pcd and img's meta info.
-            cfg (None | :obj:`ConfigDict`): Training or testing config.
+            cfg (:obj:`ConfigDict`): Training or testing config.
             rescale (list[torch.Tensor]): Whether th rescale bbox.
 
         Returns:
@@ -439,7 +439,7 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
             mlvl_anchors (List[torch.Tensor]): Multi-level anchors
                 in single batch.
             input_meta (list[dict]): Contain pcd and img's meta info.
-            cfg (None | :obj:`ConfigDict`): Training or testing config.
+            cfg (:obj:`ConfigDict`): Training or testing config.
             rescale (list[torch.Tensor]): whether th rescale bbox.
 
         Returns:

mmdet3d/models/dense_heads/anchor_free_mono3d_head.py

@@ -18,35 +18,45 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
         num_classes (int): Number of categories excluding the background
             category.
         in_channels (int): Number of channels in the input feature map.
-        feat_channels (int): Number of hidden channels. Used in child classes.
-        stacked_convs (int): Number of stacking convs of the head.
-        strides (tuple): Downsample factor of each feature map.
-        dcn_on_last_conv (bool): If true, use dcn in the last layer of
-            towers. Default: False.
-        conv_bias (bool | str): If specified as `auto`, it will be decided by
-            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
-            None, otherwise False. Default: "auto".
-        background_label (int | None): Label ID of background, set as 0 for
-            RPN and num_classes for other heads. It will automatically set as
-            num_classes if None is given.
-        use_direction_classifier (bool): Whether to add a direction classifier.
-        diff_rad_by_sin (bool): Whether to change the difference into sin
-            difference for box regression loss.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        loss_dir (dict): Config of direction classifier loss.
-        loss_attr (dict): Config of attribute classifier loss, which is only
-            active when pred_attrs=True.
-        bbox_code_size (int): Dimensions of predicted bounding boxes.
-        pred_attrs (bool): Whether to predict attributes. Default to False.
-        num_attrs (int): The number of attributes to be predicted. Default: 9.
-        pred_velo (bool): Whether to predict velocity. Default to False.
-        pred_bbox2d (bool): Whether to predict 2D boxes. Default to False.
-        group_reg_dims (tuple[int]): The dimension of each regression target
-            group. Default: (2, 1, 3, 1, 2).
-        cls_branch (tuple[int]): Channels for classification branch.
+        feat_channels (int, optional): Number of hidden channels.
+            Used in child classes. Defaults to 256.
+        stacked_convs (int, optional): Number of stacking convs of the head.
+        strides (tuple, optional): Downsample factor of each feature map.
+        dcn_on_last_conv (bool, optional): If true, use dcn in the last
+            layer of towers. Default: False.
+        conv_bias (bool | str, optional): If specified as `auto`, it will be
+            decided by the norm_cfg. Bias of conv will be set as True
+            if `norm_cfg` is None, otherwise False. Default: 'auto'.
+        background_label (int, optional): Label ID of background,
+            set as 0 for RPN and num_classes for other heads.
+            It will automatically set as `num_classes` if None is given.
+        use_direction_classifier (bool, optional):
+            Whether to add a direction classifier.
+        diff_rad_by_sin (bool, optional): Whether to change the difference
+            into sin difference for box regression loss. Defaults to True.
+        dir_offset (float, optional): Parameter used in direction
+            classification. Defaults to 0.
+        dir_limit_offset (float, optional): Parameter used in direction
+            classification. Defaults to 0.
+        loss_cls (dict, optional): Config of classification loss.
+        loss_bbox (dict, optional): Config of localization loss.
+        loss_dir (dict, optional): Config of direction classifier loss.
+        loss_attr (dict, optional): Config of attribute classifier loss,
+            which is only active when `pred_attrs=True`.
+        bbox_code_size (int, optional): Dimensions of predicted bounding boxes.
+        pred_attrs (bool, optional): Whether to predict attributes.
+            Defaults to False.
+        num_attrs (int, optional): The number of attributes to be predicted.
+            Default: 9.
+        pred_velo (bool, optional): Whether to predict velocity.
+            Defaults to False.
+        pred_bbox2d (bool, optional): Whether to predict 2D boxes.
+            Defaults to False.
+        group_reg_dims (tuple[int], optional): The dimension of each regression
+            target group. Default: (2, 1, 3, 1, 2).
+        cls_branch (tuple[int], optional): Channels for classification branch.
             Default: (128, 64).
-        reg_branch (tuple[tuple]): Channels for regression branch.
+        reg_branch (tuple[tuple], optional): Channels for regression branch.
             Default: (
                 (128, 64),  # offset
                 (128, 64),  # depth
@@ -54,14 +64,16 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
                 (64, ),  # rot
                 ()  # velo
             ),
-        dir_branch (tuple[int]): Channels for direction classification branch.
+        dir_branch (tuple[int], optional): Channels for direction
+            classification branch. Default: (64, ).
+        attr_branch (tuple[int], optional): Channels for classification branch.
             Default: (64, ).
-        attr_branch (tuple[int]): Channels for classification branch.
-            Default: (64, ).
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        train_cfg (dict): Training config of anchor head.
-        test_cfg (dict): Testing config of anchor head.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        train_cfg (dict, optional): Training config of anchor head.
+        test_cfg (dict, optional): Testing config of anchor head.
     """  # noqa: W605
 
     _version = 1
@@ -126,6 +138,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
         self.use_direction_classifier = use_direction_classifier
         self.diff_rad_by_sin = diff_rad_by_sin
         self.dir_offset = dir_offset
+        self.dir_limit_offset = dir_limit_offset
         self.loss_cls = build_loss(loss_cls)
         self.loss_bbox = build_loss(loss_bbox)
         self.loss_dir = build_loss(loss_dir)
@@ -290,7 +303,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
                 a 4D-tensor.
 
         Returns:
-            tuple: Usually contain classification scores, bbox predictions, \
+            tuple: Usually contain classification scores, bbox predictions,
                 and direction class predictions.
                 cls_scores (list[Tensor]): Box scores for each scale level,
                     each is a 4D-tensor, the channel number is
@@ -402,7 +415,7 @@ class AnchorFreeMono3DHead(BaseMono3DDenseHead):
                 corresponding to each box
             img_metas (list[dict]): Meta information of each image, e.g.,
                 image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+            gt_bboxes_ignore (list[Tensor]): specify which bounding
                 boxes can be ignored when computing the loss.
         """