Update docstrings

.gitlab-ci.yml

@@ -47,7 +47,7 @@ pages:
   stage: deploy
   script:
     - pip install numba==0.48.0
-    - pip install sphinx sphinx_rtd_theme recommonmark sphinx_markdown_tables
+    - pip install sphinx sphinx_rtd_theme recommonmark sphinx_markdown_tables m2r
     - cd docs
     - make html
     - cd ..

README.md

 <div align="center">
-  <img src="demo/mmdet3d-logo.png" width="600"/>
+  <img src="resources/mmdet3d-logo.png" width="600"/>
 </div>
 
 **News**: We released the codebase v0.1.0.

docs/api.rst

@@ -24,11 +24,6 @@ post_processing
 .. automodule:: mmdet3d.core.post_processing
     :members:
 
-utils
-^^^^^^^^^^
-.. automodule:: mmdet3d.core.utils
-    :members:
-
 mmdet3d.datasets
 ----------------
 
@@ -70,21 +65,6 @@ roi_heads
 .. automodule:: mmdet3d.models.roi_heads
     :members:
 
-roi_heads.bbox_heads
-^^^^^^^^^^^^^^^^^^^^
-.. automodule:: mmdet3d.models.roi_heads.bbox_heads
-    :members:
-
-roi_heads.mask_heads
-^^^^^^^^^^^^^^^^^^^^
-.. automodule:: mmdet3d.models.roi_heads.mask_heads
-    :members:
-
-roi_heads.roi_extractors
-^^^^^^^^^^^^^^^^^^^^^^^^^
-.. automodule:: mmdet3d.models.roi_heads.roi_extractors
-    :members:
-
 fusion_layers
 ^^^^^^^^^^^^^
 .. automodule:: mmdet3d.models.fusion_layers

docs/getting_started.md

@@ -6,7 +6,7 @@ For installation instructions, please see [install.md](install.md).
 ## Prepare datasets
 
 It is recommended to symlink the dataset root to `$MMDETECTION3D/data`.
-If your folder structure is different, you may need to change the corresponding paths in config files.
+If your folder structure is different from the following, you may need to change the corresponding paths in config files.
 
 ```
 mmdetection3d

mmdet3d/core/anchor/anchor_3d_generator.py

@@ -88,10 +88,10 @@ class Anchor3DRangeGenerator(object):
             device (str): Device where the anchors will be put on.
 
         Returns:
-            list[torch.Tensor]: Anchors in multiple feature levels.
-                The sizes of each tensor should be [N, 4], where
-                N = width * height * num_base_anchors, width and height
-                are the sizes of the corresponding feature lavel,
+            list[torch.Tensor]: Anchors in multiple feature levels. \
+                The sizes of each tensor should be [N, 4], where \
+                N = width * height * num_base_anchors, width and height \
+                are the sizes of the corresponding feature lavel, \
                 num_base_anchors is the number of anchors for that level.
         """
         assert self.num_levels == len(featmap_sizes)
@@ -168,7 +168,7 @@ class Anchor3DRangeGenerator(object):
             device (str): Devices that the anchors will be put on.
 
         Returns:
-            torch.Tensor: anchors with shape
+            torch.Tensor: anchors with shape \
                 [*feature_size, num_sizes, num_rots, 7].
         """
         if len(feature_size) == 2:
@@ -250,11 +250,21 @@ class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
         """Generate anchors in a single range.
 
         Args:
-            feature_size: list [D, H, W](zyx)
-            sizes: [N, 3] list of list or array, size of anchors, xyz
+            feature_size (list[float] | tuple[float]): Feature map size. It is
+                either a list of a tuple of [D, H, W](in order of z, y, and x).
+            anchor_range (torch.Tensor | list[float]): Range of anchors with
+                shape [6]. The order is consistent with that of anchors, i.e.,
+                (x_min, y_min, z_min, x_max, y_max, z_max).
+            scale (float | int, optional): The scale factor of anchors.
+            sizes (list[list] | np.ndarray | torch.Tensor): Anchor size with
+                shape [N, 3], in order of x, y, z.
+            rotations (list[float] | np.ndarray | torch.Tensor): Rotations of
+                anchors in a single feature grid.
+            device (str): Devices that the anchors will be put on.
 
         Returns:
-            anchors: [*feature_size, num_sizes, num_rots, 7] tensor.
+            torch.Tensor: anchors with shape \
+                [*feature_size, num_sizes, num_rots, 7].
         """
         if len(feature_size) == 2:
             feature_size = [1, feature_size[0], feature_size[1]]
@@ -305,12 +315,11 @@ class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
         rets.insert(3, sizes)
 
         ret = torch.cat(rets, dim=-1).permute([2, 1, 0, 3, 4, 5])
-        # [1, 200, 176, N, 2, 7] for kitti after permute
 
         if len(self.custom_values) > 0:
             custom_ndim = len(self.custom_values)
             custom = ret.new_zeros([*ret.shape[:-1], custom_ndim])
+            # TODO: check the support of custom values
             # custom[:] = self.custom_values
             ret = torch.cat([ret, custom], dim=-1)
-            # [1, 200, 176, N, 2, 9] for nus dataset after permute
         return ret

mmdet3d/core/bbox/iou_calculators/iou3d_calculator.py

@@ -9,7 +9,7 @@ class BboxOverlapsNearest3D(object):
 
     Note:
         This IoU calculator first finds the nearest 2D boxes in bird eye view
-        (BEV), and then calculate the 2D IoU using ``:meth:bbox_overlaps``.
+        (BEV), and then calculate the 2D IoU using :meth:`bbox_overlaps`.
 
     Args:
         coordinate (str): 'camera', 'lidar', or 'depth' coordinate system
@@ -35,8 +35,8 @@ class BboxOverlapsNearest3D(object):
             is_aligned (bool): Whether the calculation is aligned
 
         Return:
-            torch.Tensor: If ``is_aligned`` is ``True``, return ious between
-                bboxes1 and bboxes2 with shape (M, N). If ``is_aligned`` is
+            torch.Tensor: If ``is_aligned`` is ``True``, return ious between \
+                bboxes1 and bboxes2 with shape (M, N). If ``is_aligned`` is \
                 ``False``, return shape is M.
         """
         return bbox_overlaps_nearest_3d(bboxes1, bboxes2, mode, is_aligned,
@@ -77,7 +77,7 @@ class BboxOverlaps3D(object):
                 iof (intersection over foreground).
 
         Return:
-            torch.Tensor: Bbox overlaps results of bboxes1 and bboxes2
+            torch.Tensor: Bbox overlaps results of bboxes1 and bboxes2 \
                 with shape (M, N) (aligned mode is not supported currently).
         """
         return bbox_overlaps_3d(bboxes1, bboxes2, mode, self.coordinate)
@@ -114,8 +114,8 @@ def bbox_overlaps_nearest_3d(bboxes1,
         is_aligned (bool): Whether the calculation is aligned
 
     Return:
-        torch.Tensor: If ``is_aligned`` is ``True``, return ious between
-            bboxes1 and bboxes2 with shape (M, N). If ``is_aligned`` is
+        torch.Tensor: If ``is_aligned`` is ``True``, return ious between \
+            bboxes1 and bboxes2 with shape (M, N). If ``is_aligned`` is \
             ``False``, return shape is M.
     """
     assert bboxes1.size(-1) == bboxes2.size(-1) >= 7
@@ -141,7 +141,7 @@ def bbox_overlaps_3d(bboxes1, bboxes2, mode='iou', coordinate='camera'):
 
     Note:
         This function calculate the IoU of 3D boxes based on their volumes.
-        IoU calculator ``:class:BboxOverlaps3D`` uses this function to
+        IoU calculator :class:`BboxOverlaps3D` uses this function to
         calculate the actual IoUs of boxes.
 
     Args:
@@ -152,7 +152,7 @@ def bbox_overlaps_3d(bboxes1, bboxes2, mode='iou', coordinate='camera'):
         coordinate (str): 'camera' or 'lidar' coordinate system.
 
     Return:
-        torch.Tensor: Bbox overlaps results of bboxes1 and bboxes2
+        torch.Tensor: Bbox overlaps results of bboxes1 and bboxes2 \
             with shape (M, N) (aligned mode is not supported currently).
     """
     assert bboxes1.size(-1) == bboxes2.size(-1) >= 7

mmdet3d/core/bbox/structures/base_box3d.py

@@ -195,11 +195,10 @@ class BaseInstance3DBoxes(object):
             In the original implementation of SECOND, checking whether
             a box in the range checks whether the points are in a convex
             polygon, we try to reduce the burdun for simpler cases.
-            TODO: check whether this will effect the performance
 
         Returns:
-            a binary vector, indicating whether each box is inside
-            the reference range.
+            torch.Tensor: A binary vector indicating whether each box is \
+                inside the reference range.
         """
         in_range_flags = ((self.tensor[:, 0] > box_range[0])
                           & (self.tensor[:, 1] > box_range[1])
@@ -214,8 +213,8 @@ class BaseInstance3DBoxes(object):
         """Check whether the boxes are in the given range.
 
         Args:
-            box_range (list | torch.Tensor): the range of box
-                (x_min, y_min, x_max, y_max)
+            box_range (list | torch.Tensor): The range of box
+                in order of (x_min, y_min, x_max, y_max).
 
         Returns:
             torch.Tensor: Indicating whether each box is inside
@@ -236,7 +235,7 @@ class BaseInstance3DBoxes(object):
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            A new object of :class:`xxx` after indexing:
+            A new object of :class:`BaseInstance3DBoxes` after indexing: \
                 The converted box of the same type in the `dst` mode.
         """
         pass
@@ -269,7 +268,7 @@ class BaseInstance3DBoxes(object):
             threshold (float): The threshold of minimal sizes.
 
         Returns:
-            torch.Tensor: A binary vector which represents whether each
+            torch.Tensor: A binary vector which represents whether each \
                 box is empty (False) or non-empty (True).
         """
         box = self.tensor
@@ -359,7 +358,7 @@ class BaseInstance3DBoxes(object):
         """Clone the Boxes.
 
         Returns:
-            :obj:`BaseInstance3DBoxes`: Box object with the same properties
+            :obj:`BaseInstance3DBoxes`: Box object with the same properties \
                 as self.
         """
         original_type = type(self)
@@ -479,7 +478,7 @@ class BaseInstance3DBoxes(object):
                 returned Tensor copies.
 
         Returns:
-            :obj:`BaseInstance3DBoxes`: A new bbox with data and other
+            :obj:`BaseInstance3DBoxes`: A new bbox with data and other \
                 properties are similar to self.
         """
         new_tensor = self.tensor.new_tensor(data) \

mmdet3d/core/bbox/structures/box_3d_mode.py

@@ -76,7 +76,7 @@ class Box3DMode(IntEnum):
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            (tuple | list | np.dnarray | torch.Tensor | BaseInstance3DBoxes):
+            (tuple | list | np.dnarray | torch.Tensor | BaseInstance3DBoxes): \
                 The converted box of the same type.
         """
         if src == dst:

mmdet3d/core/bbox/structures/cam_box3d.py

@@ -100,7 +100,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
                     down y
 
         Returns:
-            torch.Tensor: Corners of each box with size (N, 8, 3)
+            torch.Tensor: Corners of each box with size (N, 8, 3).
         """
         # TODO: rotation_3d_in_axis function do not support
         #  empty tensor currently.
@@ -163,8 +163,8 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
                 Defaults to None.
 
         Returns:
-            tuple or None: When ``points`` is None, the function returns None,
-                otherwise it returns the rotated points and the
+            tuple or None: When ``points`` is None, the function returns \
+                None, otherwise it returns the rotated points and the \
                 rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
@@ -287,7 +287,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            BaseInstance3DBoxes:
+            :obj:`BaseInstance3DBoxes`:  \
                 The converted box of the same type in the `dst` mode.
         """
         from .box_3d_mode import Box3DMode

mmdet3d/core/bbox/structures/depth_box3d.py

@@ -131,8 +131,8 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
                 Defaults to None.
 
         Returns:
-            tuple or None: When ``points`` is None, the function returns None,
-                otherwise it returns the rotated points and the
+            tuple or None: When ``points`` is None, the function returns \
+                None, otherwise it returns the rotated points and the \
                 rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
@@ -207,10 +207,9 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
             In the original implementation of SECOND, checking whether
             a box in the range checks whether the points are in a convex
             polygon, we try to reduce the burdun for simpler cases.
-            TODO: check whether this will effect the performance
 
         Returns:
-            torch.Tensor: Indicating whether each box is inside
+            torch.Tensor: Indicating whether each box is inside \
                 the reference range.
         """
         in_range_flags = ((self.tensor[:, 0] > box_range[0])
@@ -231,7 +230,7 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BaseInstance3DBoxes`:
+            :obj:`BaseInstance3DBoxes`: \
                 The converted box of the same type in the `dst` mode.
         """
         from .box_3d_mode import Box3DMode

mmdet3d/core/bbox/structures/lidar_box3d.py

@@ -131,8 +131,8 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
                 Defaults to None.
 
         Returns:
-            tuple or None: When ``points`` is None, the function returns None,
-                otherwise it returns the rotated points and the
+            tuple or None: When ``points`` is None, the function returns \
+                None, otherwise it returns the rotated points and the \
                 rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
@@ -204,7 +204,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             TODO: check whether this will effect the performance
 
         Returns:
-            torch.Tensor: Indicating whether each box is inside
+            torch.Tensor: Indicating whether each box is inside \
                 the reference range.
         """
         in_range_flags = ((self.tensor[:, 0] > box_range[0])
@@ -225,7 +225,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
                 to LiDAR. This requires a transformation matrix.
 
         Returns:
-            :obj:`BaseInstance3DBoxes`:
+            :obj:`BaseInstance3DBoxes`: \
                 The converted box of the same type in the `dst` mode.
         """
         from .box_3d_mode import Box3DMode
@@ -239,7 +239,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             extra_width (float | torch.Tensor): extra width to enlarge the box
 
         Returns:
-            :obj:`LiDARInstance3DBoxes`: enlarged boxes
+            :obj:`LiDARInstance3DBoxes`: Enlarged boxes.
         """
         enlarged_boxes = self.tensor.clone()
         enlarged_boxes[:, 3:6] += extra_width * 2
@@ -251,7 +251,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
         """Find the box which the points are in.
 
         Args:
-            points (torch.Tensor): Points in shape Nx3
+            points (torch.Tensor): Points in shape Nx3.
 
         Returns:
             torch.Tensor: The index of box where each point are in.

mmdet3d/core/bbox/structures/utils.py

@@ -12,7 +12,7 @@ def limit_period(val, offset=0.5, period=np.pi):
