def test_batch_match(self, device, dtype):
# The batch size of voxelgrids and odms must match.
with pytest.raises(
ValueError,
match="Expected voxelgrids and odms' batch size to be the same, "
"but got 2 for odms and 3 for voxelgrid."):
voxelgrids = torch.ones((3, 3, 3, 3), device=device, dtype=dtype)
odms = torch.ones((2, 6, 3, 3), device=device, dtype=dtype)
vg.project_odms(odms, voxelgrids)
def test_dimension_match(self, device, dtype):
# The dimension of voxelgrids and odms must match
with pytest.raises(
ValueError,
match=
"Expected voxelgrids and odms' dimension size to be the same, "
"but got 3 for odms and 4 for voxelgrid."):
voxelgrids = torch.ones((2, 4, 4, 4), device=device, dtype=dtype)
odms = torch.ones((2, 6, 3, 3), device=device, dtype=dtype)
vg.project_odms(odms, voxelgrids)
def test_empty_filled_odms(self, device, dtype):
# If the input is an empty odms, the output should be a filled voxel grid
odms1 = torch.zeros((6, 3, 3), device=device, dtype=dtype)
# If the input odms is a filled odms. the output should be an empty voxel grid
odms2 = torch.ones((6, 3, 3), device=device, dtype=dtype) * 3
odms = torch.stack((odms1, odms2))
voxelgrids = vg.project_odms(odms)
assert torch.equal(
voxelgrids[0],
torch.ones((3, 3, 3), device=device, dtype=torch.bool))
assert torch.equal(
voxelgrids[1],
torch.zeros((3, 3, 3), device=device, dtype=torch.bool))
def test_handmade_input_vote4(self, device, dtype):
# The input is hand-made.
odms = torch.tensor([[[[2, 0, 0], [2, 0, 0], [1, 1, 0]],
[[0, 1, 1], [0, 1, 2], [1, 0, 2]],
[[2, 0, 0], [2, 1, 0], [1, 1, 0]],
[[0, 1, 1], [0, 1, 1], [1, 0, 2]],
[[1, 0, 3], [0, 0, 1], [3, 2, 0]],
[[0, 2, 3], [2, 0, 0], [3, 0, 0]]]],
device=device,
dtype=dtype)
expected_votes = torch.tensor([[[[1, 1, 0], [1, 1, 1], [0, 1, 1]],
[[1, 1, 0], [1, 1, 1], [0, 1, 1]],
[[1, 1, 0], [1, 1, 1], [0, 1, 1]]]],
device=device,
dtype=torch.bool)
output_votes = vg.project_odms(odms, votes=4)
assert torch.equal(output_votes, expected_votes)