[Fix] Fix tests dir (#1704)

* format ut dir tree

* ad pytest skip

mmdet3d/evaluation/metrics/kitti_metric.py

@@ -67,7 +67,7 @@ class KittiMetric(BaseMetric):
         self.pred_box_type_3d = pred_box_type_3d
         self.default_cam_key = default_cam_key
         self.file_client_args = file_client_args
-
+        self.default_cam_key = default_cam_key
         allowed_metrics = ['bbox', 'img_bbox', 'mAP']
         self.metrics = metric if isinstance(metric, list) else [metric]
         for metric in self.metrics:

mmdet3d/models/backbones/multi_backbone.py

@@ -115,10 +115,11 @@ class MultiBackbone(BaseModule):
             cur_ret = self.backbone_list[ind](points)
             cur_suffix = self.suffixes[ind]
             fp_features.append(cur_ret['fp_features'][-1])
+            cur_ret_new = dict()
             if cur_suffix != '':
                 for k in cur_ret.keys():
-                    cur_ret[k + '_' + cur_suffix] = cur_ret.pop(k)
-            ret.update(cur_ret)
+                    cur_ret_new[k + '_' + cur_suffix] = cur_ret[k]
+            ret.update(cur_ret_new)
 
         # Combine the features here
         hd_feature = torch.cat(fp_features, dim=1)

tests/test_models/test_backbones/test_dgcnn.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet3d.models import build_backbone
+
+
+def test_dgcnn_gf():
+    if not torch.cuda.is_available():
+        pytest.skip()
+
+    # DGCNNGF used in segmentation
+    cfg = dict(
+        type='DGCNNBackbone',
+        in_channels=6,
+        num_samples=(20, 20, 20),
+        knn_modes=['D-KNN', 'F-KNN', 'F-KNN'],
+        radius=(None, None, None),
+        gf_channels=((64, 64), (64, 64), (64, )),
+        fa_channels=(1024, ),
+        act_cfg=dict(type='ReLU'))
+
+    self = build_backbone(cfg)
+    self.cuda()
+
+    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
+    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
+    # test forward
+    ret_dict = self(xyz)
+    gf_points = ret_dict['gf_points']
+    fa_points = ret_dict['fa_points']
+
+    assert len(gf_points) == 4
+    assert gf_points[0].shape == torch.Size([1, 100, 6])
+    assert gf_points[1].shape == torch.Size([1, 100, 64])
+    assert gf_points[2].shape == torch.Size([1, 100, 64])
+    assert gf_points[3].shape == torch.Size([1, 100, 64])
+    assert fa_points.shape == torch.Size([1, 100, 1216])

tests/test_models/test_backbones/test_dla.py

+import torch
+
+from mmdet3d.models import build_backbone
+
+
+def test_dla_net():
+    # test DLANet used in SMOKE
+    # test list config
+    cfg = dict(
+        type='DLANet',
+        depth=34,
+        in_channels=3,
+        norm_cfg=dict(type='GN', num_groups=32))
+
+    img = torch.randn((4, 3, 32, 32))
+    self = build_backbone(cfg)
+    self.init_weights()
+
+    results = self(img)
+    assert len(results) == 6
+    assert results[0].shape == torch.Size([4, 16, 32, 32])
+    assert results[1].shape == torch.Size([4, 32, 16, 16])
+    assert results[2].shape == torch.Size([4, 64, 8, 8])
+    assert results[3].shape == torch.Size([4, 128, 4, 4])
+    assert results[4].shape == torch.Size([4, 256, 2, 2])
+    assert results[5].shape == torch.Size([4, 512, 1, 1])

tests/test_models/test_backbones/test_mink_resnet.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet3d.models import build_backbone
+
+
+def test_mink_resnet():
+    if not torch.cuda.is_available():
+        pytest.skip('test requires GPU and torch+cuda')
+
+    try:
+        import MinkowskiEngine as ME
+    except ImportError:
+        pytest.skip('test requires MinkowskiEngine installation')
+
+    coordinates, features = [], []
+    np.random.seed(42)
+    # batch of 2 point clouds
+    for i in range(2):
+        c = torch.from_numpy(np.random.rand(500, 3) * 100)
+        coordinates.append(c.float().cuda())
+        f = torch.from_numpy(np.random.rand(500, 3))
+        features.append(f.float().cuda())
+    tensor_coordinates, tensor_features = ME.utils.sparse_collate(
+        coordinates, features)
+    x = ME.SparseTensor(
+        features=tensor_features, coordinates=tensor_coordinates)
+
+    # MinkResNet34 with 4 outputs
+    cfg = dict(type='MinkResNet', depth=34, in_channels=3)
+    self = build_backbone(cfg).cuda()
+    self.init_weights()
+
+    y = self(x)
+    assert len(y) == 4
+    assert y[0].F.shape == torch.Size([900, 64])
+    assert y[0].tensor_stride[0] == 8
+    assert y[1].F.shape == torch.Size([472, 128])
+    assert y[1].tensor_stride[0] == 16
+    assert y[2].F.shape == torch.Size([105, 256])
+    assert y[2].tensor_stride[0] == 32
+    assert y[3].F.shape == torch.Size([16, 512])
+    assert y[3].tensor_stride[0] == 64
+
+    # MinkResNet50 with 2 outputs
+    cfg = dict(
+        type='MinkResNet', depth=34, in_channels=3, num_stages=2, pool=False)
+    self = build_backbone(cfg).cuda()
+    self.init_weights()
+
+    y = self(x)
+    assert len(y) == 2
+    assert y[0].F.shape == torch.Size([985, 64])
+    assert y[0].tensor_stride[0] == 4
+    assert y[1].F.shape == torch.Size([900, 128])
+    assert y[1].tensor_stride[0] == 8

tests/test_models/test_backbones/test_multi_backbone.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet3d.models import build_backbone
+
+
+def test_multi_backbone():
+    if not torch.cuda.is_available():
+        pytest.skip()
+
+    # test list config
+    cfg_list = dict(
+        type='MultiBackbone',
+        num_streams=4,
+        suffixes=['net0', 'net1', 'net2', 'net3'],
+        backbones=[
+            dict(
+                type='PointNet2SASSG',
+                in_channels=4,
+                num_points=(256, 128, 64, 32),
+                radius=(0.2, 0.4, 0.8, 1.2),
+                num_samples=(64, 32, 16, 16),
+                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                             (128, 128, 256)),
+                fp_channels=((256, 256), (256, 256)),
+                norm_cfg=dict(type='BN2d')),
+            dict(
+                type='PointNet2SASSG',
+                in_channels=4,
+                num_points=(256, 128, 64, 32),
+                radius=(0.2, 0.4, 0.8, 1.2),
+                num_samples=(64, 32, 16, 16),
+                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                             (128, 128, 256)),
+                fp_channels=((256, 256), (256, 256)),
+                norm_cfg=dict(type='BN2d')),
+            dict(
+                type='PointNet2SASSG',
+                in_channels=4,
+                num_points=(256, 128, 64, 32),
+                radius=(0.2, 0.4, 0.8, 1.2),
+                num_samples=(64, 32, 16, 16),
+                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                             (128, 128, 256)),
+                fp_channels=((256, 256), (256, 256)),
+                norm_cfg=dict(type='BN2d')),
+            dict(
+                type='PointNet2SASSG',
+                in_channels=4,
+                num_points=(256, 128, 64, 32),
+                radius=(0.2, 0.4, 0.8, 1.2),
+                num_samples=(64, 32, 16, 16),
+                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                             (128, 128, 256)),
+                fp_channels=((256, 256), (256, 256)),
+                norm_cfg=dict(type='BN2d'))
+        ])
+
+    self = build_backbone(cfg_list)
+    self.cuda()
+
+    assert len(self.backbone_list) == 4
+
+    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
+    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
+    # test forward
+    ret_dict = self(xyz[:, :, :4])
+
+    assert ret_dict['hd_feature'].shape == torch.Size([1, 256, 128])
+    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
+    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])
+
+    # test dict config
+    cfg_dict = dict(
+        type='MultiBackbone',
+        num_streams=2,
+        suffixes=['net0', 'net1'],
+        aggregation_mlp_channels=[512, 128],
+        backbones=dict(
+            type='PointNet2SASSG',
+            in_channels=4,
+            num_points=(256, 128, 64, 32),
+            radius=(0.2, 0.4, 0.8, 1.2),
+            num_samples=(64, 32, 16, 16),
+            sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                         (128, 128, 256)),
+            fp_channels=((256, 256), (256, 256)),
+            norm_cfg=dict(type='BN2d')))
+
+    self = build_backbone(cfg_dict)
+    self.cuda()
+
+    assert len(self.backbone_list) == 2
+
+    # test forward
+    ret_dict = self(xyz[:, :, :4])
+
+    assert ret_dict['hd_feature'].shape == torch.Size([1, 128, 128])
+    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
+    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])
+
+    # Length of backbone configs list should be equal to num_streams
+    with pytest.raises(AssertionError):
+        cfg_list['num_streams'] = 3
+        build_backbone(cfg_list)
+
+    # Length of suffixes list should be equal to num_streams
+    with pytest.raises(AssertionError):
+        cfg_dict['suffixes'] = ['net0', 'net1', 'net2']
+        build_backbone(cfg_dict)
+
+    # Type of 'backbones' should be Dict or List[Dict].
+    with pytest.raises(AssertionError):
+        cfg_dict['backbones'] = 'PointNet2SASSG'
+        build_backbone(cfg_dict)

tests/test_models/test_backbones.py → tests/test_models/test_backbones/test_pointnet2_sa_msg.py

@@ -6,185 +6,6 @@ import torch
 from mmdet3d.models import build_backbone
 
 
-def test_pointnet2_sa_ssg():
-    if not torch.cuda.is_available():
-        pytest.skip()
-
-    cfg = dict(
-        type='PointNet2SASSG',
-        in_channels=6,
-        num_points=(32, 16),
-        radius=(0.8, 1.2),
-        num_samples=(16, 8),
-        sa_channels=((8, 16), (16, 16)),
-        fp_channels=((16, 16), (16, 16)))
-    self = build_backbone(cfg)
-    self.cuda()
-    assert self.SA_modules[0].mlps[0].layer0.conv.in_channels == 6
-    assert self.SA_modules[0].mlps[0].layer0.conv.out_channels == 8
-    assert self.SA_modules[0].mlps[0].layer1.conv.out_channels == 16
-    assert self.SA_modules[1].mlps[0].layer1.conv.out_channels == 16
-    assert self.FP_modules[0].mlps.layer0.conv.in_channels == 32
-    assert self.FP_modules[0].mlps.layer0.conv.out_channels == 16
-    assert self.FP_modules[1].mlps.layer0.conv.in_channels == 19
-
-    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
-    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
-    # test forward
-    ret_dict = self(xyz)
-    fp_xyz = ret_dict['fp_xyz']
-    fp_features = ret_dict['fp_features']
-    fp_indices = ret_dict['fp_indices']
-    sa_xyz = ret_dict['sa_xyz']
-    sa_features = ret_dict['sa_features']
-    sa_indices = ret_dict['sa_indices']
-    assert len(fp_xyz) == len(fp_features) == len(fp_indices) == 3
-    assert len(sa_xyz) == len(sa_features) == len(sa_indices) == 3
-    assert fp_xyz[0].shape == torch.Size([1, 16, 3])
-    assert fp_xyz[1].shape == torch.Size([1, 32, 3])
-    assert fp_xyz[2].shape == torch.Size([1, 100, 3])
-    assert fp_features[0].shape == torch.Size([1, 16, 16])
-    assert fp_features[1].shape == torch.Size([1, 16, 32])
-    assert fp_features[2].shape == torch.Size([1, 16, 100])
-    assert fp_indices[0].shape == torch.Size([1, 16])
-    assert fp_indices[1].shape == torch.Size([1, 32])
-    assert fp_indices[2].shape == torch.Size([1, 100])
-    assert sa_xyz[0].shape == torch.Size([1, 100, 3])
-    assert sa_xyz[1].shape == torch.Size([1, 32, 3])
-    assert sa_xyz[2].shape == torch.Size([1, 16, 3])
-    assert sa_features[0].shape == torch.Size([1, 3, 100])
-    assert sa_features[1].shape == torch.Size([1, 16, 32])
-    assert sa_features[2].shape == torch.Size([1, 16, 16])
-    assert sa_indices[0].shape == torch.Size([1, 100])
-    assert sa_indices[1].shape == torch.Size([1, 32])
-    assert sa_indices[2].shape == torch.Size([1, 16])
-
-    # test only xyz input without features
-    cfg['in_channels'] = 3
-    self = build_backbone(cfg)
-    self.cuda()
-    ret_dict = self(xyz[..., :3])
-    assert len(fp_xyz) == len(fp_features) == len(fp_indices) == 3
-    assert len(sa_xyz) == len(sa_features) == len(sa_indices) == 3
-    assert fp_features[0].shape == torch.Size([1, 16, 16])
-    assert fp_features[1].shape == torch.Size([1, 16, 32])
-    assert fp_features[2].shape == torch.Size([1, 16, 100])
-    assert sa_features[0].shape == torch.Size([1, 3, 100])
-    assert sa_features[1].shape == torch.Size([1, 16, 32])
-    assert sa_features[2].shape == torch.Size([1, 16, 16])
-
-
-def test_multi_backbone():
-    if not torch.cuda.is_available():
-        pytest.skip()
-
-    # test list config
-    cfg_list = dict(
-        type='MultiBackbone',
-        num_streams=4,
-        suffixes=['net0', 'net1', 'net2', 'net3'],
-        backbones=[
-            dict(
-                type='PointNet2SASSG',
-                in_channels=4,
-                num_points=(256, 128, 64, 32),
-                radius=(0.2, 0.4, 0.8, 1.2),
-                num_samples=(64, 32, 16, 16),
