Reformat docstrings in code

tests/test_dataset/test_indoor_eval.py

@@ -5,7 +5,7 @@ from mmdet3d.core.evaluation.indoor_eval import average_precision, indoor_eval
 
 
 def test_indoor_eval():
-    from mmdet3d.core.bbox.structures import DepthInstance3DBoxes, Box3DMode
+    from mmdet3d.core.bbox.structures import Box3DMode, DepthInstance3DBoxes
     det_infos = [{
         'labels_3d':
         torch.tensor([0, 1, 2, 2, 0, 3, 1, 2, 3, 2]),

tests/test_forward.py

-"""
-Test model forward process
+"""Test model forward process.
 
 CommandLine:
     pytest tests/test_forward.py
     xdoctest tests/test_forward.py zero
-
-
-
 """
 import copy
-from os.path import dirname, exists, join
-
 import numpy as np
 import torch
+from os.path import dirname, exists, join
 
 
 def _get_config_directory():
-    """ Find the predefined detector config directory """
+    """Find the predefined detector config directory."""
     try:
         # Assume we are running in the source mmdetection repo
         repo_dpath = dirname(dirname(__file__))
@@ -31,9 +26,7 @@ def _get_config_directory():
 
 
 def _get_config_module(fname):
-    """
-    Load a configuration as a python module
-    """
+    """Load a configuration as a python module."""
     from mmcv import Config
     config_dpath = _get_config_directory()
     config_fpath = join(config_dpath, fname)
@@ -42,9 +35,10 @@ def _get_config_module(fname):
 
 
 def _get_detector_cfg(fname):
-    """
-    Grab configs necessary to create a detector. These are deep copied to allow
-    for safe modification of parameters without influencing other tests.
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
     """
     import mmcv
     config = _get_config_module(fname)
@@ -154,8 +148,7 @@ def _test_single_stage_forward(cfg_file):
 
 def _demo_mm_inputs(input_shape=(1, 3, 300, 300),
                     num_items=None, num_classes=10):  # yapf: disable
-    """
-    Create a superset of inputs needed to run test or train batches.
+    """Create a superset of inputs needed to run test or train batches.
 
     Args:
         input_shape (tuple):

tests/test_heads.py

 import copy
-from os.path import dirname, exists, join
-
 import pytest
 import torch
+from os.path import dirname, exists, join
 
 from mmdet3d.core.bbox import Box3DMode, LiDARInstance3DBoxes
 
 
 def _get_config_directory():
-    """ Find the predefined detector config directory """
+    """Find the predefined detector config directory."""
     try:
         # Assume we are running in the source mmdetection repo
         repo_dpath = dirname(dirname(__file__))
@@ -23,9 +22,7 @@ def _get_config_directory():
 
 
 def _get_config_module(fname):
-    """
-    Load a configuration as a python module
-    """
+    """Load a configuration as a python module."""
     from mmcv import Config
     config_dpath = _get_config_directory()
     config_fpath = join(config_dpath, fname)
@@ -34,9 +31,10 @@ def _get_config_module(fname):
 
 
 def _get_head_cfg(fname):
-    """
-    Grab configs necessary to create a bbox_head. These are deep copied to
-    allow for safe modification of parameters without influencing other tests.
+    """Grab configs necessary to create a bbox_head.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
     """
     import mmcv
     config = _get_config_module(fname)
@@ -51,9 +49,10 @@ def _get_head_cfg(fname):
 
 
 def _get_rpn_head_cfg(fname):
-    """
-    Grab configs necessary to create a rpn_head. These are deep copied to allow
-    for safe modification of parameters without influencing other tests.
+    """Grab configs necessary to create a rpn_head.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
     """
     import mmcv
     config = _get_config_module(fname)

tests/test_pipeline/test_indoor_pipeline.py

-import os.path as osp
-
 import mmcv
 import numpy as np
 import torch
+from os import path as osp
 
 from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.datasets.pipelines import Compose

tests/test_pipeline/test_loading.py

-import os.path as osp
-
 import mmcv
 import numpy as np
 import pytest
+from os import path as osp
 
 from mmdet3d.core.bbox import DepthInstance3DBoxes
 from mmdet3d.datasets.pipelines import LoadAnnotations3D, LoadPointsFromFile

tests/test_sparse_unet.py

 import torch
 
-import mmdet3d.ops.spconv as spconv
 from mmdet3d.ops import SparseBasicBlock
+from mmdet3d.ops import spconv as spconv
 
 
 def test_SparseUNet():

tools/analyze_logs.py

 import argparse
 import json
-from collections import defaultdict
-
-import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
+from collections import defaultdict
+from matplotlib import pyplot as plt
 
 
 def cal_train_time(log_dicts, args):

tools/benchmark.py

 import argparse
 import time
-
 import torch
 from mmcv import Config
 from mmcv.parallel import MMDataParallel
 from mmcv.runner import load_checkpoint
-from tools.fuse_conv_bn import fuse_module
 
 from mmdet3d.datasets import build_dataloader, build_dataset
 from mmdet3d.models import build_detector
 from mmdet.core import wrap_fp16_model
+from tools.fuse_conv_bn import fuse_module
 
 
 def parse_args():

tools/create_data.py

 import argparse
-import os.path as osp
+from os import path as osp
 
-import tools.data_converter.indoor_converter as indoor
-import tools.data_converter.kitti_converter as kitti
-import tools.data_converter.lyft_converter as lyft_converter
-import tools.data_converter.nuscenes_converter as nuscenes_converter
+from tools.data_converter import indoor_converter as indoor
+from tools.data_converter import kitti_converter as kitti
+from tools.data_converter import lyft_converter as lyft_converter
+from tools.data_converter import nuscenes_converter as nuscenes_converter
 from tools.data_converter.create_gt_database import create_groundtruth_database
 
 

tools/data_converter/create_gt_database.py

-import os.path as osp
-import pickle
-
 import mmcv
 import numpy as np
-import pycocotools.mask as maskUtils
+import pickle
 from mmcv import track_iter_progress
+from os import path as osp
+from pycocotools import mask as maskUtils
 from pycocotools.coco import COCO
 
-import mmdet3d.core.bbox.box_np_ops as box_np_ops
+from mmdet3d.core.bbox import box_np_ops as box_np_ops
 from mmdet3d.datasets import build_dataset
 from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
 from mmdet.ops import roi_align

tools/data_converter/indoor_converter.py

+import mmcv
 import os
 
-import mmcv
 from tools.data_converter.scannet_data_utils import ScanNetData
 from tools.data_converter.sunrgbd_data_utils import SUNRGBDData
 

tools/data_converter/kitti_converter.py

-import pickle
-from pathlib import Path
-
 import numpy as np
+import pickle
 from mmcv import track_iter_progress
+from pathlib import Path
 
 from mmdet3d.core.bbox import box_np_ops
 from .kitti_data_utils import get_kitti_image_info

tools/data_converter/kitti_data_utils.py

-import concurrent.futures as futures
+import numpy as np
 from collections import OrderedDict
+from concurrent import futures as futures
 from pathlib import Path
-
-import numpy as np
 from skimage import io
 
 

tools/data_converter/lyft_converter.py

-import os.path as osp
-
 import mmcv
 import numpy as np
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
+from os import path as osp
 from pyquaternion import Quaternion
 
 from mmdet3d.datasets import LyftDataset

tools/data_converter/nuscenes_converter.py

-import os.path as osp
-from collections import OrderedDict
-from typing import List, Tuple, Union
-
 import mmcv
 import numpy as np
+from collections import OrderedDict
 from nuscenes.nuscenes import NuScenes
 from nuscenes.utils.geometry_utils import view_points
+from os import path as osp
 from pyquaternion import Quaternion
 from shapely.geometry import MultiPoint, box
+from typing import List, Tuple, Union
 
 from mmdet3d.datasets import NuScenesDataset
 
@@ -457,10 +456,8 @@ def get_2d_boxes(nusc, sample_data_token: str,
 def post_process_coords(
     corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
 ) -> Union[Tuple[float, float, float, float], None]:
-    """
-    Get the intersection of the convex hull of the reprojected
-    bbox corners and the image canvas, return None if no
-    intersection.
+    """Get the intersection of the convex hull of the reprojected bbox corners
+    and the image canvas, return None if no intersection.
 
     Args:
         corner_coords (list[int]): Corner coordinates of reprojected
@@ -491,9 +488,8 @@ def post_process_coords(
 
 def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
                     sample_data_token: str, filename: str) -> OrderedDict:
-    """
-    Generate one 2D annotation record given various informations on
-    top of the 2D bounding box coordinates.
+    """Generate one 2D annotation record given various informations on top of
+    the 2D bounding box coordinates.
 
     Args:
         ann_rec (dict): Original 3d annotation record.

tools/data_converter/scannet_data_utils.py

-import concurrent.futures as futures
-import os.path as osp
-
 import mmcv
 import numpy as np
+from concurrent import futures as futures
+from os import path as osp
 
 
 class ScanNetData(object):

tools/data_converter/sunrgbd_data_utils.py

-import concurrent.futures as futures
-import os.path as osp
-
 import mmcv
 import numpy as np
-import scipy.io as sio
+from concurrent import futures as futures
+from os import path as osp
+from scipy import io as sio
 
 
 def random_sampling(points, num_points, replace=None, return_choices=False):

tools/publish_model.py

 import argparse
 import subprocess
-
 import torch
 
 

tools/regnet2mmdet.py

 import argparse
-from collections import OrderedDict
-
 import torch
+from collections import OrderedDict
 
 
 def convert_stem(model_key, model_weight, state_dict, converted_names):

tools/test.py

 import argparse
-import os
-
 import mmcv
+import os
 import torch
 from mmcv import Config, DictAction
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import get_dist_info, init_dist, load_checkpoint
-from tools.fuse_conv_bn import fuse_module
 
 from mmdet3d.apis import single_gpu_test
 from mmdet3d.datasets import build_dataloader, build_dataset
 from mmdet3d.models import build_detector
 from mmdet.apis import multi_gpu_test, set_random_seed
 from mmdet.core import wrap_fp16_model
+from tools.fuse_conv_bn import fuse_module
 
 
 def parse_args():