def test_load_points_from_indoor_file():
sunrgbd_info = mmcv.load('./tests/data/sunrgbd/sunrgbd_infos.pkl')
sunrgbd_load_points_from_file = LoadPointsFromFile(6, shift_height=True)
sunrgbd_results = dict()
data_path = './tests/data/sunrgbd'
sunrgbd_info = sunrgbd_info[0]
sunrgbd_results['pts_filename'] = osp.join(data_path,
sunrgbd_info['pts_path'])
sunrgbd_results = sunrgbd_load_points_from_file(sunrgbd_results)
sunrgbd_point_cloud = sunrgbd_results['points']
assert sunrgbd_point_cloud.shape == (100, 4)
scannet_info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')
scannet_load_data = LoadPointsFromFile(shift_height=True)
scannet_results = dict()
data_path = './tests/data/scannet'
scannet_info = scannet_info[0]
scannet_results['pts_filename'] = osp.join(data_path,
scannet_info['pts_path'])
scannet_results = scannet_load_data(scannet_results)
scannet_point_cloud = scannet_results['points']
repr_str = repr(scannet_load_data)
expected_repr_str = 'LoadPointsFromFile(shift_height=True, ' \
'file_client_args={\'backend\': \'disk\'}), ' \
'load_dim=6, use_dim=[0, 1, 2])'
assert repr_str == expected_repr_str
assert scannet_point_cloud.shape == (100, 4)
def test_load_points_from_outdoor_file():
data_path = 'tests/data/kitti/a.bin'
load_points_from_file = LoadPointsFromFile(coord_type='LIDAR',
load_dim=4,
use_dim=4)
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
points = results['points'].tensor.numpy()
assert points.shape == (50, 4)
assert np.allclose(points.sum(), 2637.479)
load_points_from_file = LoadPointsFromFile(coord_type='LIDAR',
load_dim=4,
use_dim=[0, 1, 2, 3])
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
new_points = results['points'].tensor.numpy()
assert new_points.shape == (50, 4)
assert np.allclose(points.sum(), 2637.479)
np.equal(points, new_points)
with pytest.raises(AssertionError):
LoadPointsFromFile(coord_type='LIDAR', load_dim=4, use_dim=5)
def test_load_points_from_indoor_file():
sunrgbd_info = mmcv.load('./tests/data/sunrgbd/sunrgbd_infos.pkl')
sunrgbd_load_points_from_file = LoadPointsFromFile(
coord_type='DEPTH', load_dim=6, shift_height=True)
sunrgbd_results = dict()
data_path = './tests/data/sunrgbd'
sunrgbd_info = sunrgbd_info[0]
sunrgbd_results['pts_filename'] = osp.join(data_path,
sunrgbd_info['pts_path'])
sunrgbd_results = sunrgbd_load_points_from_file(sunrgbd_results)
sunrgbd_point_cloud = sunrgbd_results['points'].tensor.numpy()
assert sunrgbd_point_cloud.shape == (100, 4)
scannet_info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')
scannet_load_data = LoadPointsFromFile(
coord_type='DEPTH', shift_height=True)
scannet_results = dict()
data_path = './tests/data/scannet'
scannet_info = scannet_info[0]
scannet_results['pts_filename'] = osp.join(data_path,
scannet_info['pts_path'])
scannet_results = scannet_load_data(scannet_results)
scannet_point_cloud = scannet_results['points'].tensor.numpy()
repr_str = repr(scannet_load_data)
expected_repr_str = 'LoadPointsFromFile(shift_height=True, ' \
'use_color=False, ' \
'file_client_args={\'backend\': \'disk\'}, ' \
'load_dim=6, use_dim=[0, 1, 2])'
assert repr_str == expected_repr_str
assert scannet_point_cloud.shape == (100, 4)
# test load point cloud with both shifted height and color
scannet_load_data = LoadPointsFromFile(
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
shift_height=True,
use_color=True)
scannet_results = dict()
scannet_results['pts_filename'] = osp.join(data_path,
scannet_info['pts_path'])
scannet_results = scannet_load_data(scannet_results)
scannet_point_cloud = scannet_results['points']
assert scannet_point_cloud.points_dim == 7
assert scannet_point_cloud.attribute_dims == dict(
height=3, color=[4, 5, 6])
scannet_point_cloud = scannet_point_cloud.tensor.numpy()
assert scannet_point_cloud.shape == (100, 7)
def test_load_points_from_outdoor_file():
data_path = 'tests/data/kitti/a.bin'
load_points_from_file = LoadPointsFromFile(4, 4)
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
points = results['points']
assert points.shape == (50, 4)
assert np.allclose(points.sum(), 2637.479)
load_points_from_file = LoadPointsFromFile(4, [0, 1, 2, 3])
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
new_points = results['points']
assert new_points.shape == (50, 4)
assert np.allclose(points.sum(), 2637.479)
np.equal(points, new_points)
with pytest.raises(AssertionError):
LoadPointsFromFile(4, 5)
def test_voxelization():
voxel_size = [0.5, 0.5, 0.5]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
max_num_points = 1000
self = VoxelGenerator(voxel_size, point_cloud_range, max_num_points)
data_path = './tests/data/kitti/training/velodyne_reduced/000000.bin'
load_points_from_file = LoadPointsFromFile(
coord_type='LIDAR', load_dim=4, use_dim=4)
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
points = results['points'].tensor.numpy()
voxels_generator = self.generate(points)
