Reformat docstrings in code

mmdet3d/core/post_processing/box3d_nms.py

@@ -10,7 +10,7 @@ def box3d_multiclass_nms(mlvl_bboxes,
                          max_num,
                          cfg,
                          mlvl_dir_scores=None):
-    """Multi-class nms for 3D boxes
+    """Multi-class nms for 3D boxes.
 
     Args:
         mlvl_bboxes (torch.Tensor): Multi-level boxes with shape (N, M).

mmdet3d/core/visualizer/show_result.py

-import os.path as osp
-
 import mmcv
 import numpy as np
 import trimesh
+from os import path as osp
 
 
 def _write_ply(points, out_filename):
-    """Write points into ply format for meshlab visualization
+    """Write points into ply format for meshlab visualization.
 
     Args:
         points (np.ndarray): Points in shape (N, dim).
@@ -28,7 +27,7 @@ def _write_ply(points, out_filename):
 
 
 def _write_oriented_bbox(scene_bbox, out_filename):
-    """Export oriented (around Z axis) scene bbox to meshes
+    """Export oriented (around Z axis) scene bbox to meshes.
 
     Args:
         scene_bbox(list[ndarray] or ndarray): xyz pos of center and

mmdet3d/core/voxel/builder.py

@@ -4,7 +4,7 @@ from . import voxel_generator
 
 
 def build_voxel_generator(cfg, **kwargs):
-    """Builder of voxel generator"""
+    """Builder of voxel generator."""
     if isinstance(cfg, voxel_generator.VoxelGenerator):
         return cfg
     elif isinstance(cfg, dict):

mmdet3d/core/voxel/voxel_generator.py

@@ -3,7 +3,7 @@ import numpy as np
 
 
 class VoxelGenerator(object):
-    """Voxel generator in numpy implementation
+    """Voxel generator in numpy implementation.
 
     Args:
         voxel_size (list[float]): Size of a single voxel
@@ -33,7 +33,7 @@ class VoxelGenerator(object):
         self._grid_size = grid_size
 
     def generate(self, points):
-        """Generate voxels given points"""
+        """Generate voxels given points."""
         return points_to_voxel(points, self._voxel_size,
                                self._point_cloud_range, self._max_num_points,
                                True, self._max_voxels)

mmdet3d/datasets/custom_3d.py

-import os.path as osp
-import tempfile
-
 import mmcv
 import numpy as np
+import tempfile
+from os import path as osp
 from torch.utils.data import Dataset
 
 from mmdet.datasets import DATASETS
@@ -12,7 +11,7 @@ from .pipelines import Compose
 
 @DATASETS.register_module()
 class Custom3DDataset(Dataset):
-    """Customized 3D dataset
+    """Customized 3D dataset.
 
     This is the base dataset of SUNRGB-D, ScanNet, nuScenes, and KITTI
     dataset.
@@ -179,7 +178,7 @@ class Custom3DDataset(Dataset):
         from mmdet3d.core.evaluation import indoor_eval
         assert isinstance(
             results, list), f'Expect results to be list, got {type(results)}.'
-        assert len(results) > 0, f'Expect length of results > 0.'
+        assert len(results) > 0, 'Expect length of results > 0.'
         assert len(results) == len(self.data_infos)
         assert isinstance(
             results[0], dict
@@ -220,8 +219,7 @@ class Custom3DDataset(Dataset):
         """Set flag according to image aspect ratio.
 
         Images with aspect ratio greater than 1 will be set as group 1,
-        otherwise group 0.
-        In 3D datasets, they are all the same, thus are all zeros
-
+        otherwise group 0. In 3D datasets, they are all the same, thus are all
+        zeros
         """
         self.flag = np.zeros(len(self), dtype=np.uint8)

mmdet3d/datasets/kitti_dataset.py

 import copy
-import os
-import os.path as osp
-import tempfile
-
 import mmcv
 import numpy as np
+import os
+import tempfile
 import torch
 from mmcv.utils import print_log
+from os import path as osp
 
 from mmdet.datasets import DATASETS
 from ..core import show_result
@@ -16,7 +15,7 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class KittiDataset(Custom3DDataset):
-    """KITTI Dataset
+    """KITTI Dataset.
 
