Add "properties_path" in BundleWorkflow

monai/bundle/properties.py

@@ -43,11 +43,6 @@
         BundleProperty.REQUIRED: True,
         BundlePropertyConfig.ID: "dataset_dir",
     },
-    "trainer": {
-        BundleProperty.DESC: "training workflow engine.",
-        BundleProperty.REQUIRED: True,
-        BundlePropertyConfig.ID: f"train{ID_SEP_KEY}trainer",
-    },
     "network_def": {
         BundleProperty.DESC: "network module for the training.",
         BundleProperty.REQUIRED: False,
@@ -63,23 +58,12 @@
         BundleProperty.REQUIRED: True,
         BundlePropertyConfig.ID: f"train{ID_SEP_KEY}dataset",
     },
-    "train_inferer": {
-        BundleProperty.DESC: "MONAI Inferer object to execute the model computation in training.",
-        BundleProperty.REQUIRED: True,
-        BundlePropertyConfig.ID: f"train{ID_SEP_KEY}inferer",
-    },
     "train_dataset_data": {
         BundleProperty.DESC: "data source for the training dataset.",
         BundleProperty.REQUIRED: False,
         BundlePropertyConfig.ID: f"train{ID_SEP_KEY}dataset{ID_SEP_KEY}data",
         BundlePropertyConfig.REF_ID: None,  # no reference to this ID
     },
-    "train_handlers": {
-        BundleProperty.DESC: "event-handlers for the training logic.",
-        BundleProperty.REQUIRED: False,
-        BundlePropertyConfig.ID: f"train{ID_SEP_KEY}handlers",
-        BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}train_handlers",
-    },
     "train_preprocessing": {
         BundleProperty.DESC: "preprocessing for the training input data.",
         BundleProperty.REQUIRED: False,
@@ -98,12 +82,6 @@
         BundlePropertyConfig.ID: f"train{ID_SEP_KEY}key_metric",
         BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}key_train_metric",
     },
-    "evaluator": {
-        BundleProperty.DESC: "validation workflow engine.",
-        BundleProperty.REQUIRED: False,
-        BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}evaluator",
-        BundlePropertyConfig.REF_ID: "validator",  # this REF_ID is the arg name of `ValidationHandler`
-    },
     "val_interval": {
         BundleProperty.DESC: "validation interval during the training.",
         BundleProperty.REQUIRED: False,
@@ -175,33 +153,17 @@
         BundleProperty.REQUIRED: True,
         BundlePropertyConfig.ID: "dataset",
     },
-    "evaluator": {
-        BundleProperty.DESC: "inference / evaluation workflow engine.",
-        BundleProperty.REQUIRED: True,
-        BundlePropertyConfig.ID: "evaluator",
-    },
     "network_def": {
         BundleProperty.DESC: "network module for the inference.",
         BundleProperty.REQUIRED: True,
         BundlePropertyConfig.ID: "network_def",
     },
-    "inferer": {
-        BundleProperty.DESC: "MONAI Inferer object to execute the model computation in inference.",
-        BundleProperty.REQUIRED: True,
-        BundlePropertyConfig.ID: "inferer",
-    },
     "dataset_data": {
         BundleProperty.DESC: "data source for the inference / evaluation dataset.",
         BundleProperty.REQUIRED: False,
         BundlePropertyConfig.ID: f"dataset{ID_SEP_KEY}data",
         BundlePropertyConfig.REF_ID: None,  # no reference to this ID
     },
-    "handlers": {
-        BundleProperty.DESC: "event-handlers for the inference / evaluation logic.",
-        BundleProperty.REQUIRED: False,
-        BundlePropertyConfig.ID: "handlers",
-        BundlePropertyConfig.REF_ID: f"evaluator{ID_SEP_KEY}val_handlers",
-    },
     "preprocessing": {
         BundleProperty.DESC: "preprocessing for the input data.",
         BundleProperty.REQUIRED: False,

monai/bundle/workflows.py

@@ -11,6 +11,7 @@
 
 from __future__ import annotations
 
+import json
 import os
 import sys
 import time
@@ -24,6 +25,7 @@
 from monai.bundle.config_parser import ConfigParser
 from monai.bundle.properties import InferProperties, MetaProperties, TrainProperties
 from monai.bundle.utils import DEFAULT_EXP_MGMT_SETTINGS, EXPR_KEY, ID_REF_KEY, ID_SEP_KEY
+from monai.config import PathLike
 from monai.utils import BundleProperty, BundlePropertyConfig, deprecated_arg, deprecated_arg_default, ensure_tuple
 
 __all__ = ["BundleWorkflow", "ConfigWorkflow"]
@@ -46,6 +48,7 @@ class BundleWorkflow(ABC):
             or "infer", "inference", "eval", "evaluation" for a inference workflow,
             other unsupported string will raise a ValueError.
             default to `None` for common workflow.
+        properties_path: the path to the JSON file of properties.
 
     """
 
@@ -59,16 +62,26 @@ class BundleWorkflow(ABC):
         new_name="workflow_type",
         msg_suffix="please use `workflow_type` instead.",
     )
-    def __init__(self, workflow_type: str | None = None, workflow: str | None = None):
+    def __init__(
+        self, workflow_type: str | None = None, workflow: str | None = None, properties_path: PathLike | None = None
+    ):
         workflow_type = workflow if workflow is not None else workflow_type
-        if workflow_type is None:
+        if workflow_type is None and properties_path is None:
             self.properties = copy(MetaProperties)
             self.workflow_type = None
             return
-        if workflow_type.lower() in self.supported_train_type:
+        if properties_path is not None:
+            properties_path = Path(properties_path)
+            if not properties_path.is_file():
+                raise ValueError(f"Property file {properties_path} does not exist.")
+            with open(properties_path) as json_file:
+                self.properties = json.load(json_file)
+            self.workflow_type = None
+            return
+        if workflow_type.lower() in self.supported_train_type:  # type: ignore[union-attr]
             self.properties = {**TrainProperties, **MetaProperties}
             self.workflow_type = "train"
-        elif workflow_type.lower() in self.supported_infer_type:
+        elif workflow_type.lower() in self.supported_infer_type:  # type: ignore[union-attr]
             self.properties = {**InferProperties, **MetaProperties}
             self.workflow_type = "infer"
         else:
@@ -206,6 +219,7 @@ class ConfigWorkflow(BundleWorkflow):
             or "infer", "inference", "eval", "evaluation" for a inference workflow,
             other unsupported string will raise a ValueError.
             default to `None` for common workflow.
+        properties_path: the path to the JSON file of properties.
         override: id-value pairs to override or add the corresponding config content.
             e.g. ``--net#input_chns 42``, ``--net %/data/other.json#net_arg``
 
@@ -230,10 +244,11 @@ def __init__(
         tracking: str | dict | None = None,
         workflow_type: str | None = None,
         workflow: str | None = None,
+        properties_path: PathLike | None = None,
         **override: Any,
     ) -> None:
         workflow_type = workflow if workflow is not None else workflow_type
-        super().__init__(workflow_type=workflow_type)
+        super().__init__(workflow_type=workflow_type, properties_path=properties_path)
         if config_file is not None:
             _config_files = ensure_tuple(config_file)
             self.config_root_path = Path(_config_files[0]).parent

tests/test_bundle_workflow.py

@@ -33,7 +33,10 @@
 
 TEST_CASE_2 = [os.path.join(os.path.dirname(__file__), "testing_data", "inference.yaml")]
 
-TEST_CASE_3 = [os.path.join(os.path.dirname(__file__), "testing_data", "config_fl_train.json")]
+TEST_CASE_3 = [
+    os.path.join(os.path.dirname(__file__), "testing_data", "config_fl_train.json"),
+    os.path.join(os.path.dirname(__file__), "testing_data", "fl_train_properties.json"),
+]
 
 
 class TestBundleWorkflow(unittest.TestCase):
@@ -101,10 +104,11 @@ def test_inference_config(self, config_file):
             logging_file=os.path.join(os.path.dirname(__file__), "testing_data", "logging.conf"),
             **override,
         )
+        inferer.add_property(name="inferer", required=True, config_id="inferer")
         self._test_inferer(inferer)
 
     @parameterized.expand([TEST_CASE_3])
-    def test_train_config(self, config_file):
+    def test_train_config(self, config_file, properties_path):
         # test standard MONAI model-zoo config workflow
         trainer = ConfigWorkflow(
             workflow_type="train",
@@ -113,6 +117,7 @@ def test_train_config(self, config_file):
             init_id="initialize",
             run_id="run",
             final_id="finalize",
+            properties_path=properties_path,
         )
         # should initialize before parsing any bundle content
         trainer.initialize()
@@ -144,6 +149,7 @@ def test_train_config(self, config_file):
     def test_non_config(self):
         # test user defined python style workflow
         inferer = NonConfigWorkflow(self.filename, self.data_dir)
+        inferer.add_property(name="inferer", required=True)
         self._test_inferer(inferer)
 
 

tests/testing_data/fl_train_properties.json

@@ -0,0 +1,126 @@
+{
+    "bundle_root": {
+        "description": "root path of the bundle.",
+        "required": true,
+        "id": "bundle_root"
+    },
+    "device": {
+        "description": "target device to execute the bundle workflow.",
+        "required": true,
+        "id": "device"
+    },
+    "dataset_dir": {
+        "description": "directory path of the dataset.",
+        "required": true,
+        "id": "dataset_dir"
+    },
+    "trainer": {
+        "description": "training workflow engine.",
+        "required": true,
+        "id": "train::trainer"
+    },
+    "network_def": {
+        "description": "network module for the training.",
+        "required": false,
+        "id": "network_def"
+    },
+    "max_epochs": {
+        "description": "max number of epochs to execute the training.",
+        "required": true,
+        "id": "train::trainer::max_epochs"
+    },
+    "train_dataset": {
+        "description": "PyTorch dataset object for the training logic.",
+        "required": true,
+        "id": "train::dataset"
+    },
+    "train_inferer": {
+        "description": "MONAI Inferer object to execute the model computation in training.",
+        "required": true,
+        "id": "train::inferer"
+    },
+    "train_dataset_data": {
+        "description": "data source for the training dataset.",
+        "required": false,
+        "id": "train::dataset::data",
+        "refer_id": null
+    },
+    "train_handlers": {
+        "description": "event-handlers for the training logic.",
+        "required": false,
+        "id": "train::handlers",
+        "refer_id": "train::trainer::train_handlers"
+    },
+    "train_preprocessing": {
+        "description": "preprocessing for the training input data.",
+        "required": false,
+        "id": "train::preprocessing",
+        "refer_id": "train::dataset::transform"
+    },
+    "train_postprocessing": {
+        "description": "postprocessing for the training model output data.",
+        "required": false,
+        "id": "train::postprocessing",
+        "refer_id": "train::trainer::postprocessing"
+    },
+    "train_key_metric": {
+        "description": "key metric to compute on the training data.",
+        "required": false,
+        "id": "train::key_metric",
+        "refer_id": "train::trainer::key_train_metric"
+    },
+    "evaluator": {
+        "description": "validation workflow engine.",
+        "required": false,
+        "id": "validate::evaluator",
+        "refer_id": "validator"
+    },
+    "val_interval": {
+        "description": "validation interval during the training.",
+        "required": false,
+        "id": "val_interval",
+        "refer_id": "interval"
+    },
+    "val_handlers": {
+        "description": "event-handlers for the validation logic.",
+        "required": false,
+        "id": "validate::handlers",
+        "refer_id": "validate::evaluator::val_handlers"
+    },
+    "val_dataset": {
+        "description": "PyTorch dataset object for the validation logic.",
+        "required": false,
+        "id": "validate::dataset",
+        "refer_id": "validate::dataloader::dataset"
+    },
+    "val_dataset_data": {
+        "description": "data source for the validation dataset.",
+        "required": false,
+        "id": "validate::dataset::data",
+        "refer_id": null
+    },
+    "val_inferer": {
+        "description": "MONAI Inferer object to execute the model computation in validation.",
+        "required": false,
+        "id": "validate::inferer",
+        "refer_id": "validate::evaluator::inferer"
+    },
+    "val_preprocessing": {
+        "description": "preprocessing for the validation input data.",
+        "required": false,
+        "id": "validate::preprocessing",
+        "refer_id": "validate::dataset::transform"
+    },
+    "val_postprocessing": {
+        "description": "postprocessing for the validation model output data.",
+        "required": false,
+        "id": "validate::postprocessing",
+        "refer_id": "validate::evaluator::postprocessing"
+    },
+    "val_key_metric": {
+        "description": "key metric to compute on the validation data.",
+        "required": false,
+        "id": "validate::key_metric",
+        "refer_id": "validate::evaluator::key_val_metric"
+    }
+}