[Enhance] Update data structures

mmdet3d/structures/det3d_data_sample.py

@@ -14,219 +14,196 @@ class Det3DDataSample(DetDataSample):
 
  The attributes in ``Det3DDataSample`` are divided into several parts:
 
- - ``proposals``(InstanceData): Region proposals used in two-stage
-  detectors.
- - ``ignored_instances``(InstanceData): Instances to be ignored during
-  training/testing.
- - ``gt_instances_3d``(InstanceData): Ground truth of 3D instance
-  annotations.
- - ``gt_instances``(InstanceData): Ground truth of 2D instance
-  annotations.
- - ``pred_instances_3d``(InstanceData): 3D instances of model
-  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
+ - ``proposals`` (InstanceData): Region proposals used in two-stage
+ detectors.
+ - ``ignored_instances`` (InstanceData): Instances to be ignored during
+ training/testing.
+ - ``gt_instances_3d`` (InstanceData): Ground truth of 3D instance
+ annotations.
+ - ``gt_instances`` (InstanceData): Ground truth of 2D instance
+ annotations.
+ - ``pred_instances_3d`` (InstanceData): 3D instances of model
+ 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
  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
- `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
+ - ``pred_instances`` (InstanceData): 2D instances of model 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
  `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
+ - ``img_pred_instances_3d`` (InstanceData): 3D instances of model
+ 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.
- - ``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.
+ - ``gt_pts_seg`` (PointData): Ground truth of point cloud segmentation.
+ - ``pred_pts_seg`` (PointData): Prediction of point cloud segmentation.
+ - ``eval_ann_info`` (dict or None): Raw annotation, which will be
+ passed to evaluator and do the online evaluation.
 
