fix 5665 (#5667)

Signed-off-by: Mingxin Zheng
<18563433+mingxin-zheng@users.noreply.github.com>

Fixes #5665 .


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [x] New tests added to cover the changes.
- [x] Integration tests passed locally by running `python
/workspace/monai/monai-in-dev/tests/test_integration_autorunner.py`.
- [x] In-line docstrings updated.

Signed-off-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com>

Author: mingxin-zheng

monai/apps/auto3dseg/auto_runner.py

@@ -265,14 +265,16 @@ def __init__(
         self.ensemble = ensemble  # last step, no need to check
 
         # intermediate variables
-        self.set_num_fold(num_fold=5)
+        self.num_fold = 5
+        self.ensemble_method_name = "AlgoEnsembleBestN"
         self.set_training_params()
         self.set_prediction_params()
         self.set_analyze_params()
 
         self.save_image = self.set_image_save_transform(kwargs)
         self.ensemble_method: AlgoEnsemble
-        self.set_ensemble_method()
+        self.set_ensemble_method(self.ensemble_method_name)
+        self.set_num_fold(num_fold=self.num_fold)
 
         # hpo
         if hpo_backend.lower() != "nni":
@@ -334,10 +336,16 @@ def set_num_fold(self, num_fold: int = 5):
 
         Args:
             num_fold: a positive integer to define the number of folds.
+
+        Notes:
+            If the ensemble method is ``AlgoEnsembleBestByFold``, this function automatically updates the ``n_fold``
+            parameter in the ``ensemble_method`` to avoid inconsistency between the training and the ensemble.
         """
         if num_fold <= 0:
             raise ValueError(f"num_fold is expected to be an integer greater than zero. Now it gets {num_fold}")
         self.num_fold = num_fold
+        if self.ensemble_method_name == "AlgoEnsembleBestByFold":
+            self.ensemble_method.n_fold = self.num_fold  # type: ignore
 
     def set_training_params(self, params: Optional[Dict[str, Any]] = None):
         """

setup.cfg

@@ -132,6 +132,7 @@ ignore =
     C408
     N812
     B023
+    B905
 per_file_ignores = __init__.py: F401, __main__.py: F401
 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py
 

tests/test_integration_autorunner.py

@@ -107,6 +107,16 @@ def test_autorunner(self) -> None:
         with skip_if_downloading_fails():
             runner.run()
 
+    @skip_if_no_cuda
+    def test_autorunner_ensemble(self) -> None:
+        work_dir = os.path.join(self.test_path, "work_dir")
+        runner = AutoRunner(work_dir=work_dir, input=self.data_src_cfg)
+        runner.set_training_params(train_param)  # 2 epochs
+        runner.set_ensemble_method("AlgoEnsembleBestByFold")
+        runner.set_num_fold(1)
+        with skip_if_downloading_fails():
+            runner.run()
+
     @skip_if_no_cuda
     @unittest.skipIf(not has_nni, "nni required")
     def test_autorunner_hpo(self) -> None: