(data)
| 19 | |
| 20 | |
| 21 | def from_predefined_split(data): |
| 22 | assert all([mask is not None for mask in [data.train_mask, data.test_mask, data.val_mask]]) |
| 23 | num_samples = data.num_nodes |
| 24 | indices = torch.arange(num_samples) |
| 25 | return { |
| 26 | 'train': indices[data.train_mask], |
| 27 | 'valid': indices[data.val_mask], |
| 28 | 'test': indices[data.test_mask] |
| 29 | } |
| 30 | |
| 31 | |
| 32 | def split_to_numpy(x, y, split): |