update template hash (#5146)

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

Fixes # .

### 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).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com>
Signed-off-by: Wenqi Li <wenqil@nvidia.com>
Co-authored-by: Wenqi Li <wenqil@nvidia.com>
Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com>

Author: mingxin-zheng

CONTRIBUTING.md

@@ -193,7 +193,8 @@ Integration tests with minimal requirements are deployed to ensure this strategy
 To add new optional dependencies, please communicate with the core team during pull request reviews,
 and add the necessary information (at least) to the following files:
 - [setup.cfg](https://github.com/Project-MONAI/MONAI/blob/dev/setup.cfg)  (for package's `[options.extras_require]` config)
-- [docs/requirements.txt](https://github.com/Project-MONAI/MONAI/blob/dev/docs/requirements.txt) (pip requirements.txt file)
+- [requirements-dev.txt](https://github.com/Project-MONAI/MONAI/blob/dev/requirements-dev.txt) (pip requirements file)
+- [docs/requirements.txt](https://github.com/Project-MONAI/MONAI/blob/dev/docs/requirements.txt) (docs pip requirements file)
 - [environment-dev.yml](https://github.com/Project-MONAI/MONAI/blob/dev/environment-dev.yml) (conda environment file)
 - [installation.md](https://github.com/Project-MONAI/MONAI/blob/dev/docs/source/installation.md) (documentation)
 

docs/source/apps.rst

@@ -214,6 +214,6 @@ Applications
 `auto3dseg`
 -----------
 
-.. automodule:: monai.apps.auto3dseg.data_analyzer
+.. automodule:: monai.apps.auto3dseg
   :members:
   :imported-members:

monai/apps/auto3dseg/auto_runner.py

@@ -45,12 +45,14 @@ class AutoRunner:
     An interface for handling Auto3Dseg with minimal inputs and understanding of the internal states in Auto3Dseg.
     The users can run the Auto3Dseg with default settings in one line of code. They can also customize the advanced
     features Auto3Dseg in a few additional lines. Examples of customization include
+
         - change cross-validation folds
         - change training/prediction parameters
         - change ensemble methods
         - automatic hyperparameter optimization.
 
     The output of the interface is a directory that contains
+
         - data statistics analysis report
         - algorithm definition files (scripts, configs, pickle objects) and training results (checkpoints, accuracies)
         - the predictions on the testing datasets from the final algorithm ensemble
@@ -92,6 +94,7 @@ class AutoRunner:
         - User can also save the input dictionary as a input YAML file and use the following one-liner
 
         .. code-block:: bash
+
             python -m monai.apps.auto3dseg AutoRunner run --input=./input.yaml
 
         - User can specify work_dir and data source config input and run AutoRunner:
@@ -159,7 +162,8 @@ class AutoRunner:
             runner.run()
 
     Notes:
-        Expected results in the work_dir as below.
+        Expected results in the work_dir as below::
+
             work_dir/
             ├── algorithm_templates # bundle algo templates (scripts/configs)
             ├── cache.yaml          # Autorunner will automatically cache results to save time
@@ -170,6 +174,7 @@ class AutoRunner:
             ├── segresnet_0         # network scripts/configs/checkpoints and pickle object of the algo
             ├── segresnet2d_0       # network scripts/configs/checkpoints and pickle object of the algo
             └── swinunetr_0         # network scripts/configs/checkpoints and pickle object of the algo
+
     """
 
     def __init__(
@@ -360,9 +365,10 @@ def set_prediction_params(self, params: Optional[Dict[str, Any]] = None):
 
         Args:
             params: a dict that defines the overriding key-value pairs during prediction. The overriding method
-            is defined by the algo class.
+                is defined by the algo class.
 
         Examples:
+
             For BundleAlgo objects, this set of param will specify the algo ensemble to only inference the first
                 two files in the testing datalist {"file_slices": slice(0, 2)}
 
@@ -377,6 +383,7 @@ def set_hpo_params(self, params: Optional[Dict[str, Any]] = None):
         Set parameters for the HPO module and the algos before the training. It will attempt to (1) override bundle
         templates with the key-value pairs in ``params`` (2) chagne the config of the HPO module (e.g. NNI) if the
         key is found to be one of:
+
             - "trialCodeDirectory"
             - "trialGpuNumber"
             - "trialConcurrency"
@@ -417,7 +424,7 @@ def set_image_save_transform(self, kwargs):
 
         Args:
             kwargs: image writing parameters for the ensemble inference. The kwargs format follows SaveImage
-            transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage .
+                transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage .
 
