MONet Bundle Integration into MONAI Deploy

[SimoneBendazzoli93]

This PR introduces support for the MONet Bundle (an nnUNet wrapper for the MONAI Bundle) into MONAI Deploy.

Key Features:

• Added a new operator: MONetBundleInferenceOperator, extending MonaiBundleInferenceOperator

• Included an example application demonstrating spleen segmentation using the MONetBundleInferenceOperator

Summary by CodeRabbit

• New Features

  • Added MONetBundleInferenceOperator — a specialized inference operator for MONet bundle nnUNet-style models with multimodal input support, automatic predictor setup, network validation, and convenient predict behavior.

• Bug Fixes

  • Fixed YAML extension handling when reading bundle configurations.
  • Normalized metadata to a dictionary to ensure consistent validation.

[sonarqubecloud]

Quality Gate failed

Failed conditions
7.3% Duplication on New Code (required ≤ 3%)

See analysis details on SonarQube Cloud

[chezhia]

This breaks the existing project structure by introducing a new folder 'devel', can this not be part of the file under ''operators" folder? - Also, can you add some links in the docstrings on how to generate the MONET bundle. Does this currently support all versions of nnunet?

[SimoneBendazzoli93]

The devel folder was accidentally included in the PR. I have now removed, adding also some references to the MONet Bundle in the docstrings

[coderabbitai]

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⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

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Run ID: ff5bc0ec-e9c9-46b2-9499-c6544cf29ab2

📥 Commits

Reviewing files that changed from the base of the PR and between 67d24d6 and 635cd5b.

📒 Files selected for processing (1)

• monai/deploy/operators/monet_bundle_inference_operator.py

Walkthrough

Adds a new MONetBundleInferenceOperator for MONet/nnUNet-style multimodal inference, exposes it in the public API, and fixes YAML bundle suffix handling plus metadata initialization in MonaiBundleInferenceOperator.

Changes

Cohort / File(s)	Summary
MONet operator & bundle-inference fixes monai/deploy/operators/monet_bundle_inference_operator.py, monai/deploy/operators/monai_bundle_inference_operator.py	Adds MONetBundleInferenceOperator implementing nnUNet-style prediction (model network validation, multimodal input resampling/concat, batch dim handling, metadata propagation). Also fixes bundle YAML suffix to include leading dot and ensures meta_data is initialized as a dict.
Public API export monai/deploy/operators/__init__.py	Imports and exports MONetBundleInferenceOperator (added to __all__ and module autosummary) to make the new operator part of the package public API.

Sequence Diagram

sequenceDiagram
    actor Client
    participant MONetOp as MONetBundleInferenceOperator
    participant Transform as ResampleToMatch / ConcatItemsd
    participant Predictor as nnUNet Predictor

    Client->>MONetOp: predict(data, **kwargs)
    MONetOp->>MONetOp: _init_config / _set_model_network
    alt multimodal kwargs present
        MONetOp->>Transform: resample extra modalities to match image
        Transform-->>MONetOp: resampled modalities
        MONetOp->>Transform: concat modalities into "image" tensor
        Transform-->>MONetOp: multimodal input tensor
    end
    MONetOp->>MONetOp: ensure batch dimension
    MONetOp->>Predictor: run predictor(input)
    Predictor-->>MONetOp: prediction
    MONetOp->>MONetOp: copy input meta to prediction
    MONetOp-->>Client: return prediction

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Poem

🐇 I hop and stitch modalities bright,
New MONet minds ready to light.
YAML dot fixed, metadata neat,
Predictions jump in a multimodal beat. 🎉

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 71.43% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title 'MONet Bundle Integration into MONAI Deploy' clearly and specifically describes the main change: introducing MONet Bundle support to MONAI Deploy through a new operator and example application.

