[Refactor] update data flow and ut (#1776)

* update data flow and ut

* update ut

* update code

* fix mapping bug

* fix comments

mmdet3d/models/detectors/fcos_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict
+
+from torch import Tensor
+
 from mmdet3d.registry import MODELS
 from mmdet3d.utils import ConfigType, OptConfigType, OptMultiConfig
+from ...structures.det3d_data_sample import SampleList
 from .single_stage_mono3d import SingleStageMono3DDetector
 
 
@@ -43,3 +48,53 @@ class FCOSMono3D(SingleStageMono3DDetector):
             test_cfg=test_cfg,
             data_preprocessor=data_preprocessor,
             init_cfg=init_cfg)
+
+    def predict(self,
+                batch_inputs_dict: Dict[str, Tensor],
+                batch_data_samples: SampleList,
+                rescale: bool = True) -> SampleList:
+        """Predict results from a batch of inputs and data samples with post-
+        processing.
+
+        Args:
+            batch_inputs_dict (dict): The model input dict which include
+                'imgs' keys
+
+                - imgs (torch.Tensor: Image of each sample.
+
+            batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
+                Samples. It usually includes information such as
+                `gt_instance_3d`.
+            rescale (bool): Whether to rescale the results.
+                Defaults to True.
+
+        Returns:
+            list[:obj:`Det3DDataSample`]: Detection results of the
+            input. Each Det3DDataSample usually contains
+            'pred_instances_3d'. And the ``pred_instances_3d`` normally
+            contains following keys.
+
+            - scores_3d (Tensor): Classification scores, has a shape
+              (num_instance, )
+            - labels_3d (Tensor): Labels of 3D bboxes, has a shape
+              (num_instances, ).
+            - bboxes_3d (Tensor): Contains a tensor with shape
+              (num_instances, C) where C >=7.
+
+            When there are 2D prediction in models, it should
+            contains  `pred_instances`, And the ``pred_instances`` normally
+            contains following keys.
+
+            - scores (Tensor): Classification scores of image, has a shape
+              (num_instance, )
+            - labels (Tensor): Predict Labels of 2D bboxes, has a shape
+              (num_instances, ).
+            - bboxes (Tensor): Contains a tensor with shape
+              (num_instances, 4).
+        """
+        x = self.extract_feat(batch_inputs_dict)
+        results_list, results_list_2d = self.bbox_head.predict(
+            x, batch_data_samples, rescale=rescale)
+        predictions = self.convert_to_datasample(batch_data_samples,
+                                                 results_list, results_list_2d)
+        return predictions

mmdet3d/models/detectors/groupfree3dnet.py

@@ -82,5 +82,6 @@ class GroupFree3DNet(SingleStage3DDetector):
         points = batch_inputs_dict['points']
         results_list = self.bbox_head.predict(points, x, batch_data_samples,
                                               **kwargs)
-        predictions = self.convert_to_datasample(results_list)
+        predictions = self.convert_to_datasample(batch_data_samples,
+                                                 results_list)
         return predictions

mmdet3d/models/detectors/h3dnet.py

@@ -154,4 +154,4 @@ class H3DNet(TwoStage3DDetector):
             feats_dict,
             batch_data_samples,
             suffix='_optimized')
-        return self.convert_to_datasample(results_list)
+        return self.convert_to_datasample(batch_data_samples, results_list)

mmdet3d/models/detectors/imvotenet.py

@@ -433,7 +433,8 @@ class ImVoteNet(Base3DDetector):
         if points is None:
             assert imgs is not None
             results_2d = self.predict_img_only(imgs, batch_data_samples)
-            return self.convert_to_datasample(results_list_2d=results_2d)
+            return self.convert_to_datasample(
+                batch_data_samples, data_instances_2d=results_2d)
 
         else:
             results_2d = self.predict_img_only(
@@ -487,7 +488,7 @@ class ImVoteNet(Base3DDetector):
                 batch_data_samples,
                 rescale=True)
 
