update

tests/tests_pytorch/__init__.py

@@ -13,24 +13,19 @@
 # limitations under the License.
 import os
 import warnings
+from pathlib import Path
 
 import pytest
 
-_TEST_ROOT = os.path.dirname(__file__)
-_PROJECT_ROOT = os.path.dirname(_TEST_ROOT)
-_TEMP_PATH = os.path.join(_PROJECT_ROOT, "test_temp")
-_PATH_DATASETS = os.path.join(_PROJECT_ROOT, "Datasets")
-_PATH_LEGACY = os.path.join(_PROJECT_ROOT, "legacy")
+_TEST_ROOT = Path(__file__).parent
+_PROJECT_ROOT = _TEST_ROOT.parent.parent
+_PATH_DATASETS = _PROJECT_ROOT / "Datasets"
+_PATH_LEGACY = _PROJECT_ROOT / "legacy"
 
 # todo: this setting `PYTHONPATH` may not be used by other evns like Conda for import packages
-if _PROJECT_ROOT not in os.getenv("PYTHONPATH", ""):
+if str(_PROJECT_ROOT) not in os.getenv("PYTHONPATH", ""):
     splitter = ":" if os.environ.get("PYTHONPATH", "") else ""
     os.environ["PYTHONPATH"] = f'{_PROJECT_ROOT}{splitter}{os.environ.get("PYTHONPATH", "")}'
 
-
-if not os.path.isdir(_TEMP_PATH):
-    os.mkdir(_TEMP_PATH)
-
-
 # Ignore cleanup warnings from pytest (rarely happens due to a race condition when executing pytest in parallel)
 warnings.filterwarnings("ignore", category=pytest.PytestWarning, message=r".*\(rm_rf\) error removing.*")

tests/tests_pytorch/trainer/flags/test_val_check_interval.py

@@ -107,6 +107,8 @@ def test_validation_check_interval_exceed_data_length_wrong():
     trainer = Trainer(
         limit_train_batches=10,
         val_check_interval=100,
+        logger=False,
+        enable_checkpointing=False,
     )
 
     model = BoringModel()

tests/tests_pytorch/trainer/optimization/test_backward_calls.py

@@ -25,13 +25,13 @@ def test_backward_count_simple(torch_backward, num_steps):
 def test_backward_count_with_grad_accumulation(torch_backward):
     """Test that backward is called the correct number of times when accumulating gradients."""
     model = BoringModel()
-    trainer = Trainer(max_epochs=1, limit_train_batches=6, accumulate_grad_batches=2)
+    trainer = Trainer(max_epochs=1, limit_train_batches=6, accumulate_grad_batches=2, logger=False, enable_checkpointing=False)
     trainer.fit(model)
     assert torch_backward.call_count == 6
 
     torch_backward.reset_mock()
 
-    trainer = Trainer(max_steps=6, accumulate_grad_batches=2)
+    trainer = Trainer(max_steps=6, accumulate_grad_batches=2, logger=False, enable_checkpointing=False)
     trainer.fit(model)
     assert torch_backward.call_count == 12
 

tests/tests_pytorch/trainer/test_dataloaders.py

@@ -679,7 +679,7 @@ def test_warning_with_small_dataloader_and_logging_interval(tmp_path):
 
     with pytest.warns(UserWarning, match=r"The number of training batches \(1\) is smaller than the logging interval"):
         trainer = Trainer(
-            default_root_dir=tmp_path, max_epochs=1, log_every_n_steps=2, limit_train_batches=1, logger=CSVLogger(".")
+            default_root_dir=tmp_path, max_epochs=1, log_every_n_steps=2, limit_train_batches=1, logger=CSVLogger(tmp_path)
         )
         trainer.fit(model)