[Fix]fix datasample

mmdet3d/core/data_structures/det3d_data_sample.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmengine.data import BaseDataElement, InstanceData, PixelData
+from mmengine.data import InstanceData, PixelData
 
+from mmdet.core.data_structures import DetDataSample
 
-class Det3DDataSample(BaseDataElement):
+
+class Det3DDataSample(DetDataSample):
     """A data structure interface of MMDetection3D. They are used as interfaces
     between different components.
 
@@ -14,8 +16,33 @@ class Det3DDataSample(BaseDataElement):
             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
+            `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
+            `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_sem_seg``(PixelData): Ground truth of point cloud
             semantic segmentation.
         - ``pred_pts_sem_seg``(PixelData): Prediction of point cloud
@@ -76,9 +103,16 @@ class Det3DDataSample(BaseDataElement):
                     [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 = BaseInstance3DBoxes(torch.rand((5, 7)))
-    >>> pred_instances_3d.scores = torch.rand((5, ))
+    >>> 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
 
@@ -126,30 +160,6 @@ class Det3DDataSample(BaseDataElement):
     >>> assert 'segm_seg' in data_sample.gt_pts_sem_seg
     """
 
-    @property
-    def proposals(self) -> InstanceData:
-        return self._proposals
-
-    @proposals.setter
-    def proposals(self, value: InstanceData):
-        self.set_field(value, '_proposals', dtype=InstanceData)
-
-    @proposals.deleter
-    def proposals(self):
-        del self._proposals
-
-    @property
-    def ignored_instances(self) -> InstanceData:
-        return self._ignored_instances
-
-    @ignored_instances.setter
-    def ignored_instances(self, value: InstanceData):
-        self.set_field(value, '_ignored_instances', dtype=InstanceData)
-
-    @ignored_instances.deleter
-    def ignored_instances(self):
-        del self._ignored_instances
-
     @property
     def gt_instances_3d(self) -> InstanceData:
         return self._gt_instances_3d
@@ -174,6 +184,30 @@ class Det3DDataSample(BaseDataElement):
     def pred_instances_3d(self):
         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):
+        self.set_field(value, '_pts_pred_instances_3d', dtype=InstanceData)
+
+    @pts_pred_instances_3d.deleter
+    def pts_pred_instances_3d(self):
+        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):
+        self.set_field(value, '_img_pred_instances_3d', dtype=InstanceData)
+
+    @img_pred_instances_3d.deleter
+    def img_pred_instances_3d(self):
+        del self._img_pred_instances_3d
+
     @property
     def gt_pts_sem_seg(self) -> PixelData:
         return self._gt_pts_sem_seg

tests/test_core/test_data_structure/test_det_data_sample.py

@@ -5,7 +5,7 @@ import pytest
 import torch
 from mmengine.data import InstanceData, PixelData
 
-from mmdet3d.core import Det3DDataSample
+from mmdet3d.core.data_structures import Det3DDataSample
 
 
 def _equal(a, b):
@@ -15,7 +15,7 @@ def _equal(a, b):
         return a == b
 
 
-class TestDetDataSample(TestCase):
+class TestDet3DataSample(TestCase):
 
     def test_init(self):
         meta_info = dict(
@@ -32,54 +32,59 @@ class TestDetDataSample(TestCase):
         det3d_data_sample = Det3DDataSample()
         # test gt_instances_3d
         gt_instances_3d_data = dict(
-            bboxes=torch.rand(4, 4),
-            labels=torch.rand(4),
-            masks=np.random.rand(4, 2, 2))
+            bboxes_3d=torch.rand(4, 4), labels_3d=torch.rand(4))
         gt_instances_3d = InstanceData(**gt_instances_3d_data)
         det3d_data_sample.gt_instances_3d = gt_instances_3d
         assert 'gt_instances_3d' in det3d_data_sample
-        assert _equal(det3d_data_sample.gt_instances_3d.bboxes,
-                      gt_instances_3d_data['bboxes'])
-        assert _equal(det3d_data_sample.gt_instances_3d.labels,
-                      gt_instances_3d_data['labels'])
-        assert _equal(det3d_data_sample.gt_instances_3d.masks,
-                      gt_instances_3d_data['masks'])
-
-        # test pred_instances
+        assert _equal(det3d_data_sample.gt_instances_3d.bboxes_3d,
+                      gt_instances_3d_data['bboxes_3d'])
+        assert _equal(det3d_data_sample.gt_instances_3d.labels_3d,
+                      gt_instances_3d_data['labels_3d'])
+
+        # test pred_instances_3d
         pred_instances_3d_data = dict(
-            bboxes=torch.rand(2, 4),
-            labels=torch.rand(2),
-            masks=np.random.rand(2, 2, 2))
+            bboxes_3d=torch.rand(2, 4),
+            labels_3d=torch.rand(2),
+            scores_3d=torch.rand(2))
         pred_instances_3d = InstanceData(**pred_instances_3d_data)
         det3d_data_sample.pred_instances_3d = pred_instances_3d
         assert 'pred_instances_3d' in det3d_data_sample
-        assert _equal(det3d_data_sample.pred_instances_3d.bboxes,
-                      pred_instances_3d_data['bboxes'])
-        assert _equal(det3d_data_sample.pred_instances_3d.labels,
-                      pred_instances_3d_data['labels'])
-        assert _equal(det3d_data_sample.pred_instances_3d.masks,
-                      pred_instances_3d_data['masks'])
-
-        # test proposals
-        proposals_data = dict(bboxes=torch.rand(4, 4), labels=torch.rand(4))
-        proposals = InstanceData(**proposals_data)
-        det3d_data_sample.proposals = proposals
-        assert 'proposals' in det3d_data_sample
-        assert _equal(det3d_data_sample.proposals.bboxes,
-                      proposals_data['bboxes'])
-        assert _equal(det3d_data_sample.proposals.labels,
-                      proposals_data['labels'])
-
-        # test ignored_instances
-        ignored_instances_data = dict(
-            bboxes=torch.rand(4, 4), labels=torch.rand(4))
-        ignored_instances = InstanceData(**ignored_instances_data)
-        det3d_data_sample.ignored_instances = ignored_instances
-        assert 'ignored_instances' in det3d_data_sample
-        assert _equal(det3d_data_sample.ignored_instances.bboxes,
-                      ignored_instances_data['bboxes'])
-        assert _equal(det3d_data_sample.ignored_instances.labels,
-                      ignored_instances_data['labels'])
+        assert _equal(det3d_data_sample.pred_instances_3d.bboxes_3d,
+                      pred_instances_3d_data['bboxes_3d'])
+        assert _equal(det3d_data_sample.pred_instances_3d.labels_3d,
+                      pred_instances_3d_data['labels_3d'])
+        assert _equal(det3d_data_sample.pred_instances_3d.scores_3d,
+                      pred_instances_3d_data['scores_3d'])
+
+        # test pts_pred_instances_3d
+        pts_pred_instances_3d_data = dict(
+            bboxes_3d=torch.rand(2, 4),
+            labels_3d=torch.rand(2),
+            scores_3d=torch.rand(2))
+        pts_pred_instances_3d = InstanceData(**pts_pred_instances_3d_data)
+        det3d_data_sample.pts_pred_instances_3d = pts_pred_instances_3d
+        assert 'pts_pred_instances_3d' in det3d_data_sample
+        assert _equal(det3d_data_sample.pts_pred_instances_3d.bboxes_3d,
+                      pts_pred_instances_3d_data['bboxes_3d'])
+        assert _equal(det3d_data_sample.pts_pred_instances_3d.labels_3d,
+                      pts_pred_instances_3d_data['labels_3d'])
+        assert _equal(det3d_data_sample.pts_pred_instances_3d.scores_3d,
+                      pts_pred_instances_3d_data['scores_3d'])
+
+        # test img_pred_instances_3d
+        img_pred_instances_3d_data = dict(
+            bboxes_3d=torch.rand(2, 4),
+            labels_3d=torch.rand(2),
+            scores_3d=torch.rand(2))
+        img_pred_instances_3d = InstanceData(**img_pred_instances_3d_data)
+        det3d_data_sample.img_pred_instances_3d = img_pred_instances_3d
+        assert 'img_pred_instances_3d' in det3d_data_sample
+        assert _equal(det3d_data_sample.img_pred_instances_3d.bboxes_3d,
+                      img_pred_instances_3d_data['bboxes_3d'])
+        assert _equal(det3d_data_sample.img_pred_instances_3d.labels_3d,
+                      img_pred_instances_3d_data['labels_3d'])
+        assert _equal(det3d_data_sample.img_pred_instances_3d.scores_3d,
+                      img_pred_instances_3d_data['scores_3d'])
 
         # test gt_panoptic_seg
         gt_pts_panoptic_seg_data = dict(panoptic_seg=torch.rand(5, 4))
@@ -124,18 +129,34 @@ class TestDetDataSample(TestCase):
             det3d_data_sample.pred_pts_sem_seg = torch.rand(2, 4)
 
     def test_deleter(self):
-        gt_instances_3d_data = dict(
-            bboxes=torch.rand(4, 4),
-            labels=torch.rand(4),
-            masks=np.random.rand(4, 2, 2))
+        tmp_instances_3d_data = dict(
+            bboxes_3d=torch.rand(4, 4), labels_3d=torch.rand(4))
 
         det3d_data_sample = Det3DDataSample()
-        gt_instances_3d = InstanceData(data=gt_instances_3d_data)
+        gt_instances_3d = InstanceData(data=tmp_instances_3d_data)
         det3d_data_sample.gt_instances_3d = gt_instances_3d
         assert 'gt_instances_3d' in det3d_data_sample
         del det3d_data_sample.gt_instances_3d
         assert 'gt_instances_3d' not in det3d_data_sample
 
+        pred_instances_3d = InstanceData(data=tmp_instances_3d_data)
+        det3d_data_sample.pred_instances_3d = pred_instances_3d
+        assert 'pred_instances_3d' in det3d_data_sample
+        del det3d_data_sample.pred_instances_3d
+        assert 'pred_instances_3d' not in det3d_data_sample
+
+        pts_pred_instances_3d = InstanceData(data=tmp_instances_3d_data)
+        det3d_data_sample.pts_pred_instances_3d = pts_pred_instances_3d
+        assert 'pts_pred_instances_3d' in det3d_data_sample
+        del det3d_data_sample.pts_pred_instances_3d
+        assert 'pts_pred_instances_3d' not in det3d_data_sample
+
+        img_pred_instances_3d = InstanceData(data=tmp_instances_3d_data)
+        det3d_data_sample.img_pred_instances_3d = img_pred_instances_3d
+        assert 'img_pred_instances_3d' in det3d_data_sample
+        del det3d_data_sample.img_pred_instances_3d
+        assert 'img_pred_instances_3d' not in det3d_data_sample
+
         pred_pts_panoptic_seg_data = torch.rand(5, 4)
         pred_pts_panoptic_seg_data = PixelData(data=pred_pts_panoptic_seg_data)
         det3d_data_sample.pred_pts_panoptic_seg_data = \