Reformat docstrings in code

.dev_scripts/linter.sh

 yapf -r -i --style .style.yapf mmdet3d/ configs/ tests/ tools/
-isort -rc mmdet3d/ configs/ tests/ tools/
+isort mmdet3d/ configs/ tests/ tools/
 flake8 .

.gitlab-ci.yml

@@ -20,7 +20,7 @@ linting:
   script:
     - pip install flake8==3.7.9 yapf isort
     - flake8 .
-    - isort -rc --check-only --diff mmdet3d/ tools/ tests/
+    - isort --check-only --diff mmdet3d/ tools/ tests/
     - yapf -r -d mmdet3d/ tools/ tests/ configs/
 
 .test_template: &test_template_def

.pre-commit-config.yaml

 repos:
   - repo: https://gitlab.com/pycqa/flake8.git
-    rev: 3.7.9
+    rev: 3.8.3
     hooks:
       - id: flake8
   - repo: https://github.com/asottile/seed-isort-config
-    rev: v2.1.0
+    rev: v2.2.0
     hooks:
       - id: seed-isort-config
   - repo: https://github.com/timothycrosley/isort
-    rev: 4.3.21
+    rev: 5.0.2
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
     rev: v0.30.0
     hooks:
       - id: yapf
-
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.5.0
+    rev: v3.1.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -27,3 +26,10 @@ repos:
       - id: check-merge-conflict
       - id: fix-encoding-pragma
         args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]

mmdet3d/apis/inference.py

-import os.path as osp
-from copy import deepcopy
-
 import mmcv
 import torch
+from copy import deepcopy
 from mmcv.parallel import collate, scatter
 from mmcv.runner import load_checkpoint
+from os import path as osp
 
 from mmdet3d.core import Box3DMode, show_result
 from mmdet3d.core.bbox import get_box_type

mmdet3d/core/anchor/anchor_3d_generator.py

@@ -6,7 +6,7 @@ from mmdet.core.anchor import ANCHOR_GENERATORS
 
 @ANCHOR_GENERATORS.register_module()
 class Anchor3DRangeGenerator(object):
-    """3D Anchor Generator by range
+    """3D Anchor Generator by range.
 
     This anchor generator generates anchors by the given range in different
     feature levels.
@@ -80,7 +80,7 @@ class Anchor3DRangeGenerator(object):
         return len(self.scales)
 
     def grid_anchors(self, featmap_sizes, device='cuda'):
-        """Generate grid anchors in multiple feature levels
+        """Generate grid anchors in multiple feature levels.
 
         Args:
             featmap_sizes (list[tuple]): List of feature map sizes in
@@ -152,7 +152,7 @@ class Anchor3DRangeGenerator(object):
                              sizes=[[1.6, 3.9, 1.56]],
                              rotations=[0, 1.5707963],
                              device='cuda'):
-        """Generate anchors in a single range
+        """Generate anchors in a single range.
 
         Args:
             feature_size (list[float] | tuple[float]): Feature map size. It is
@@ -212,7 +212,7 @@ class Anchor3DRangeGenerator(object):
 
 @ANCHOR_GENERATORS.register_module()
 class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
-    """Aligned 3D Anchor Generator by range
+    """Aligned 3D Anchor Generator by range.
 
     This anchor generator uses a different manner to generate the positions
     of anchors' centers from `Anchor3DRangeGenerator`.
@@ -247,7 +247,7 @@ class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
                              sizes=[[1.6, 3.9, 1.56]],
                              rotations=[0, 1.5707963],
                              device='cuda'):
-        """Generate anchors in a single range
+        """Generate anchors in a single range.
 
         Args:
             feature_size: list [D, H, W](zyx)

mmdet3d/core/bbox/box_np_ops.py

@@ -22,8 +22,7 @@ def box_camera_to_lidar(data, r_rect, velo2cam):
 
 
 def corners_nd(dims, origin=0.5):
-    """Generate relative box corners based on length per dim and
-    origin point.
+    """Generate relative box corners based on length per dim and origin point.
 
