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

… for `PointSample` (#1875)

* update dataset transforms

* update dbsampler docstring and add typehints

* add type hints and fix potential point sample bug

* fix lint

* fix

* fix

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})'