-                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
-                             (128, 128, 256)),
-                fp_channels=((256, 256), (256, 256)),
-                norm_cfg=dict(type='BN2d')),
-            dict(
-                type='PointNet2SASSG',
-                in_channels=4,
-                num_points=(256, 128, 64, 32),
-                radius=(0.2, 0.4, 0.8, 1.2),
-                num_samples=(64, 32, 16, 16),
-                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
-                             (128, 128, 256)),
-                fp_channels=((256, 256), (256, 256)),
-                norm_cfg=dict(type='BN2d')),
-            dict(
-                type='PointNet2SASSG',
-                in_channels=4,
-                num_points=(256, 128, 64, 32),
-                radius=(0.2, 0.4, 0.8, 1.2),
-                num_samples=(64, 32, 16, 16),
-                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
-                             (128, 128, 256)),
-                fp_channels=((256, 256), (256, 256)),
-                norm_cfg=dict(type='BN2d')),
-            dict(
-                type='PointNet2SASSG',
-                in_channels=4,
-                num_points=(256, 128, 64, 32),
-                radius=(0.2, 0.4, 0.8, 1.2),
-                num_samples=(64, 32, 16, 16),
-                sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
-                             (128, 128, 256)),
-                fp_channels=((256, 256), (256, 256)),
-                norm_cfg=dict(type='BN2d'))
-        ])
-
-    self = build_backbone(cfg_list)
-    self.cuda()
-
-    assert len(self.backbone_list) == 4
-
-    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
-    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
-    # test forward
-    ret_dict = self(xyz[:, :, :4])
-
-    assert ret_dict['hd_feature'].shape == torch.Size([1, 256, 128])
-    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
-    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])
-
-    # test dict config
-    cfg_dict = dict(
-        type='MultiBackbone',
-        num_streams=2,
-        suffixes=['net0', 'net1'],
-        aggregation_mlp_channels=[512, 128],
-        backbones=dict(
-            type='PointNet2SASSG',
-            in_channels=4,
-            num_points=(256, 128, 64, 32),
-            radius=(0.2, 0.4, 0.8, 1.2),
-            num_samples=(64, 32, 16, 16),
-            sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
-                         (128, 128, 256)),
-            fp_channels=((256, 256), (256, 256)),
-            norm_cfg=dict(type='BN2d')))
-
-    self = build_backbone(cfg_dict)
-    self.cuda()
-
-    assert len(self.backbone_list) == 2
-
-    # test forward
-    ret_dict = self(xyz[:, :, :4])
-
-    assert ret_dict['hd_feature'].shape == torch.Size([1, 128, 128])
-    assert ret_dict['fp_xyz_net0'][-1].shape == torch.Size([1, 128, 3])
-    assert ret_dict['fp_features_net0'][-1].shape == torch.Size([1, 256, 128])
-
-    # Length of backbone configs list should be equal to num_streams
-    with pytest.raises(AssertionError):
-        cfg_list['num_streams'] = 3
-        build_backbone(cfg_list)
-
-    # Length of suffixes list should be equal to num_streams
-    with pytest.raises(AssertionError):
-        cfg_dict['suffixes'] = ['net0', 'net1', 'net2']
-        build_backbone(cfg_dict)
-
-    # Type of 'backbones' should be Dict or List[Dict].
-    with pytest.raises(AssertionError):
-        cfg_dict['backbones'] = 'PointNet2SASSG'
-        build_backbone(cfg_dict)
-
-
 def test_pointnet2_sa_msg():
     if not torch.cuda.is_available():
         pytest.skip()
@@ -297,111 +118,3 @@ def test_pointnet2_sa_msg():
     assert sa_indices[2].shape == torch.Size([1, 256])
     assert sa_indices[3].shape == torch.Size([1, 64])
     assert sa_indices[4].shape == torch.Size([1, 16])
-
-
-def test_dgcnn_gf():
-    if not torch.cuda.is_available():
-        pytest.skip()
-
-    # DGCNNGF used in segmentation
-    cfg = dict(
-        type='DGCNNBackbone',
-        in_channels=6,
-        num_samples=(20, 20, 20),
-        knn_modes=['D-KNN', 'F-KNN', 'F-KNN'],
-        radius=(None, None, None),
-        gf_channels=((64, 64), (64, 64), (64, )),
-        fa_channels=(1024, ),
-        act_cfg=dict(type='ReLU'))
-
-    self = build_backbone(cfg)
-    self.cuda()
-
-    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
-    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
-    # test forward
-    ret_dict = self(xyz)
-    gf_points = ret_dict['gf_points']
-    fa_points = ret_dict['fa_points']
-
-    assert len(gf_points) == 4
-    assert gf_points[0].shape == torch.Size([1, 100, 6])
-    assert gf_points[1].shape == torch.Size([1, 100, 64])
-    assert gf_points[2].shape == torch.Size([1, 100, 64])
-    assert gf_points[3].shape == torch.Size([1, 100, 64])
-    assert fa_points.shape == torch.Size([1, 100, 1216])
-
-
-def test_dla_net():
-    # test DLANet used in SMOKE
-    # test list config
-    cfg = dict(
-        type='DLANet',
-        depth=34,
-        in_channels=3,
-        norm_cfg=dict(type='GN', num_groups=32))
-
-    img = torch.randn((4, 3, 32, 32))
-    self = build_backbone(cfg)
-    self.init_weights()
-
-    results = self(img)
-    assert len(results) == 6
-    assert results[0].shape == torch.Size([4, 16, 32, 32])
-    assert results[1].shape == torch.Size([4, 32, 16, 16])
-    assert results[2].shape == torch.Size([4, 64, 8, 8])
-    assert results[3].shape == torch.Size([4, 128, 4, 4])
-    assert results[4].shape == torch.Size([4, 256, 2, 2])
-    assert results[5].shape == torch.Size([4, 512, 1, 1])
-
-
-def test_mink_resnet():
-    if not torch.cuda.is_available():
-        pytest.skip('test requires GPU and torch+cuda')
-
-    try:
-        import MinkowskiEngine as ME
-    except ImportError:
-        pytest.skip('test requires MinkowskiEngine installation')
-
-    coordinates, features = [], []
-    np.random.seed(42)
-    # batch of 2 point clouds
-    for i in range(2):
-        c = torch.from_numpy(np.random.rand(500, 3) * 100)
-        coordinates.append(c.float().cuda())
-        f = torch.from_numpy(np.random.rand(500, 3))
-        features.append(f.float().cuda())
-    tensor_coordinates, tensor_features = ME.utils.sparse_collate(
-        coordinates, features)
-    x = ME.SparseTensor(
-        features=tensor_features, coordinates=tensor_coordinates)
-
-    # MinkResNet34 with 4 outputs
-    cfg = dict(type='MinkResNet', depth=34, in_channels=3)
-    self = build_backbone(cfg).cuda()
-    self.init_weights()
-
-    y = self(x)
-    assert len(y) == 4
-    assert y[0].F.shape == torch.Size([900, 64])
-    assert y[0].tensor_stride[0] == 8
-    assert y[1].F.shape == torch.Size([472, 128])
-    assert y[1].tensor_stride[0] == 16
-    assert y[2].F.shape == torch.Size([105, 256])
-    assert y[2].tensor_stride[0] == 32
-    assert y[3].F.shape == torch.Size([16, 512])
-    assert y[3].tensor_stride[0] == 64
-
-    # MinkResNet50 with 2 outputs
-    cfg = dict(
-        type='MinkResNet', depth=34, in_channels=3, num_stages=2, pool=False)
-    self = build_backbone(cfg).cuda()
-    self.init_weights()
-
-    y = self(x)
-    assert len(y) == 2
-    assert y[0].F.shape == torch.Size([985, 64])
-    assert y[0].tensor_stride[0] == 4
-    assert y[1].F.shape == torch.Size([900, 128])
-    assert y[1].tensor_stride[0] == 8

tests/test_models/test_backbones/test_pointnet2_sa_ssg.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet3d.models import build_backbone
+
+
+def test_pointnet2_sa_ssg():
+    if not torch.cuda.is_available():
+        pytest.skip()
+
+    cfg = dict(
+        type='PointNet2SASSG',
+        in_channels=6,
+        num_points=(32, 16),
+        radius=(0.8, 1.2),
+        num_samples=(16, 8),
+        sa_channels=((8, 16), (16, 16)),
+        fp_channels=((16, 16), (16, 16)))
+    self = build_backbone(cfg)
+    self.cuda()
+    assert self.SA_modules[0].mlps[0].layer0.conv.in_channels == 6
+    assert self.SA_modules[0].mlps[0].layer0.conv.out_channels == 8
+    assert self.SA_modules[0].mlps[0].layer1.conv.out_channels == 16
+    assert self.SA_modules[1].mlps[0].layer1.conv.out_channels == 16
+    assert self.FP_modules[0].mlps.layer0.conv.in_channels == 32
+    assert self.FP_modules[0].mlps.layer0.conv.out_channels == 16
+    assert self.FP_modules[1].mlps.layer0.conv.in_channels == 19
+
+    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin', dtype=np.float32)
+    xyz = torch.from_numpy(xyz).view(1, -1, 6).cuda()  # (B, N, 6)
+    # test forward
+    ret_dict = self(xyz)
+    fp_xyz = ret_dict['fp_xyz']
+    fp_features = ret_dict['fp_features']
+    fp_indices = ret_dict['fp_indices']
+    sa_xyz = ret_dict['sa_xyz']
+    sa_features = ret_dict['sa_features']
+    sa_indices = ret_dict['sa_indices']
+    assert len(fp_xyz) == len(fp_features) == len(fp_indices) == 3
+    assert len(sa_xyz) == len(sa_features) == len(sa_indices) == 3
+    assert fp_xyz[0].shape == torch.Size([1, 16, 3])
+    assert fp_xyz[1].shape == torch.Size([1, 32, 3])
+    assert fp_xyz[2].shape == torch.Size([1, 100, 3])
+    assert fp_features[0].shape == torch.Size([1, 16, 16])
+    assert fp_features[1].shape == torch.Size([1, 16, 32])
+    assert fp_features[2].shape == torch.Size([1, 16, 100])
+    assert fp_indices[0].shape == torch.Size([1, 16])
+    assert fp_indices[1].shape == torch.Size([1, 32])
+    assert fp_indices[2].shape == torch.Size([1, 100])
+    assert sa_xyz[0].shape == torch.Size([1, 100, 3])
+    assert sa_xyz[1].shape == torch.Size([1, 32, 3])
+    assert sa_xyz[2].shape == torch.Size([1, 16, 3])
+    assert sa_features[0].shape == torch.Size([1, 3, 100])
+    assert sa_features[1].shape == torch.Size([1, 16, 32])
+    assert sa_features[2].shape == torch.Size([1, 16, 16])
+    assert sa_indices[0].shape == torch.Size([1, 100])
+    assert sa_indices[1].shape == torch.Size([1, 32])
+    assert sa_indices[2].shape == torch.Size([1, 16])
+
+    # test only xyz input without features
+    cfg['in_channels'] = 3
+    self = build_backbone(cfg)
+    self.cuda()
+    ret_dict = self(xyz[..., :3])
+    assert len(fp_xyz) == len(fp_features) == len(fp_indices) == 3
+    assert len(sa_xyz) == len(sa_features) == len(sa_indices) == 3
+    assert fp_features[0].shape == torch.Size([1, 16, 16])
+    assert fp_features[1].shape == torch.Size([1, 16, 32])
+    assert fp_features[2].shape == torch.Size([1, 16, 100])
+    assert sa_features[0].shape == torch.Size([1, 3, 100])
+    assert sa_features[1].shape == torch.Size([1, 16, 32])
+    assert sa_features[2].shape == torch.Size([1, 16, 16])