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[coderabbitai]

Actionable comments posted: 4

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

monai/deploy/operators/monai_bundle_inference_operator.py (1)

149-149: ⚠️ Potential issue | 🔴 Critical

Bug: Missing leading dot on "yml" suffix in _read_directory_bundle_config.

bundle_suffixes here has "yml" without a leading dot, so constructing f"{config_name_base}{suffix}" at Line 170 would produce e.g. "inferenceyml" instead of "inference.yml". The archive-based reader at Line 189 was correctly fixed to ".yml", but this directory-based reader was missed.

🐛 Proposed fix

-    bundle_suffixes = (".json", ".yaml", "yml")  # The only supported file ext(s)
+    bundle_suffixes = (".json", ".yaml", ".yml")  # The only supported file ext(s)

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monai_bundle_inference_operator.py` at line 149, In
_read_directory_bundle_config the bundle_suffixes tuple is missing a leading dot
for "yml", causing f"{config_name_base}{suffix}" to produce filenames like
"inferenceyml"; update bundle_suffixes in monai_bundle_inference_operator.py to
include the leading dot (".yml") so that filenames built by config_name_base +
suffix are correct; confirm the change in the _read_directory_bundle_config
function where config_name_base and suffix are concatenated.

🧹 Nitpick comments (2)

monai/deploy/operators/monet_bundle_inference_operator.py (2)

90-95: Non-MetaTensor kwargs (e.g. from base class) are silently dropped from multimodal data.

The base class compute passes **other_inputs to predict, which may include non-tensor entries. The if len(kwargs) > 0 guard enters the multimodal path for any kwargs, but only MetaTensor values are added to multimodal_data. Non-MetaTensor kwargs are silently ignored. Consider filtering kwargs more explicitly — e.g. only enter multimodal path if there are actually MetaTensor values:

Proposed fix

-        if len(kwargs) > 0:
-            multimodal_data = {"image": data}
-            for key in kwargs.keys():
-                if isinstance(kwargs[key], MetaTensor):
-                    multimodal_data[key] = ResampleToMatch(mode="bilinear")(kwargs[key], img_dst=data)
-            data = ConcatItemsd(keys=list(multimodal_data.keys()), name="image")(multimodal_data)["image"]
+        meta_tensor_kwargs = {k: v for k, v in kwargs.items() if isinstance(v, MetaTensor)}
+        if meta_tensor_kwargs:
+            multimodal_data = {"image": data}
+            for key, value in meta_tensor_kwargs.items():
+                multimodal_data[key] = ResampleToMatch(mode="bilinear")(value, img_dst=data)
+            data = ConcatItemsd(keys=list(multimodal_data.keys()), name="image")(multimodal_data)["image"]

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` around lines 90 -
95, The multimodal branch currently triggers for any kwargs but only adds
MetaTensor values and silently drops others; change the logic in predict
(referencing kwargs, multimodal_data, MetaTensor, ResampleToMatch, ConcatItemsd)
to first filter kwargs for MetaTensor entries (e.g., meta_kwargs = {k:v for k,v
in kwargs.items() if isinstance(v, MetaTensor)}), only enter the multimodal path
when meta_kwargs is non-empty, build multimodal_data from meta_kwargs
(resampling via ResampleToMatch and concatenating with ConcatItemsd) and leave
non-MetaTensor kwargs untouched so compute/predict (and other_inputs) still
receive them.

---

17-17: Hard import of monai.transforms breaks the optional_import pattern used elsewhere.

The base operator and this file use optional_import for torch and MetaTensor, but ConcatItemsd and ResampleToMatch are imported directly. If monai is not installed (or partially installed), this will raise ImportError at module load time rather than deferring it to usage.

Proposed fix

-from monai.transforms import ConcatItemsd, ResampleToMatch
+ConcatItemsd, _ = optional_import("monai.transforms", name="ConcatItemsd")
+ResampleToMatch, _ = optional_import("monai.transforms", name="ResampleToMatch")

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` at line 17,
Replace the hard import of ConcatItemsd and ResampleToMatch with the same
optional_import pattern used for torch/MetaTensor: use
optional_import("monai.transforms") to get the transforms module (or None), then
assign ConcatItemsd = transforms.ConcatItemsd and ResampleToMatch =
transforms.ResampleToMatch if transforms is not None; if they are None, ensure
any code that uses ConcatItemsd/ResampleToMatch checks for None and raises a
clear ImportError or defers functionality until monai is available. Reference
the symbols ConcatItemsd and ResampleToMatch in
monet_bundle_inference_operator.py and follow the existing optional_import usage
style in the file for consistency.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@monai/deploy/operators/monet_bundle_inference_operator.py`:
- Line 1: Update the file header in monet_bundle_inference_operator.py to
correct the copyright year: replace the incorrect "2002" with "2025" in the
top-of-file comment so the copyright line accurately reflects the current year.
- Around line 26-46: Remove the duplicated sentence in the module/class
docstring that repeats "A specialized operator for performing inference using
the MONet bundle"; edit the docstring (above MonetBundleInferenceOperator / the
class definition containing _init_config and predict) to keep a single, coherent
opening sentence, preserve the rest of the docstring content and formatting
(attributes/methods sections), and ensure the triple-quoted string remains
properly closed and PEP257-style spacing is preserved.
- Around line 58-64: The _init_config implementation is re-parsing the bundle
and overwriting self._parser after calling super()._init_config, which causes
double I/O and a mismatch with objects the parent initialized (e.g.,
self._device, self._inferer, self._preproc, self._postproc); remove the extra
get_bundle_config call and instead reuse the parser the parent already created
(use self._parser) to obtain network_def via
self._parser.get_parsed_content("network_def") and assign that to
self._nnunet_predictor without reassigning self._parser.
- Around line 75-81: The runtime type-check block for model_network is using
torch.jit.isinstance (meant for TorchScript refinement) which is incorrect for
eager Python; replace torch.jit.isinstance(model_network,
torch.jit.ScriptModule) with the standard isinstance(model_network,
torch.jit.ScriptModule) in the validation that checks model_network in the
MonetBundleInferenceOperator (the block referencing model_network,
torch.jit.ScriptModule, TorchScriptModel, TritonModel) so the condition uses
only Python isinstance checks and the TypeError remains unchanged.

---

Outside diff comments:
In `@monai/deploy/operators/monai_bundle_inference_operator.py`:
- Line 149: In _read_directory_bundle_config the bundle_suffixes tuple is
missing a leading dot for "yml", causing f"{config_name_base}{suffix}" to
produce filenames like "inferenceyml"; update bundle_suffixes in
monai_bundle_inference_operator.py to include the leading dot (".yml") so that
filenames built by config_name_base + suffix are correct; confirm the change in
the _read_directory_bundle_config function where config_name_base and suffix are
concatenated.

---

Nitpick comments:
In `@monai/deploy/operators/monet_bundle_inference_operator.py`:
- Around line 90-95: The multimodal branch currently triggers for any kwargs but
only adds MetaTensor values and silently drops others; change the logic in
predict (referencing kwargs, multimodal_data, MetaTensor, ResampleToMatch,
ConcatItemsd) to first filter kwargs for MetaTensor entries (e.g., meta_kwargs =
{k:v for k,v in kwargs.items() if isinstance(v, MetaTensor)}), only enter the
multimodal path when meta_kwargs is non-empty, build multimodal_data from
meta_kwargs (resampling via ResampleToMatch and concatenating with ConcatItemsd)
and leave non-MetaTensor kwargs untouched so compute/predict (and other_inputs)
still receive them.
- Line 17: Replace the hard import of ConcatItemsd and ResampleToMatch with the
same optional_import pattern used for torch/MetaTensor: use
optional_import("monai.transforms") to get the transforms module (or None), then
assign ConcatItemsd = transforms.ConcatItemsd and ResampleToMatch =
transforms.ResampleToMatch if transforms is not None; if they are None, ensure
any code that uses ConcatItemsd/ResampleToMatch checks for None and raises a
clear ImportError or defers functionality until monai is available. Reference
the symbols ConcatItemsd and ResampleToMatch in
monet_bundle_inference_operator.py and follow the existing optional_import usage
style in the file for consistency.

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Double parsing: _init_config parses the bundle config twice.

super()._init_config(config_names) already calls get_bundle_config and assigns self._parser. Lines 61–62 immediately re-parse the same bundle and overwrite self._parser, discarding the one the parent just set up. This doubles the I/O and parsing work. More importantly, the parent's _init_config configures self._device, self._inferer, self._preproc, self._postproc, etc., all using the first parser. Overwriting self._parser afterward creates a divergence between those cached objects and the active parser.

If the intent is just to get network_def, you can use the parser that super() already set:

Proposed fix

     def _init_config(self, config_names):
 
         super()._init_config(config_names)
-        parser = get_bundle_config(str(self._bundle_path), config_names)
-        self._parser = parser
-
-        self._nnunet_predictor = parser.get_parsed_content("network_def")
+        self._nnunet_predictor = self._parser.get_parsed_content("network_def")

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	def _init_config(self, config_names):
	super()._init_config(config_names)
	parser = get_bundle_config(str(self._bundle_path), config_names)
	self._parser = parser
	self._nnunet_predictor = parser.get_parsed_content("network_def")
	def _init_config(self, config_names):
	super()._init_config(config_names)
	self._nnunet_predictor = self._parser.get_parsed_content("network_def")

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` around lines 58 -
64, The _init_config implementation is re-parsing the bundle and overwriting
self._parser after calling super()._init_config, which causes double I/O and a
mismatch with objects the parent initialized (e.g., self._device, self._inferer,
self._preproc, self._postproc); remove the extra get_bundle_config call and
instead reuse the parser the parent already created (use self._parser) to obtain
network_def via self._parser.get_parsed_content("network_def") and assign that
to self._nnunet_predictor without reassigning self._parser.

[chezhia]

This suggestion from copilot needs review - If the parent class is initializing the model properly, we can accept the suggestion, but I am not sure if get_bundle_config is needed to apply any config patch.

[chezhia]

@SimoneBendazzoli93 - please review this suggestion above

[coderabbitai]

Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!

[coderabbitai]

Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!

[coderabbitai]

Actionable comments posted: 2

♻️ Duplicate comments (2)

monai/deploy/operators/monet_bundle_inference_operator.py (2)

57-63: ⚠️ Potential issue | 🟠 Major

Avoid reparsing and overwriting self._parser after super()._init_config.

Line 60 and Line 61 reinitialize parser state already built by the base class. This duplicates parsing work and can desync parser-dependent fields initialized in MonaiBundleInferenceOperator._init_config.

Proposed fix

     def _init_config(self, config_names):
 
         super()._init_config(config_names)
-        parser = get_bundle_config(str(self._bundle_path), config_names)
-        self._parser = parser
-
-        self._nnunet_predictor = parser.get_parsed_content("network_def")
+        self._nnunet_predictor = self._parser.get_parsed_content("network_def")

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` around lines 57 -
63, The code reinitializes the parser after calling super()._init_config, which
duplicates work and can overwrite parser state; in _init_config avoid calling
get_bundle_config again and do not assign to self._parser a new parser—use the
parser instance already initialized by super()._init_config (self._parser) and
set self._nnunet_predictor = self._parser.get_parsed_content("network_def") (or
call get_parsed_content on the existing parser variable) instead of reassigning
self._parser via get_bundle_config.

---

74-80: ⚠️ Potential issue | 🟠 Major

Use isinstance for eager runtime checks and align the error message with accepted types.