     Args:
         dims (np.ndarray, shape=[N, ndim]): Array of length per dim
@@ -206,8 +205,8 @@ def corner_to_standup_nd_jit(boxes_corner):
 
 @numba.jit(nopython=True)
 def corner_to_surfaces_3d_jit(corners):
-    """Convert 3d box corners from corner function above
-    to surfaces that normal vectors all direct to internal.
+    """Convert 3d box corners from corner function above to surfaces that
+    normal vectors all direct to internal.
 
     Args:
         corners (np.ndarray, [N, 8, 3]): 3d box corners
@@ -272,8 +271,8 @@ def box3d_to_bbox(box3d, rect, Trv2c, P2):
 
 
 def corner_to_surfaces_3d(corners):
-    """convert 3d box corners from corner function above
-    to surfaces that normal vectors all direct to internal.
+    """convert 3d box corners from corner function above to surfaces that
+    normal vectors all direct to internal.
 
     Args:
         corners (np.ndarray, [N, 8, 3]): 3d box corners.

mmdet3d/core/bbox/coders/delta_xyzwhlr_bbox_coder.py

@@ -6,7 +6,7 @@ from mmdet.core.bbox.builder import BBOX_CODERS
 
 @BBOX_CODERS.register_module()
 class DeltaXYZWLHRBBoxCoder(BaseBBoxCoder):
-    """Bbox Coder for 3D boxes
+    """Bbox Coder for 3D boxes.
 
     Args:
         code_size (int): The dimension of boxes to be encoded.
@@ -18,9 +18,9 @@ class DeltaXYZWLHRBBoxCoder(BaseBBoxCoder):
 
     @staticmethod
     def encode(src_boxes, dst_boxes):
-        """Get box regression transformation deltas
-            (dx, dy, dz, dw, dh, dl, dr, dv*) that can be used
-            to transform the `src_boxes` into the `target_boxes`.
+        """Get box regression transformation deltas (dx, dy, dz, dw, dh, dl,
+        dr, dv*) that can be used to transform the `src_boxes` into the
+        `target_boxes`.
 
         Args:
             src_boxes (torch.Tensor): source boxes, e.g., object proposals.
@@ -55,7 +55,8 @@ class DeltaXYZWLHRBBoxCoder(BaseBBoxCoder):
 
     @staticmethod
     def decode(anchors, deltas):
-        """Apply transformation `deltas` (dx, dy, dz, dw, dh, dl, dr, dv*) to `boxes`.
+        """Apply transformation `deltas` (dx, dy, dz, dw, dh, dl, dr, dv*) to
+        `boxes`.
 
         Args:
             anchors (torch.Tensor): Parameters of anchors with shape (N, 7).

mmdet3d/core/bbox/coders/partial_bin_based_bbox_coder.py

@@ -7,7 +7,7 @@ from mmdet.core.bbox.builder import BBOX_CODERS
 
 @BBOX_CODERS.register_module()
 class PartialBinBasedBBoxCoder(BaseBBoxCoder):
-    """Partial bin based bbox coder
+    """Partial bin based bbox coder.
 
     Args:
         num_dir_bins (int): Number of bins to encode direction angle.
@@ -175,7 +175,7 @@ class PartialBinBasedBBoxCoder(BaseBBoxCoder):
         return angle_cls.long(), angle_res
 
     def class2angle(self, angle_cls, angle_res, limit_period=True):
-        """Inverse function to angle2class
+        """Inverse function to angle2class.
 
         Args:
             angle_cls (torch.Tensor): Angle class to decode.

mmdet3d/core/bbox/iou_calculators/iou3d_calculator.py

@@ -5,7 +5,7 @@ from ..structures import get_box_type
 
 @IOU_CALCULATORS.register_module()
 class BboxOverlapsNearest3D(object):
-    """Nearest 3D IoU Calculator
+    """Nearest 3D IoU Calculator.
 
     Note:
         This IoU calculator first finds the nearest 2D boxes in bird eye view
@@ -20,7 +20,7 @@ class BboxOverlapsNearest3D(object):
         self.coordinate = coordinate
 
     def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
-        """Calculate nearest 3D IoU
+        """Calculate nearest 3D IoU.
 
         Note:
             If ``is_aligned`` is ``False``, then it calculates the ious between
@@ -51,7 +51,7 @@ class BboxOverlapsNearest3D(object):
 
 @IOU_CALCULATORS.register_module()
 class BboxOverlaps3D(object):
-    """3D IoU Calculator
+    """3D IoU Calculator.
 
     Args:
         coordinate (str): The coordinate system, valid options are
@@ -63,7 +63,7 @@ class BboxOverlaps3D(object):
         self.coordinate = coordinate
 
     def __call__(self, bboxes1, bboxes2, mode='iou'):
-        """Calculate 3D IoU using cuda implementation
+        """Calculate 3D IoU using cuda implementation.
 
         Note:
             This function calculate the IoU of 3D boxes based on their volumes.
@@ -94,7 +94,7 @@ def bbox_overlaps_nearest_3d(bboxes1,
                              mode='iou',
                              is_aligned=False,
                              coordinate='lidar'):
-    """Calculate nearest 3D IoU
+    """Calculate nearest 3D IoU.
 
     Note:
         This function first finds the nearest 2D boxes in bird eye view
@@ -117,7 +117,6 @@ def bbox_overlaps_nearest_3d(bboxes1,
         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
 
@@ -138,7 +137,7 @@ def bbox_overlaps_nearest_3d(bboxes1,
 
 
 def bbox_overlaps_3d(bboxes1, bboxes2, mode='iou', coordinate='camera'):
-    """Calculate 3D IoU using cuda implementation
+    """Calculate 3D IoU using cuda implementation.
 
     Note:
         This function calculate the IoU of 3D boxes based on their volumes.

mmdet3d/core/bbox/structures/base_box3d.py

-from abc import abstractmethod
-
 import numpy as np
 import torch
+from abc import abstractmethod
 
 from mmdet3d.ops.iou3d import iou3d_cuda
 from .utils import limit_period, xywhr2xyxyr
 
 
 class BaseInstance3DBoxes(object):
-    """Base class for 3D Boxes
+    """Base class for 3D Boxes.
 
     Note:
         The box is bottom centered, i.e. the relative position of origin in
@@ -162,7 +161,7 @@ class BaseInstance3DBoxes(object):
 
     @abstractmethod
     def rotate(self, angles, axis=0):
-        """Calculate whether the points is in any of the boxes
+        """Calculate whether the points is in any of the boxes.
 
         Args:
             angles (float): rotation angles
@@ -172,12 +171,11 @@ class BaseInstance3DBoxes(object):
 
     @abstractmethod
     def flip(self, bev_direction='horizontal'):
-        """Flip the boxes in BEV along given BEV direction
-        """
+        """Flip the boxes in BEV along given BEV direction."""
         pass
 
     def translate(self, trans_vector):
-        """Calculate whether the points is in any of the boxes
+        """Calculate whether the points is in any of the boxes.
 
         Args:
             trans_vector (torch.Tensor): translation vector of size 1x3
@@ -187,7 +185,7 @@ class BaseInstance3DBoxes(object):
         self.tensor[:, :3] += trans_vector
 
     def in_range_3d(self, box_range):
-        """Check whether the boxes are in the given range
+        """Check whether the boxes are in the given range.
 
         Args:
             box_range (list | torch.Tensor): the range of box
@@ -213,7 +211,7 @@ class BaseInstance3DBoxes(object):
 
     @abstractmethod
     def in_range_bev(self, box_range):
-        """Check whether the boxes are in the given range
+        """Check whether the boxes are in the given range.
 
         Args:
             box_range (list | torch.Tensor): the range of box
@@ -244,7 +242,7 @@ class BaseInstance3DBoxes(object):
         pass
 
     def scale(self, scale_factor):
-        """Scale the box with horizontal and vertical scaling factors
+        """Scale the box with horizontal and vertical scaling factors.
 
