-
Notifications
You must be signed in to change notification settings - Fork 3.7k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Code Coverage]
models/tgn.py (#6662)
Add test to TGN Update typing.
- Loading branch information
Showing
3 changed files
with
99 additions
and
15 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| import torch | ||
|
|
||
| from torch_geometric.data import TemporalData | ||
| from torch_geometric.loader import TemporalDataLoader | ||
| from torch_geometric.nn import TGNMemory | ||
| from torch_geometric.nn.models.tgn import ( | ||
| IdentityMessage, | ||
| LastAggregator, | ||
| LastNeighborLoader, | ||
| ) | ||
|
|
||
|
|
||
| def test_tgn(): | ||
| memory_dim = 16 | ||
| time_dim = 16 | ||
| src = torch.tensor([0, 1, 0, 2, 0, 3, 1, 4, 2, 3]) | ||
| dst = torch.tensor([1, 2, 1, 1, 3, 2, 4, 3, 3, 4]) | ||
| t = torch.arange(10) | ||
| msg = torch.randn(10, 16) | ||
| data = TemporalData(src=src, dst=dst, t=t, msg=msg) | ||
| loader = TemporalDataLoader(data, batch_size=5) | ||
|
|
||
| neighbor_loader = LastNeighborLoader(data.num_nodes, size=3) | ||
| assert neighbor_loader.cur_e_id == 0 | ||
| assert neighbor_loader.e_id.size() == (data.num_nodes, 3) | ||
|
|
||
| memory = TGNMemory( | ||
| num_nodes=data.num_nodes, | ||
| raw_msg_dim=data.msg.size(-1), | ||
| memory_dim=memory_dim, | ||
| time_dim=time_dim, | ||
| message_module=IdentityMessage(data.msg.size(-1), memory_dim, | ||
| time_dim), | ||
| aggregator_module=LastAggregator(), | ||
| ) | ||
| assert memory.memory.size() == (data.num_nodes, memory_dim) | ||
| assert memory.last_update.size() == (data.num_nodes, ) | ||
|
|
||
| # Test during TGNMemory training: | ||
| memory.train() | ||
| for i, batch in enumerate(loader): | ||
| n_id = torch.cat([batch.src, batch.dst]).unique() | ||
| n_id, edge_index, e_id = neighbor_loader(n_id) | ||
| z, last_update = memory(n_id) | ||
| memory.update_state(batch.src, batch.dst, batch.t, batch.msg) | ||
| neighbor_loader.insert(batch.src, batch.dst) | ||
| if i == 0: | ||
| assert n_id.size(0) == 4 | ||
| assert edge_index.numel() == 0 | ||
| assert e_id.numel() == 0 | ||
| assert z.size() == (n_id.size(0), memory_dim) | ||
| assert torch.sum(last_update) == 0 | ||
| else: | ||
| assert n_id.size(0) == 5 | ||
| assert edge_index.numel() == 12 | ||
| assert e_id.numel() == 6 | ||
| assert z.size() == (n_id.size(0), memory_dim) | ||
| assert torch.equal(last_update, torch.tensor([4, 3, 3, 4, 0])) | ||
|
|
||
| # Test after TGNMemory training: | ||
| memory.eval() | ||
| all_n_id = torch.arange(data.num_nodes) | ||
| z, last_update = memory(all_n_id) | ||
| assert z.size() == (data.num_nodes, memory_dim) | ||
| assert torch.equal(last_update, torch.tensor([4, 6, 8, 9, 9])) | ||
|
|
||
| post_src = torch.tensor([3, 4]) | ||
| post_dst = torch.tensor([4, 3]) | ||
| post_t = torch.tensor([10, 10]) | ||
| post_msg = torch.randn(2, 16) | ||
| memory.update_state(post_src, post_dst, post_t, post_msg) | ||
| post_z, post_last_update = memory(all_n_id) | ||
| assert torch.allclose(z[0:3], post_z[0:3]) | ||
| assert torch.equal(post_last_update, torch.tensor([4, 6, 8, 10, 10])) | ||
|
|
||
| memory.reset_state() | ||
| assert memory.memory.sum() == 0 | ||
| assert memory.last_update.sum() == 0 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters