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test: replacing tmpdir with tmp_path in tests_pytorch/checkpointing #19642

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merged 5 commits into from Mar 16, 2024
Merged

test: replacing tmpdir with tmp_path in tests_pytorch/checkpointing #19642

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a56b9ad
refactored tmpdir from the tests_pytorch/checkpointing dir
fnhirwa Mar 15, 2024
1fb42cf
Merge branch 'master' into checkpointing
fnhirwa Mar 15, 2024
8962b2e
Merge branch 'master' into checkpointing
fnhirwa Mar 16, 2024
928e217
fixes
awaelchli Mar 16, 2024
d4f181b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 16, 2024
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18 changes: 9 additions & 9 deletions tests/tests_pytorch/checkpointing/test_checkpoint_callback_frequency.py
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Expand Up @@ -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
Expand All @@ -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,
Expand All @@ -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__()
Expand All @@ -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,
Expand All @@ -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"))
Expand All @@ -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,
Expand Down
10 changes: 5 additions & 5 deletions tests/tests_pytorch/checkpointing/test_legacy_checkpoints.py
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Expand Up @@ -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"])
Expand All @@ -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}"'
Expand All @@ -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}")))
Expand All @@ -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"),
Expand Down