[Enhance]: Add typehints for dataset transforms and fix potential bug for PointSample

mmdet3d/datasets/transforms/compose.py

@@ -32,7 +32,7 @@ def __call__(self, data):
             data (dict): A result dict contains the data to transform.
 
         Returns:
-           dict: Transformed data.
+            dict: Transformed data.
         """
 
         for t in self.transforms:

mmdet3d/datasets/transforms/dbsampler.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
 import os
-import warnings
+from typing import List, Optional
 
 import mmengine
 import numpy as np
@@ -16,18 +16,19 @@ class BatchSampler:
 
     Args:
         sample_list (list[dict]): List of samples.
-        name (str, optional): The category of samples. Default: None.
-        epoch (int, optional): Sampling epoch. Default: None.
-        shuffle (bool, optional): Whether to shuffle indices. Default: False.
-        drop_reminder (bool, optional): Drop reminder. Default: False.
+        name (str, optional): The category of samples. Defaults to None.
+        epoch (int, optional): Sampling epoch. Defaults to None.
+        shuffle (bool, optional): Whether to shuffle indices.
+            Defaults to False.
+        drop_reminder (bool, optional): Drop reminder. Defaults to False.
     """
 
     def __init__(self,
-                 sampled_list,
-                 name=None,
-                 epoch=None,
-                 shuffle=True,
-                 drop_reminder=False):
+                 sampled_list: List[dict],
+                 name: Optional[str] = None,
+                 epoch: Optional[int] = None,
+                 shuffle: bool = True,
+                 drop_reminder: bool = False) -> None:
         self._sampled_list = sampled_list
         self._indices = np.arange(len(sampled_list))
         if shuffle:
@@ -40,7 +41,7 @@ def __init__(self,
         self._epoch_counter = 0
         self._drop_reminder = drop_reminder
 
-    def _sample(self, num):
+    def _sample(self, num: int) -> List[int]:
         """Sample specific number of ground truths and return indices.
 
         Args:
@@ -57,15 +58,15 @@ def _sample(self, num):
             self._idx += num
         return ret
 
-    def _reset(self):
+    def _reset(self) -> None:
         """Reset the index of batchsampler to zero."""
         assert self._name is not None
         # print("reset", self._name)
         if self._shuffle:
             np.random.shuffle(self._indices)
         self._idx = 0
 
-    def sample(self, num):
+    def sample(self, num: int) -> List[dict]:
         """Sample specific number of ground truths.
 
         Args:
@@ -88,24 +89,28 @@ class DataBaseSampler(object):
         rate (float): Rate of actual sampled over maximum sampled number.
         prepare (dict): Name of preparation functions and the input value.
         sample_groups (dict): Sampled classes and numbers.
-        classes (list[str], optional): List of classes. Default: None.
-        points_loader(dict, optional): Config of points loader. Default:
-            dict(type='LoadPointsFromFile', load_dim=4, use_dim=[0,1,2,3])
+        classes (list[str], optional): List of classes. Defaults to None.
+        points_loader(dict, optional): Config of points loader. Defaults to
+            dict(type='LoadPointsFromFile', load_dim=4, use_dim=[0, 1, 2, 3]).
+        file_client_args (dict, optional): Config dict of file clients,
+            refer to
+            https://github.com/open-mmlab/mmengine/blob/main/mmengine/fileio/file_client.py
+            for more details. Defaults to dict(backend='disk').
     """
 
     def __init__(self,
-                 info_path,
-                 data_root,
-                 rate,
-                 prepare,
-                 sample_groups,
-                 classes=None,
-                 points_loader=dict(
+                 info_path: str,
+                 data_root: str,
+                 rate: float,
+                 prepare: dict,
+                 sample_groups: dict,
+                 classes: Optional[List[str]] = None,
+                 points_loader: dict = dict(
                      type='LoadPointsFromFile',
                      coord_type='LIDAR',
                      load_dim=4,
                      use_dim=[0, 1, 2, 3]),
-                 file_client_args=dict(backend='disk')):
+                 file_client_args: dict = dict(backend='disk')) -> None:
         super().__init__()
         self.data_root = data_root
         self.info_path = info_path
@@ -118,18 +123,9 @@ def __init__(self,
         self.file_client = mmengine.FileClient(**file_client_args)
 
         # load data base infos
-        if hasattr(self.file_client, 'get_local_path'):
-            with self.file_client.get_local_path(info_path) as local_path:
-                # loading data from a file-like object needs file format
-                db_infos = mmengine.load(
-                    open(local_path, 'rb'), file_format='pkl')
-        else:
-            warnings.warn(
-                'The used MMCV version does not have get_local_path. '
-                f'We treat the {info_path} as local paths and it '
-                'might cause errors if the path is not a local path. '
-                'Please use MMCV>= 1.3.16 if you meet errors.')
-            db_infos = mmengine.load(info_path)
+        with self.file_client.get_local_path(info_path) as local_path:
+            # loading data from a file-like object needs file format
+            db_infos = mmengine.load(open(local_path, 'rb'), file_format='pkl')
 
         # filter database infos
         from mmengine.logging import MMLogger
@@ -163,7 +159,7 @@ def __init__(self,
         # TODO: No group_sampling currently
 
     @staticmethod
-    def filter_by_difficulty(db_infos, removed_difficulty):
+    def filter_by_difficulty(db_infos: dict, removed_difficulty: list) -> dict:
         """Filter ground truths by difficulties.
 
