Add more unit tests

ci/unit_tests/runner.py

@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 import argparse
+import importlib
 import inspect
 import os
 import sys
@@ -107,7 +108,13 @@ def get_default_pattern(loader):
     test_file_name = f"test_{args.bundle}_dist" if args.dist is True else f"test_{args.bundle}"
     test_file = os.path.join(os.path.dirname(__file__), f"{test_file_name}.py")
     if os.path.exists(test_file):
-        tests = unittest.TestLoader().loadTestsFromNames([test_file_name])
+        loader = unittest.TestLoader()
+        # if having the "test_order" function, will use it as the load order
+        sys.path.append(os.path.dirname(__file__))
+        module = importlib.import_module(test_file_name)
+        if hasattr(module, "test_order"):
+            loader.sortTestMethodsUsing = module.test_order
+        tests = loader.loadTestsFromNames([test_file_name])
         test_runner = unittest.runner.TextTestRunner(
             resultclass=TimeLoggingTestResult, verbosity=args.verbosity, failfast=args.failfast
         )

ci/unit_tests/test_brats_mri_axial_slices_generative_diffusion.py

@@ -20,6 +20,7 @@
 import numpy as np
 from monai.bundle import ConfigWorkflow
 from parameterized import parameterized
+from utils import check_workflow
 
 TEST_CASE_1 = [
     {
@@ -98,9 +99,7 @@ def test_autoencoder_train(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_2])
     def test_autoencoder_infer(self, override):
@@ -115,9 +114,7 @@ def test_autoencoder_infer(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_1])
     def test_diffusion_train(self, override):
@@ -134,14 +131,7 @@ def test_diffusion_train(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        # TODO: uncomment the following check after we have monai > 1.2.0
-        # https://github.com/Project-MONAI/MONAI/issues/6602
-        # check_result = trainer.check_properties()
-        # if check_result is not None and len(check_result) > 0:
-        #     raise ValueError(f"check properties for overrided train config failed: {check_result}")
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_2])
     def test_diffusion_infer(self, override):
@@ -156,9 +146,7 @@ def test_diffusion_infer(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=False)
 
 
 if __name__ == "__main__":

ci/unit_tests/test_brats_mri_generative_diffusion.py

@@ -20,6 +20,7 @@
 import numpy as np
 from monai.bundle import ConfigWorkflow
 from parameterized import parameterized
+from utils import check_workflow
 
 TEST_CASE_1 = [
     {
@@ -112,9 +113,7 @@ def test_autoencoder_train(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_3])
     def test_autoencoder_infer(self, override):
@@ -129,9 +128,7 @@ def test_autoencoder_infer(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_2])
     def test_diffusion_train(self, override):
@@ -148,14 +145,7 @@ def test_diffusion_train(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        # TODO: uncomment the following check after we have monai > 1.2.0
-        # https://github.com/Project-MONAI/MONAI/issues/6602
-        # check_result = trainer.check_properties()
-        # if check_result is not None and len(check_result) > 0:
-        #     raise ValueError(f"check properties for overrided train config failed: {check_result}")
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=False)
 
     @parameterized.expand([TEST_CASE_3])
     def test_diffusion_infer(self, override):
@@ -170,9 +160,7 @@ def test_diffusion_infer(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=False)
 
 
 if __name__ == "__main__":

ci/unit_tests/test_brats_mri_segmentation.py

@@ -19,6 +19,7 @@
 import numpy as np
 from monai.bundle import ConfigWorkflow
 from parameterized import parameterized
+from utils import check_workflow
 
 TEST_CASE_1 = [  # train, evaluate
     {
@@ -75,13 +76,7 @@ def test_train_eval_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        # check required and optional properties
-        check_result = trainer.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for train config failed: {check_result}")
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=True)
 
         validator = ConfigWorkflow(
             # override train.json, thus set the workflow to "train" rather than "eval"
@@ -91,12 +86,7 @@ def test_train_eval_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        validator.initialize()
-        check_result = validator.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for overrided train config failed: {check_result}")
-        validator.run()
-        validator.finalize()
+        check_workflow(validator, check_properties=True)
 
     @parameterized.expand([TEST_CASE_2])
     def test_infer_config(self, override):
@@ -110,13 +100,7 @@ def test_infer_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        # check required and optional properties
-        check_result = inferrer.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for inference config failed: {check_result}")
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=True)
 
 
 if __name__ == "__main__":

ci/unit_tests/test_endoscopic_inbody_classification.py

@@ -19,6 +19,7 @@
 from monai.bundle import ConfigWorkflow
 from monai.data import PILWriter
 from parameterized import parameterized
+from utils import check_workflow
 
 TEST_CASE_1 = [  # train, evaluate
     {
@@ -77,13 +78,7 @@ def test_train_eval_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        trainer.initialize()
-        # check required and optional properties
-        check_result = trainer.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for train config failed: {check_result}")
-        trainer.run()
-        trainer.finalize()
+        check_workflow(trainer, check_properties=True)
 
         validator = ConfigWorkflow(
             # override train.json, thus set the workflow to "train" rather than "eval"
@@ -93,12 +88,7 @@ def test_train_eval_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        validator.initialize()
-        check_result = validator.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for overrided train config failed: {check_result}")
-        validator.run()
-        validator.finalize()
+        check_workflow(validator, check_properties=True)
 
     @parameterized.expand([TEST_CASE_2])
     def test_infer_config(self, override):
@@ -112,13 +102,7 @@ def test_infer_config(self, override):
             meta_file=os.path.join(bundle_root, "configs/metadata.json"),
             **override,
         )
-        inferrer.initialize()
-        # check required and optional properties
-        check_result = inferrer.check_properties()
-        if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for inference config failed: {check_result}")
-        inferrer.run()
-        inferrer.finalize()
+        check_workflow(inferrer, check_properties=True)
 
 
 if __name__ == "__main__":