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Unify LightningNodeData and LightningLinkData #6473

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Jan 19, 2023
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Unify LightningNodeData and LightningLinkData #6473

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aa7e45a
update
rusty1s Jan 19, 2023
0cdf266
update
rusty1s Jan 19, 2023
b9b5b51
typo
rusty1s Jan 19, 2023
5c6718e
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rusty1s Jan 19, 2023
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Changed

- Unified `LightningNodeData` and `LightningLinkData` code paths ([#6473](https://github.com/pyg-team/pytorch_geometric/pull/6473))
- Allow indices with any integer type in `RGCNConv` ([#6463](https://github.com/pyg-team/pytorch_geometric/pull/6463))
- Re-structured the documentation ([#6420](https://github.com/pyg-team/pytorch_geometric/pull/6420), [#6423](https://github.com/pyg-team/pytorch_geometric/pull/6423), [#6429](https://github.com/pyg-team/pytorch_geometric/pull/6429), [#6440](https://github.com/pyg-team/pytorch_geometric/pull/6440), [#6443](https://github.com/pyg-team/pytorch_geometric/pull/6443), [#6445](https://github.com/pyg-team/pytorch_geometric/pull/6445), [#6452](https://github.com/pyg-team/pytorch_geometric/pull/6452), [#6453](https://github.com/pyg-team/pytorch_geometric/pull/6453), [#6458](https://github.com/pyg-team/pytorch_geometric/pull/6458), [#6459](https://github.com/pyg-team/pytorch_geometric/pull/6459), [#6460](https://github.com/pyg-team/pytorch_geometric/pull/6460))
- Fix the default arguments of `DataParallel` class ([#6376](https://github.com/pyg-team/pytorch_geometric/pull/6376))
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18 changes: 9 additions & 9 deletions test/data/lightning/test_datamodule.py
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Expand Up @@ -45,7 +45,7 @@ def __init__(self, in_channels, hidden_channels, out_channels):

def forward(self, x, batch):
# Basic test to ensure that the dataset is not replicated:
self.trainer.datamodule.train_dataset.data.x.add_(1)
self.trainer.datamodule.train_dataset._data.x.add_(1)

x = self.lin1(x).relu()
x = global_mean_pool(x, batch)
Expand Down Expand Up @@ -93,14 +93,14 @@ def test_lightning_dataset(get_dataset, strategy_type):
max_epochs=1, log_every_n_steps=1)
datamodule = LightningDataset(train_dataset, val_dataset, test_dataset,
batch_size=5, num_workers=3)
old_x = train_dataset.data.x.clone()
old_x = train_dataset._data.x.clone()
assert str(datamodule) == ('LightningDataset(train_dataset=MUTAG(50), '
'val_dataset=MUTAG(30), '
'test_dataset=MUTAG(10), batch_size=5, '
'num_workers=3, pin_memory=True, '
'persistent_workers=True)')
trainer.fit(model, datamodule)
new_x = train_dataset.data.x
new_x = train_dataset._data.x
offset = 10 + 6 + 2 * devices # `train_steps` + `val_steps` + `sanity`
assert torch.all(new_x > (old_x + offset - 4)) # Ensure shared data.
if strategy_type is None:
Expand Down Expand Up @@ -274,7 +274,7 @@ def test_lightning_hetero_node_data(get_dataset):
max_epochs=5, log_every_n_steps=1)
datamodule = LightningNodeData(data, loader='neighbor', num_neighbors=[5],
batch_size=32, num_workers=3)
assert isinstance(datamodule.neighbor_sampler, NeighborSampler)
assert isinstance(datamodule.graph_sampler, NeighborSampler)
old_x = data['author'].x.clone()
trainer.fit(model, datamodule)
new_x = data['author'].x
Expand All @@ -298,12 +298,12 @@ def sample_from_nodes(self, *args, **kwargs):

datamodule = LightningNodeData(data, node_sampler=DummySampler(),
input_train_nodes=torch.arange(2))
assert isinstance(datamodule.neighbor_sampler, DummySampler)
assert isinstance(datamodule.graph_sampler, DummySampler)

datamodule = LightningLinkData(
data, link_sampler=DummySampler(),
input_train_edges=torch.tensor([[0, 1], [0, 1]]))
assert isinstance(datamodule.neighbor_sampler, DummySampler)
assert isinstance(datamodule.graph_sampler, DummySampler)


@onlyCUDA
Expand All @@ -330,7 +330,7 @@ def test_lightning_hetero_link_data():
batch_size=32,
num_workers=0,
)
assert isinstance(datamodule.neighbor_sampler, NeighborSampler)
assert isinstance(datamodule.graph_sampler, NeighborSampler)
for batch in datamodule.train_dataloader():
assert 'edge_label_index' in batch['author', 'paper']

Expand Down Expand Up @@ -404,9 +404,9 @@ def test_eval_loader_kwargs(get_dataset):
)

assert datamodule.loader_kwargs['batch_size'] == 32
assert datamodule.neighbor_sampler.num_neighbors == [5]
assert datamodule.graph_sampler.num_neighbors == [5]
assert datamodule.eval_loader_kwargs['batch_size'] == 64
assert datamodule.eval_neighbor_sampler.num_neighbors == [-1]
assert datamodule.eval_graph_sampler.num_neighbors == [-1]

train_loader = datamodule.train_dataloader()
assert math.ceil(int(data.train_mask.sum()) / 32) == len(train_loader)
Expand Down
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