(self)
| 74 | print(input1.coordinate_map_key, input2.coordinate_map_key) |
| 75 | |
| 76 | def test_device(self): |
| 77 | print(f"{self.__class__.__name__}: test_device SparseTensor") |
| 78 | if not is_cuda_available(): |
| 79 | return |
| 80 | |
| 81 | coords = torch.IntTensor( |
| 82 | [[0, 1], [0, 1], [0, 2], [0, 2], [1, 0], [1, 0], [1, 1]] |
| 83 | ) |
| 84 | feats = torch.FloatTensor([[0, 1, 2, 3, 5, 6, 7]]).T |
| 85 | |
| 86 | SparseTensor(feats.to(0), coords.to(0)) |
| 87 | feats = torch.FloatTensor([[0, 1, 2, 3, 5, 6, 7]]).T.to(0) |
| 88 | st = SparseTensor(feats, coords, device=feats.device) |
| 89 | print(st) |
| 90 | |
| 91 | def test_device_unique(self): |
| 92 | print(f"{self.__class__.__name__}: test_device_unique SparseTensor") |
nothing calls this directly
no test coverage detected