def quantize(coords):
r"""Returns a unique index map and an inverse index map.
Args:
:attr:`coords` (:attr:`numpy.ndarray` or :attr:`torch.Tensor`): a
matrix of size :math:`N \times D` where :math:`N` is the number of
points in the :math:`D` dimensional space.
Returns:
:attr:`unique_map` (:attr:`numpy.ndarray` or :attr:`torch.Tensor`): a
list of indices that defines unique coordinates.
:attr:`coords[unique_map]` is the unique coordinates.
:attr:`inverse_map` (:attr:`numpy.ndarray` or :attr:`torch.Tensor`): a
list of indices that defines the inverse map that recovers the original
coordinates. :attr:`coords[unique_map[inverse_map]] == coords`
Example::
>>> unique_map, inverse_map = quantize(coords)
>>> unique_coords = coords[unique_map]
>>> print(unique_coords[inverse_map] == coords) # True, ..., True
>>> print(coords[unique_map[inverse_map]] == coords) # True, ..., True
"""
assert isinstance(coords, np.ndarray) or isinstance(
coords, torch.Tensor), "Invalid coords type"
if isinstance(coords, np.ndarray):
assert (coords.dtype == np.int32
), f"Invalid coords type {coords.dtype} != np.int32"
return MEB.quantize_np(coords.astype(np.int32))
else:
# Type check done inside
return MEB.quantize_th(coords.int())
def test_mapping(self):
N = 16575
coords = (np.random.rand(N, 3) * 100).astype(np.int32)
mapping, inverse_mapping = MEB.quantize_np(coords)
print("N unique:", len(mapping), "N:", N)
self.assertTrue((coords == coords[mapping][inverse_mapping]).all())
self.assertTrue((coords == coords[mapping[inverse_mapping]]).all())
coords = torch.from_numpy(coords)
mapping, inverse_mapping = MEB.quantize_th(coords)
print("N unique:", len(mapping), "N:", N)
self.assertTrue((coords == coords[mapping[inverse_mapping]]).all())
unique_coords, index, reverse_index = sparse_quantize(
coords, return_index=True, return_inverse=True)
self.assertTrue((coords == coords[index[reverse_index]]).all())