[Enhance] Update data structures

mmdet3d/structures/bbox_3d/base_box3d.py

@@ -463,17 +463,11 @@ def overlaps(cls, boxes1, boxes2, mode='iou'):
  # height overlap
  overlaps_h = cls.height_overlaps(boxes1, boxes2)
 
- # Restrict the min values of W and H to avoid memory overflow in
- # ``box_iou_rotated``.
- boxes1_bev, boxes2_bev = boxes1.bev, boxes2.bev
- boxes1_bev[:, 2:4] = boxes1_bev[:, 2:4].clamp(min=1e-4)
- boxes2_bev[:, 2:4] = boxes2.bev[:, 2:4].clamp(min=1e-4)
-
  # bev overlap
- iou2d = box_iou_rotated(boxes1_bev, boxes2_bev)
- areas1 = (boxes1_bev[:, 2] * boxes1_bev[:, 3]).unsqueeze(1).expand(
+ iou2d = box_iou_rotated(boxes1.bev, boxes2.bev)
+ areas1 = (boxes1.bev[:, 2] * boxes1.bev[:, 3]).unsqueeze(1).expand(
  rows, cols)
- areas2 = (boxes2_bev[:, 2] * boxes2_bev[:, 3]).unsqueeze(0).expand(
+ areas2 = (boxes2.bev[:, 2] * boxes2.bev[:, 3]).unsqueeze(0).expand(
  rows, cols)
  overlaps_bev = iou2d * (areas1 + areas2) / (1 + iou2d)
 

mmdet3d/structures/det3d_data_sample.py

@@ -15,46 +15,47 @@ class Det3DDataSample(DetDataSample):
  The attributes in ``Det3DDataSample`` are divided into several parts:
 
  - ``proposals``(InstanceData): Region proposals used in two-stage
-  detectors.
+ detectors.
  - ``ignored_instances``(InstanceData): Instances to be ignored during
-  training/testing.
+ training/testing.
  - ``gt_instances_3d``(InstanceData): Ground truth of 3D instance
-  annotations.
+ annotations.
  - ``gt_instances``(InstanceData): Ground truth of 2D instance
-  annotations.
+ annotations.
  - ``pred_instances_3d``(InstanceData): 3D instances of model
-  predictions.
-  - For point-cloud 3d object detection task whose input modality
+ predictions.
+ - For point-cloud 3D object detection task whose input modality
  is `use_lidar=True, use_camera=False`, the 3D predictions results
  are saved in `pred_instances_3d`.
-  - For vision-only(monocular/multi-view) 3D object detection task
+ - For vision-only (monocular/multi-view) 3D object detection task
  whose input modality is `use_lidar=False, use_camera=True`, the 3D
  predictions are saved in `pred_instances_3d`.
  - ``pred_instances``(InstanceData): 2D instances of model
-  predictions.
-  -  For multi-modality 3D detection task whose input modality is
+ predictions.
+ - For multi-modality 3D detection task whose input modality is
  `use_lidar=True, use_camera=True`, the 2D predictions
  are saved in `pred_instances`.
  - ``pts_pred_instances_3d``(InstanceData): 3D instances of model
-  predictions based on point cloud.
-  -  For multi-modality 3D detection task whose input modality is
+ predictions based on point cloud.
+ - For multi-modality 3D detection task whose input modality is
  `use_lidar=True, use_camera=True`, the 3D predictions based on
  point cloud are saved in `pts_pred_instances_3d` to distinguish
  with `img_pred_instances_3d` which based on image.
  - ``img_pred_instances_3d``(InstanceData): 3D instances of model
-  predictions based on image.
-  -  For multi-modality 3D detection task whose input modality is
+ predictions based on image.
+ - For multi-modality 3D detection task whose input modality is
  `use_lidar=True, use_camera=True`, the 3D predictions based on
  image are saved in `img_pred_instances_3d` to distinguish with
  `pts_pred_instances_3d` which based on point cloud.
  - ``gt_pts_seg``(PointData): Ground truth of point cloud
-  segmentation.
+ segmentation.
  - ``pred_pts_seg``(PointData): Prediction of point cloud
-  segmentation.
- - ``eval_ann_info``(dict): Raw annotation, which will be passed to
-   evaluator and do the online evaluation.
+ segmentation.
+ - ``eval_ann_info``(dict or None): Raw annotation, which will be passed
+ to evaluator and do the online evaluation.
 
  Examples:
+ >>> import torch
  >>> from mmengine.structures import InstanceData
 
  >>> from mmdet3d.structures import Det3DDataSample
@@ -64,8 +65,8 @@ class Det3DDataSample(DetDataSample):
  >>> meta_info = dict(img_shape=(800, 1196, 3),
  ... pad_shape=(800, 1216, 3))
  >>> gt_instances_3d = InstanceData(metainfo=meta_info)
- >>> gt_instances_3d.bboxes = BaseInstance3DBoxes(torch.rand((5, 7)))
- >>> gt_instances_3d.labels = torch.randint(0,3,(5, ))
+ >>> gt_instances_3d.bboxes_3d = BaseInstance3DBoxes(torch.rand((5, 7)))
+ >>> gt_instances_3d.labels_3d = torch.randint(0, 3, (5,))
  >>> data_sample.gt_instances_3d = gt_instances_3d
  >>> assert 'img_shape' in data_sample.gt_instances_3d.metainfo_keys()
  >>> print(data_sample)
@@ -81,8 +82,8 @@ class Det3DDataSample(DetDataSample):
  img_shape: (800, 1196, 3)
 
  DATA FIELDS
- labels: tensor([0, 0, 1, 0, 2])
- bboxes: BaseInstance3DBoxes(
+ labels_3d: tensor([0, 0, 1, 0, 2])
+ bboxes_3d: BaseInstance3DBoxes(
  tensor([[0.2874, 0.3078, 0.8368, 0.2326, 0.9845, 0.6199, 0.9944],
  [0.6222, 0.8778, 0.7306, 0.3320, 0.3973, 0.7662, 0.7326],
  [0.8547, 0.6082, 0.1660, 0.1676, 0.9810, 0.3092, 0.0917],
@@ -96,8 +97,8 @@ class Det3DDataSample(DetDataSample):
  img_shape: (800, 1196, 3)
 
  DATA FIELDS
- labels: tensor([0, 0, 1, 0, 2])
- bboxes: BaseInstance3DBoxes(
+ labels_3d: tensor([0, 0, 1, 0, 2])
+ bboxes_3d: BaseInstance3DBoxes(
  tensor([[0.2874, 0.3078, 0.8368, 0.2326, 0.9845, 0.6199, 0.9944],
  [0.6222, 0.8778, 0.7306, 0.3320, 0.3973, 0.7662, 0.7326],
  [0.8547, 0.6082, 0.1660, 0.1676, 0.9810, 0.3092, 0.0917],
@@ -120,15 +121,16 @@ class Det3DDataSample(DetDataSample):
 
  >>> data_sample = Det3DDataSample()
  >>> gt_instances_3d_data = dict(
- ... bboxes=BaseInstance3DBoxes(torch.rand((2, 7))),
- ... labels=torch.rand(2))
+ ... bboxes_3d=BaseInstance3DBoxes(torch.rand((2, 7))),
+ ... labels_3d=torch.rand(2))
  >>> gt_instances_3d = InstanceData(**gt_instances_3d_data)
  >>> data_sample.gt_instances_3d = gt_instances_3d
  >>> assert 'gt_instances_3d' in data_sample
- >>> assert 'bboxes' in data_sample.gt_instances_3d
+ >>> assert 'bboxes_3d' in data_sample.gt_instances_3d
 
+ >>> from mmdet3d.structures import PointData
  >>> data_sample = Det3DDataSample()
- ... gt_pts_seg_data = dict(
+ >>> gt_pts_seg_data = dict(
  ... pts_instance_mask=torch.rand(2),
  ... pts_semantic_mask=torch.rand(2))
  >>> data_sample.gt_pts_seg = PointData(**gt_pts_seg_data)
@@ -162,73 +164,88 @@ def gt_instances_3d(self) -> InstanceData:
  return self._gt_instances_3d
 
  @gt_instances_3d.setter
- def gt_instances_3d(self, value: InstanceData):
+ def gt_instances_3d(self, value: InstanceData) -> None:
  self.set_field(value, '_gt_instances_3d', dtype=InstanceData)
 
  @gt_instances_3d.deleter
- def gt_instances_3d(self):
+ def gt_instances_3d(self) -> None:
  del self._gt_instances_3d
 
  @property
  def pred_instances_3d(self) -> InstanceData:
  return self._pred_instances_3d
 
  @pred_instances_3d.setter
- def pred_instances_3d(self, value: InstanceData):
+ def pred_instances_3d(self, value: InstanceData) -> None:
  self.set_field(value, '_pred_instances_3d', dtype=InstanceData)
 
  @pred_instances_3d.deleter
- def pred_instances_3d(self):
+ def pred_instances_3d(self) -> None:
  del self._pred_instances_3d
 
  @property
  def pts_pred_instances_3d(self) -> InstanceData:
  return self._pts_pred_instances_3d
 
  @pts_pred_instances_3d.setter
- def pts_pred_instances_3d(self, value: InstanceData):
+ def pts_pred_instances_3d(self, value: InstanceData) -> None:
  self.set_field(value, '_pts_pred_instances_3d', dtype=InstanceData)
 
  @pts_pred_instances_3d.deleter
- def pts_pred_instances_3d(self):
+ def pts_pred_instances_3d(self) -> None:
  del self._pts_pred_instances_3d
 
  @property
  def img_pred_instances_3d(self) -> InstanceData:
  return self._img_pred_instances_3d
 
  @img_pred_instances_3d.setter
- def img_pred_instances_3d(self, value: InstanceData):
+ def img_pred_instances_3d(self, value: InstanceData) -> None:
  self.set_field(value, '_img_pred_instances_3d', dtype=InstanceData)
 
  @img_pred_instances_3d.deleter
- def img_pred_instances_3d(self):
+ def img_pred_instances_3d(self) -> None:
  del self._img_pred_instances_3d
 
  @property
  def gt_pts_seg(self) -> PointData:
  return self._gt_pts_seg
 
  @gt_pts_seg.setter
- def gt_pts_seg(self, value: PointData):
+ def gt_pts_seg(self, value: PointData) -> None:
  self.set_field(value, '_gt_pts_seg', dtype=PointData)
 
  @gt_pts_seg.deleter
- def gt_pts_seg(self):
+ def gt_pts_seg(self) -> None:
  del self._gt_pts_seg
 
  @property
  def pred_pts_seg(self) -> PointData:
  return self._pred_pts_seg
 
  @pred_pts_seg.setter
- def pred_pts_seg(self, value: PointData):
+ def pred_pts_seg(self, value: PointData) -> None:
  self.set_field(value, '_pred_pts_seg', dtype=PointData)
 
  @pred_pts_seg.deleter
- def pred_pts_seg(self):
+ def pred_pts_seg(self) -> None:
  del self._pred_pts_seg
 
+ @property
+ def eval_ann_info(self) -> Union[dict, None]:
+ return self._eval_ann_info
+
+ @eval_ann_info.setter
+ def eval_ann_info(self, value: Union[dict, None]) -> None:
+ if value is None:
+ self.set_field(value, '_eval_ann_info')
+ else:
+ self.set_field(value, '_eval_ann_info', dtype=dict)
+
+ @eval_ann_info.deleter
+ def eval_ann_info(self) -> None:
+ del self._eval_ann_info
+
 
 SampleList = List[Det3DDataSample]
 OptSampleList = Optional[SampleList]

mmdet3d/structures/point_data.py

@@ -12,7 +12,7 @@
 
 
 class PointData(BaseDataElement):
- """Data structure for point-level annnotations or predictions.
+ """Data structure for point-level annotations or predictions.
 
  All data items in ``data_fields`` of ``PointData`` meet the following
  requirements:
@@ -27,58 +27,70 @@ class PointData(BaseDataElement):
 
  Examples:
  >>> metainfo = dict(
- ... sample_id=random.randint(0, 100))
+ ... sample_idx=random.randint(0, 100))
  >>> points = np.random.randint(0, 255, (100, 3))
  >>> point_data = PointData(metainfo=metainfo,
  ... points=points)
  >>> print(len(point_data))
- >>> (100)
+ 100
 
  >>> # slice
- >>> slice_data = pixel_data[10:60]
- >>> assert slice_data.shape == (50,)
+ >>> slice_data = point_data[10:60]
+ >>> assert len(slice_data) == 50
 
  >>> # set
- >>> point_data.pts_semantic_mask = torch.randint(0, 255, (100))
- >>> point_data.pts_instance_mask = torch.randint(0, 255, (100))
- >>> assert tuple(point_data.pts_semantic_mask.shape) == (100)
- >>> assert tuple(point_data.pts_instance_mask.shape) == (100)
+ >>> point_data.pts_semantic_mask = torch.randint(0, 255, (100,))
+ >>> point_data.pts_instance_mask = torch.randint(0, 255, (100,))
+ >>> assert tuple(point_data.pts_semantic_mask.shape) == (100,)
+ >>> assert tuple(point_data.pts_instance_mask.shape) == (100,)
  """
 
- def __setattr__(self, name: str, value: Sized):
+ def __setattr__(self, name: str, value: Sized) -> None:
  """setattr is only used to set data.
 
