add GLEM model, TAGDataset and example of GLEM #9662
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## master #9662 +/- ##
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- Coverage 87.54% 86.92% -0.62%
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Files 482 483 +1
Lines 31414 31585 +171
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- Hits 27501 27455 -46
- Misses 3913 4130 +217 ☔ View full report in Codecov by Sentry. |
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@rusty1s @akihironitta ready for your reviews |
| # Add the parent directory to sys.path | ||
| parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) | ||
| sys.path.append(parent_dir) |
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| ## Run GLEM for getting SOTA result on ogbn-products dataset | ||
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| `python glem.py` |
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| ## Run GLEM for getting SOTA result on ogbn-products dataset | |
| `python glem.py` |
| ext_pred_path = download_google_url( | ||
| id='15sO2m7BeW7C1Upmdw3Cx1JS__6nxTAzY', | ||
| folder='/work/users/junhaos/glem_data/ogbn_products/ext_preds', | ||
| filename='giant_sagn_scr.pt', log=True) |
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Let's use a relative path for other people to use.
| pretrain_augmented = True | ||
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| seed_everything(42) | ||
| from ogb.nodeproppred import PygNodePropPredDataset |
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nit: Let's move the import statement at the start of the file.
| if em_phase == 'gnn': | ||
| gnn_test_acc = max(gnn_test_acc, final_test_acc) | ||
| model.gnn = model.gnn.to('cpu', non_blocking=True) | ||
| em_phase = 'lm' | ||
| else: | ||
| lm_test_acc = max(lm_test_acc, final_test_acc) | ||
| model.lm = model.lm.to('cpu', non_blocking=True) | ||
| em_phase = 'gnn' | ||
| torch.cuda.empty_cache() | ||
| print(f'Best GNN acc: {gnn_test_acc}, LM acc: {lm_test_acc}') |
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This is the same comment as #9467 (comment), but we shouldn't pick the best metric evaluated on the test set at the end of every EM step.
reopened #9591
Feature summary: