Tidy up some docstrings for utils.py

python-sdk/nuscenes/eval/lidarseg/utils.py

@@ -1,10 +1,18 @@
+from typing import Dict
+
 import numpy as np
 
 from nuscenes import NuScenes
 
 
 class LidarsegChallengeAdaptor:
+    """
+
+    """
     def __init__(self, nusc: NuScenes):
+        """
+
+        """
         self.nusc = nusc
 
         self.raw_name_2_merged_name_mapping = self.get_raw2merged()
@@ -15,7 +23,11 @@ def __init__(self, nusc: NuScenes):
         self.raw_idx_2_merged_idx_mapping = self.get_raw_idx_2_merged_idx()
 
     @staticmethod
-    def get_raw2merged():
+    def get_raw2merged() -> Dict:
+        """
+        Returns the mapping from
+        :return:
+        """
         return {'noise': 'void_ignore',
                 'human.pedestrian.adult': 'pedestrian',
                 'human.pedestrian.child': 'pedestrian',
@@ -50,8 +62,10 @@ def get_raw2merged():
                 'vehicle.ego': 'void_ignore'}
 
     @staticmethod
-    def get_merged2idx():
+    def get_merged2idx() -> Dict:
         """
+        Returns the mapping from the merged class names to the merged class indices.
+        :return: A dictionary containing the mapping from the merged class names to the merged class indices.
         """
         return {'void_ignore': 0,
                 'barrier': 1,
@@ -71,14 +85,20 @@ def get_merged2idx():
                 'manmade': 15,
                 'vegetation': 16}
 
-    def get_raw_idx_2_merged_idx(self):
+    def get_raw_idx_2_merged_idx(self) -> Dict:
+        """
+
+        """
         raw_idx_2_merged_idx_mapping = dict()
         for raw_name, raw_idx in self.nusc.lidarseg_name2idx_mapping.items():
             raw_idx_2_merged_idx_mapping[raw_idx] = self.merged_name_2_merged_idx_mapping[
                 self.raw_name_2_merged_name_mapping[raw_name]]
         return raw_idx_2_merged_idx_mapping
 
-    def check_mapping(self):
+    def check_mapping(self) -> None:
+        """
+
+        """
         merged_set = set()
         for raw_name, merged_name in self.raw_name_2_merged_name_mapping.items():
             merged_set.add(merged_name)
@@ -88,7 +108,7 @@ def check_mapping(self):
 
     def convert_label(self, points_label: np.ndarray) -> np.ndarray:
         """
-        Convert the labels in a single .bin file according to the provided mapping
+        Convert the labels in a single .bin file according to the provided mapping.
         :param points_label: The .bin to be converted (e.g. './i_contain_the_labels_for_a_pointcloud.bin')
         """
         counter_before = self.get_stats(points_label)  # get stats before conversion
@@ -112,7 +132,7 @@ def compare_stats(self, counter_before: np.array, counter_after: np.array) -> bo
                                no points, class 1 has 1 point, class 2 has 34 points, etc.
         :param counter_after: A numPy array which contains the counts of each class (the index of the array corresponds
                               to the class label) after conversion
-        :returns: True or False; True if the stats before and after conversion are the same, and False if otherwise.
+        :return: True or False; True if the stats before and after conversion are the same, and False if otherwise.
         """
         counter_check = [0] * len(counter_after)
         for i, count in enumerate(counter_before):  # Note that it is expected that the class labels are 0-indexed.
@@ -125,8 +145,9 @@ def compare_stats(self, counter_before: np.array, counter_after: np.array) -> bo
     def get_stats(self, points_label: np.array) -> np.ndarray:
         """
         Get frequency of each label in a point cloud.
-        :param points_label: A numPy array which contains the labels of the point cloud; e.g. np.array([2, 1, 34, ..., 38])
-        :returns: An array which contains the counts of each label in the point cloud. The index of the point cloud
+        :param points_label: A numPy array which contains the labels of the point cloud;
+                             e.g. np.array([2, 1, 34, ..., 38])
+        :return: An array which contains the counts of each label in the point cloud. The index of the point cloud
                   corresponds to the index of the class label. E.g. [0, 2345, 12, 451] means that there are no points
                   in class 0, there are 2345 points in class 1, there are 12 points in class 2 etc.
         """