         """
 
@@ -473,8 +480,8 @@ def _train_algo_in_sequence(self, history: List[Dict[str, Any]]):
 
         Note:
             The final results of the model training will be written to all the generated algorithm's output
-                folders under the working directory. The results include the model checkpoints, a
-                progress.yaml, accuracies in CSV and a pickle file of the Algo object.
+            folders under the working directory. The results include the model checkpoints, a
+            progress.yaml, accuracies in CSV and a pickle file of the Algo object.
         """
         for task in history:
             for _, algo in task.items():
@@ -493,12 +500,13 @@ def _train_algo_in_nni(self, history):
 
         Note:
             The final results of the model training will not be written to all the previously generated
-                algorithm's output folders. Instead, HPO will generate a new algo during the searching, and
-                the new algo will be saved under the working directory with a different format of the name.
-                For example, if the searching space has "learning_rate", the result of HPO will be written to
-                a folder name with original task name and the param (e.g. "dints_0_learning_rate_0.001").
-                The results include the model checkpoints, a progress.yaml, accuracies in CSV and a pickle
-                file of the Algo object.
+            algorithm's output folders. Instead, HPO will generate a new algo during the searching, and
+            the new algo will be saved under the working directory with a different format of the name.
+            For example, if the searching space has "learning_rate", the result of HPO will be written to
+            a folder name with original task name and the param (e.g. "dints_0_learning_rate_0.001").
+            The results include the model checkpoints, a progress.yaml, accuracies in CSV and a pickle
+            file of the Algo object.
+
         """
         default_nni_config = {
             "trialCodeDirectory": ".",

monai/apps/auto3dseg/bundle_gen.py

@@ -28,7 +28,7 @@
 from monai.utils import ensure_tuple
 
 logger = get_logger(module_name=__name__)
-ALGO_HASH = os.environ.get("MONAI_ALGO_HASH", "f76fb58")
+ALGO_HASH = os.environ.get("MONAI_ALGO_HASH", "004a63c")
 
 __all__ = ["BundleAlgo", "BundleGen"]
 
@@ -39,7 +39,7 @@ class BundleAlgo(Algo):
 
     ``BundleAlgo.cfg`` is a ``monai.bundle.ConfigParser`` instance.
 
-    ..code-block:: python
+    .. code-block:: python
 
         from monai.apps.auto3dseg import BundleAlgo
 
@@ -215,26 +215,26 @@ def get_score(self, *args, **kwargs):
     def get_inferer(self, *args, **kwargs):
         """
         Load the InferClass from the infer.py. The InferClass should be defined in the template under the path of
-        "scripts/infer.py". It is required to define the "InferClass" (name is fixed) with two functions at least
-        ("__init__" and "infer"). The init class has an override kwargs that can be used to override parameters in
+        `"scripts/infer.py"`. It is required to define the "InferClass" (name is fixed) with two functions at least
+        (``__init__`` and ``infer``). The init class has an override kwargs that can be used to override parameters in
         the run-time optionally.
 
         Examples:
 
-            ..code-block:: python
+        .. code-block:: python
 
-                class InferClass
-                    def __init__(self, config_file: Optional[Union[str, Sequence[str]]] = None, **override):
-                        # read configs from config_file (sequence)
-                        # set up transforms
-                        # set up model
-                        # set up other hyper parameters
-                        return
+            class InferClass
+                def __init__(self, config_file: Optional[Union[str, Sequence[str]]] = None, **override):
+                    # read configs from config_file (sequence)
+                    # set up transforms
+                    # set up model
+                    # set up other hyper parameters
+                    return
 
-                    @torch.no_grad()
-                    def infer(self, image_file):
-                        # infer the model and save the results to output
-                        return output
+                @torch.no_grad()
+                def infer(self, image_file):
+                    # infer the model and save the results to output
+                    return output
 
         """
         infer_py = os.path.join(self.output_path, "scripts", "infer.py")

monai/apps/auto3dseg/ensemble_builder.py

@@ -243,7 +243,8 @@ class AlgoEnsembleBuilder:
 
     Examples:
 
-        ..code-block:: python
+        .. code-block:: python
+
             builder = AlgoEnsembleBuilder(history, data_src_cfg)
             builder.set_ensemble_method(BundleAlgoEnsembleBestN(3))
             ensemble = builder.get_ensemble()

monai/apps/auto3dseg/hpo_gen.py

@@ -70,7 +70,8 @@ class NNIGen(HPOGen):
             and supports saving to and loading from pickle files via ``algo_from_pickle`` and ``algo_to_pickle``.
         params: a set of parameter to override the algo if override is supported by Algo subclass.
 
-    Examples:
+    Examples::
+
         The experiment will keep generating new folders to save the model checkpoints, scripts, and configs if available.
         ├── algorithm_templates
         │   └── unet
@@ -245,7 +246,8 @@ class OptunaGen(HPOGen):
             and supports saving to and loading from pickle files via ``algo_from_pickle`` and ``algo_to_pickle``.
         params: a set of parameter to override the algo if override is supported by Algo subclass.
 
-    Examples:
+    Examples::
+
         The experiment will keep generating new folders to save the model checkpoints, scripts, and configs if available.
         ├── algorithm_templates
         │   └── unet