Fix docstring problems

mmdet3d/core/evaluation/indoor_eval.py

@@ -5,7 +5,7 @@ from mmdet3d.core.bbox.iou_calculators.iou3d_calculator import bbox_overlaps_3d
 
 
 def boxes3d_depth_to_lidar(boxes3d, mid_to_bottom=True):
-    """Boxes3d Depth to Lidar.
+    """Boxes3d depth to lidar.
 
     Flip X-right,Y-forward,Z-up to X-forward,Y-left,Z-up.
 
@@ -93,7 +93,7 @@ def eval_det_cls(pred, gt, iou_thr=None):
     Args:
         pred (dict): {img_id: [(bbox, score)]} where bbox is numpy array.
         gt (dict): {img_id: [bbox]}.
-        iou_thr (List[float]): a list, iou threshold.
+        iou_thr (list[float]): a list, iou threshold.
 
     Return:
         ndarray: numpy array of length nd.
@@ -193,16 +193,16 @@ def eval_det_cls(pred, gt, iou_thr=None):
 
 
 def eval_map_recall(det_infos, gt_infos, ovthresh=None):
-    """Evaluate mAP and Recall.
+    """Evaluate mAP and recall.
 
     Generic functions to compute precision/recall for object detection
         for multiple classes.
 
     Args:
-        det_infos (List[ListList[[tuple]]]): Label, bbox and
+        det_infos (list[list[list[tuple]]]): Label, bbox and
             score of the detection result.
-        gt_infos (List[List[List]]): Label, bbox of the groundtruth.
-        ovthresh (List[float]): iou threshold.
+        gt_infos (list[list[list]]): Label, bbox of the groundtruth.
+        ovthresh (list[float]): iou threshold.
             Default: None.
 
     Return:
@@ -267,9 +267,9 @@ def indoor_eval(gt_annos, dt_annos, metric, label2cat):
     Evaluate the result of the detection.
 
     Args:
-        gt_annos (List[List[dict]]): GT annotations.
-        dt_annos (List[List[List[tuple]]]): Detection annotations.
-        metric (List[float]): AP IoU thresholds.
+        gt_annos (list[list[dict]]): GT annotations.
+        dt_annos (list[list[List[tuple]]]): Detection annotations.
+        metric (list[float]): AP IoU thresholds.
         label2cat (dict): {label: cat}.
 
     Return:

mmdet3d/datasets/indoor_base_dataset.py

@@ -98,12 +98,12 @@ class IndoorBaseDataset(torch_data.Dataset):
         return class_names
 
     def _generate_annotations(self, output):
-        """Generate Annotations.
+        """Generate annotations.
 
         Transform results of the model to the form of the evaluation.
 
         Args:
-            output (List): The output of the model.
+            output (list): The output of the model.
         """
         result = []
         bs = len(output)
@@ -140,8 +140,8 @@ class IndoorBaseDataset(torch_data.Dataset):
         Evaluation in indoor protocol.
 
         Args:
-            results (List): List of result.
-            metric (List[float]): AP IoU thresholds.
+            results (list): List of result.
+            metric (list[float]): AP IoU thresholds.
         """
         results = self.format_results(results)
         from mmdet3d.core.evaluation import indoor_eval

mmdet3d/datasets/pipelines/indoor_augment.py

@@ -5,10 +5,10 @@ from mmdet.datasets.builder import PIPELINES
 
 @PIPELINES.register_module()
 class IndoorFlipData(object):
-    """Indoor Flip Data.
+    """Indoor flip data.
 
     Flip point cloud and ground truth boxes.
-    The point cloud will ve flipped along the yz plane
+    The point cloud will be flipped along the yz plane
     and the xz plane with a certain probability.
 
     Args:
@@ -53,19 +53,19 @@ class IndoorFlipData(object):
 
 @PIPELINES.register_module()
 class IndoorPointsColorJitter(object):
-    """Indoor Points Color Jitter.
+    """Indoor points color jitter.
 
     Randomly change the brightness and color of the point cloud, and
     drop out the points' colors with a certain range and probability.
 
     Args:
-        color_mean (List[float]): Mean color of the point cloud.
+        color_mean (list[float]): Mean color of the point cloud.
             Default: [0.5, 0.5, 0.5].
-        bright_range (List[float]): Range of brightness.
+        bright_range (list[float]): Range of brightness.
             Default: [0.8, 1.2].
-        color_shift_range (List[float]): Range of color shift.
+        color_shift_range (list[float]): Range of color shift.
             Default: [0.95, 1.05].
-        jitter_range (List[float]): Range of jittering.
+        jitter_range (list[float]): Range of jittering.
             Default: [-0.025, 0.025].
         drop_prob (float): Probability to drop out points' color.
             Default: 0.3
@@ -118,16 +118,16 @@ class IndoorPointsColorJitter(object):
 # TODO: try transform noise.
 @PIPELINES.register_module()
 class IndoorGlobalRotScale(object):
-    """Indoor Global Rotate Scale.
+    """Indoor global rotate and scale.
 
     Augment sunrgbd and scannet data with global rotating and scaling.
 
     Args:
         use_height (bool): Whether to use height.
             Default: True.
-        rot_range (List[float]): Range of rotation.
+        rot_range (list[float]): Range of rotation.
             Default: None.
-        scale_range (List[float]): Range of scale.
+        scale_range (list[float]): Range of scale.
             Default: None.
     """
 
@@ -153,7 +153,7 @@ class IndoorGlobalRotScale(object):
         return rot_mat
 
     def _rotate_aligned_boxes(self, input_boxes, rot_mat):
-        """Rotate Aligned Boxes.
+        """Rotate aligned boxes.
 
         Rotate function for the aligned boxes.
 

mmdet3d/datasets/pipelines/indoor_loading.py

@@ -6,12 +6,12 @@ from mmdet.datasets.builder import PIPELINES
 
 @PIPELINES.register_module()
 class IndoorPointsColorNormalize(object):
-    """Indoor Points Color Normalize
+    """Indoor points color normalize
 
     Normalize color of the points.
 
     Args:
-        color_mean (List[float]): Mean color of the point cloud.
+        color_mean (list[float]): Mean color of the point cloud.
     """
 
     def __init__(self, color_mean):
@@ -33,7 +33,7 @@ class IndoorPointsColorNormalize(object):
 
 @PIPELINES.register_module()
 class IndoorLoadPointsFromFile(object):
-    """Indoor Load Points From File.
+    """Indoor load points from file.
 
     Load sunrgbd and scannet points from file.
 
@@ -41,7 +41,7 @@ class IndoorLoadPointsFromFile(object):
         use_height (bool): Whether to use height.
         load_dim (int): The dimension of the loaded points.
             Default: 6.
-        use_dim (List[int]): Which dimensions of the points to be used.
+        use_dim (list[int]): Which dimensions of the points to be used.
             Default: [0, 1, 2].
     """
 
@@ -78,7 +78,7 @@ class IndoorLoadPointsFromFile(object):
 
 @PIPELINES.register_module()
 class IndoorLoadAnnotations3D(object):
-    """Indoor Load Annotations3D.
+    """Indoor load annotations3D.
 
     Load instance mask and semantic mask of points.
     """

mmdet3d/datasets/pipelines/indoor_sample.py

@@ -5,7 +5,7 @@ from mmdet.datasets.builder import PIPELINES
 
 @PIPELINES.register_module()
 class IndoorPointSample(object):
-    """Point Sample.
+    """Indoor point sample.
 
     Sampling data to a certain number.
 
@@ -22,7 +22,7 @@ class IndoorPointSample(object):
                                num_samples,
                                replace=None,
                                return_choices=False):
-        """Points Random Sampling.
+        """Points random sampling.
 
         Sample points to a certain number.
 
@@ -34,7 +34,7 @@ class IndoorPointSample(object):
 
         Returns:
             points (ndarray): 3D Points.
-            choices (ndarray): The generated random samples
+            choices (ndarray): The generated random samples.
         """
         if replace is None:
             replace = (points.shape[0] < num_samples)

tools/data_converter/indoor_converter.py

@@ -9,7 +9,7 @@ def create_indoor_info_file(data_path,
                             pkl_prefix='sunrgbd',
                             save_path=None,
                             use_v1=False):
-    """Create indoor  information file.
+    """Create indoor information file.
 
     Get information of the raw data and save it to the pkl file.
 

tools/data_converter/scannet_data_utils.py

@@ -6,12 +6,12 @@ import numpy as np
 
 
 class ScanNetData(object):
-    """ScanNet Data
+    """ScanNet data.
 
-    Generate scannet infos for scannet_converter
+    Generate scannet infos for scannet_converter.
 
     Args:
-        root_path (str): Root path of the raw data
+        root_path (str): Root path of the raw data.
         split (str): Set split type of the data. Default: 'train'.
     """
 
@@ -56,11 +56,11 @@ class ScanNetData(object):
         Args:
             num_workers (int): Number of threads to be used. Default: 4.
             has_label (bool): Whether the data has label. Default: True.
-            sample_id_list (List[int]): Index list of the sample.
+            sample_id_list (list[int]): Index list of the sample.
                 Default: None.
 
         Returns:
-            infos (List[dict]): Information of the raw data.
+            infos (list[dict]): Information of the raw data.
         """
 
         def process_single_scene(sample_idx):

tools/data_converter/sunrgbd_data_utils.py

@@ -7,7 +7,7 @@ import scipy.io as sio
 
 
 def random_sampling(points, num_points, replace=None, return_choices=False):
-    """Random Sampling.
+    """Random sampling.
 
     Sampling point cloud to a certain number of points.
 
@@ -57,9 +57,9 @@ class SUNRGBDInstance(object):
 
 
 class SUNRGBDData(object):
-    """SUNRGBD Data
+    """SUNRGBD data.
 
-    Generate scannet infos for sunrgbd_converter
+    Generate scannet infos for sunrgbd_converter.
 
     Args:
         root_path (str): Root path of the raw data.
@@ -130,11 +130,11 @@ class SUNRGBDData(object):
         Args:
             num_workers (int): Number of threads to be used. Default: 4.
             has_label (bool): Whether the data has label. Default: True.
-            sample_id_list (List[int]): Index list of the sample.
+            sample_id_list (list[int]): Index list of the sample.
                 Default: None.
 
         Returns:
-            infos (List[dict]): Information of the raw data.
+            infos (list[dict]): Information of the raw data.
         """
 
         def process_single_scene(sample_idx):