[Fixed] modify vote_head to support 3dssd (#396)

* modify vote_head to support 3dssd

* delete .keys()

* add 3dssd unittest

* 3dssd->ssd3d

mmdet3d/models/dense_heads/vote_head.py

@@ -119,9 +119,19 @@ def _extract_input(self, feat_dict):
  torch.Tensor: Features of input points.
  torch.Tensor: Indices of input points.
  """
- seed_points = feat_dict['seed_points']
- seed_features = feat_dict['seed_features']
- seed_indices = feat_dict['seed_indices']
+
+ # for imvotenet
+ if 'seed_points' in feat_dict and \
+ 'seed_features' in feat_dict and \
+ 'seed_indices' in feat_dict:
+ seed_points = feat_dict['seed_points']
+ seed_features = feat_dict['seed_features']
+ seed_indices = feat_dict['seed_indices']
+ # for votenet
+ else:
+ seed_points = feat_dict['fp_xyz'][-1]
+ seed_features = feat_dict['fp_features'][-1]
+ seed_indices = feat_dict['fp_indices'][-1]
 
  return seed_points, seed_features, seed_indices
 

mmdet3d/models/detectors/votenet.py

@@ -22,28 +22,6 @@ def __init__(self,
  test_cfg=test_cfg,
  pretrained=pretrained)
 
- def extract_feat(self, points, img_metas=None):
- """Directly extract features from the backbone+neck.
-
- Args:
- points (torch.Tensor): Input points.
- """
- x = self.backbone(points)
- if self.with_neck:
- x = self.neck(x)
-
- seed_points = x['fp_xyz'][-1]
- seed_features = x['fp_features'][-1]
- seed_indices = x['fp_indices'][-1]
-
- feat_dict = {
- 'seed_points': seed_points,
- 'seed_features': seed_features,
- 'seed_indices': seed_indices
- }
-
- return feat_dict
-
  def forward_train(self,
  points,
  img_metas,

tests/test_models/test_detectors.py

@@ -121,6 +121,47 @@ def test_voxel_net():
  assert labels_3d.shape == torch.Size([50])
 
 
+def test_3dssd():
+ if not torch.cuda.is_available():
+ pytest.skip('test requires GPU and torch+cuda')
+ _setup_seed(0)
+ ssd3d_cfg = _get_detector_cfg('3dssd/3dssd_kitti-3d-car.py')
+ self = build_detector(ssd3d_cfg).cuda()
+ points_0 = torch.rand([2000, 4], device='cuda')
+ points_1 = torch.rand([2000, 4], device='cuda')
+ points = [points_0, points_1]
+ img_meta_0 = dict(box_type_3d=DepthInstance3DBoxes)
+ img_meta_1 = dict(box_type_3d=DepthInstance3DBoxes)
+ img_metas = [img_meta_0, img_meta_1]
+ gt_bbox_0 = DepthInstance3DBoxes(torch.rand([10, 7], device='cuda'))
+ gt_bbox_1 = DepthInstance3DBoxes(torch.rand([10, 7], device='cuda'))
+ gt_bboxes = [gt_bbox_0, gt_bbox_1]
+ gt_labels_0 = torch.randint(0, 10, [10], device='cuda')
+ gt_labels_1 = torch.randint(0, 10, [10], device='cuda')
+ gt_labels = [gt_labels_0, gt_labels_1]
+
+ # test forward_train
+ losses = self.forward_train(points, img_metas, gt_bboxes, gt_labels)
+ assert losses['vote_loss'] >= 0
+ assert losses['objectness_loss'] >= 0
+ assert losses['semantic_loss'] >= 0
+ assert losses['center_loss'] >= 0
+ assert losses['dir_class_loss'] >= 0
+ assert losses['dir_res_loss'] >= 0
+ assert losses['size_class_loss'] >= 0
+ assert losses['size_res_loss'] >= 0
+
+ # test simple_test
+ results = self.simple_test(points, img_metas)
+ boxes_3d = results[0]['boxes_3d']
+ scores_3d = results[0]['scores_3d']
+ labels_3d = results[0]['labels_3d']
+ assert boxes_3d.tensor.shape[0] >= 0
+ assert boxes_3d.tensor.shape[1] == 7
+ assert scores_3d.shape[0] >= 0
+ assert labels_3d.shape[0] >= 0
+
+
 def test_vote_net():
  if not torch.cuda.is_available():
  pytest.skip('test requires GPU and torch+cuda')