         Args:
             scale_factors (float): Scale factors to scale the boxes.
@@ -253,7 +251,7 @@ class BaseInstance3DBoxes(object):
         self.tensor[:, 7:] *= scale_factor
 
     def limit_yaw(self, offset=0.5, period=np.pi):
-        """Limit the yaw to a given period and offset
+        """Limit the yaw to a given period and offset.
 
         Args:
             offset (float): The offset of the yaw.
@@ -321,7 +319,7 @@ class BaseInstance3DBoxes(object):
 
     @classmethod
     def cat(cls, boxes_list):
-        """Concatenates a list of Boxes into a single Boxes
+        """Concatenates a list of Boxes into a single Boxes.
 
         Args:
             boxes_list (list[Boxes]): List of boxes.
@@ -343,7 +341,7 @@ class BaseInstance3DBoxes(object):
         return cat_boxes
 
     def to(self, device):
-        """Convert current boxes to a specific device
+        """Convert current boxes to a specific device.
 
         Args:
             device (str | :obj:`torch.device`): The name of the device.
@@ -383,7 +381,7 @@ class BaseInstance3DBoxes(object):
 
     @classmethod
     def height_overlaps(cls, boxes1, boxes2, mode='iou'):
-        """Calculate height overlaps of two boxes
+        """Calculate height overlaps of two boxes.
 
         Note:
             This function calculate the height overlaps between boxes1 and
@@ -415,7 +413,7 @@ class BaseInstance3DBoxes(object):
 
     @classmethod
     def overlaps(cls, boxes1, boxes2, mode='iou'):
-        """Calculate 3D overlaps of two boxes
+        """Calculate 3D overlaps of two boxes.
 
         Note:
             This function calculate the overlaps between boxes1 and boxes2,

mmdet3d/core/bbox/structures/box_3d_mode.py

-from enum import IntEnum, unique
-
 import numpy as np
 import torch
+from enum import IntEnum, unique
 
 from .base_box3d import BaseInstance3DBoxes
 from .cam_box3d import CameraInstance3DBoxes

mmdet3d/core/bbox/structures/cam_box3d.py

@@ -121,7 +121,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
 
     @property
     def bev(self):
-        """Calculate the 2D bounding boxes in BEV with rotation
+        """Calculate the 2D bounding boxes in BEV with rotation.
 
         Returns:
             torch.Tensor: A nx5 tensor of 2D BEV box of each box.
@@ -131,7 +131,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
 
     @property
     def nearest_bev(self):
-        """Calculate the 2D bounding boxes in BEV without rotation
+        """Calculate the 2D bounding boxes in BEV without rotation.
 
         Returns:
             torch.Tensor: A tensor of 2D BEV box of each box.
@@ -187,7 +187,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
             return points, rot_mat_T
 
     def flip(self, bev_direction='horizontal', points=None):
-        """Flip the boxes in BEV along given BEV direction
+        """Flip the boxes in BEV along given BEV direction.
 
         In CAM coordinates, it flips the x (horizontal) or z (vertical) axis.
 
@@ -218,7 +218,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
             return points
 
     def in_range_bev(self, box_range):
-        """Check whether the boxes are in the given range
+        """Check whether the boxes are in the given range.
 
         Args:
             box_range (list | torch.Tensor): The range of box
@@ -242,7 +242,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
 
     @classmethod
     def height_overlaps(cls, boxes1, boxes2, mode='iou'):
-        """Calculate height overlaps of two boxes
+        """Calculate height overlaps of two boxes.
 
         Note:
             This function calculate the height overlaps between boxes1 and

mmdet3d/core/bbox/structures/depth_box3d.py

@@ -165,7 +165,7 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
             return points, rot_mat_T
 
     def flip(self, bev_direction='horizontal', points=None):
-        """Flip the boxes in BEV along given BEV direction
+        """Flip the boxes in BEV along given BEV direction.
 
         In Depth coordinates, it flips x (horizontal) or y (vertical) axis.
 

mmdet3d/core/bbox/structures/lidar_box3d.py

@@ -7,7 +7,7 @@ from .utils import limit_period, rotation_3d_in_axis
 
 
 class LiDARInstance3DBoxes(BaseInstance3DBoxes):
-    """3D boxes of instances in LIDAR coordinates
+    """3D boxes of instances in LIDAR coordinates.
 
     Coordinates in LiDAR:
     .. code-block:: none
@@ -89,7 +89,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
 
     @property
     def bev(self):
-        """Calculate the 2D bounding boxes in BEV with rotation
+        """Calculate the 2D bounding boxes in BEV with rotation.
 
         Returns:
             torch.Tensor: A nx5 tensor of 2D BEV box of each box.
@@ -99,7 +99,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
 
     @property
     def nearest_bev(self):
-        """Calculate the 2D bounding boxes in BEV without rotation
+        """Calculate the 2D bounding boxes in BEV without rotation.
 
         Returns:
             torch.Tensor: A tensor of 2D BEV box of each box.
@@ -159,7 +159,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             return points, rot_mat_T
 
     def flip(self, bev_direction='horizontal', points=None):
-        """Flip the boxes in BEV along given BEV direction
+        """Flip the boxes in BEV along given BEV direction.
 
         In LIDAR coordinates, it flips the y (horizontal) or x (vertical) axis.
 
@@ -190,7 +190,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             return points
 
     def in_range_bev(self, box_range):
-        """Check whether the boxes are in the given range
+        """Check whether the boxes are in the given range.
 
         Args:
             box_range (list | torch.Tensor): the range of box
@@ -232,7 +232,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             box=self, src=Box3DMode.LIDAR, dst=dst, rt_mat=rt_mat)
 
     def enlarged_box(self, extra_width):
-        """Enlarge the length, width and height boxes
+        """Enlarge the length, width and height boxes.
 
         Args:
             extra_width (float | torch.Tensor): extra width to enlarge the box

mmdet3d/core/bbox/structures/utils.py

@@ -19,7 +19,7 @@ def limit_period(val, offset=0.5, period=np.pi):
 
 
 def rotation_3d_in_axis(points, angles, axis=0):
-    """Rotate points by angles according to axis
+    """Rotate points by angles according to axis.
 
     Args:
         points (torch.Tensor): Points of shape (N, M, 3).
@@ -92,8 +92,8 @@ def get_box_type(box_type):
     Returns:
         tuple: box type and box mode.
     """
-    from .box_3d_mode import (LiDARInstance3DBoxes, CameraInstance3DBoxes,
-                              DepthInstance3DBoxes, Box3DMode)
+    from .box_3d_mode import (Box3DMode, CameraInstance3DBoxes,
+                              DepthInstance3DBoxes, LiDARInstance3DBoxes)
     box_type_lower = box_type.lower()
     if box_type_lower == 'lidar':
         box_type_3d = LiDARInstance3DBoxes
@@ -112,7 +112,7 @@ def get_box_type(box_type):
 
 
 def points_cam2img(points_3d, proj_mat):
-    """Project points from camera coordicates to image coordinates
+    """Project points from camera coordicates to image coordinates.
 
     Args:
         points_3d (torch.Tensor): Points in shape (N, 3)

mmdet3d/core/bbox/transforms.py

@@ -2,7 +2,7 @@ import torch
 
 
 def bbox3d_mapping_back(bboxes, scale_factor, flip_horizontal, flip_vertical):
-    """Map bboxes from testing scale to original image scale"""
+    """Map bboxes from testing scale to original image scale."""
     new_bboxes = bboxes.clone()
     if flip_horizontal:
         new_bboxes.flip('horizontal')

mmdet3d/core/evaluation/indoor_eval.py

@@ -51,8 +51,8 @@ def average_precision(recalls, precisions, mode='area'):
 
 
 def eval_det_cls(pred, gt, iou_thr=None):
-    """Generic functions to compute precision/recall for object detection
-        for a single class.
+    """Generic functions to compute precision/recall for object detection for a
+    single class.
 
     Args:
         pred (dict): {img_id: [(bbox, score)]} where bbox is numpy array.

mmdet3d/core/evaluation/kitti_utils/eval.py

 import gc
 import io as sysio
-
 import numba
 import numpy as np
 
@@ -340,8 +339,8 @@ def fused_compute_statistics(overlaps,
 
 
 def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50):
-    """Fast iou algorithm. this function can be used independently to
-    do result analysis. Must be used in CAMERA coordinate system.
+    """Fast iou algorithm. this function can be used independently to do result
+    analysis. Must be used in CAMERA coordinate system.
 
     Args:
         gt_annos (dict): Must from get_label_annos() in kitti_common.py.
@@ -487,7 +486,7 @@ def eval_class(gt_annos,
         [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
     aos = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
     for m, current_class in enumerate(current_classes):
-        for l, difficulty in enumerate(difficultys):
+        for idx_l, difficulty in enumerate(difficultys):
             rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)
             (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets,
              dontcares, total_dc_num, total_num_valid_gt) = rets
@@ -540,16 +539,19 @@ def eval_class(gt_annos,
                         compute_aos=compute_aos)
                     idx += num_part
                 for i in range(len(thresholds)):
-                    recall[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
-                    precision[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])
+                    recall[m, idx_l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
+                    precision[m, idx_l, k, i] = pr[i, 0] / (
+                        pr[i, 0] + pr[i, 1])
                     if compute_aos:
-                        aos[m, l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
+                        aos[m, idx_l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
                 for i in range(len(thresholds)):
-                    precision[m, l, k, i] = np.max(
-                        precision[m, l, k, i:], axis=-1)
-                    recall[m, l, k, i] = np.max(recall[m, l, k, i:], axis=-1)
+                    precision[m, idx_l, k, i] = np.max(
+                        precision[m, idx_l, k, i:], axis=-1)
+                    recall[m, idx_l, k, i] = np.max(
+                        recall[m, idx_l, k, i:], axis=-1)
                     if compute_aos:
-                        aos[m, l, k, i] = np.max(aos[m, l, k, i:], axis=-1)
+                        aos[m, idx_l, k, i] = np.max(
+                            aos[m, idx_l, k, i:], axis=-1)
     ret_dict = {
         'recall': recall,
         'precision': precision,

mmdet3d/core/evaluation/kitti_utils/rotate_iou.py

@@ -4,7 +4,6 @@
 # Author: yanyan, scrin@foxmail.com
 #####################
 import math
-
 import numba
 import numpy as np
 from numba import cuda
@@ -304,10 +303,9 @@ def rotate_iou_kernel_eval(N,
 
 
 def rotate_iou_gpu_eval(boxes, query_boxes, criterion=-1, device_id=0):
-    """Rotated box iou running in gpu. 500x faster than cpu version
-    (take 5ms in one example with numba.cuda code).
-    convert from [this project](
-        https://github.com/hongzhenwang/RRPN-revise/tree/master/lib/rotation).
+    """Rotated box iou running in gpu. 500x faster than cpu version (take 5ms
+    in one example with numba.cuda code). convert from [this project](
+    https://github.com/hongzhenwang/RRPN-revise/tree/master/lib/rotation).
 
     Args:
         boxes (torch.Tensor): rbboxes. format: centers, dims,

mmdet3d/core/evaluation/lyft_eval.py

-import os.path as osp
-
 import mmcv
 import numpy as np
 from lyft_dataset_sdk.eval.detection.mAP_evaluation import (Box3D, get_ap,
@@ -8,6 +6,7 @@ from lyft_dataset_sdk.eval.detection.mAP_evaluation import (Box3D, get_ap,
                                                             group_by_key,
                                                             wrap_in_box)
 from mmcv.utils import print_log
+from os import path as osp
 from terminaltables import AsciiTable
 
 
@@ -198,8 +197,8 @@ def get_classwise_aps(gt: list, predictions: list, class_names: list,
 
 
 def get_single_class_aps(gt, predictions, iou_thresholds):
-    """Compute recall and precision for all iou thresholds.
-    Adapted from LyftDatasetDevkit.
+    """Compute recall and precision for all iou thresholds. Adapted from
+    LyftDatasetDevkit.
 
     Args:
         gt (list[dict]): list of dictionaries in the format described above.