-            return self.convert_to_datasample(results_3d)
+            return self.convert_to_datasample(batch_data_samples, results_3d)
 
     def predict_img_only(self,
                          imgs: Tensor,

mmdet3d/models/detectors/imvoxelnet.py

@@ -5,7 +5,6 @@ import torch
 
 from mmdet3d.models.layers.fusion_layers.point_fusion import point_sample
 from mmdet3d.registry import MODELS, TASK_UTILS
-from mmdet3d.structures import Det3DDataSample
 from mmdet3d.structures.det3d_data_sample import SampleList
 from mmdet3d.utils import ConfigType, InstanceList, OptConfigType
 from mmdet.models.detectors import BaseDetector
@@ -58,13 +57,13 @@ class ImVoxelNet(BaseDetector):
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
 
-    def convert_to_datasample(self, results_list: InstanceList) -> SampleList:
-        """Convert results list to `Det3DDataSample`.
-
+    def convert_to_datasample(self, data_samples: SampleList,
+                              data_instances: InstanceList) -> SampleList:
+        """ Convert results list to `Det3DDataSample`.
         Args:
-            results_list (list[:obj:`InstanceData`]): 3D Detection results of
-                each image.
-
+            inputs (list[:obj:`Det3DDataSample`]): The input data.
+            data_instances (list[:obj:`InstanceData`]): 3D Detection
+                results of each image.
         Returns:
             list[:obj:`Det3DDataSample`]: 3D Detection results of the
             input images. Each Det3DDataSample usually contain
@@ -78,12 +77,10 @@ class ImVoxelNet(BaseDetector):
                 - bboxes_3d (Tensor): Contains a tensor with shape
                     (num_instances, C) where C >=7.
             """
-        out_results_list = []
-        for i in range(len(results_list)):
-            result = Det3DDataSample()
-            result.pred_instances_3d = results_list[i]
-            out_results_list.append(result)
-        return out_results_list
+        for data_sample, pred_instances_3d in zip(data_samples,
+                                                  data_instances):
+            data_sample.pred_instances_3d = pred_instances_3d
+        return data_samples
 
     def extract_feat(self, batch_inputs_dict: dict,
                      batch_data_samples: SampleList):
@@ -188,7 +185,8 @@ class ImVoxelNet(BaseDetector):
         """
         x = self.extract_feat(batch_inputs_dict, batch_data_samples)
         results_list = self.bbox_head.predict(x, batch_data_samples, **kwargs)
-        predictions = self.convert_to_datasample(results_list)
+        predictions = self.convert_to_datasample(batch_data_samples,
+                                                 results_list)
         return predictions
 
     def _forward(self, batch_inputs_dict: dict, batch_data_samples: SampleList,

mmdet3d/models/detectors/mvx_two_stage.py

@@ -218,7 +218,7 @@ class MVXTwoStageDetector(Base3DDetector):
             x = self.pts_neck(x)
         return x
 
-    def extract_feat(self, batch_inputs_dict: List[Tensor],
+    def extract_feat(self, batch_inputs_dict: dict,
                      batch_input_metas: List[dict]) -> tuple:
         """Extract features from images and points.
 
@@ -235,9 +235,9 @@ class MVXTwoStageDetector(Base3DDetector):
              tuple: Two elements in tuple arrange as
              image features and point cloud features.
         """
-        voxel_dict = batch_inputs_dict['voxels']
-        imgs = batch_inputs_dict['imgs']
-        points = batch_inputs_dict['points']
+        voxel_dict = batch_inputs_dict.get('voxels', None)
+        imgs = batch_inputs_dict.get('imgs', None)
+        points = batch_inputs_dict.get('points', None)
         img_feats = self.extract_img_feat(imgs, batch_input_metas)
         pts_feats = self.extract_pts_feat(
             voxel_dict,
@@ -401,6 +401,7 @@ class MVXTwoStageDetector(Base3DDetector):
         else:
             results_list_2d = None
 
-        detsamples = self.convert_to_datasample(results_list_3d,
+        detsamples = self.convert_to_datasample(batch_data_samples,
+                                                results_list_3d,
                                                 results_list_2d)
         return detsamples

mmdet3d/models/detectors/single_stage.py

@@ -108,7 +108,8 @@ class SingleStage3DDetector(Base3DDetector):
         """
         x = self.extract_feat(batch_inputs_dict)
         results_list = self.bbox_head.predict(x, batch_data_samples, **kwargs)
-        predictions = self.convert_to_datasample(results_list)
+        predictions = self.convert_to_datasample(batch_data_samples,
+                                                 results_list)
         return predictions
 
     def _forward(self,
@@ -137,7 +138,7 @@ class SingleStage3DDetector(Base3DDetector):
         return results
 
     def extract_feat(
-        self, batch_inputs_dict: torch.Tensor
+        self, batch_inputs_dict: Dict[str, Tensor]
     ) -> Union[Tuple[torch.Tensor], Dict[str, Tensor]]:
         """Directly extract features from the backbone+neck.
 

mmdet3d/models/detectors/single_stage_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Tuple
 
+from mmengine.structures import InstanceData
 from torch import Tensor
 
 from mmdet3d.registry import MODELS
-from mmdet3d.structures import Det3DDataSample
 from mmdet3d.structures.det3d_data_sample import SampleList
-from mmdet3d.utils import InstanceList
+from mmdet3d.utils import OptInstanceList
 from mmdet.models.detectors.single_stage import SingleStageDetector
 
 
@@ -18,38 +18,63 @@ class SingleStageMono3DDetector(SingleStageDetector):
     boxes on the output features of the backbone+neck.
     """
 
-    def convert_to_datasample(self, results_list: InstanceList) -> SampleList:
-        """ Convert results list to `Det3DDataSample`.
+    def convert_to_datasample(
+        self,
+        data_samples: SampleList,
+        data_instances_3d: OptInstanceList = None,
+        data_instances_2d: OptInstanceList = None,
+    ) -> SampleList:
+        """Convert results list to `Det3DDataSample`.
+
         Args:
-            results_list (list[:obj:`InstanceData`]):Detection results
-            of each image. For each image, it could contains two results
-            format:
-                1. pred_instances_3d
-                2. (pred_instances_3d, pred_instances)
+            data_samples (list[:obj:`Det3DDataSample`]): The input data.
+            data_instances_3d (list[:obj:`InstanceData`], optional): 3D
+                Detection results of each image. Defaults to None.
+            data_instances_2d (list[:obj:`InstanceData`], optional): 2D
+                Detection results of each image. Defaults to None.
 
         Returns:
-            list[:obj:`Det3DDataSample`]: 3D Detection results of the
-            input images. Each Det3DDataSample usually contain
-            'pred_instances_3d'. And the ``pred_instances_3d`` usually
+            list[:obj:`Det3DDataSample`]: Detection results of the
+            input. Each Det3DDataSample usually contains
+            'pred_instances_3d'. And the ``pred_instances_3d`` normally
             contains following keys.
 
             - scores_3d (Tensor): Classification scores, has a shape
               (num_instance, )
-            - labels_3d (Tensor): Labels of bboxes, has a shape
+            - labels_3d (Tensor): Labels of 3D bboxes, has a shape
               (num_instances, ).
             - bboxes_3d (Tensor): Contains a tensor with shape
               (num_instances, C) where C >=7.
+
+            When there are 2D prediction in some models, it should
+            contains  `pred_instances`, And the ``pred_instances`` normally
+            contains following keys.
+
+            - scores (Tensor): Classification scores of image, has a shape
+              (num_instance, )
+            - labels (Tensor): Predict Labels of 2D bboxes, has a shape
+              (num_instances, ).
+            - bboxes (Tensor): Contains a tensor with shape
+              (num_instances, 4).
         """
-        out_results_list = []
-        for i in range(len(results_list)):
-            result = Det3DDataSample()
-            if len(results_list[i]) == 2:
-                result.pred_instances_3d = results_list[i][0]
-                result.pred_instances = results_list[i][1]
-            else:
-                result.pred_instances_3d = results_list[i]
-            out_results_list.append(result)
-        return out_results_list
+
+        assert (data_instances_2d is not None) or \
+               (data_instances_3d is not None),\
+               'please pass at least one type of data_samples'
+
+        if data_instances_2d is None:
+            data_instances_2d = [
+                InstanceData() for _ in range(len(data_instances_3d))
+            ]
+        if data_instances_3d is None:
+            data_instances_3d = [
+                InstanceData() for _ in range(len(data_instances_2d))
+            ]
+
+        for i, data_sample in enumerate(data_samples):
+            data_sample.pred_instances_3d = data_instances_3d[i]
+            data_sample.pred_instances = data_instances_2d[i]
+        return data_samples
 
     def extract_feat(self, batch_inputs_dict: dict) -> Tuple[Tensor]:
         """Extract features.

mmdet3d/models/detectors/two_stage.py

@@ -161,7 +161,8 @@ class TwoStage3DDetector(Base3DDetector):
                                              batch_data_samples)
 
         # connvert to Det3DDataSample
-        results_list = self.convert_to_datasample(results_list)
+        results_list = self.convert_to_datasample(batch_data_samples,
+                                                  results_list)
 
         return results_list
 

mmdet3d/models/detectors/votenet.py

@@ -99,7 +99,8 @@ class VoteNet(SingleStage3DDetector):
         points = batch_inputs_dict['points']
         results_list = self.bbox_head.predict(points, feats_dict,
                                               batch_data_samples, **kwargs)
-        data_3d_samples = self.convert_to_datasample(results_list)
+        data_3d_samples = self.convert_to_datasample(batch_data_samples,
+                                                     results_list)
         return data_3d_samples
 
     def aug_test(self, aug_inputs_list: List[dict],
@@ -142,5 +143,6 @@ class VoteNet(SingleStage3DDetector):
                                                   self.bbox_head.test_cfg)
 
         merged_results = InstanceData(**merged_results_dict)
-        data_3d_samples = self.convert_to_datasample([merged_results])
+        data_3d_samples = self.convert_to_datasample(batch_data_samples,
+                                                     [merged_results])
         return data_3d_samples

mmdet3d/visualization/local_visualizer.py

@@ -470,8 +470,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
     def add_datasample(self,
                        name: str,
                        data_input: dict,
-                       gt_sample: Optional['Det3DDataSample'] = None,
-                       pred_sample: Optional['Det3DDataSample'] = None,
+                       data_sample: Optional['Det3DDataSample'] = None,
                        draw_gt: bool = True,
                        draw_pred: bool = True,
                        show: bool = False,
@@ -495,9 +494,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             name (str): The image identifier.
             data_input (dict): It should include the point clouds or image
                 to draw.
-            gt_sample (:obj:`Det3DDataSample`, optional): GT Det3DDataSample.
-                Defaults to None.
-            pred_sample (:obj:`Det3DDataSample`, optional): Prediction
+            data_sample (:obj:`Det3DDataSample`, optional): Prediction
                 Det3DDataSample. Defaults to None.
             draw_gt (bool): Whether to draw GT Det3DDataSample.
                 Default to True.
@@ -524,20 +521,20 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         gt_img_data = None
         pred_img_data = None
 
-        if draw_gt and gt_sample is not None:
-            if 'gt_instances_3d' in gt_sample:
-                gt_data_3d = self._draw_instances_3d(data_input,
-                                                     gt_sample.gt_instances_3d,
-                                                     gt_sample.metainfo,
-                                                     vis_task, palette)
-            if 'gt_instances' in gt_sample:
+        if draw_gt and data_sample is not None:
+            if 'gt_instances_3d' in data_sample:
+                gt_data_3d = self._draw_instances_3d(
+                    data_input, data_sample.gt_instances_3d,
+                    data_sample.metainfo, vis_task, palette)
+            if 'gt_instances' in data_sample:
                 assert 'img' in data_input
                 if isinstance(data_input['img'], Tensor):
                     img = data_input['img'].permute(1, 2, 0).numpy()
                     img = img[..., [2, 1, 0]]  # bgr to rgb
-                gt_img_data = self._draw_instances(img, gt_sample.gt_instances,
+                gt_img_data = self._draw_instances(img,
+                                                   data_sample.gt_instances,
                                                    classes, palette)
-            if 'gt_pts_seg' in gt_sample:
+            if 'gt_pts_seg' in data_sample:
                 assert classes is not None, 'class information is ' \
                                             'not provided when ' \
                                             'visualizing panoptic ' \
@@ -545,23 +542,23 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                 assert 'points' in data_input
                 gt_seg_data_3d = \
                     self._draw_pts_sem_seg(data_input['points'],
-                                           pred_sample.pred_pts_seg,
+                                           data_sample.pred_pts_seg,
                                            palette, ignore_index)
 
-        if draw_pred and pred_sample is not None:
-            if 'pred_instances_3d' in pred_sample:
-                pred_instances_3d = pred_sample.pred_instances_3d
+        if draw_pred and data_sample is not None:
+            if 'pred_instances_3d' in data_sample:
+                pred_instances_3d = data_sample.pred_instances_3d
                 # .cpu can not be used for BaseInstancesBoxes3D
                 # so we need to use .to('cpu')
                 pred_instances_3d = pred_instances_3d[
                     pred_instances_3d.scores_3d > pred_score_thr].to('cpu')
                 pred_data_3d = self._draw_instances_3d(data_input,
                                                        pred_instances_3d,
-                                                       pred_sample.metainfo,
+                                                       data_sample.metainfo,
                                                        vis_task, palette)
-            if 'pred_instances' in pred_sample:
-                if 'img' in data_input and len(pred_sample.pred_instances) > 0:
-                    pred_instances = pred_sample.pred_instances
+            if 'pred_instances' in data_sample:
+                if 'img' in data_input and len(data_sample.pred_instances) > 0:
+                    pred_instances = data_sample.pred_instances
                     pred_instances = pred_instances_3d[
                         pred_instances.scores > pred_score_thr].cpu()
                     if isinstance(data_input['img'], Tensor):
@@ -569,7 +566,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                         img = img[..., [2, 1, 0]]  # bgr to rgb
                     pred_img_data = self._draw_instances(
                         img, pred_instances, classes, palette)
-            if 'pred_pts_seg' in pred_sample:
+            if 'pred_pts_seg' in data_sample:
                 assert classes is not None, 'class information is ' \
                                             'not provided when ' \
                                             'visualizing panoptic ' \
@@ -577,7 +574,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                 assert 'points' in data_input
                 pred_seg_data_3d = \
                     self._draw_pts_sem_seg(data_input['points'],
-                                           pred_sample.pred_pts_seg,
+                                           data_sample.pred_pts_seg,
                                            palette, ignore_index)
 
         # monocular 3d object detection image

tests/test_datasets/test_lyft_dataset.py

@@ -34,7 +34,7 @@ def _generate_nus_dataset_config():
         dict(type='Identity'),
     ]
     modality = dict(use_lidar=True, use_camera=False)
-    data_prefix = dict(pts='lidar', img='')
+    data_prefix = dict(pts='lidar', img='', sweeps='sweeps/LIDAR_TOP')
     return data_root, ann_file, classes, data_prefix, pipeline, modality
 
 

tests/test_datasets/test_nuscenes_dataset.py

@@ -34,7 +34,10 @@ def _generate_nus_dataset_config():
         dict(type='Identity'),
     ]
     modality = dict(use_lidar=True, use_camera=True)
-    data_prefix = dict(pts='samples/LIDAR_TOP', img='samples/CAM_BACK_LEFT')
+    data_prefix = dict(
+        pts='samples/LIDAR_TOP',
+        img='samples/CAM_BACK_LEFT',
+        sweeps='sweeps/LIDAR_TOP')
     return data_root, ann_file, classes, data_prefix, pipeline, modality
 
 

tests/test_datasets/test_s3dis_dataset.py

@@ -76,7 +76,7 @@ class TestS3DISDataset(unittest.TestCase):
         input_dict = s3dis_seg_dataset.prepare_data(0)
 
         points = input_dict['inputs']['points']
-        data_sample = input_dict['data_sample']
+        data_sample = input_dict['data_samples']
         pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
 
         expected_points = torch.tensor([[

tests/test_datasets/test_scannet_dataset.py

@@ -178,7 +178,7 @@ class TestScanNetDataset(unittest.TestCase):
         input_dict = scannet_seg_dataset.prepare_data(0)
 
         points = input_dict['inputs']['points']
-        data_sample = input_dict['data_sample']
+        data_sample = input_dict['data_samples']
         pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
 
         expected_points = torch.tensor([[

tests/test_datasets/test_semantickitti_dataset.py

@@ -80,6 +80,6 @@ class TestSemanticKITTIDataset(unittest.TestCase):
         input_dict = semantickitti_dataset.prepare_data(0)
 
         points = input_dict['inputs']['points']
-        data_sample = input_dict['data_sample']
+        data_sample = input_dict['data_samples']
         pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
         self.assertEqual(points.shape[0], pts_semantic_mask.shape[0])

tests/test_datasets/test_transforms/test_formating.py

@@ -19,7 +19,7 @@ class TestPack3DDetInputs(unittest.TestCase):
         inputs = packed_results['inputs']
 
         # annotations
-        gt_instances = packed_results['data_sample'].gt_instances_3d
+        gt_instances = packed_results['data_samples'].gt_instances_3d
         self.assertIn('points', inputs)
         self.assertIsInstance(inputs['points'], torch.Tensor)
         assert_allclose(inputs['points'].sum(), torch.tensor(13062.6436))

tests/test_evaluation/test_metrics/test_indoor_metric.py

@@ -14,9 +14,6 @@ class TestIndoorMetric(unittest.TestCase):
     @patch('sys.stdout', new_callable=StringIO)
     def test_process(self, stdout):
         indoor_metric = IndoorMetric()
-
-        dummy_batch = dict(data_sample=dict())
-
         eval_ann_info = {
             'gt_bboxes_3d':
             DepthInstance3DBoxes(
@@ -40,7 +37,6 @@ class TestIndoorMetric(unittest.TestCase):
             'gt_labels_3d':
             np.array([2, 2, 2, 3, 4, 17, 4, 7, 2, 8, 17, 11])
         }
-        dummy_batch['data_sample']['eval_ann_info'] = eval_ann_info
 
         pred_instances_3d = dict()
         pred_instances_3d['scores_3d'] = torch.ones(
@@ -50,6 +46,8 @@ class TestIndoorMetric(unittest.TestCase):
             eval_ann_info['gt_labels_3d'])
         pred_dict = dict()
         pred_dict['pred_instances_3d'] = pred_instances_3d
+        pred_dict['eval_ann_info'] = eval_ann_info
+
         indoor_metric.dataset_meta = {
             'CLASSES': ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                         'window', 'bookshelf', 'picture', 'counter', 'desk',
@@ -58,7 +56,8 @@ class TestIndoorMetric(unittest.TestCase):
             'box_type_3d':
             'Depth',
         }
-        indoor_metric.process([dummy_batch], [pred_dict])
+
+        indoor_metric.process({}, [pred_dict])
 
         eval_results = indoor_metric.evaluate(1)
         for v in eval_results.values():

tests/test_evaluation/test_metrics/test_instance_seg_metric.py

@@ -3,7 +3,7 @@ import unittest
 
 import numpy as np
 import torch
-from mmengine.data import BaseDataElement
+from mmengine.structures import BaseDataElement
 
 from mmdet3d.evaluation.metrics import InstanceSegMetric
 from mmdet3d.structures import Det3DDataSample, PointData
@@ -11,10 +11,9 @@ from mmdet3d.structures import Det3DDataSample, PointData
 
 class TestInstanceSegMetric(unittest.TestCase):
 
-    def _demo_mm_inputs(self):
+    def _demo_mm_model_output(self):
         """Create a superset of inputs needed to run test or train batches."""
-        packed_inputs = []
-        mm_inputs = dict()
+
         n_points = 3300
         gt_labels = [0, 0, 0, 0, 0, 0, 14, 14, 2, 1]
         gt_instance_mask = np.ones(n_points, dtype=np.int) * -1
@@ -29,13 +28,6 @@ class TestInstanceSegMetric(unittest.TestCase):
         ann_info_data['pts_instance_mask'] = torch.tensor(gt_instance_mask)
         ann_info_data['pts_semantic_mask'] = torch.tensor(gt_semantic_mask)
 
-        mm_inputs['data_sample'] = dict(eval_ann_info=ann_info_data)
-        packed_inputs.append(mm_inputs)
-
-        return packed_inputs
-
-    def _demo_mm_model_output(self):
-        """Create a superset of inputs needed to run test or train batches."""
         results_dict = dict()
         n_points = 3300
         gt_labels = [0, 0, 0, 0, 0, 0, 14, 14, 2, 1]
@@ -54,6 +46,7 @@ class TestInstanceSegMetric(unittest.TestCase):
         results_dict['instance_scores'] = torch.tensor(scores)
         data_sample = Det3DDataSample()
         data_sample.pred_pts_seg = PointData(**results_dict)
+        data_sample.eval_ann_info = ann_info_data
         batch_data_samples = [data_sample]
 
         predictions = []
@@ -65,7 +58,7 @@ class TestInstanceSegMetric(unittest.TestCase):
         return predictions
 
     def test_evaluate(self):
-        data_batch = self._demo_mm_inputs()
+        data_batch = {}
         predictions = self._demo_mm_model_output()
         seg_valid_class_ids = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
                                33, 34, 36, 39)

tests/test_evaluation/test_metrics/test_kitti_metric.py

@@ -10,7 +10,7 @@ data_root = 'tests/data/kitti'
 
 
 def _init_evaluate_input():
-    data_batch = [dict(data_sample=dict(sample_idx=0))]
+    metainfo = dict(sample_idx=0)
     predictions = Det3DDataSample()
     pred_instances_3d = InstanceData()
     pred_instances_3d.bboxes_3d = LiDARInstance3DBoxes(
@@ -21,12 +21,13 @@ def _init_evaluate_input():
 
     predictions.pred_instances_3d = pred_instances_3d
     predictions.pred_instances = InstanceData()
+    predictions.set_metainfo(metainfo)
     predictions = predictions.to_dict()
-    return data_batch, [predictions]
+    return {}, [predictions]
 
 
 def _init_multi_modal_evaluate_input():
-    data_batch = [dict(data_sample=dict(sample_idx=0))]
+    metainfo = dict(sample_idx=0)
     predictions = Det3DDataSample()
     pred_instances_3d = InstanceData()
     pred_instances = InstanceData()
@@ -42,8 +43,9 @@ def _init_multi_modal_evaluate_input():
 
     predictions.pred_instances_3d = pred_instances_3d
     predictions.pred_instances = pred_instances
+    predictions.set_metainfo(metainfo)
     predictions = predictions.to_dict()
-    return data_batch, [predictions]
+    return {}, [predictions]
 
 
 def test_multi_modal_kitti_metric():