💚 Parallel tests

Makefile

@@ -59,7 +59,7 @@ lint: ## check style with flake8
 	flake8 tiatoolbox tests
 
 test: ## run tests quickly with the default Python
-	pytest
+	pytest -n auto
 
 coverage: ## check code coverage quickly with the default Python
 	pytest  --cov=tiatoolbox --cov-report=term --cov-report=html --cov-report=xml

requirements/requirements_dev.txt

@@ -10,6 +10,7 @@ pre-commit>=2.20.0
 pytest>=7.2.0
 pytest-cov>=4.0.0
 pytest-runner>=6.0
+pytest-xdist[psutil]
 ruff==0.0.285  # This will be updated by pre-commit bot to latest version
 toml>=0.10.2
 twine>=4.0.1

tests/conftest.py

@@ -1,5 +1,5 @@
 """pytest fixtures."""
-
+import os
 import shutil
 from pathlib import Path
 from typing import Callable
@@ -525,3 +525,36 @@ def moving_mask(remote_sample: Callable) -> Path:
     Download moving mask for pytest.
     """
     return remote_sample("moving_mask")
+
+
+@pytest.fixture(scope="session")
+def chdir() -> Callable:
+    """Return a context manager to change the current working directory.
+
+    Todo: switch to chdir from contextlib when Python 3.11 is required
+
+    """
+    try:
+        from contextlib import chdir
+    except ImportError:
+        from contextlib import AbstractContextManager
+
+        class chdir(AbstractContextManager):  # noqa: N801
+            """Non thread-safe context manager to change the current working directory.
+
+            See Also: https://github.com/python/cpython/blob/main/Lib/contextlib.py.
+
+            """
+
+            def __init__(self, path):
+                self.path = path
+                self._old_cwd = []
+
+            def __enter__(self):
+                self._old_cwd.append(os.getcwd())  # noqa: PTH109
+                os.chdir(self.path)
+
+            def __exit__(self, *excinfo):
+                os.chdir(self._old_cwd.pop())
+
+    return chdir

tests/models/test_hovernetplus.py

@@ -1,6 +1,5 @@
 """Unit test package for HoVerNet+."""
 
-from pathlib import Path
 from typing import Callable
 
 import torch
@@ -11,9 +10,8 @@
 from tiatoolbox.utils.transforms import imresize
 
 
-def test_functionality(remote_sample: Callable, tmp_path: Path) -> None:
+def test_functionality(remote_sample: Callable) -> None:
     """Functionality test."""
-    tmp_path = str(tmp_path)
     sample_patch = str(remote_sample("stainnorm-source"))
     patch_pre = imread(sample_patch)
     patch_pre = imresize(patch_pre, scale_factor=0.5)

tests/models/test_patch_predictor.py

@@ -471,7 +471,7 @@ def test_io_patch_predictor_config() -> None:
 # -------------------------------------------------------------------------------------
 
 
-def test_predictor_crash() -> None:
+def test_predictor_crash(tmp_path: Path) -> None:
     """Test for crash when making predictor."""
     # without providing any model
     with pytest.raises(ValueError, match=r"Must provide.*"):
@@ -489,20 +489,19 @@ def test_predictor_crash() -> None:
     predictor = PatchPredictor(pretrained_model="resnet18-kather100k", batch_size=32)
 
     with pytest.raises(ValueError, match=r".*not a valid mode.*"):
-        predictor.predict("aaa", mode="random")
+        predictor.predict("aaa", mode="random", save_dir=tmp_path)
     # remove previously generated data
-    if Path.exists(Path("output")):
-        shutil.rmtree("output", ignore_errors=True)
+    shutil.rmtree(tmp_path / "output", ignore_errors=True)
     with pytest.raises(TypeError, match=r".*must be a list of file paths.*"):
-        predictor.predict("aaa", mode="wsi")
+        predictor.predict("aaa", mode="wsi", save_dir=tmp_path)
     # remove previously generated data
-    shutil.rmtree("output", ignore_errors=True)
+    shutil.rmtree(tmp_path / "output", ignore_errors=True)
     with pytest.raises(ValueError, match=r".*masks.*!=.*imgs.*"):
-        predictor.predict([1, 2, 3], masks=[1, 2], mode="wsi")
+        predictor.predict([1, 2, 3], masks=[1, 2], mode="wsi", save_dir=tmp_path)
     with pytest.raises(ValueError, match=r".*labels.*!=.*imgs.*"):
-        predictor.predict([1, 2, 3], labels=[1, 2], mode="patch")
+        predictor.predict([1, 2, 3], labels=[1, 2], mode="patch", save_dir=tmp_path)
     # remove previously generated data
-    shutil.rmtree("output", ignore_errors=True)
+    shutil.rmtree(tmp_path / "output", ignore_errors=True)
 
 
 def test_io_config_delegation(remote_sample: Callable, tmp_path: Path) -> None:
@@ -622,34 +621,41 @@ def test_patch_predictor_api(sample_patch1, sample_patch2, tmp_path: Path) -> No
     output = predictor.predict(
         inputs,
         on_gpu=ON_GPU,
+        save_dir=save_dir_path,
     )
     assert sorted(output.keys()) == ["predictions"]
     assert len(output["predictions"]) == 2
+    shutil.rmtree(save_dir_path, ignore_errors=True)
 
     output = predictor.predict(
         inputs,
         labels=[1, "a"],
         return_labels=True,
         on_gpu=ON_GPU,
+        save_dir=save_dir_path,
     )
     assert sorted(output.keys()) == sorted(["labels", "predictions"])
     assert len(output["predictions"]) == len(output["labels"])
     assert output["labels"] == [1, "a"]
+    shutil.rmtree(save_dir_path, ignore_errors=True)
 
     output = predictor.predict(
         inputs,
         return_probabilities=True,
         on_gpu=ON_GPU,
+        save_dir=save_dir_path,
     )
     assert sorted(output.keys()) == sorted(["predictions", "probabilities"])
     assert len(output["predictions"]) == len(output["probabilities"])
+    shutil.rmtree(save_dir_path, ignore_errors=True)
 
     output = predictor.predict(
         inputs,
         return_probabilities=True,
         labels=[1, "a"],
         return_labels=True,
         on_gpu=ON_GPU,
+        save_dir=save_dir_path,
     )
     assert sorted(output.keys()) == sorted(["labels", "predictions", "probabilities"])
     assert len(output["predictions"]) == len(output["labels"])
@@ -693,13 +699,14 @@ def test_patch_predictor_api(sample_patch1, sample_patch2, tmp_path: Path) -> No
         labels=[1, "a"],
         return_labels=True,
         on_gpu=ON_GPU,
+        save_dir=save_dir_path,
     )
     assert sorted(output.keys()) == sorted(["labels", "predictions", "probabilities"])
     assert len(output["predictions"]) == len(output["labels"])
     assert len(output["predictions"]) == len(output["probabilities"])
 
 
-def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
+def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path, chdir: Callable) -> None:
     """Test normal run of wsi predictor."""
     save_dir_path = tmp_path
 
@@ -711,6 +718,8 @@ def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
     patch_size = np.array([224, 224])
     predictor = PatchPredictor(pretrained_model="resnet18-kather100k", batch_size=32)
 
+    save_dir = f"{save_dir_path}/model_wsi_output"
+
     # wrapper to make this more clean
     kwargs = {
         "return_probabilities": True,
@@ -720,6 +729,7 @@ def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
         "stride_shape": patch_size,
         "resolution": 1.0,
         "units": "baseline",
+        "save_dir": save_dir,
     }
     # ! add this test back once the read at `baseline` is fixed
     # sanity check, both output should be the same with same resolution read args
@@ -730,6 +740,8 @@ def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
         **kwargs,
     )
 
+    shutil.rmtree(save_dir, ignore_errors=True)
+
     tile_output = predictor.predict(
         [mini_wsi_jpg],
         masks=[mini_wsi_msk],
@@ -744,7 +756,6 @@ def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
     assert accuracy > 0.9, np.nonzero(~diff)
 
     # remove previously generated data
-    save_dir = save_dir_path / "model_wsi_output"
     shutil.rmtree(save_dir, ignore_errors=True)
 
     kwargs = {
@@ -793,26 +804,26 @@ def test_wsi_predictor_api(sample_wsi_dict, tmp_path: Path) -> None:
         )
     # remove previously generated data
     shutil.rmtree(_kwargs["save_dir"], ignore_errors=True)
-    shutil.rmtree("output", ignore_errors=True)
 
-    # test reading of multiple whole-slide images
-    _kwargs = copy.deepcopy(kwargs)
-    _kwargs["save_dir"] = None  # default coverage
-    _kwargs["return_probabilities"] = False
-    output = predictor.predict(
-        [mini_wsi_svs, mini_wsi_svs],
-        masks=[mini_wsi_msk, mini_wsi_msk],
-        mode="wsi",
-        **_kwargs,
-    )
-    assert Path.exists(Path("output"))
-    for output_info in output.values():
-        assert Path(output_info["raw"]).exists()
-        assert "merged" in output_info
-        assert Path(output_info["merged"]).exists()
+    with chdir(save_dir_path):
+        # test reading of multiple whole-slide images
+        _kwargs = copy.deepcopy(kwargs)
+        _kwargs["save_dir"] = None  # default coverage
+        _kwargs["return_probabilities"] = False
+        output = predictor.predict(
+            [mini_wsi_svs, mini_wsi_svs],
+            masks=[mini_wsi_msk, mini_wsi_msk],
+            mode="wsi",
+            **_kwargs,
+        )
+        assert Path.exists(Path("output"))
+        for output_info in output.values():
+            assert Path(output_info["raw"]).exists()
+            assert "merged" in output_info
+            assert Path(output_info["merged"]).exists()
 
-    # remove previously generated data
-    shutil.rmtree("output", ignore_errors=True)
+        # remove previously generated data
+        shutil.rmtree("output", ignore_errors=True)
 
 
 def test_wsi_predictor_merge_predictions(sample_wsi_dict) -> None: