[Feature]Support centerpoint

mmdeploy/codebase/mmdet3d/deploy/voxel_detection_model.py

@@ -200,7 +200,7 @@ def post_process(model_cfg: Union[str, mmcv.Config],
         elif 'pts_bbox_head' in model_cfg.model.keys():
             head_cfg = dict(**model_cfg.model['pts_bbox_head'])
         else:
-            raise
+            raise NotImplementedError('Not supported model.')
         head_cfg['train_cfg'] = None
         head_cfg['test_cfg'] = model_cfg.model['test_cfg']
         head = build_head(head_cfg)

mmdeploy/codebase/mmdet3d/models/centerpoint.py

@@ -87,16 +87,13 @@ def centerpoint__get_bbox(ctx,
         list[dict]: Decoded bbox, scores and labels after nms.
     """
     rets = []
-    # common_heads = self.task_heads[0].heads
-    batch_size = 1
     scores_range = [0]
     bbox_range = [0]
     dir_range = [0]
     self.test_cfg = self.test_cfg['pts']
     for i, task_head in enumerate(self.task_heads):
         scores_range.append(scores_range[i] + self.num_classes[i])
-        bbox_range.append(bbox_range[i] +
-                          8 if 'vel' in task_head.heads.keys() else 6)
+        bbox_range.append(bbox_range[i] + 8)
         dir_range.append(dir_range[i] + 2)
     for task_id in range(len(self.num_classes)):
         num_class_with_bg = self.num_classes[task_id]
@@ -129,11 +126,6 @@ def centerpoint__get_bbox(ctx,
                                    dir_range[task_id]:dir_range[task_id + 1],
                                    ...][:, 1].unsqueeze(1)
 
-        # if 'vel' in preds_dict[0]:
-        #     batch_vel = preds_dict[0]['vel']
-        # else:
-        #     batch_vel = None
-
         temp = self.bbox_coder.decode(
             batch_heatmap,
             batch_rots,
@@ -148,26 +140,25 @@ def centerpoint__get_bbox(ctx,
         batch_cls_preds = [box['scores'] for box in temp]
         batch_cls_labels = [box['labels'] for box in temp]
         if self.test_cfg['nms_type'] == 'circle':
-            ret_task = []
-            for i in range(batch_size):
-                boxes3d = temp[i]['bboxes']
-                scores = temp[i]['scores']
-                labels = temp[i]['labels']
-                centers = boxes3d[:, [0, 1]]
-                boxes = torch.cat([centers, scores.view(-1, 1)], dim=1)
-                keep = torch.tensor(
-                    circle_nms(
-                        boxes.detach().cpu().numpy(),
-                        self.test_cfg['min_radius'][task_id],
-                        post_max_size=self.test_cfg['post_max_size']),
-                    dtype=torch.long,
-                    device=boxes.device)
-
-                boxes3d = boxes3d[keep]
-                scores = scores[keep]
-                labels = labels[keep]
-                ret = dict(bboxes=boxes3d, scores=scores, labels=labels)
-                ret_task.append(ret)
+
+            boxes3d = temp[0]['bboxes']
+            scores = temp[0]['scores']
+            labels = temp[0]['labels']
+            centers = boxes3d[:, [0, 1]]
+            boxes = torch.cat([centers, scores.view(-1, 1)], dim=1)
+            keep = torch.tensor(
+                circle_nms(
+                    boxes.detach().cpu().numpy(),
+                    self.test_cfg['min_radius'][task_id],
+                    post_max_size=self.test_cfg['post_max_size']),
+                dtype=torch.long,
+                device=boxes.device)
+
+            boxes3d = boxes3d[keep]
+            scores = scores[keep]
+            labels = labels[keep]
+            ret = dict(bboxes=boxes3d, scores=scores, labels=labels)
+            ret_task = [ret]
             rets.append(ret_task)
         else:
             rets.append(

tests/test_codebase/test_mmdet3d/test_mmdet3d_models.py

@@ -111,15 +111,17 @@ def test_pointpillars_scatter(backend_type: Backend):
 
 def get_centerpoint():
     from mmdet3d.models.detectors.centerpoint import CenterPoint
-    model_cfg = load_config('data/model_cfg.py')[0]
+    model_cfg = load_config(
+        'tests/test_codebase/test_mmdet3d/data/model_cfg.py')[0]
     model = CenterPoint(**model_cfg.centerpoint_model)
     model.requires_grad_(False)
     return model
 
 
 def get_centerpoint_head():
     from mmdet3d.models import builder
-    model_cfg = load_config('data/model_cfg.py')[0]
+    model_cfg = load_config(
+        'tests/test_codebase/test_mmdet3d/data/model_cfg.py')[0]
     model_cfg.centerpoint_model.pts_bbox_head.test_cfg = model_cfg.\
         centerpoint_model.test_cfg
     head = builder.build_head(model_cfg.centerpoint_model.pts_bbox_head)
@@ -144,9 +146,12 @@ def test_centerpoint(backend_type: Backend):
                 output_names=['outputs']),
             codebase_config=dict(
                 type=Codebase.MMDET3D.value, task=Task.VOXEL_DETECTION.value)))
-    model_cfg = load_config('data/model_cfg.py')[0]
+    model_cfg = load_config(
+        'tests/test_codebase/test_mmdet3d/data/model_cfg.pytests/'
+        'test_codebase/test_mmdet3d/data/model_cfg.py')[0]
     voxeldetection = VoxelDetection(model_cfg, deploy_cfg, 'cpu')
-    inputs, data = voxeldetection.create_input('data/kitti/kitti_000008.bin')
+    inputs, data = voxeldetection.create_input(
+        'tests/test_codebase/test_mmdet3d/data/kitti/kitti_000008.bin')
 
     with RewriterContext(
             cfg=deploy_cfg,

tests/test_codebase/test_mmdet3d/test_voxel_detection_model.py

@@ -41,7 +41,11 @@ def setup_class(cls):
         deploy_cfg = mmcv.Config({
             'onnx_config': {
                 'input_names': ['voxels', 'num_points', 'coors'],
-                'output_names': ['scores', 'bbox_preds', 'dir_scores']
+                'output_names': ['scores', 'bbox_preds', 'dir_scores'],
+                'opset_version': 11
+            },
+            'backend_config': {
+                'type': 'tensorrt'
             }
         })
 
@@ -71,7 +75,7 @@ def test_forward_and_show_result(self):
 
 
 @backend_checker(Backend.ONNXRUNTIME)
-def test_build_pose_detection_model():
+def test_build_voxel_detection_model():
     from mmdeploy.utils import load_config
     model_cfg_path = 'tests/test_codebase/test_mmdet3d/data/model_cfg.py'
     model_cfg = load_config(model_cfg_path)[0]