def test_two_spheres(self, init_res, upsampling_steps):
final_res = init_res * 2**upsampling_steps + 1
assert (torch.equal(
sdf.sdf_to_voxelgrids([self.two_spheres],
init_res=init_res,
upsampling_steps=upsampling_steps),
self.sdf_to_voxelgrids_naive([self.two_spheres], final_res)))
def test_upsampling_steps_type(self):
with pytest.raises(TypeError,
match=r"Expected upsampling_steps to be int "
r"but got <class 'float'>."):
sdf.sdf_to_voxelgrids([self.sphere], upsampling_steps=0.5)
def test_init_res_type(self):
with pytest.raises(TypeError,
match=r"Expected init_res to be int "
r"but got <class 'float'>."):
sdf.sdf_to_voxelgrids([self.sphere], init_res=0.5)
def test_bbox_dim_type(self):
with pytest.raises(TypeError,
match=r"Expected bbox_dim to be int or float "
r"but got <class 'str'>."):
sdf.sdf_to_voxelgrids([self.sphere], bbox_dim=' ')
def test_each_sdf_type(self):
with pytest.raises(TypeError,
match=r"Expected sdf\[0\] to be callable "
r"but got <class 'int'>."):
sdf.sdf_to_voxelgrids([0])
def test_sdf_type(self):
with pytest.raises(TypeError,
match=r"Expected sdf to be list "
r"but got <class 'int'>."):
sdf.sdf_to_voxelgrids(0)