[CI] Add github action CI && refactor `tests/utils/` (#2012)

* update github action ci

* install torch in ci

* fix ci on win

* add __init__ in tests/utils

* add mmdet3d/testing for modeule importing in UT in win

* fix diff path sep on UT

* reduce RAM in voxel_generator

* reduce RAM in anchor_generator

* remove print in UT

* fix lint

* fix pvrcnn ut

* fix lint

* reduce channels in second_fpn

tests/test_models/test_detectors/test_fcaf3d.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestFCAF3d(unittest.TestCase):
@@ -19,12 +19,12 @@ class TestFCAF3d(unittest.TestCase):
         import mmdet3d.models
         assert hasattr(mmdet3d.models, 'MinkSingleStage3DDetector')
         DefaultScope.get_instance('test_fcaf3d', scope_name='mmdet3d')
-        _setup_seed(0)
-        fcaf3d_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        fcaf3d_net_cfg = get_detector_cfg(
             'fcaf3d/fcaf3d_2xb8_scannet-3d-18class.py')
         model = MODELS.build(fcaf3d_net_cfg)
         num_gt_instance = 3
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             num_gt_instance=num_gt_instance,
             num_classes=1,
             points_feat_dim=6,

tests/test_models/test_detectors/test_groupfree3d.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestGroupfree3d(unittest.TestCase):
@@ -15,12 +15,12 @@ class TestGroupfree3d(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'GroupFree3DNet')
         DefaultScope.get_instance('test_groupfree3d', scope_name='mmdet3d')
-        _setup_seed(0)
-        voxel_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        voxel_net_cfg = get_detector_cfg(
             'groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py')
         model = MODELS.build(voxel_net_cfg)
         num_gt_instance = 5
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             num_gt_instance=num_gt_instance,
             points_feat_dim=3,
             with_pts_semantic_mask=True,

tests/test_models/test_detectors/test_h3dnet.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestH3D(unittest.TestCase):
@@ -15,11 +15,11 @@ class TestH3D(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'H3DNet')
         DefaultScope.get_instance('test_H3DNet', scope_name='mmdet3d')
-        _setup_seed(0)
-        voxel_net_cfg = _get_detector_cfg('h3dnet/h3dnet_8xb3_scannet-seg.py')
+        setup_seed(0)
+        voxel_net_cfg = get_detector_cfg('h3dnet/h3dnet_8xb3_scannet-seg.py')
         model = MODELS.build(voxel_net_cfg)
         num_gt_instance = 5
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             num_gt_instance=num_gt_instance,
             points_feat_dim=4,
             bboxes_3d_type='depth',

tests/test_models/test_detectors/test_imvotenet.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestImvoteNet(unittest.TestCase):
@@ -15,12 +15,12 @@ class TestImvoteNet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'ImVoteNet')
         DefaultScope.get_instance('test_imvotenet_img', scope_name='mmdet3d')
-        _setup_seed(0)
-        votenet_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        votenet_net_cfg = get_detector_cfg(
             'imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py')
         model = MODELS.build(votenet_net_cfg)
 
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             with_points=False, with_img=True, img_size=128)
 
         if torch.cuda.is_available():
@@ -49,12 +49,12 @@ class TestImvoteNet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'ImVoteNet')
         DefaultScope.get_instance('test_imvotenet', scope_name='mmdet3d')
-        _setup_seed(0)
-        votenet_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        votenet_net_cfg = get_detector_cfg(
             'imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py')
         model = MODELS.build(votenet_net_cfg)
 
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             with_points=True,
             with_img=True,
             img_size=128,

tests/test_models/test_detectors/test_imvoxelnet.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestImVoxelNet(unittest.TestCase):
@@ -15,12 +15,12 @@ class TestImVoxelNet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'ImVoxelNet')
         DefaultScope.get_instance('test_ImVoxelNet', scope_name='mmdet3d')
-        _setup_seed(0)
-        imvoxel_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        imvoxel_net_cfg = get_detector_cfg(
             'imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py')
         model = MODELS.build(imvoxel_net_cfg)
         num_gt_instance = 1
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             with_points=False,
             with_img=True,
             img_size=(128, 128),

tests/test_models/test_detectors/test_mvxnet.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestMVXNet(unittest.TestCase):
@@ -15,14 +15,14 @@ class TestMVXNet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'DynamicMVXFasterRCNN')
 
