[Refactor] Refactor code structure and docstrings (#803)

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning

tools/data_converter/s3dis_data_utils.py

@@ -13,7 +13,7 @@ class S3DISData(object):
 
     Args:
         root_path (str): Root path of the raw data.
-        split (str): Set split type of the data. Default: 'Area_1'.
+        split (str, optional): Set split type of the data. Default: 'Area_1'.
     """
 
     def __init__(self, root_path, split='Area_1'):
@@ -48,9 +48,11 @@ class S3DISData(object):
         This method gets information from the raw data.
 
         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.
+            num_workers (int, optional): Number of threads to be used.
+                Default: 4.
+            has_label (bool, optional): Whether the data has label.
+                Default: True.
+            sample_id_list (list[int], optional): Index list of the sample.
                 Default: None.
 
         Returns:
@@ -154,10 +156,11 @@ class S3DISSegData(object):
     Args:
         data_root (str): Root path of the raw data.
         ann_file (str): The generated scannet infos.
-        split (str): Set split type of the data. Default: 'train'.
-        num_points (int): Number of points in each data input. Default: 8192.
-        label_weight_func (function): Function to compute the label weight.
-            Default: None.
+        split (str, optional): Set split type of the data. Default: 'train'.
+        num_points (int, optional): Number of points in each data input.
+            Default: 8192.
+        label_weight_func (function, optional): Function to compute the
+            label weight. Default: None.
     """
 
     def __init__(self,
@@ -209,7 +212,7 @@ class S3DISSegData(object):
         return label
 
     def get_scene_idxs_and_label_weight(self):
-        """Compute scene_idxs for data sampling and label weight for loss \
+        """Compute scene_idxs for data sampling and label weight for loss
         calculation.
 
         We sample more times for scenes with more points. Label_weight is

tools/data_converter/scannet_data_utils.py

@@ -13,7 +13,7 @@ class ScanNetData(object):
 
     Args:
         root_path (str): Root path of the raw data.
-        split (str): Set split type of the data. Default: 'train'.
+        split (str, optional): Set split type of the data. Default: 'train'.
     """
 
     def __init__(self, root_path, split='train'):
@@ -90,9 +90,11 @@ class ScanNetData(object):
         This method gets information from the raw data.
 
         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.
+            num_workers (int, optional): Number of threads to be used.
+                Default: 4.
+            has_label (bool, optional): Whether the data has label.
+                Default: True.
+            sample_id_list (list[int], optional): Index list of the sample.
                 Default: None.
 
         Returns:
@@ -201,10 +203,11 @@ class ScanNetSegData(object):
     Args:
         data_root (str): Root path of the raw data.
         ann_file (str): The generated scannet infos.
-        split (str): Set split type of the data. Default: 'train'.
-        num_points (int): Number of points in each data input. Default: 8192.
-        label_weight_func (function): Function to compute the label weight.
-            Default: None.
+        split (str, optional): Set split type of the data. Default: 'train'.
+        num_points (int, optional): Number of points in each data input.
+            Default: 8192.
+        label_weight_func (function, optional): Function to compute the
+            label weight. Default: None.
     """
 
     def __init__(self,
@@ -261,7 +264,7 @@ class ScanNetSegData(object):
         return label
 
     def get_scene_idxs_and_label_weight(self):
-        """Compute scene_idxs for data sampling and label weight for loss \
+        """Compute scene_idxs for data sampling and label weight for loss
         calculation.
 
         We sample more times for scenes with more points. Label_weight is

tools/data_converter/sunrgbd_data_utils.py

@@ -42,7 +42,7 @@ class SUNRGBDInstance(object):
         self.ymax = data[2] + data[4]
         self.box2d = np.array([self.xmin, self.ymin, self.xmax, self.ymax])
         self.centroid = np.array([data[5], data[6], data[7]])
-        # data[9] is dx (l), data[8] is dy (w), data[10] is dz (h)
+        # data[9] is x_size (l), data[8] is y_size (w), data[10] is z_size (h)
         # in our depth coordinate system,
         # l corresponds to the size along the x axis
         self.size = np.array([data[9], data[8], data[10]]) * 2
@@ -62,8 +62,8 @@ class SUNRGBDData(object):
 
     Args:
         root_path (str): Root path of the raw data.
-        split (str): Set split type of the data. Default: 'train'.
-        use_v1 (bool): Whether to use v1. Default: False.
+        split (str, optional): Set split type of the data. Default: 'train'.
+        use_v1 (bool, optional): Whether to use v1. Default: False.
     """
 
     def __init__(self, root_path, split='train', use_v1=False):
@@ -128,9 +128,11 @@ class SUNRGBDData(object):
         This method gets information from the raw data.
 
         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.
+            num_workers (int, optional): Number of threads to be used.
+                Default: 4.
+            has_label (bool, optional): Whether the data has label.
+                Default: True.
+            sample_id_list (list[int], optional): Index list of the sample.
                 Default: None.
 
         Returns:

tools/data_converter/waymo_converter.py

@@ -31,8 +31,8 @@ class Waymo2KITTI(object):
         save_dir (str): Directory to save data in KITTI format.
         prefix (str): Prefix of filename. In general, 0 for training, 1 for
             validation and 2 for testing.
-        workers (str): Number of workers for the parallel process.
-        test_mode (bool): Whether in the test_mode. Default: False.
+        workers (int, optional): Number of workers for the parallel process.
+        test_mode (bool, optional): Whether in the test_mode. Default: False.
     """
 
     def __init__(self,
@@ -402,8 +402,8 @@ class Waymo2KITTI(object):
                 camera projections corresponding with two returns.
             range_image_top_pose (:obj:`Transform`): Range image pixel pose for
                 top lidar.
-            ri_index (int): 0 for the first return, 1 for the second return.
-                Default: 0.
+            ri_index (int, optional): 0 for the first return,
+                1 for the second return. Default: 0.
 
         Returns:
             tuple[list[np.ndarray]]: (List of points with shape [N, 3],