autorunner params from config

monai/apps/auto3dseg/auto_runner.py

@@ -214,22 +214,11 @@ def __init__(
         mlflow_tracking_uri: str | None = None,
         **kwargs: Any,
     ):
-        logger.info(f"AutoRunner using work directory {work_dir}")
-        os.makedirs(work_dir, exist_ok=True)
-
-        self.work_dir = os.path.abspath(work_dir)
-        self.data_src_cfg = dict()
-        self.data_src_cfg_name = os.path.join(self.work_dir, "input.yaml")
-        self.algos = algos
-        self.templates_path_or_url = templates_path_or_url
-        self.allow_skip = allow_skip
-        self.mlflow_tracking_uri = mlflow_tracking_uri
-        self.kwargs = deepcopy(kwargs)
-
-        if input is None and os.path.isfile(self.data_src_cfg_name):
-            input = self.data_src_cfg_name
+        if input is None and os.path.isfile(os.path.join(os.path.abspath(work_dir), "input.yaml")):
+            input = os.path.join(os.path.abspath(work_dir), "input.yaml")
             logger.info(f"Input config is not provided, using the default {input}")
 
+        self.data_src_cfg = dict()
         if isinstance(input, dict):
             self.data_src_cfg = input
         elif isinstance(input, str) and os.path.isfile(input):
@@ -238,6 +227,51 @@ def __init__(
         else:
             raise ValueError(f"{input} is not a valid file or dict")
 
+        if "work_dir" in self.data_src_cfg:  # override from config
+            work_dir = self.data_src_cfg["work_dir"]
+        self.work_dir = os.path.abspath(work_dir)
+
+        logger.info(f"AutoRunner using work directory {self.work_dir}")
+        os.makedirs(self.work_dir, exist_ok=True)
+        self.data_src_cfg_name = os.path.join(self.work_dir, "input.yaml")
+
+        self.algos = algos
+        self.templates_path_or_url = templates_path_or_url
+        self.allow_skip = allow_skip
+
+        # cache.yaml
+        self.not_use_cache = not_use_cache
+        self.cache_filename = os.path.join(self.work_dir, "cache.yaml")
+        self.cache = self.read_cache()
+        self.export_cache()
+
+        # determine if we need to analyze, algo_gen or train from cache, unless manually provided
+        self.analyze = not self.cache["analyze"] if analyze is None else analyze
+        self.algo_gen = not self.cache["algo_gen"] if algo_gen is None else algo_gen
+        self.train = train
+        self.ensemble = ensemble  # last step, no need to check
+        self.hpo = hpo and has_nni
+        self.hpo_backend = hpo_backend
+        self.mlflow_tracking_uri = mlflow_tracking_uri
+        self.kwargs = deepcopy(kwargs)
+
+        # parse input config for AutoRunner param overrides
+        for param in [
+            "analyze",
+            "algo_gen",
+            "train",
+            "hpo",
+            "ensemble",
+            "not_use_cache",
+            "allow_skip",
+        ]:  # override from config
+            if param in self.data_src_cfg and isinstance(self.data_src_cfg[param], bool):
+                setattr(self, param, self.data_src_cfg[param])  # e.g. self.analyze = self.data_src_cfg["analyze"]
+
+        for param in ["algos", "hpo_backend", "templates_path_or_url", "mlflow_tracking_uri"]:  # override from config
+            if param in self.data_src_cfg:
+                setattr(self, param, self.data_src_cfg[param])  # e.g. self.algos = self.data_src_cfg["algos"]
+
         missing_keys = {"dataroot", "datalist", "modality"}.difference(self.data_src_cfg.keys())
         if len(missing_keys) > 0:
             raise ValueError(f"Config keys are missing {missing_keys}")
@@ -256,6 +290,8 @@ def __init__(
 
         # inspect and update folds
         num_fold = self.inspect_datalist_folds(datalist_filename=datalist_filename)
+        if "num_fold" in self.data_src_cfg:
+            num_fold = int(self.data_src_cfg["num_fold"])  # override from config
 
         self.data_src_cfg["datalist"] = datalist_filename  # update path to a version in work_dir and save user input
         ConfigParser.export_config_file(
@@ -266,17 +302,6 @@ def __init__(
         self.datastats_filename = os.path.join(self.work_dir, "datastats.yaml")
         self.datalist_filename = datalist_filename
 
-        self.not_use_cache = not_use_cache
-        self.cache_filename = os.path.join(self.work_dir, "cache.yaml")
-        self.cache = self.read_cache()
-        self.export_cache()
-
-        # determine if we need to analyze, algo_gen or train from cache, unless manually provided
-        self.analyze = not self.cache["analyze"] if analyze is None else analyze
-        self.algo_gen = not self.cache["algo_gen"] if algo_gen is None else algo_gen
-        self.train = train
-        self.ensemble = ensemble  # last step, no need to check
-
         self.set_training_params()
         self.set_device_info()
         self.set_prediction_params()
@@ -288,9 +313,9 @@ def __init__(
         self.gpu_customization_specs: dict[str, Any] = {}
 
         # hpo
-        if hpo_backend.lower() != "nni":
+        if self.hpo_backend.lower() != "nni":
             raise NotImplementedError("HPOGen backend only supports NNI")
-        self.hpo = hpo and has_nni
+        self.hpo = self.hpo and has_nni
         self.set_hpo_params()
         self.search_space: dict[str, dict[str, Any]] = {}
         self.hpo_tasks = 0

tests/test_vis_gradcam.py

@@ -20,7 +20,7 @@
 
 from monai.networks.nets import DenseNet, DenseNet121, SEResNet50
 from monai.visualize import GradCAM, GradCAMpp
-from tests.utils import assert_allclose
+from tests.utils import assert_allclose, skip_if_quick
 
 
 class DenseNetAdjoint(DenseNet121):
@@ -147,6 +147,7 @@ def __call__(self, x, adjoint_info):
     TESTS_ILL.append([cam])
 
 
+@skip_if_quick
 class TestGradientClassActivationMap(unittest.TestCase):
     @parameterized.expand(TESTS)
     def test_shape(self, cam_class, input_data, expected_shape):