coors, voxels, num_points_per_voxel = voxels_generator
expected_coors = coors
expected_voxels = voxels
expected_num_points_per_voxel = num_points_per_voxel
points = torch.tensor(points)
max_num_points = -1
dynamic_voxelization = Voxelization(voxel_size, point_cloud_range,
max_num_points)
max_num_points = 1000
hard_voxelization = Voxelization(voxel_size, point_cloud_range,
max_num_points)
# test hard_voxelization on cpu
coors, voxels, num_points_per_voxel = hard_voxelization.forward(points)
coors = coors.detach().numpy()
voxels = voxels.detach().numpy()
num_points_per_voxel = num_points_per_voxel.detach().numpy()
assert np.all(coors == expected_coors)
assert np.all(voxels == expected_voxels)
assert np.all(num_points_per_voxel == expected_num_points_per_voxel)
# test dynamic_voxelization on cpu
coors = dynamic_voxelization.forward(points)
coors = coors.detach().numpy()
points = points.detach().numpy()
for i in range(expected_voxels.shape[0]):
indices = _get_voxel_points_indices(points, coors, expected_voxels[i])
num_points_current_voxel = points[indices].shape[0]
assert num_points_current_voxel > 0
assert np.all(
points[indices] == expected_coors[i][:num_points_current_voxel])
assert num_points_current_voxel == expected_num_points_per_voxel[i]
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
# test hard_voxelization on gpu
points = torch.tensor(points).contiguous().to(device='cuda:0')
coors, voxels, num_points_per_voxel = hard_voxelization.forward(points)
coors = coors.cpu().detach().numpy()
voxels = voxels.cpu().detach().numpy()
num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy()
assert np.all(coors == expected_coors)
assert np.all(voxels == expected_voxels)
assert np.all(num_points_per_voxel == expected_num_points_per_voxel)
# test dynamic_voxelization on gpu
coors = dynamic_voxelization.forward(points)
coors = coors.cpu().detach().numpy()
points = points.cpu().detach().numpy()
for i in range(expected_voxels.shape[0]):
indices = _get_voxel_points_indices(points, coors, expected_voxels[i])
num_points_current_voxel = points[indices].shape[0]
assert num_points_current_voxel > 0
assert np.all(
points[indices] == expected_coors[i][:num_points_current_voxel])
assert num_points_current_voxel == expected_num_points_per_voxel[i]
def test_voxelization_nondeterministic():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
voxel_size = [0.5, 0.5, 0.5]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
data_path = './tests/data/kitti/training/velodyne_reduced/000000.bin'
load_points_from_file = LoadPointsFromFile(coord_type='LIDAR',
load_dim=4,
use_dim=4)
results = dict()
results['pts_filename'] = data_path
results = load_points_from_file(results)
points = results['points'].tensor.numpy()
points = torch.tensor(points)
max_num_points = -1
dynamic_voxelization = Voxelization(voxel_size, point_cloud_range,
max_num_points)
max_num_points = 10
max_voxels = 50
hard_voxelization = Voxelization(voxel_size,
point_cloud_range,
max_num_points,
max_voxels,
deterministic=False)
# test hard_voxelization (non-deterministic version) on gpu
points = torch.tensor(points).contiguous().to(device='cuda:0')
voxels, coors, num_points_per_voxel = hard_voxelization.forward(points)
coors = coors.cpu().detach().numpy().tolist()
voxels = voxels.cpu().detach().numpy().tolist()
num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy().tolist()
coors_all = dynamic_voxelization.forward(points)
coors_all = coors_all.cpu().detach().numpy().tolist()
coors_set = set([tuple(c) for c in coors])
coors_all_set = set([tuple(c) for c in coors_all])
assert len(coors_set) == len(coors)
assert len(coors_set - coors_all_set) == 0
points = points.cpu().detach().numpy().tolist()
coors_points_dict = {}
for c, ps in zip(coors_all, points):
if tuple(c) not in coors_points_dict:
coors_points_dict[tuple(c)] = set()
coors_points_dict[tuple(c)].add(tuple(ps))
for c, ps, n in zip(coors, voxels, num_points_per_voxel):
ideal_voxel_points_set = coors_points_dict[tuple(c)]
voxel_points_set = set([tuple(p) for p in ps[:n]])
assert len(voxel_points_set) == n
if n < max_num_points:
assert voxel_points_set == ideal_voxel_points_set
for p in ps[n:]:
assert max(p) == min(p) == 0
else:
assert len(voxel_points_set - ideal_voxel_points_set) == 0
# test hard_voxelization (non-deterministic version) on gpu
# with all input point in range
points = torch.tensor(points).contiguous().to(device='cuda:0')[:max_voxels]
coors_all = dynamic_voxelization.forward(points)
valid_mask = coors_all.ge(0).all(-1)
points = points[valid_mask]
coors_all = coors_all[valid_mask]
coors_all = coors_all.cpu().detach().numpy().tolist()
voxels, coors, num_points_per_voxel = hard_voxelization.forward(points)
coors = coors.cpu().detach().numpy().tolist()
coors_set = set([tuple(c) for c in coors])
coors_all_set = set([tuple(c) for c in coors_all])
assert len(coors_set) == len(coors) == len(coors_all_set)