test: replacing tmpdir with tmp_path in `tests_pytorch/checkpoint…

…ing` (#19642)

* refactored tmpdir from the tests_pytorch/checkpointing dir
* fixes

---------

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

tests/tests_pytorch/checkpointing/test_checkpoint_callback_frequency.py

@@ -22,7 +22,7 @@
 from tests_pytorch.helpers.runif import RunIf
 
 
-def test_disabled_checkpointing(tmpdir):
+def test_disabled_checkpointing():
     # no callback
     trainer = Trainer(max_epochs=3, enable_checkpointing=False)
     assert not trainer.checkpoint_callbacks
@@ -34,10 +34,10 @@ def test_disabled_checkpointing(tmpdir):
 @pytest.mark.parametrize(
     ("epochs", "val_check_interval", "expected"), [(1, 1.0, 1), (2, 1.0, 2), (1, 0.25, 4), (2, 0.3, 6)]
 )
-def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_interval: float, expected: int):
+def test_default_checkpoint_freq(save_mock, tmp_path, epochs: int, val_check_interval: float, expected: int):
     model = BoringModel()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         max_epochs=epochs,
         enable_model_summary=False,
         val_check_interval=val_check_interval,
@@ -55,7 +55,7 @@ def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_inter
     ("k", "epochs", "val_check_interval", "expected"), [(1, 1, 1.0, 1), (2, 2, 1.0, 2), (2, 1, 0.25, 4), (2, 2, 0.3, 6)]
 )
 @pytest.mark.parametrize("save_last", [False, True, "link"])
-def test_top_k(save_mock, tmpdir, k, epochs, val_check_interval, expected, save_last):
+def test_top_k(save_mock, tmp_path, k, epochs, val_check_interval, expected, save_last):
     class TestModel(BoringModel):
         def __init__(self):
             super().__init__()
@@ -71,8 +71,8 @@ def training_step(self, batch, batch_idx):
 
     model = TestModel()
     trainer = Trainer(
-        callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss", save_top_k=k, save_last=save_last)],
-        default_root_dir=tmpdir,
+        callbacks=[callbacks.ModelCheckpoint(dirpath=tmp_path, monitor="my_loss", save_top_k=k, save_last=save_last)],
+        default_root_dir=tmp_path,
         max_epochs=epochs,
         enable_model_summary=False,
         val_check_interval=val_check_interval,
@@ -87,7 +87,7 @@ def training_step(self, batch, batch_idx):
 @mock.patch("torch.save")
 @RunIf(min_cuda_gpus=2, standalone=True)
 @pytest.mark.parametrize(("k", "epochs", "val_check_interval", "expected"), [(1, 1, 1.0, 1), (2, 2, 0.3, 4)])
-def test_top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
+def test_top_k_ddp(save_mock, tmp_path, k, epochs, val_check_interval, expected):
     class TestModel(BoringModel):
         def training_step(self, batch, batch_idx):
             local_rank = int(os.getenv("LOCAL_RANK"))
@@ -106,8 +106,8 @@ def on_train_epoch_end(self):
 
     model = TestModel()
     trainer = Trainer(
-        callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss_step", save_top_k=k, mode="max")],
-        default_root_dir=tmpdir,
+        callbacks=[callbacks.ModelCheckpoint(dirpath=tmp_path, monitor="my_loss_step", save_top_k=k, mode="max")],
+        default_root_dir=tmp_path,
         enable_progress_bar=False,
         max_epochs=epochs,
         enable_model_summary=False,

tests/tests_pytorch/checkpointing/test_legacy_checkpoints.py

@@ -38,15 +38,15 @@
 
 @pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
 @RunIf(sklearn=True)
-def test_load_legacy_checkpoints(tmpdir, pl_version: str):
+def test_load_legacy_checkpoints(tmp_path, pl_version: str):
     PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
     with patch("sys.path", [PATH_LEGACY] + sys.path):
         path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
         assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
         path_ckpt = path_ckpts[-1]
 
         model = ClassificationModel.load_from_checkpoint(path_ckpt, num_features=24)
-        trainer = Trainer(default_root_dir=str(tmpdir))
+        trainer = Trainer(default_root_dir=tmp_path)
         dm = ClassifDataModule(num_features=24, length=6000, batch_size=128, n_clusters_per_class=2, n_informative=8)
         res = trainer.test(model, datamodule=dm)
         assert res[0]["test_loss"] <= 0.85, str(res[0]["test_loss"])
@@ -67,7 +67,7 @@ def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningMo
 
 @pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
 @RunIf(sklearn=True)
-def test_legacy_ckpt_threading(tmpdir, pl_version: str):
+def test_legacy_ckpt_threading(pl_version: str):
     PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
     path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
     assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
@@ -93,7 +93,7 @@ def load_model():
 
 @pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
 @RunIf(sklearn=True)
-def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
+def test_resume_legacy_checkpoints(tmp_path, pl_version: str):
     PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
     with patch("sys.path", [PATH_LEGACY] + sys.path):
         path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
@@ -105,7 +105,7 @@ def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
         stop = LimitNbEpochs(1)
 
         trainer = Trainer(
-            default_root_dir=str(tmpdir),
+            default_root_dir=tmp_path,
             accelerator="auto",
             devices=1,
             precision=("16-mixed" if torch.cuda.is_available() else "32-true"),