- the value must have the attribute of `__len__` and have the same length
- of PointData.
+ The value must have the attribute of `__len__` and have the same length
+ of `PointData`.
  """
  if name in ('_metainfo_fields', '_data_fields'):
  if not hasattr(self, name):
  super().__setattr__(name, value)
  else:
- raise AttributeError(
- f'{name} has been used as a '
- f'private attribute, which is immutable. ')
+ raise AttributeError(f'{name} has been used as a '
+ 'private attribute, which is immutable.')
 
  else:
  assert isinstance(value,
- Sized), 'value must contain `_len__` attribute'
+ Sized), 'value must contain `__len__` attribute'
+
+ if len(self) > 0:
+ assert len(value) == len(self), 'the length of ' \
+ f'values {len(value)} is ' \
+ 'not consistent with ' \
+ 'the length of this ' \
+ ':obj:`PointData` ' \
+ f'{len(self)}'
  super().__setattr__(name, value)
 
  __setitem__ = __setattr__
 
  def __getitem__(self, item: IndexType) -> 'PointData':
  """
  Args:
- item (str, obj:`slice`,
- obj`torch.LongTensor`, obj:`torch.BoolTensor`):
+ item (str, :obj:`slice`,
+ :obj:`torch.LongTensor`, :obj:`torch.BoolTensor`):
  get the corresponding values according to item.
 
  Returns:
- obj:`PointData`: Corresponding values.
+ :obj:`PointData`: Corresponding values.
  """
  if isinstance(item, list):
  item = np.array(item)
  if isinstance(item, np.ndarray):
+ # The default int type of numpy is platform dependent, int32 for
+ # windows and int64 for linux. `torch.Tensor` requires the index
+ # should be int64, therefore we simply convert it to int64 here.
+ # Mode details in https://github.com/numpy/numpy/issues/9464
+ item = item.astype(np.int64) if item.dtype == np.int32 else item
  item = torch.from_numpy(item)
  assert isinstance(
  item, (str, slice, int, torch.LongTensor, torch.cuda.LongTensor,
@@ -87,8 +99,8 @@ def __getitem__(self, item: IndexType) -> 'PointData':
  if isinstance(item, str):
  return getattr(self, item)
 
- if type(item) == int:
- if item >= len(self) or item < -len(self): # type:ignore
+ if isinstance(item, int):
+ if item >= len(self) or item < -len(self): # type: ignore
  raise IndexError(f'Index {item} out of range!')
  else:
  # keep the dimension
@@ -99,14 +111,14 @@ def __getitem__(self, item: IndexType) -> 'PointData':
  assert item.dim() == 1, 'Only support to get the' \
  ' values along the first dimension.'
  if isinstance(item, (torch.BoolTensor, torch.cuda.BoolTensor)):
- assert len(item) == len(self), f'The shape of the' \
- f' input(BoolTensor)) ' \
+ assert len(item) == len(self), 'The shape of the ' \
+ 'input(BoolTensor) ' \
  f'{len(item)} ' \
- f' does not match the shape ' \
- f'of the indexed tensor ' \
- f'in results_filed ' \
+ 'does not match the shape ' \
+ 'of the indexed tensor ' \
+ 'in results_field ' \
  f'{len(self)} at ' \
- f'first dimension. '
+ 'first dimension.'
 
  for k, v in self.items():
  if isinstance(v, torch.Tensor):
@@ -116,7 +128,7 @@ def __getitem__(self, item: IndexType) -> 'PointData':
  elif isinstance(
  v, (str, list, tuple)) or (hasattr(v, '__getitem__')
  and hasattr(v, 'cat')):
- # convert to indexes from boolTensor
+ # convert to indexes from BoolTensor
  if isinstance(item,
  (torch.BoolTensor, torch.cuda.BoolTensor)):
  indexes = torch.nonzero(item).view(
@@ -141,16 +153,15 @@ def __getitem__(self, item: IndexType) -> 'PointData':
  raise ValueError(
  f'The type of `{k}` is `{type(v)}`, which has no '
  'attribute of `cat`, so it does not '
- f'support slice with `bool`')
-
+ 'support slice with `bool`')
  else:
  # item is a slice
  for k, v in self.items():
  new_data[k] = v[item]
- return new_data # type:ignore
+ return new_data # type: ignore
 
  def __len__(self) -> int:
- """int: the length of PointData"""
+ """int: the length of `PointData`."""
  if len(self._data_fields) > 0:
  return len(self.values()[0])
  else: