(self)
| 66 | print(len(unique_map)) |
| 67 | |
| 68 | def test_mapping(self): |
| 69 | N = 16575 |
| 70 | coords = (np.random.rand(N, 3) * 100).astype(np.int32) |
| 71 | mapping, inverse_mapping = MEB.quantize_np(coords) |
| 72 | print("N unique:", len(mapping), "N:", N) |
| 73 | self.assertTrue((coords == coords[mapping][inverse_mapping]).all()) |
| 74 | self.assertTrue((coords == coords[mapping[inverse_mapping]]).all()) |
| 75 | |
| 76 | coords = torch.from_numpy(coords) |
| 77 | mapping, inverse_mapping = MEB.quantize_th(coords) |
| 78 | print("N unique:", len(mapping), "N:", N) |
| 79 | self.assertTrue((coords == coords[mapping[inverse_mapping]]).all()) |
| 80 | |
| 81 | unique_coords, index, reverse_index = sparse_quantize( |
| 82 | coords, return_index=True, return_inverse=True |
| 83 | ) |
| 84 | self.assertTrue((coords == coords[index[reverse_index]]).all()) |
| 85 | |
| 86 | def test_label(self): |
| 87 | N = 16575 |
nothing calls this directly
no test coverage detected