         period ([type], optional): Period of the value. Defaults to np.pi.
 
     Returns:
-        torch.Tensor: value in the range of
+        torch.Tensor: value in the range of \
             [-offset * period, (1-offset) * period]
     """
     return val - torch.floor(val / period + offset) * period
@@ -27,11 +27,11 @@ def rotation_3d_in_axis(points, angles, axis=0):
         axis (int, optional): The axis to be rotated. Defaults to 0.
 
     Raises:
-        ValueError: when the axis is not in range [0, 1, 2], it will
+        ValueError: when the axis is not in range [0, 1, 2], it will \
             raise value error.
 
     Returns:
-        torch.Tensor: rotated points in shape (N, M, 3)
+        torch.Tensor: Rotated points in shape (N, M, 3)
     """
     rot_sin = torch.sin(angles)
     rot_cos = torch.cos(angles)

mmdet3d/core/evaluation/lyft_eval.py

@@ -90,7 +90,7 @@ def lyft_eval(lyft, data_root, res_path, eval_set, output_dir, logger=None):
     """Evaluation API for Lyft dataset.
 
     Args:
-        lyft (:obj:``LyftDataset``): Lyft class in the sdk.
+        lyft (:obj:`LyftDataset`): Lyft class in the sdk.
         data_root (str): Root of data for reading splits.
         res_path (str): Path of result json file recording detections.
         eval_set (str): Name of the split for evaluation.

mmdet3d/datasets/custom_3d.py

@@ -84,8 +84,8 @@ class Custom3DDataset(Dataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Standard input_dict consists of the
-                data information.
+            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
@@ -141,7 +141,7 @@ class Custom3DDataset(Dataset):
             index (int): Index for accessing the target data.
 
         Returns:
-            dict: Training data dict corresponding to the index.
+            dict: Training data dict of the corresponding index.
         """
         input_dict = self.get_data_info(index)
         if input_dict is None:
@@ -160,7 +160,7 @@ class Custom3DDataset(Dataset):
             index (int): Index for accessing the target data.
 
         Returns:
-            dict: Testing data dict corresponding to the index.
+            dict: Testing data dict of the corresponding index.
         """
         input_dict = self.get_data_info(index)
         self.pre_pipeline(input_dict)
@@ -207,9 +207,9 @@ class Custom3DDataset(Dataset):
                 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
-                files when jsonfile_prefix is not specified.
+            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:
             tmp_dir = tempfile.TemporaryDirectory()

mmdet3d/datasets/kitti2d_dataset.py

@@ -80,8 +80,7 @@ class Kitti2DDataset(CustomDataset):
             index (int): Index of the annotation data to get.
 
         Returns:
-            dict: Standard annotation dictionary
-                consists of the data information.
+            dict: annotation information consists of the following keys:
 
                 - bboxes (np.ndarray): ground truth bboxes
                 - labels (np.ndarray): labels of ground truths

mmdet3d/datasets/kitti_dataset.py

@@ -15,14 +15,10 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class KittiDataset(Custom3DDataset):
-    """KITTI Dataset.
+    r"""KITTI Dataset.
 
-    This class serves as the API for experiments on the KITTI Dataset.
-
-    Please refer to
-    `<http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d>`_
-    for data downloading. It is recommended to symlink the dataset root to
-    $MMDETECTION3D/data and organize them as the doc shows.
+    This class serves as the API for experiments on the `KITTI Dataset
+    <http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d>`_.
 
     Args:
         data_root (str): Path of dataset root.
@@ -89,15 +85,15 @@ class KittiDataset(Custom3DDataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Standard input_dict consists of the
-                data information.
+            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_info (dict): image info
-                - lidar2img (list[np.ndarray], optional): transformations from
-                    lidar to different cameras
+                - lidar2img (list[np.ndarray], optional): transformations \
+                    from lidar to different cameras
                 - ann_info (dict): annotation info
         """
         info = self.data_infos[index]
@@ -132,10 +128,9 @@ class KittiDataset(Custom3DDataset):
             index (int): Index of the annotation data to get.
 
         Returns:
-            dict: Standard annotation dictionary
-                consists of the data information.
+            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
@@ -249,8 +244,8 @@ class KittiDataset(Custom3DDataset):
                 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:
@@ -458,7 +453,8 @@ class KittiDataset(Custom3DDataset):
                             class_names,
                             pklfile_prefix=None,
                             submission_prefix=None):
-        """Convert results to kitti format for evaluation and test submission.
+        """Convert 2D detection results to kitti format for evaluation and test
+        submission.
 
         Args:
             net_outputs (list[np.ndarray]): list of array storing the

mmdet3d/datasets/lyft_dataset.py

@@ -22,8 +22,7 @@ class LyftDataset(Custom3DDataset):
 
     Please refer to
     `<https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/data>`_  # noqa
-    for data downloading. It is recommended to symlink the dataset
-    root to $MMDETECTION3D/data and organize them as the doc shows.
+    for data downloading.
 
     Args:
         ann_file (str): Path of annotation file.
@@ -127,16 +126,16 @@ class LyftDataset(Custom3DDataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Standard input_dict consists of the
-                data information.
+            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
                 - sweeps (list[dict]): infos of sweeps
                 - timestamp (float): sample timestamp
                 - img_filename (str, optional): image filename
-                - lidar2img (list[np.ndarray], optional): transformations from
-                    lidar to different cameras
+                - lidar2img (list[np.ndarray], optional): transformations \
+                    from lidar to different cameras
                 - ann_info (dict): annotation info
         """
         info = self.data_infos[index]
@@ -186,10 +185,9 @@ class LyftDataset(Custom3DDataset):
             index (int): Index of the annotation data to get.
 
         Returns:
-            dict: Standard annotation dictionary
-                consists of the data information.
+            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
@@ -320,9 +318,10 @@ class LyftDataset(Custom3DDataset):
                 the result will not be converted to csv file.
 
         Returns:
-            tuple (dict, str): 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.
+            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'
         assert len(results) == len(self), (
@@ -472,7 +471,7 @@ def output_to_lyft_box(detection):
         detection (dict): Detection results.
 
     Returns:
-        list[:obj:``LyftBox``]: List of standard LyftBoxes.
+        list[:obj:`LyftBox`]: List of standard LyftBoxes.
     """
     box3d = detection['boxes_3d']
     scores = detection['scores_3d'].numpy()
@@ -504,7 +503,7 @@ def lidar_lyft_box_to_global(info, boxes):
     Args:
         info (dict): Info for a specific sample data, including the
             calibration information.
-        boxes (list[:obj:``LyftBox``]): List of predicted LyftBoxes.
+        boxes (list[:obj:`LyftBox`]): List of predicted LyftBoxes.
 
     Returns:
         list: List of standard LyftBoxes in the global

mmdet3d/datasets/nuscenes_dataset.py

@@ -17,9 +17,8 @@ class NuScenesDataset(Custom3DDataset):
 
     This class serves as the API for experiments on the NuScenes Dataset.
 
-    Please refer to `<https://www.nuscenes.org/download>`_for data
-    downloading. It is recommended to symlink the dataset root to
-    $MMDETECTION3D/data and organize them as the doc shows.
+    Please refer to `NuScenes Dataset <https://www.nuscenes.org/download>`_
+    for data downloading.
 
     Args:
         ann_file (str): Path of annotation file.
@@ -161,16 +160,16 @@ class NuScenesDataset(Custom3DDataset):
             index (int): Index of the sample data to get.
 
         Returns:
-            dict: Standard input_dict consists of the
-                data information.
+            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
                 - sweeps (list[dict]): infos of sweeps
                 - timestamp (float): sample timestamp
                 - img_filename (str, optional): image filename
-                - lidar2img (list[np.ndarray], optional): transformations from
-                    lidar to different cameras
+                - lidar2img (list[np.ndarray], optional): transformations \
+                    from lidar to different cameras
                 - ann_info (dict): annotation info
         """
         info = self.data_infos[index]
@@ -220,10 +219,9 @@ class NuScenesDataset(Custom3DDataset):
             index (int): Index of the annotation data to get.
 
