[Fix] Fixed wrong config paths (#641)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

configs/3dssd/README.md

@@ -29,6 +29,6 @@ Some settings in our implementation are different from the [official implementat
 
 |  Backbone   |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP |Download |
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
-|    [PointNet2SAMSG](./3dssd_kitti-3d-car.py)| Car |72e|4.7||78.69(81.27)<sup>1</sup>|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438-b4276f56.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438.log.json)|
+|    [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py)| Car |72e|4.7||78.69(81.27)<sup>1</sup>|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438-b4276f56.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438.log.json)|
 
 [1]: We report two different 3D object detection performance here. 78.69mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implemention in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).

configs/3dssd/metafile.yml

@@ -11,9 +11,9 @@ Collections:
     README: configs/3dssd/README.md
 
 Models:
-  - Name: 3dssd_kitti-3d-car
+  - Name: 3dssd_4x4_kitti-3d-car
     In Collection: 3DSSD
-    Config: configs/3dssd/3dssd_kitti-3d-car.py
+    Config: configs/3dssd/3dssd_4x4_kitti-3d-car.py
     Metadata:
       Training Memory (GB): 4.7
     Results:

tests/test_models/test_detectors.py

@@ -127,7 +127,7 @@ 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')
+    ssd3d_cfg = _get_detector_cfg('3dssd/3dssd_4x4_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')

tests/test_models/test_heads/test_heads.py

@@ -665,7 +665,7 @@ def test_h3d_head():
 
     targets = (vote_targets, vote_target_masks, size_class_targets,
                size_res_targets, dir_class_targets, dir_res_targets,
-               center_targets, mask_targets, valid_gt_masks,
+               center_targets, None, mask_targets, valid_gt_masks,
                objectness_targets, objectness_weights, box_loss_weights,
                valid_gt_weights)
 
@@ -889,7 +889,7 @@ def test_ssd3d_head():
     if not torch.cuda.is_available():
         pytest.skip('test requires GPU and torch+cuda')
     _setup_seed(0)
-    ssd3d_head_cfg = _get_vote_head_cfg('3dssd/3dssd_kitti-3d-car.py')
+    ssd3d_head_cfg = _get_vote_head_cfg('3dssd/3dssd_4x4_kitti-3d-car.py')
     ssd3d_head_cfg.vote_module_cfg.num_points = 64
     self = build_head(ssd3d_head_cfg).cuda()
     sa_xyz = [torch.rand([2, 128, 3], dtype=torch.float32).cuda()]

tests/test_models/test_segmentors.py

@@ -55,7 +55,7 @@ def test_pointnet2_ssg():
 
     set_random_seed(0, True)
     pn2_ssg_cfg = _get_segmentor_cfg(
-        'pointnet2/pointnet2_ssg_16x2_scannet-3d-20class.py')
+        'pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py')
     pn2_ssg_cfg.test_cfg.num_points = 32
     self = build_segmentor(pn2_ssg_cfg).cuda()
     points = [torch.rand(1024, 6).float().cuda() for _ in range(2)]
@@ -122,7 +122,7 @@ def test_pointnet2_msg():
 
     set_random_seed(0, True)
     pn2_msg_cfg = _get_segmentor_cfg(
-        'pointnet2/pointnet2_msg_16x2_scannet-3d-20class.py')
+        'pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class.py')
     pn2_msg_cfg.test_cfg.num_points = 32
     self = build_segmentor(pn2_msg_cfg).cuda()
     points = [torch.rand(1024, 6).float().cuda() for _ in range(2)]

tests/test_runtime/test_apis.py

@@ -321,7 +321,7 @@ def test_inference_segmentor():
         pytest.skip('test requires GPU and torch+cuda')
     pcd = 'tests/data/scannet/points/scene0000_00.bin'
     segmentor_cfg = 'configs/pointnet2/pointnet2_ssg_' \
-                    '16x2_scannet-3d-20class.py'
+                    '16x2_cosine_200e_scannet_seg-3d-20class.py'
     segmentor = init_model(segmentor_cfg, device='cuda:0')
     results = inference_segmentor(segmentor, pcd)
     seg_3d = results[0][0]['semantic_mask']