-        _setup_seed(0)
+        setup_seed(0)
         DefaultScope.get_instance('test_mvxnet', scope_name='mmdet3d')
-        mvx_net_cfg = _get_detector_cfg(
+        mvx_net_cfg = get_detector_cfg(
             'mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py'  # noqa
         )
         model = MODELS.build(mvx_net_cfg)
         num_gt_instance = 1
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             with_img=False, num_gt_instance=num_gt_instance, points_feat_dim=4)
 
         if torch.cuda.is_available():

tests/test_models/test_detectors/test_parta2.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestPartA2(unittest.TestCase):
@@ -15,18 +15,17 @@ class TestPartA2(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'PartA2')
         DefaultScope.get_instance('test_parta2', scope_name='mmdet3d')
-        _setup_seed(0)
-        parta2_cfg = _get_detector_cfg(
+        setup_seed(0)
+        parta2_cfg = get_detector_cfg(
             'parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py')
         model = MODELS.build(parta2_cfg)
         num_gt_instance = 2
-        packed_inputs = _create_detector_inputs(
-            num_gt_instance=num_gt_instance)
+        packed_inputs = create_detector_inputs(num_gt_instance=num_gt_instance)
 
         # TODO: Support aug data test
         # aug_packed_inputs = [
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance),
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance + 1)
+        #     create_detector_inputs(num_gt_instance=num_gt_instance),
+        #     create_detector_inputs(num_gt_instance=num_gt_instance + 1)
         # ]
         # test_aug_test
         # metainfo = {

tests/test_models/test_detectors/test_pointrcnn.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestPointRCNN(unittest.TestCase):
@@ -15,12 +15,12 @@ class TestPointRCNN(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'PointRCNN')
         DefaultScope.get_instance('test_pointrcnn', scope_name='mmdet3d')
-        _setup_seed(0)
-        pointrcnn_cfg = _get_detector_cfg(
+        setup_seed(0)
+        pointrcnn_cfg = get_detector_cfg(
             'point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py')
         model = MODELS.build(pointrcnn_cfg)
         num_gt_instance = 2
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             num_points=10101, num_gt_instance=num_gt_instance)
 
         if torch.cuda.is_available():

tests/test_models/test_detectors/test_pvrcnn.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestPVRCNN(unittest.TestCase):
@@ -15,18 +15,17 @@ class TestPVRCNN(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'PointVoxelRCNN')
         DefaultScope.get_instance('test_pvrcnn', scope_name='mmdet3d')
-        _setup_seed(0)
-        pvrcnn_cfg = _get_detector_cfg(
+        setup_seed(0)
+        pvrcnn_cfg = get_detector_cfg(
             'pvrcnn/pvrcnn_8xb2-80e_kitti-3d-3class.py')
         model = MODELS.build(pvrcnn_cfg)
         num_gt_instance = 2
-        packed_inputs = _create_detector_inputs(
-            num_gt_instance=num_gt_instance)
+        packed_inputs = create_detector_inputs(num_gt_instance=num_gt_instance)
 
         # TODO: Support aug data test
         # aug_packed_inputs = [
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance),
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance + 1)
+        #     create_detector_inputs(num_gt_instance=num_gt_instance),
+        #     create_detector_inputs(num_gt_instance=num_gt_instance + 1)
         # ]
         # test_aug_test
         # metainfo = {

tests/test_models/test_detectors/test_sassd.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestSDSSD(unittest.TestCase):
@@ -15,12 +15,12 @@ class TestSDSSD(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'SASSD')
         DefaultScope.get_instance('test_sassd', scope_name='mmdet3d')
-        _setup_seed(0)
-        voxel_net_cfg = _get_detector_cfg(
+        setup_seed(0)
+        voxel_net_cfg = get_detector_cfg(
             'sassd/sassd_8xb6-80e_kitti-3d-3class.py')
         model = MODELS.build(voxel_net_cfg)
         num_gt_instance = 3
-        packed_inputs = _create_detector_inputs(
+        packed_inputs = create_detector_inputs(
             num_gt_instance=num_gt_instance, num_classes=1)
 
         if torch.cuda.is_available():

tests/test_models/test_detectors/test_votenet.py

@@ -4,8 +4,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestVotenet(unittest.TestCase):
@@ -15,18 +15,16 @@ class TestVotenet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'VoteNet')
         DefaultScope.get_instance('test_vote_net', scope_name='mmdet3d')
-        _setup_seed(0)
-        voxel_net_cfg = _get_detector_cfg(
-            'votenet/votenet_8xb16_sunrgbd-3d.py')
+        setup_seed(0)
+        voxel_net_cfg = get_detector_cfg('votenet/votenet_8xb16_sunrgbd-3d.py')
         model = MODELS.build(voxel_net_cfg)
         num_gt_instance = 50
-        packed_inputs = _create_detector_inputs(
-            num_gt_instance=num_gt_instance)
+        packed_inputs = create_detector_inputs(num_gt_instance=num_gt_instance)
 
         # TODO: Support aug test
         # aug_data = [
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance),
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance + 1)
+        #     create_detector_inputs(num_gt_instance=num_gt_instance),
+        #     create_detector_inputs(num_gt_instance=num_gt_instance + 1)
         # ]
         # # test_aug_test
         # metainfo = {

tests/test_models/test_detectors/test_voxelnet.py

@@ -5,8 +5,8 @@ import torch
 from mmengine import DefaultScope
 
 from mmdet3d.registry import MODELS
-from tests.utils.model_utils import (_create_detector_inputs,
-                                     _get_detector_cfg, _setup_seed)
+from mmdet3d.testing import (create_detector_inputs, get_detector_cfg,
+                             setup_seed)
 
 
 class TestVoxelNet(unittest.TestCase):
@@ -16,18 +16,17 @@ class TestVoxelNet(unittest.TestCase):
 
         assert hasattr(mmdet3d.models, 'VoxelNet')
         DefaultScope.get_instance('test_voxelnet', scope_name='mmdet3d')
-        _setup_seed(0)
-        pointpillars_cfg = _get_detector_cfg(
+        setup_seed(0)
+        pointpillars_cfg = get_detector_cfg(
             'pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py')
         model = MODELS.build(pointpillars_cfg)
         num_gt_instance = 2
-        packed_inputs = _create_detector_inputs(
-            num_gt_instance=num_gt_instance)
+        packed_inputs = create_detector_inputs(num_gt_instance=num_gt_instance)
 
         # TODO: Support aug_test
         # aug_data = [
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance),
-        #     _create_detector_inputs(num_gt_instance=num_gt_instance + 1)
+        #     create_detector_inputs(num_gt_instance=num_gt_instance),
+        #     create_detector_inputs(num_gt_instance=num_gt_instance + 1)
         # ]
         # # test_aug_test
         # metainfo = {

tests/test_models/test_necks/test_second_fpn.py

@@ -45,8 +45,8 @@ def test_centerpoint_fpn():
 
     second_cfg = dict(
         type='SECOND',
-        in_channels=64,
-        out_channels=[64, 128, 256],
+        in_channels=2,
+        out_channels=[2, 2, 2],
         layer_nums=[3, 5, 5],
         layer_strides=[2, 2, 2],
         norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
@@ -57,8 +57,8 @@ def test_centerpoint_fpn():
     # centerpoint usage of fpn
     centerpoint_fpn_cfg = dict(
         type='SECONDFPN',
-        in_channels=[64, 128, 256],
-        out_channels=[128, 128, 128],
+        in_channels=[2, 2, 2],
+        out_channels=[2, 2, 2],
         upsample_strides=[0.5, 1, 2],
         norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
         upsample_cfg=dict(type='deconv', bias=False),
@@ -67,17 +67,17 @@ def test_centerpoint_fpn():
     # original usage of fpn
     fpn_cfg = dict(
         type='SECONDFPN',
-        in_channels=[64, 128, 256],
+        in_channels=[2, 2, 2],
         upsample_strides=[1, 2, 4],
-        out_channels=[128, 128, 128])
+        out_channels=[2, 2, 2])
 
     second_fpn = build_neck(fpn_cfg)
 
     centerpoint_second_fpn = build_neck(centerpoint_fpn_cfg)
 
-    input = torch.rand([4, 64, 512, 512])
+    input = torch.rand([2, 2, 32, 32])
     sec_output = second(input)
     centerpoint_output = centerpoint_second_fpn(sec_output)
     second_output = second_fpn(sec_output)
-    assert centerpoint_output[0].shape == torch.Size([4, 384, 128, 128])
-    assert second_output[0].shape == torch.Size([4, 384, 256, 256])
+    assert centerpoint_output[0].shape == torch.Size([2, 6, 8, 8])
+    assert second_output[0].shape == torch.Size([2, 6, 16, 16])

tests/test_models/test_task_modules/test_anchor/test_anchor_3d_generator.py

@@ -45,10 +45,10 @@ def test_anchor_3d_range_generator():
                         '\nrotations=[0, 1.57],\nreshape_out=False,' \
                         '\nsize_per_range=True)'
     assert repr_str == expected_repr_str
-    featmap_size = (256, 256)
+    featmap_size = (8, 8)
     mr_anchors = anchor_generator.single_level_grid_anchors(
         featmap_size, 1.1, device=device)
-    assert mr_anchors.shape == torch.Size([1, 256, 256, 3, 2, 7])
+    assert mr_anchors.shape == torch.Size([1, 8, 8, 3, 2, 7])
 
 
 def test_aligned_anchor_generator():
@@ -80,108 +80,108 @@ def test_aligned_anchor_generator():
         size_per_range=False,
         reshape_out=True)
 
-    featmap_sizes = [(256, 256), (128, 128), (64, 64)]
+    featmap_sizes = [(16, 16), (8, 8), (4, 4)]
     anchor_generator = TASK_UTILS.build(anchor_generator_cfg)
     assert anchor_generator.num_base_anchors == 8
 
     # check base anchors
     expected_grid_anchors = [
         torch.tensor([[
-            -51.0000, -51.0000, -1.8000, 2.5981, 0.8660, 1.0000, 0.0000,
+            -48.0000, -48.0000, -1.8000, 2.5981, 0.8660, 1.0000, 0.0000,
             0.0000, 0.0000
         ],
                       [
-                          -51.0000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
+                          -48.0000, -48.0000, -1.8000, 0.4000, 0.4000, 1.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -50.6000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
+                          -41.6000, -48.0000, -1.8000, 0.4000, 0.4000, 1.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -50.2000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
+                          -35.2000, -48.0000, -1.8000, 1.0000, 1.0000, 1.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -49.8000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
+                          -28.8000, -48.0000, -1.8000, 1.0000, 1.0000, 1.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -49.4000, -51.0000, -1.8000, 1.7321, 0.5774, 1.0000,
+                          -22.4000, -48.0000, -1.8000, 1.7321, 0.5774, 1.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -49.0000, -51.0000, -1.8000, 1.7321, 0.5774, 1.0000,
+                          -16.0000, -48.0000, -1.8000, 1.7321, 0.5774, 1.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -48.6000, -51.0000, -1.8000, 2.5981, 0.8660, 1.0000,
+                          -9.6000, -48.0000, -1.8000, 2.5981, 0.8660, 1.0000,
                           1.5700, 0.0000, 0.0000
                       ]],
                      device=device),
         torch.tensor([[
-            -50.8000, -50.8000, -1.8000, 5.1962, 1.7320, 2.0000, 0.0000,
+            -44.8000, -44.8000, -1.8000, 5.1962, 1.7320, 2.0000, 0.0000,
             0.0000, 0.0000
         ],
                       [
-                          -50.8000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
+                          -44.8000, -44.8000, -1.8000, 0.8000, 0.8000, 2.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -50.0000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
+                          -32.0000, -44.8000, -1.8000, 0.8000, 0.8000, 2.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -49.2000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
+                          -19.2000, -44.8000, -1.8000, 2.0000, 2.0000, 2.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -48.4000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
+                          -6.4000, -44.8000, -1.8000, 2.0000, 2.0000, 2.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -47.6000, -50.8000, -1.8000, 3.4642, 1.1548, 2.0000,
+                          6.4000, -44.8000, -1.8000, 3.4642, 1.1548, 2.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -46.8000, -50.8000, -1.8000, 3.4642, 1.1548, 2.0000,
+                          19.2000, -44.8000, -1.8000, 3.4642, 1.1548, 2.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -46.0000, -50.8000, -1.8000, 5.1962, 1.7320, 2.0000,
+                          32.0000, -44.8000, -1.8000, 5.1962, 1.7320, 2.0000,
                           1.5700, 0.0000, 0.0000
                       ]],
                      device=device),
         torch.tensor([[
-            -50.4000, -50.4000, -1.8000, 10.3924, 3.4640, 4.0000, 0.0000,
+            -38.4000, -38.4000, -1.8000, 10.3924, 3.4640, 4.0000, 0.0000,
             0.0000, 0.0000
         ],
                       [
-                          -50.4000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
+                          -38.4000, -38.4000, -1.8000, 1.6000, 1.6000, 4.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -48.8000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
+                          -12.8000, -38.4000, -1.8000, 1.6000, 1.6000, 4.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -47.2000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
+                          12.8000, -38.4000, -1.8000, 4.0000, 4.0000, 4.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -45.6000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
+                          38.4000, -38.4000, -1.8000, 4.0000, 4.0000, 4.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -44.0000, -50.4000, -1.8000, 6.9284, 2.3096, 4.0000,
+                          -38.4000, -12.8000, -1.8000, 6.9284, 2.3096, 4.0000,
                           1.5700, 0.0000, 0.0000
                       ],
                       [
-                          -42.4000, -50.4000, -1.8000, 6.9284, 2.3096, 4.0000,
+                          -12.8000, -12.8000, -1.8000, 6.9284, 2.3096, 4.0000,
                           0.0000, 0.0000, 0.0000
                       ],
                       [
-                          -40.8000, -50.4000, -1.8000, 10.3924, 3.4640, 4.0000,
+                          12.8000, -12.8000, -1.8000, 10.3924, 3.4640, 4.0000,
                           1.5700, 0.0000, 0.0000
                       ]],
                      device=device)
@@ -189,9 +189,9 @@ def test_aligned_anchor_generator():
     multi_level_anchors = anchor_generator.grid_anchors(
         featmap_sizes, device=device)
     expected_multi_level_shapes = [
-        torch.Size([524288, 9]),
-        torch.Size([131072, 9]),
-        torch.Size([32768, 9])
+        torch.Size([2048, 9]),
+        torch.Size([512, 9]),
+        torch.Size([128, 9])
     ]
     for i, single_level_anchor in enumerate(multi_level_anchors):
         assert single_level_anchor.shape == expected_multi_level_shapes[i]

tests/test_models/test_task_modules/test_voxel/test_voxel_generator.py

@@ -6,17 +6,15 @@ from mmdet3d.models.task_modules.voxel import VoxelGenerator
 
 def test_voxel_generator():
     np.random.seed(0)
-    voxel_size = [0.5, 0.5, 0.5]
-    point_cloud_range = [0, -40, -3, 70.4, 40, 1]
-    max_num_points = 1000
+    voxel_size = [5, 5, 1]
+    point_cloud_range = [0, 0, 0, 20, 40, 4]
+    max_num_points = 5
     self = VoxelGenerator(voxel_size, point_cloud_range, max_num_points)
-    points = np.random.rand(1000, 4)
+    points = np.random.uniform(0, 4, (20, 3))
     voxels = self.generate(points)
     voxels, coors, num_points_per_voxel = voxels
-    expected_coors = np.array([[7, 81, 1], [6, 81, 0], [7, 80, 1], [6, 81, 1],
-                               [7, 81, 0], [6, 80, 1], [7, 80, 0], [6, 80, 0]])
-    expected_num_points_per_voxel = np.array(
-        [120, 121, 127, 134, 115, 127, 125, 131])
-    assert voxels.shape == (8, 1000, 4)
+    expected_coors = np.array([[2, 0, 0], [3, 0, 0], [0, 0, 0], [1, 0, 0]])
+    expected_num_points_per_voxel = np.array([5, 5, 5, 3])
+    assert voxels.shape == (4, 5, 3)
     assert np.all(coors == expected_coors)
     assert np.all(num_points_per_voxel == expected_num_points_per_voxel)