tests/test_models/test_layers/test_dgcnn_modules/test_dgcnn_fa_module.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+
+def test_dgcnn_fa_module():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    from mmdet3d.models.layers import DGCNNFAModule
+
+    self = DGCNNFAModule(mlp_channels=[24, 16]).cuda()
+    assert self.mlps.layer0.conv.in_channels == 24
+    assert self.mlps.layer0.conv.out_channels == 16
+
+    points = [torch.rand(1, 200, 12).float().cuda() for _ in range(3)]
+
+    fa_points = self(points)
+    assert fa_points.shape == torch.Size([1, 200, 40])

tests/test_models/test_layers/test_dgcnn_modules/test_dgcnn_fp_module.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+
+def test_dgcnn_fp_module():
+    if not torch.cuda.is_available():
+        pytest.skip()
+    from mmdet3d.models.layers import DGCNNFPModule
+
+    self = DGCNNFPModule(mlp_channels=[24, 16]).cuda()
+    assert self.mlps.layer0.conv.in_channels == 24
+    assert self.mlps.layer0.conv.out_channels == 16
+
+    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin',
+                      np.float32).reshape((-1, 6))
+
+    # (B, N, 3)
+    xyz = torch.from_numpy(xyz).view(1, -1, 3).cuda()
+    points = xyz.repeat([1, 1, 8]).cuda()
+
+    fp_points = self(points)
+    assert fp_points.shape == torch.Size([1, 200, 16])

tests/test_models/test_common_modules/test_dgcnn_modules.py → tests/test_models/test_layers/test_dgcnn_modules/test_dgcnn_gf_module.py

@@ -55,38 +55,3 @@ def test_dgcnn_gf_module():
         norm_cfg=dict(type='BN2d'),
         act_cfg=dict(type='ReLU'),
         pool_mode='max').cuda()
-
-
-def test_dgcnn_fa_module():
-    if not torch.cuda.is_available():
-        pytest.skip()
-    from mmdet3d.models.layers import DGCNNFAModule
-
-    self = DGCNNFAModule(mlp_channels=[24, 16]).cuda()
-    assert self.mlps.layer0.conv.in_channels == 24
-    assert self.mlps.layer0.conv.out_channels == 16
-
-    points = [torch.rand(1, 200, 12).float().cuda() for _ in range(3)]
-
-    fa_points = self(points)
-    assert fa_points.shape == torch.Size([1, 200, 40])
-
-
-def test_dgcnn_fp_module():
-    if not torch.cuda.is_available():
-        pytest.skip()
-    from mmdet3d.models.layers import DGCNNFPModule
-
-    self = DGCNNFPModule(mlp_channels=[24, 16]).cuda()
-    assert self.mlps.layer0.conv.in_channels == 24
-    assert self.mlps.layer0.conv.out_channels == 16
-
-    xyz = np.fromfile('tests/data/sunrgbd/points/000001.bin',
-                      np.float32).reshape((-1, 6))
-
-    # (B, N, 3)
-    xyz = torch.from_numpy(xyz).view(1, -1, 3).cuda()
-    points = xyz.repeat([1, 1, 8]).cuda()
-
-    fp_points = self(points)
-    assert fp_points.shape == torch.Size([1, 200, 16])