         Args:
@@ -182,7 +178,7 @@ def filter_by_difficulty(db_infos, removed_difficulty):
         return new_db_infos
 
     @staticmethod
-    def filter_by_min_points(db_infos, min_gt_points_dict):
+    def filter_by_min_points(db_infos: dict, min_gt_points_dict: dict) -> dict:
         """Filter ground truths by number of points in the bbox.
 
         Args:
@@ -203,12 +199,19 @@ def filter_by_min_points(db_infos, min_gt_points_dict):
                 db_infos[name] = filtered_infos
         return db_infos
 
-    def sample_all(self, gt_bboxes, gt_labels, img=None, ground_plane=None):
+    def sample_all(self,
+                   gt_bboxes: np.ndarray,
+                   gt_labels: np.ndarray,
+                   img: Optional[np.ndarray] = None,
+                   ground_plane: Optional[np.ndarray] = None) -> dict:
         """Sampling all categories of bboxes.
 
         Args:
             gt_bboxes (np.ndarray): Ground truth bounding boxes.
             gt_labels (np.ndarray): Ground truth labels of boxes.
+            img (np.ndarray, optional): Image array. Defaults to None.
+            ground_plane (np.ndarray, optional): Ground plane information.
+                Defaults to None.
 
         Returns:
             dict: Dict of sampled 'pseudo ground truths'.
@@ -301,7 +304,10 @@ def sample_all(self, gt_bboxes, gt_labels, img=None, ground_plane=None):
 
         return ret
 
-    def sample_class_v2(self, name, num, gt_bboxes):
+    def sample_class_v2(self,
+                        name: str,
+                        num: int,
+                        gt_bboxes: np.ndarray) -> List[dict]:
         """Sampling specific categories of bounding boxes.
 
         Args:

mmdet3d/datasets/transforms/formating.py

@@ -63,16 +63,16 @@ class Pack3DDetInputs(BaseTransform):
 
     def __init__(
         self,
-        keys: dict,
-        meta_keys: dict = ('img_path', 'ori_shape', 'img_shape', 'lidar2img',
-                           'depth2img', 'cam2img', 'pad_shape', 'scale_factor',
-                           'flip', 'pcd_horizontal_flip', 'pcd_vertical_flip',
-                           'box_mode_3d', 'box_type_3d', 'img_norm_cfg',
-                           'num_pts_feats', 'pcd_trans', 'sample_idx',
-                           'pcd_scale_factor', 'pcd_rotation',
-                           'pcd_rotation_angle', 'lidar_path',
-                           'transformation_3d_flow', 'trans_mat',
-                           'affine_aug')):
+        keys: tuple,
+        meta_keys: tuple = ('img_path', 'ori_shape', 'img_shape', 'lidar2img',
+                            'depth2img', 'cam2img', 'pad_shape',
+                            'scale_factor', 'flip', 'pcd_horizontal_flip',
+                            'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d',
+                            'img_norm_cfg', 'num_pts_feats', 'pcd_trans',
+                            'sample_idx', 'pcd_scale_factor', 'pcd_rotation',
+                            'pcd_rotation_angle', 'lidar_path',
+                            'transformation_3d_flow', 'trans_mat',
+                            'affine_aug')) -> None:
         self.keys = keys
         self.meta_keys = meta_keys
 
@@ -99,7 +99,7 @@ def transform(self, results: Union[dict,
                 - img
 
             - 'data_samples' (obj:`Det3DDataSample`): The annotation info of
-                the sample.
+              the sample.
         """
         # augtest
         if isinstance(results, list):
@@ -116,7 +116,7 @@ def transform(self, results: Union[dict,
         else:
             raise NotImplementedError
 
-    def pack_single_results(self, results):
+    def pack_single_results(self, results: dict) -> dict:
         """Method to pack the single input data. when the value in this dict is
         a list, it usually is in Augmentations Testing.
 
@@ -132,7 +132,7 @@ def pack_single_results(self, results):
                 - points
                 - img
 
-            - 'data_samples' (obj:`Det3DDataSample`): The annotation info
+            - 'data_samples' (:obj:`Det3DDataSample`): The annotation info
               of the sample.
         """
         # Format 3D data
@@ -220,6 +220,7 @@ def pack_single_results(self, results):
         return packed_results
 
     def __repr__(self) -> str:
+        """str: Return a string that describes the module."""
         repr_str = self.__class__.__name__
         repr_str += f'(keys={self.keys})'
         repr_str += f'(meta_keys={self.meta_keys})'