test: disable logger in model checkpoint tests to avoid side effects

Author: littlebullGit

tests/tests_pytorch/callbacks/test_model_checkpoint_additional_cases.py

@@ -84,6 +84,7 @@ def test_model_checkpoint_every_n_train_steps_with_train_metric_saves_at_step(tm
         limit_val_batches=0,  # no validation needed for this test
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
     )
 
     trainer.fit(model, train_dataloaders=train_loader, val_dataloaders=val_loader)
@@ -128,6 +129,7 @@ def test_model_checkpoint_time_interval_with_val_metric_defers_until_validation(
         limit_val_batches=1,
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
     )
 
     trainer.fit(model, train_dataloaders=train_loader, val_dataloaders=val_loader)
@@ -194,6 +196,7 @@ def test_model_checkpoint_defer_until_next_validation_when_val_every_2_epochs(tm
         limit_val_batches=1,
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
         check_val_every_n_epoch=2,  # only validate every 2 epochs
     )
 

tests/tests_pytorch/callbacks/test_model_checkpoint_edge_cases.py

@@ -117,6 +117,7 @@ def test_checkpoint_defers_with_mid_epoch_validation(tmp_path, val_scores):
         limit_val_batches=1,
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
         val_check_interval=0.5,
     )
 
@@ -161,6 +162,7 @@ def test_time_interval_defers_across_epoch_until_first_validation(tmp_path, val_
         limit_val_batches=1,
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
         check_val_every_n_epoch=2,  # first validation only after 2nd epoch
     )
 

tests/tests_pytorch/callbacks/test_model_checkpoint_step_interval_val_metric.py

@@ -91,6 +91,7 @@ def test_model_checkpoint_every_n_train_steps_with_val_metric_saves_after_val(tm
         limit_val_batches=1,
         enable_checkpointing=True,
         enable_model_summary=False,
+        logger=False,
     )
 
     trainer.fit(model, train_dataloaders=train_loader, val_dataloaders=val_loader)