     This class serves as the API for experiments on the KITTI Dataset.
 

mmdet3d/datasets/lyft_dataset.py

-import os.path as osp
-import tempfile
-
 import mmcv
 import numpy as np
 import pandas as pd
+import tempfile
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
 from lyft_dataset_sdk.utils.data_classes import Box as LyftBox
+from os import path as osp
 from pyquaternion import Quaternion
 
 from mmdet3d.core.evaluation.lyft_eval import lyft_eval
@@ -16,7 +15,7 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class LyftDataset(Custom3DDataset):
-    """Lyft Dataset
+    """Lyft Dataset.
 
     This class serves as the API for experiments on the Lyft Dataset.
 

mmdet3d/datasets/nuscenes_dataset.py

-import os.path as osp
-import tempfile
-
 import mmcv
 import numpy as np
 import pyquaternion
+import tempfile
 from nuscenes.utils.data_classes import Box as NuScenesBox
+from os import path as osp
 
 from mmdet.datasets import DATASETS
 from ..core import show_result
@@ -14,7 +13,7 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class NuScenesDataset(Custom3DDataset):
-    """NuScenes Dataset
+    """NuScenes Dataset.
 
     This class serves as the API for experiments on the NuScenes Dataset.
 

mmdet3d/datasets/pipelines/data_augment_utils.py

-import warnings
-
 import numba
 import numpy as np
+import warnings
 from numba.errors import NumbaPerformanceWarning
 
 from mmdet3d.core.bbox import box_np_ops
@@ -44,11 +43,11 @@ def box_collision_test(boxes, qboxes, clockwise=True):
                     max(boxes_standup[i, 1], qboxes_standup[j, 1]))
                 if ih > 0:
                     for k in range(4):
-                        for l in range(4):
+                        for box_l in range(4):
                             A = lines_boxes[i, k, 0]
                             B = lines_boxes[i, k, 1]
-                            C = lines_qboxes[j, l, 0]
-                            D = lines_qboxes[j, l, 1]
+                            C = lines_qboxes[j, box_l, 0]
+                            D = lines_qboxes[j, box_l, 1]
                             acd = (D[1] - A[1]) * (C[0] -
                                                    A[0]) > (C[1] - A[1]) * (
                                                        D[0] - A[0])
@@ -71,15 +70,15 @@ def box_collision_test(boxes, qboxes, clockwise=True):
                         # now check complete overlap.
                         # box overlap qbox:
                         box_overlap_qbox = True
-                        for l in range(4):  # point l in qboxes
+                        for box_l in range(4):  # point l in qboxes
                             for k in range(4):  # corner k in boxes
                                 vec = boxes[i, k] - boxes[i, (k + 1) % 4]
                                 if clockwise:
                                     vec = -vec
                                 cross = vec[1] * (
-                                    boxes[i, k, 0] - qboxes[j, l, 0])
+                                    boxes[i, k, 0] - qboxes[j, box_l, 0])
                                 cross -= vec[0] * (
-                                    boxes[i, k, 1] - qboxes[j, l, 1])
+                                    boxes[i, k, 1] - qboxes[j, box_l, 1])
                                 if cross >= 0:
                                     box_overlap_qbox = False
                                     break
@@ -88,15 +87,15 @@ def box_collision_test(boxes, qboxes, clockwise=True):
 
                         if box_overlap_qbox is False:
                             qbox_overlap_box = True
-                            for l in range(4):  # point l in boxes
+                            for box_l in range(4):  # point box_l in boxes
                                 for k in range(4):  # corner k in qboxes
                                     vec = qboxes[j, k] - qboxes[j, (k + 1) % 4]
                                     if clockwise:
                                         vec = -vec
                                     cross = vec[1] * (
-                                        qboxes[j, k, 0] - boxes[i, l, 0])
+                                        qboxes[j, k, 0] - boxes[i, box_l, 0])
                                     cross -= vec[0] * (
-                                        qboxes[j, k, 1] - boxes[i, l, 1])
+                                        qboxes[j, k, 1] - boxes[i, box_l, 1])
                                     if cross >= 0:  #
                                         qbox_overlap_box = False
                                         break
@@ -264,8 +263,8 @@ def noise_per_object_v3_(gt_boxes,
                          center_noise_std=1.0,
                          global_random_rot_range=np.pi / 4,
                          num_try=100):
-    """random rotate or remove each groundtrutn independently.
-    use kitti viewer to test this function points_transform_
+    """random rotate or remove each groundtrutn independently. use kitti viewer
+    to test this function points_transform_
 
     Args:
         gt_boxes: [N, 7], gt box in lidar.points_transform_

mmdet3d/datasets/pipelines/dbsampler.py

 import copy
+import numpy as np
 import os
 import pickle
 
-import numpy as np
-
 from mmdet3d.core.bbox import box_np_ops
 from mmdet3d.datasets.pipelines import data_augment_utils
 from ..registry import OBJECTSAMPLERS

mmdet3d/datasets/pipelines/loading.py

@@ -7,7 +7,7 @@ from mmdet.datasets.pipelines import LoadAnnotations
 
 @PIPELINES.register_module()
 class LoadMultiViewImageFromFiles(object):
-    """ Load multi channel images from a list of separate channel files.
+    """Load multi channel images from a list of separate channel files.
 
     Expects results['img_filename'] to be a list of filenames
     """
@@ -43,7 +43,7 @@ class LoadMultiViewImageFromFiles(object):
 
 @PIPELINES.register_module()
 class LoadPointsFromMultiSweeps(object):
-    """Load points from multiple sweeps
+    """Load points from multiple sweeps.
 
     This is usually used for nuScenes dataset to utilize previous sweeps.
 
@@ -143,7 +143,7 @@ class PointSegClassMapping(object):
 
 @PIPELINES.register_module()
 class NormalizePointsColor(object):
-    """Normalize color of points
+    """Normalize color of points.
 
     Normalize color of the points.
 

mmdet3d/datasets/pipelines/test_time_aug.py

+import mmcv
 import warnings
 from copy import deepcopy
 
-import mmcv
-
 from mmdet.datasets.builder import PIPELINES
 from mmdet.datasets.pipelines import Compose
 
 
 @PIPELINES.register_module()
 class MultiScaleFlipAug3D(object):
-    """Test-time augmentation with multiple scales and flipping
+    """Test-time augmentation with multiple scales and flipping.
 
     Args:
         transforms (list[dict]): Transforms to apply in each augmentation.

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -91,7 +91,7 @@ class RandomFlip3D(RandomFlip):
 
 @PIPELINES.register_module()
 class ObjectSample(object):
-    """Sample GT objects to the data
+    """Sample GT objects to the data.
 
     Args:
         db_sampler (dict): Config dict of the database sampler.
@@ -168,7 +168,7 @@ class ObjectSample(object):
 
 @PIPELINES.register_module()
 class ObjectNoise(object):
-    """Apply noise to each GT objects in the scene
+    """Apply noise to each GT objects in the scene.
 
     Args:
         translation_std (list, optional): Standard deviation of the
@@ -221,7 +221,7 @@ class ObjectNoise(object):
 
 @PIPELINES.register_module()
 class GlobalRotScaleTrans(object):
-    """Apply global rotation, scaling and translation to a 3D scene
+    """Apply global rotation, scaling and translation to a 3D scene.
 
     Args:
         rot_range (list[float]): Range of rotation angle.
@@ -374,7 +374,7 @@ class PointsRangeFilter(object):
 
 @PIPELINES.register_module()
 class ObjectNameFilter(object):
-    """Filter GT objects by their names
+    """Filter GT objects by their names.
 
     Args:
         classes (list[str]): list of class names to be kept for training

mmdet3d/datasets/scannet_dataset.py

-import os.path as osp
-
 import numpy as np
+from os import path as osp
 
 from mmdet3d.core import show_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
@@ -10,7 +9,7 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class ScanNetDataset(Custom3DDataset):
-    """ScanNet Dataset
+    """ScanNet Dataset.
 
