def test_pointnet2_sa_msg():
if not torch.cuda.is_available():
pytest.skip()
cfg = dict(type='PointNet2SAMSG',
in_channels=4,
num_points=(256, 64, (32, 32)),
radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
num_samples=((8, 8, 16), (8, 8, 16), (8, 8, 8)),
sa_channels=(((8, 8, 16), (8, 8, 16), (8, 8, 16)),
((16, 16, 32), (16, 16, 32), (16, 24, 32)),
((32, 32, 64), (32, 24, 64), (32, 64, 64))),
aggregation_channels=(16, 32, 64),
fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
fps_sample_range_lists=((-1), (-1), (64, -1)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(type='PointSAModuleMSG',
pool_mod='max',
use_xyz=True,
normalize_xyz=False))
self = build_backbone(cfg)
self.cuda()
assert self.SA_modules[0].mlps[0].layer0.conv.in_channels == 4
assert self.SA_modules[0].mlps[0].layer0.conv.out_channels == 8
assert self.SA_modules[0].mlps[1].layer1.conv.out_channels == 8
assert self.SA_modules[2].mlps[2].layer2.conv.out_channels == 64
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])
sa_xyz = ret_dict['sa_xyz'][-1]
sa_features = ret_dict['sa_features'][-1]
sa_indices = ret_dict['sa_indices'][-1]
assert sa_xyz.shape == torch.Size([1, 64, 3])
assert sa_features.shape == torch.Size([1, 64, 64])
assert sa_indices.shape == torch.Size([1, 64])
# out_indices should smaller than the length of SA Modules.
with pytest.raises(AssertionError):
build_backbone(
dict(type='PointNet2SAMSG',
in_channels=4,
num_points=(256, 64, (32, 32)),
radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
num_samples=((8, 8, 16), (8, 8, 16), (8, 8, 8)),
sa_channels=(((8, 8, 16), (8, 8, 16), (8, 8, 16)),
((16, 16, 32), (16, 16, 32), (16, 24, 32)),
((32, 32, 64), (32, 24, 64), (32, 64, 64))),
aggregation_channels=(16, 32, 64),
fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
fps_sample_range_lists=((-1), (-1), (64, -1)),
out_indices=(2, 3),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(type='PointSAModuleMSG',
pool_mod='max',
use_xyz=True,
normalize_xyz=False)))
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']
assert len(fp_xyz) == len(fp_features) == len(fp_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[2].shape == torch.Size([1, 16, 100])
assert fp_indices[2].shape == torch.Size([1, 100])
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)