         Returns:
-            dict: Standard annotation dictionary
-                consists of the data information.
+            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
@@ -392,9 +390,10 @@ class NuScenesDataset(Custom3DDataset):
                 If not specified, a temp file will be created. Default: None.
 
         Returns:
-            tuple (dict, str): 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.
+            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'
         assert len(results) == len(self), (
@@ -497,12 +496,12 @@ def output_to_nusc_box(detection):
     Args:
         detection (dict): Detection results.
 
-            - boxes_3d (:obj:``BaseInstance3DBoxes``): detection bbox
+            - boxes_3d (:obj:`BaseInstance3DBoxes`): detection bbox
             - scores_3d (torch.Tensor): detection scores
             - labels_3d (torch.Tensor): predicted box labels
 
     Returns:
-        list[:obj:``NuScenesBox``]: List of standard NuScenesBoxes.
+        list[:obj:`NuScenesBox`]: List of standard NuScenesBoxes.
     """
     box3d = detection['boxes_3d']
     scores = detection['scores_3d'].numpy()
@@ -544,7 +543,7 @@ def lidar_nusc_box_to_global(info,
     Args:
         info (dict): Info for a specific sample data, including the
             calibration information.
-        boxes (list[:obj:``NuScenesBox``]): List of predicted NuScenesBoxes.
+        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.

mmdet3d/datasets/pipelines/dbsampler.py

@@ -189,9 +189,9 @@ class DataBaseSampler(object):
         Returns:
             dict: Dict of sampled 'pseudo ground truths'.
 
-                - gt_labels_3d (np.ndarray): labels of ground truths:
-                    labels of sampled ground truths
-                - gt_bboxes_3d (:obj:`BaseInstance3DBoxes`):
+                - gt_labels_3d (np.ndarray): ground truths labels \
+                    of sampled objects.
+                - gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): \
                     sampled 3D bounding boxes
                 - points (np.ndarray): sampled points
                 - group_ids (np.ndarray): ids of sampled ground truths

mmdet3d/datasets/pipelines/formating.py

@@ -22,7 +22,7 @@ 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)
     """
 
@@ -79,25 +79,26 @@ 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
-        - "filename": path to the image file
-        - "ori_shape": original shape of the image as a tuple (h, w, c)
-        - "pad_shape": image shape after padding
-        - "lidar2img": transform from lidar to image
-        - 'pcd_horizontal_flip': a boolean indicating if point cloud is
+        - 'scale_factor': a float indicating the preprocessing scale
+        - 'flip': a boolean indicating if image flip transform was used
+        - 'filename': path to the image file
+        - 'ori_shape': original shape of the image as a tuple (h, w, c)
+        - 'pad_shape': image shape after padding
+        - 'lidar2img': transform from lidar to image
+        - '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
         - 'img_norm_cfg': a dict of normalization information:
-            - mean - per channel mean subtraction
-            - std - per channel std divisor
-            - to_rgb - bool indicating if bgr was converted to rgb
+
+            - mean: per channel mean subtraction
+            - std: per channel std divisor
+            - to_rgb: bool indicating if bgr was converted to rgb
         - 'rect': rectification matrix
         - 'Trv2c': transformation from velodyne to camera coordinate
         - 'P2': transformation betweeen cameras
@@ -111,11 +112,11 @@ class Collect3D(object):
         keys (Sequence[str]): Keys of results to be collected in ``data``.
         meta_keys (Sequence[str], optional): Meta keys to be converted to
             ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
-            Default: ``('filename', 'ori_shape', 'img_shape', 'lidar2img',
-            'pad_shape', 'scale_factor', 'flip', 'pcd_horizontal_flip',
-            'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d', 'img_norm_cfg',
-            'rect', 'Trv2c', 'P2', 'pcd_trans', 'sample_idx',
-            'pcd_scale_factor', 'pcd_rotation', 'pts_filename')``
+            Default: ('filename', 'ori_shape', 'img_shape', 'lidar2img', \
+            'pad_shape', 'scale_factor', 'flip', 'pcd_horizontal_flip', \
+            'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d', \
+            'img_norm_cfg', 'rect', 'Trv2c', 'P2', 'pcd_trans', \
+            'sample_idx', 'pcd_scale_factor', 'pcd_rotation', 'pts_filename')
     """
 
     def __init__(self,