Consolidate ogbn-papers100M and ogbn-products examples and increase test acc to 61.76%

[guanxingithub]

Improve the test accuracy to >60% on ogbn-papers100M consolidate repetitive examples

Epoch 01: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:46<00:00, 25698.41it/s]
Epoch 01, Loss: 1.4240,  Train Time: 46.9759s
Val: 61.31%,
Epoch 02: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30618.20it/s]
Epoch 02, Loss: 1.1989,  Train Time: 39.4279s
Val: 63.59%,
Epoch 03: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30776.19it/s]
Epoch 03, Loss: 1.1645,  Train Time: 39.2251s
Val: 63.33%,
Epoch 04: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30780.95it/s]
Epoch 04, Loss: 1.1467,  Train Time: 39.2191s
Val: 65.75%,
Epoch 05: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30827.83it/s]
Epoch 05, Loss: 1.1330,  Train Time: 39.1595s
Val: 65.11%,
Epoch 06: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30820.94it/s]
Epoch 06, Loss: 1.1256,  Train Time: 39.1682s
Val: 63.63%,
Epoch 07: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30862.58it/s]
Epoch 07, Loss: 1.1187,  Train Time: 39.1154s
Val: 65.35%,
Epoch 08: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30837.83it/s]
Epoch 08, Loss: 1.1126,  Train Time: 39.1468s
Val: 65.58%,
Epoch 09: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30838.79it/s]
Epoch 09, Loss: 1.1097,  Train Time: 39.1455s
Val: 65.29%,
Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1207179/1207179 [00:39<00:00, 30853.25it/s]
Epoch 10, Loss: 1.1062,  Train Time: 39.1278s
Val: 64.01%,
Average Epoch Time on training: 39.9711s
Average Epoch Time on inference: 2.7252s
Average Epoch Time: 42.6964s
Median Epoch Time: 41.7139s
Best Validation Accuracy: 65.75%
Testing...
Test Accuracy: 61.76%

[akihironitta]

This is great! 🚀

[akihironitta]

Let's also make sure the filenames are updated in the entire repo, e.g.,

$ git grep ogbn_products_sage.py
README.md:- **[SAGEConv](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.SAGEConv.html)** from Hamilton *et al.*: [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216) (NIPS 2017) \[[**Example1**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/reddit.py), [**Example2**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/ogbn_products_sage.py), [**Example3**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/graph_sage_unsup.py), [**Example4**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/graph_sage_unsup_ppi.py)\]
README.md:- **[NeighborLoader](https://pytorch-geometric.readthedocs.io/en/latest/modules/loader.html#torch_geometric.loader.NeighborLoader)** from Hamilton *et al.*: [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216) (NIPS 2017) \[[**Example1**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/reddit.py), [**Example2**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/ogbn_products_sage.py), [**Example3**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/ogbn_products_gat.py), [**Example4**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/to_hetero_mag.py)\]
torch_geometric/loader/neighbor_sampler.py:        `examples/ogbn_products_sage.py
torch_geometric/loader/neighbor_sampler.py:        ogbn_products_sage.py>`_.

[akihironitta]

LGTM once all the comments are addressed. Great work!

[akihironitta]

Once comments are resolved, LGTM! Let's also make sure all filenames are updated in the repo as I commented in #9467 (review).

[akihironitta]

Great work!

[puririshi98]

@rusty1s @akihironitta anything else needed to merge?

[akihironitta]

Let me address unresolved comments later:

• Consolidate ogbn-papers100M and ogbn-products examples and increase test acc to 61.76% #9467 (review)
• Consolidate ogbn-papers100M and ogbn-products examples and increase test acc to 61.76% #9467 (comment)

n/a

[akihironitta]

LGTM!

Improve the test accuracy to >60% on ogbn-papers100M consolidate repetitive examples

I also modified the PR title and the changelog as the test metric >60% reported in the PR description is likely to be the highest metric evaluated and picked across all epochs. Feel free to adjust it back if the reported metric is the actual test metric.