     This class serves as the API for experiments on the ScanNet Dataset.
 

mmdet3d/datasets/sunrgbd_dataset.py

-import os.path as osp
-
 import numpy as np
+from os import path as osp
 
 from mmdet3d.core import show_result
 from mmdet3d.core.bbox import DepthInstance3DBoxes
@@ -10,7 +9,7 @@ from .custom_3d import Custom3DDataset
 
 @DATASETS.register_module()
 class SUNRGBDDataset(Custom3DDataset):
-    """SUNRGBD Dataset
+    """SUNRGBD Dataset.
 
     This class serves as the API for experiments on the SUNRGBD Dataset.
 

mmdet3d/models/backbones/pointnet2_sa_ssg.py

 import torch
-import torch.nn as nn
 from mmcv.runner import load_checkpoint
+from torch import nn as nn
 
 from mmdet3d.ops import PointFPModule, PointSAModule
 from mmdet.models import BACKBONES

mmdet3d/models/backbones/second.py

-import torch.nn as nn
 from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmcv.runner import load_checkpoint
+from torch import nn as nn
 
 from mmdet.models import BACKBONES
 
 
 @BACKBONES.register_module()
 class SECOND(nn.Module):
-    """Backbone network for SECOND/PointPillars/PartA2/MVXNet
+    """Backbone network for SECOND/PointPillars/PartA2/MVXNet.
 
     Args:
         in_channels (int): Input channels

mmdet3d/models/dense_heads/anchor3d_head.py

 import numpy as np
 import torch
-import torch.nn as nn
 from mmcv.cnn import bias_init_with_prob, normal_init
+from torch import nn as nn
 
 from mmdet3d.core import (PseudoSampler, box3d_multiclass_nms, limit_period,
                           xywhr2xyxyr)
@@ -244,7 +244,7 @@ class Anchor3DHead(nn.Module, AnchorTrainMixin):
 
     @staticmethod
     def add_sin_difference(boxes1, boxes2):
-        """Convert the rotation difference to difference in sine function
+        """Convert the rotation difference to difference in sine function.
 
         Args:
             boxes1 (torch.Tensor): shape (NxC), where C>=7 and

mmdet3d/models/dense_heads/free_anchor3d_head.py

 import torch
-import torch.nn.functional as F
+from torch.nn import functional as F
 
 from mmdet3d.core.bbox import bbox_overlaps_nearest_3d
 from mmdet.models import HEADS
@@ -9,7 +9,7 @@ from .train_mixins import get_direction_target
 
 @HEADS.register_module()
 class FreeAnchor3DHead(Anchor3DHead):
-    """`FreeAnchor <https://arxiv.org/abs/1909.02466>`_ head for 3D detection
+    """`FreeAnchor <https://arxiv.org/abs/1909.02466>`_ head for 3D detection.
 
     Note:
         This implementation is directly modified from the `mmdet implementation
@@ -237,7 +237,7 @@ class FreeAnchor3DHead(Anchor3DHead):
         return losses
 
     def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
-        """Generate positive bag loss
+        """Generate positive bag loss.
 
         Args:
             matched_cls_prob (torch.Tensor): Classification probability
@@ -259,7 +259,7 @@ class FreeAnchor3DHead(Anchor3DHead):
             bag_prob, torch.ones_like(bag_prob), reduction='none')
 
     def negative_bag_loss(self, cls_prob, box_prob):
-        """Generate negative bag loss
+        """Generate negative bag loss.
 
         Args:
             cls_prob (torch.Tensor): Classification probability

mmdet3d/models/dense_heads/parta2_rpn_head.py

@@ -11,7 +11,7 @@ from .anchor3d_head import Anchor3DHead
 
 @HEADS.register_module()
 class PartA2RPNHead(Anchor3DHead):
-    """RPN head for PartA2
+    """RPN head for PartA2.
 
     Note:
         The main difference between the PartA2 RPN head and the Anchor3DHead