  Examples:
- >>> from mmengine.structures import InstanceData
-
- >>> from mmdet3d.structures import Det3DDataSample
- >>> from mmdet3d.structures import BaseInstance3DBoxes
-
- >>> data_sample = Det3DDataSample()
- >>> 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, ))
- >>> data_sample.gt_instances_3d = gt_instances_3d
- >>> assert 'img_shape' in data_sample.gt_instances_3d.metainfo_keys()
- >>> print(data_sample)
- <Det3DDataSample(
-
- META INFORMATION
-
- DATA FIELDS
- _gt_instances_3d: <InstanceData(
-
+ >>> import torch
+ >>> from mmengine.structures import InstanceData
+
+ >>> from mmdet3d.structures import Det3DDataSample
+ >>> from mmdet3d.structures import BaseInstance3DBoxes
+
+ >>> data_sample = Det3DDataSample()
+ >>> meta_info = dict(
+ ... img_shape=(800, 1196, 3),
+ ... pad_shape=(800, 1216, 3))
+ >>> gt_instances_3d = InstanceData(metainfo=meta_info)
+ >>> 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()
+ >>> len(data_sample.gt_instances_3d)
+ 5
+ >>> print(data_sample)
+ <Det3DDataSample(
  META INFORMATION
- pad_shape: (800, 1216, 3)
- img_shape: (800, 1196, 3)
-
  DATA FIELDS
- labels: tensor([0, 0, 1, 0, 2])
- bboxes: 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],
- [0.4686, 0.7007, 0.4428, 0.0672, 0.3319, 0.3033, 0.8519],
- [0.9693, 0.5315, 0.4642, 0.9079, 0.2481, 0.1781, 0.9557]]))
- ) at 0x7fb0d9354280>
- gt_instances_3d: <InstanceData(
-
+ gt_instances_3d: <InstanceData(
+ META INFORMATION
+ img_shape: (800, 1196, 3)
+ pad_shape: (800, 1216, 3)
+ DATA FIELDS
+ labels_3d: tensor([1, 0, 2, 0, 1])
+ bboxes_3d: BaseInstance3DBoxes(
+ tensor([[1.9115e-01, 3.6061e-01, 6.7707e-01, 5.2902e-01, 8.0736e-01, 8.2759e-01, # noqa E501
+ 2.4328e-01],
+ [5.6272e-01, 2.7508e-01, 5.7966e-01, 9.2410e-01, 3.0456e-01, 1.8912e-01, # noqa E501
+ 3.3176e-01],
+ [8.1069e-01, 2.8684e-01, 7.7689e-01, 9.2397e-02, 5.5849e-01, 3.8007e-01, # noqa E501
+ 4.6719e-01],
+ [6.6346e-01, 4.8005e-01, 5.2318e-02, 4.4137e-01, 4.1163e-01, 8.9339e-01, # noqa E501
+ 7.2847e-01],
+ [2.4800e-01, 7.1944e-01, 3.4766e-01, 7.8583e-01, 8.5507e-01, 6.3729e-02, # noqa E501
+ 7.5161e-05]]))
+ ) at 0x7f7e29de3a00>
+ ) at 0x7f7e2a0e8640>
+ >>> pred_instances = InstanceData(metainfo=meta_info)
+ >>> pred_instances.bboxes = torch.rand((5, 4))
+ >>> pred_instances.scores = torch.rand((5, ))
+ >>> data_sample = Det3DDataSample(pred_instances=pred_instances)
+ >>> assert 'pred_instances' in data_sample
+
+ >>> pred_instances_3d = InstanceData(metainfo=meta_info)
+ >>> pred_instances_3d.bboxes_3d = BaseInstance3DBoxes(
+ ... torch.rand((5, 7)))
+ >>> pred_instances_3d.scores_3d = torch.rand((5, ))
+ >>> pred_instances_3d.labels_3d = torch.rand((5, ))
+ >>> data_sample = Det3DDataSample(pred_instances_3d=pred_instances_3d)
+ >>> assert 'pred_instances_3d' in data_sample
+
+ >>> data_sample = Det3DDataSample()
+ >>> gt_instances_3d_data = dict(
+ ... 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_3d' in data_sample.gt_instances_3d
+
+ >>> from mmdet3d.structures import PointData
+ >>> data_sample = Det3DDataSample()
+ >>> 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)
+ >>> print(data_sample)
+ <Det3DDataSample(
  META INFORMATION
- pad_shape: (800, 1216, 3)
- img_shape: (800, 1196, 3)
-
  DATA FIELDS
- labels: tensor([0, 0, 1, 0, 2])
- bboxes: 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],
- [0.4686, 0.7007, 0.4428, 0.0672, 0.3319, 0.3033, 0.8519],
- [0.9693, 0.5315, 0.4642, 0.9079, 0.2481, 0.1781, 0.9557]]))
- ) at 0x7fb0d9354280>
- ) at 0x7fb0d93543d0>
- >>> pred_instances = InstanceData(metainfo=meta_info)
- >>> pred_instances.bboxes = torch.rand((5, 4))
- >>> pred_instances.scores = torch.rand((5, ))
- >>> data_sample = Det3DDataSample(pred_instances=pred_instances)
- >>> assert 'pred_instances' in data_sample
-
- >>> pred_instances_3d = InstanceData(metainfo=meta_info)
- >>> pred_instances_3d.bboxes_3d = BaseInstance3DBoxes(torch.rand((5, 7)))
- >>> pred_instances_3d.scores_3d = torch.rand((5, ))
- >>> pred_instances_3d.labels_3d = torch.rand((5, ))
- >>> data_sample = Det3DDataSample(pred_instances_3d=pred_instances_3d)
- >>> assert 'pred_instances_3d' in data_sample
-
- >>> data_sample = Det3DDataSample()
- >>> gt_instances_3d_data = dict(
- ... bboxes=BaseInstance3DBoxes(torch.rand((2, 7))),
- ... labels=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
-
- >>> data_sample = Det3DDataSample()
- ... 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)
- >>> print(data_sample)
- <Det3DDataSample(
-
- META INFORMATION
-
- DATA FIELDS
- gt_pts_seg: <PointData(
-
- META INFORMATION
-
- DATA FIELDS
- pts_instance_mask: tensor([0.0576, 0.3067])
- pts_semantic_mask: tensor([0.9267, 0.7455])
- ) at 0x7f654a9c1590>
- _gt_pts_seg: <PointData(
-
- META INFORMATION
-
- DATA FIELDS
- pts_instance_mask: tensor([0.0576, 0.3067])
- pts_semantic_mask: tensor([0.9267, 0.7455])
- ) at 0x7f654a9c1590>
- ) at 0x7f654a9c1550>
+ gt_pts_seg: <PointData(
+ META INFORMATION
+ DATA FIELDS
+ pts_semantic_mask: tensor([0.7199, 0.4006])
+ pts_instance_mask: tensor([0.7363, 0.8096])
+ ) at 0x7f7e2962cc40>
+ ) at 0x7f7e29ff0d60>
  """
 
  @property
  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