Use non-deprecated options in tests (#9949)

tests/callbacks/test_device_stats_monitor.py

@@ -46,7 +46,7 @@ def test_device_stats_gpu_from_torch(tmpdir):
         gpus=1,
         callbacks=[device_stats],
         logger=DebugLogger(tmpdir),
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
     )
 
@@ -75,7 +75,7 @@ def test_device_stats_gpu_from_nvidia(tmpdir):
         gpus=1,
         callbacks=[device_stats],
         logger=DebugLogger(tmpdir),
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
     )
 
@@ -104,7 +104,7 @@ def test_device_stats_monitor_tpu(tmpdir):
         log_every_n_steps=1,
         callbacks=[device_stats],
         logger=DebugLogger(tmpdir),
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
     )
 
@@ -122,7 +122,7 @@ def test_device_stats_monitor_no_logger(tmpdir):
         callbacks=[device_stats],
         max_epochs=1,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
     )
 

tests/callbacks/test_model_summary.py

@@ -32,8 +32,8 @@ def test_model_summary_callback_present_trainer():
 
 
 def test_model_summary_callback_with_weights_summary_none():
-
-    trainer = Trainer(weights_summary=None)
+    with pytest.deprecated_call(match=r"weights_summary=None\)` is deprecated"):
+        trainer = Trainer(weights_summary=None)
     assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
     trainer = Trainer(enable_model_summary=False)
@@ -42,7 +42,8 @@ def test_model_summary_callback_with_weights_summary_none():
     trainer = Trainer(enable_model_summary=False, weights_summary="full")
     assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
-    trainer = Trainer(enable_model_summary=True, weights_summary=None)
+    with pytest.deprecated_call(match=r"weights_summary=None\)` is deprecated"):
+        trainer = Trainer(enable_model_summary=True, weights_summary=None)
     assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
 

tests/models/test_restore.py

@@ -153,7 +153,7 @@ def test_trainer_properties_restore_resume_from_checkpoint(tmpdir):
     state_dict = torch.load(resume_ckpt)
 
     trainer_args.update(
-        {"max_epochs": 3, "resume_from_checkpoint": resume_ckpt, "checkpoint_callback": False, "callbacks": []}
+        {"max_epochs": 3, "resume_from_checkpoint": resume_ckpt, "enable_checkpointing": False, "callbacks": []}
     )
 
     class CustomClassifModel(CustomClassifModel):

tests/trainer/test_trainer.py

@@ -468,7 +468,7 @@ def test_trainer_max_steps_and_epochs(tmpdir):
         "max_epochs": 3,
         "max_steps": num_train_samples + 10,
         "logger": False,
-        "weights_summary": None,
+        "enable_model_summary": False,
         "enable_progress_bar": False,
     }
     trainer = Trainer(**trainer_kwargs)
@@ -555,7 +555,7 @@ def test_trainer_min_steps_and_epochs(tmpdir):
         # define less min steps than 1 epoch
         "min_steps": num_train_samples // 2,
         "logger": False,
-        "weights_summary": None,
+        "enable_model_summary": False,
         "enable_progress_bar": False,
     }
     trainer = Trainer(**trainer_kwargs)
@@ -723,9 +723,9 @@ def test_tested_checkpoint_path(tmpdir, ckpt_path, save_top_k, fn):
             assert getattr(trainer, path_attr) == ckpt_path
 
 
-@pytest.mark.parametrize("checkpoint_callback", (False, True))
+@pytest.mark.parametrize("enable_checkpointing", (False, True))
 @pytest.mark.parametrize("fn", ("validate", "test", "predict"))
-def test_tested_checkpoint_path_best(tmpdir, checkpoint_callback, fn):
+def test_tested_checkpoint_path_best(tmpdir, enable_checkpointing, fn):
     class TestModel(BoringModel):
         def validation_step(self, batch, batch_idx):
             self.log("foo", -batch_idx)
@@ -746,7 +746,7 @@ def test_tested_checkpoint_path_best(tmpdir, checkpoint_callback, fn):
         limit_predict_batches=1,
         enable_progress_bar=False,
         default_root_dir=tmpdir,
-        checkpoint_callback=checkpoint_callback,
+        enable_checkpointing=enable_checkpointing,
     )
     trainer.fit(model)
 
@@ -754,7 +754,7 @@ def test_tested_checkpoint_path_best(tmpdir, checkpoint_callback, fn):
     path_attr = f"{fn}{'d' if fn == 'validate' else 'ed'}_ckpt_path"
     assert getattr(trainer, path_attr) is None
 
-    if checkpoint_callback:
+    if enable_checkpointing:
         trainer_fn(ckpt_path="best")
         assert getattr(trainer, path_attr) == trainer.checkpoint_callback.best_model_path
 

tests/utilities/test_cli.py

@@ -384,7 +384,7 @@ def test_lightning_cli_config_and_subclass_mode(tmpdir):
         "fit": {
             "model": {"class_path": "tests.helpers.BoringModel"},
             "data": {"class_path": "tests.helpers.BoringDataModule", "init_args": {"data_dir": str(tmpdir)}},
-            "trainer": {"default_root_dir": str(tmpdir), "max_epochs": 1, "weights_summary": None},
+            "trainer": {"default_root_dir": str(tmpdir), "max_epochs": 1, "enable_model_summary": False},
         }
     }
     config_path = tmpdir / "config.yaml"