(encoder_model, data, dataloader)
| 67 | |
| 68 | |
| 69 | def test(encoder_model, data, dataloader): |
| 70 | encoder_model.eval() |
| 71 | zs = [] |
| 72 | for i, (batch_size, node_id, adjs) in enumerate(dataloader): |
| 73 | adjs = [adj.to('cuda') for adj in adjs] |
| 74 | z, _, _ = encoder_model(data.x[node_id], adjs) |
| 75 | zs.append(z) |
| 76 | x = torch.cat(zs, dim=0) |
| 77 | |
| 78 | split = get_split(num_samples=x.size()[0], train_ratio=0.1, test_ratio=0.8) |
| 79 | result = LREvaluator()(x, data.y, split) |
| 80 | return result |
| 81 | |
| 82 | |
| 83 | def main(): |
no test coverage detected