[pre-commit.ci] auto fixes from pre-commit.com hooks

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pyg-team · Dec 23, 2024 · 0bea004 · 0bea004
1 parent 4a5c789
commit 0bea004
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Showing 4 changed files with 9 additions and 11 deletions.
diff --git a/examples/pytorch_lightning/gin.py b/examples/pytorch_lightning/gin.py
@@ -74,10 +74,9 @@ def configure_optimizers(self):
 
     devices = torch.cuda.device_count()
     strategy = DDPStrategy(accelerator='gpu')
-    checkpoint = ModelCheckpoint(monitor='val_acc', save_top_k=1,
-                                              mode='max')
+    checkpoint = ModelCheckpoint(monitor='val_acc', save_top_k=1, mode='max')
     trainer = Trainer(strategy=strategy, devices=devices, max_epochs=50,
-                         log_every_n_steps=5, callbacks=[checkpoint])
+                      log_every_n_steps=5, callbacks=[checkpoint])
 
     trainer.fit(model, datamodule)
     trainer.test(ckpt_path='best', datamodule=datamodule)
diff --git a/examples/pytorch_lightning/graph_sage.py b/examples/pytorch_lightning/graph_sage.py
@@ -74,10 +74,9 @@ def configure_optimizers(self):
     model = Model(dataset.num_node_features, dataset.num_classes)
 
     strategy = SingleDeviceStrategy('cuda:0')
-    checkpoint = ModelCheckpoint(monitor='val_acc', save_top_k=1,
-                                              mode='max')
+    checkpoint = ModelCheckpoint(monitor='val_acc', save_top_k=1, mode='max')
     trainer = Trainer(strategy=strategy, devices=1, max_epochs=20,
-                         callbacks=[checkpoint])
+                      callbacks=[checkpoint])
 
     trainer.fit(model, datamodule)
     trainer.test(ckpt_path='best', datamodule=datamodule)
diff --git a/test/data/lightning/test_datamodule.py b/test/data/lightning/test_datamodule.py
@@ -108,7 +108,7 @@ def expect_rank_zero_user_warning(match: str):
     model = LinearGraphModule(dataset.num_features, 64, dataset.num_classes)
 
     trainer = L.Trainer(strategy=strategy, devices=devices, max_epochs=1,
-                         log_every_n_steps=1)
+                        log_every_n_steps=1)
     with pytest.warns(UserWarning, match="'shuffle=True' option is ignored"):
         datamodule = LightningDataset(train_dataset, val_dataset, test_dataset,
                                       pred_dataset, batch_size=5,
@@ -131,7 +131,7 @@ def expect_rank_zero_user_warning(match: str):
     # Test with `val_dataset=None` and `test_dataset=None`:
     if strategy_type is None:
         trainer = L.Trainer(strategy=strategy, devices=devices, max_epochs=1,
-                             log_every_n_steps=1)
+                            log_every_n_steps=1)
 
         datamodule = LightningDataset(train_dataset, batch_size=5)
         assert str(datamodule) == ('LightningDataset(train_dataset=MUTAG(50), '
@@ -228,7 +228,7 @@ def test_lightning_node_data(get_dataset, strategy_type, loader):
         kwargs_repr += 'num_neighbors=[5], '
 
     trainer = L.Trainer(strategy=strategy, devices=devices, max_epochs=5,
-                         log_every_n_steps=1)
+                        log_every_n_steps=1)
     datamodule = LightningNodeData(data, loader=loader, batch_size=batch_size,
                                    num_workers=num_workers, **kwargs)
 
@@ -314,7 +314,7 @@ def test_lightning_hetero_node_data(preserve_context, get_dataset):
     strategy = DDPStrategy(accelerator='gpu')
 
     trainer = L.Trainer(strategy=strategy, devices=devices, max_epochs=5,
-                         log_every_n_steps=1)
+                        log_every_n_steps=1)
     datamodule = LightningNodeData(data, loader='neighbor', num_neighbors=[5],
                                    batch_size=32, num_workers=3)
     assert isinstance(datamodule.graph_sampler, NeighborSampler)

diff --git a/test/graphgym/test_graphgym.py b/test/graphgym/test_graphgym.py
@@ -196,7 +196,7 @@ def test_train(destroy_process_group, tmp_path, capfd):
     cfg.params = params_count(model)
     logger = LoggerCallback()
     trainer = L.Trainer(max_epochs=1, max_steps=4, callbacks=logger,
-                         log_every_n_steps=1)
+                        log_every_n_steps=1)
     train_loader, val_loader = loaders[0], loaders[1]
     trainer.fit(model, train_loader, val_loader)