Line 76 uses torch.jit.isinstance, which is intended for TorchScript type refinement, not regular Python runtime validation. Also, Line 80’s message omits accepted TorchScriptModel and TritonModel.

Proposed fix

         if (
             not isinstance(model_network, torch.nn.Module)
-            and not torch.jit.isinstance(model_network, torch.jit.ScriptModule)
+            and not isinstance(model_network, torch.jit.ScriptModule)
             and not isinstance(model_network, TorchScriptModel)
             and not isinstance(model_network, TritonModel)
         ):
-            raise TypeError("model_network must be an instance of torch.nn.Module or torch.jit.ScriptModule")
+            raise TypeError(
+                "model_network must be an instance of torch.nn.Module, "
+                "torch.jit.ScriptModule, TorchScriptModel, or TritonModel"
+            )

In PyTorch (including 1.10.2), is `torch.jit.isinstance` intended for TorchScript type refinement rather than regular eager-mode runtime type checks?

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` around lines 74 -
80, The runtime type check in monet_bundle_inference_operator.py incorrectly
uses torch.jit.isinstance (meant for TorchScript refinement) and the TypeError
message omits accepted types; update the conditional in the validation block
that checks model_network (the one currently testing torch.nn.Module,
torch.jit.ScriptModule, TorchScriptModel, TritonModel) to use plain
isinstance(...) for all checks (replace torch.jit.isinstance with isinstance)
and change the raised TypeError message in that same block to list all accepted
types: torch.nn.Module, torch.jit.ScriptModule, TorchScriptModel, and
TritonModel so the message accurately reflects the allowed types.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@monai/deploy/operators/monet_bundle_inference_operator.py`:
- Around line 89-94: The current loop in the inference operator only adds kwargs
entries when isinstance(..., MetaTensor), silently dropping others; update the
handling in the method (where multimodal_data, ResampleToMatch, and ConcatItemsd
are used) to validate kwargs: iterate items in kwargs and for each key either
resample and add it to multimodal_data if it's a MetaTensor, or raise a clear
TypeError/ValueError that includes the offending key name and its actual type so
callers know they passed an unsupported modality type (do not silently ignore
non-MetaTensor values).
- Line 98: The assignment prediction.meta = data.meta can raise if either
prediction or data lack a .meta attribute; update the
MonetBundleInferenceOperator where this line occurs to guard the propagation by
checking attributes (e.g., using hasattr(prediction, "meta") and hasattr(data,
"meta") or isinstance checks) and only copy data.meta when both objects expose
.meta, otherwise skip or attach a safe metadata container; ensure you reference
the variables prediction and data in the conditional so behavior remains
unchanged for tensor-like outputs.

---

Duplicate comments:
In `@monai/deploy/operators/monet_bundle_inference_operator.py`:
- Around line 57-63: The code reinitializes the parser after calling
super()._init_config, which duplicates work and can overwrite parser state; in
_init_config avoid calling get_bundle_config again and do not assign to
self._parser a new parser—use the parser instance already initialized by
super()._init_config (self._parser) and set self._nnunet_predictor =
self._parser.get_parsed_content("network_def") (or call get_parsed_content on
the existing parser variable) instead of reassigning self._parser via
get_bundle_config.
- Around line 74-80: The runtime type check in
monet_bundle_inference_operator.py incorrectly uses torch.jit.isinstance (meant
for TorchScript refinement) and the TypeError message omits accepted types;
update the conditional in the validation block that checks model_network (the
one currently testing torch.nn.Module, torch.jit.ScriptModule, TorchScriptModel,
TritonModel) to use plain isinstance(...) for all checks (replace
torch.jit.isinstance with isinstance) and change the raised TypeError message in
that same block to list all accepted types: torch.nn.Module,
torch.jit.ScriptModule, TorchScriptModel, and TritonModel so the message
accurately reflects the allowed types.

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro

Run ID: 32044423-f81a-4a86-a66f-992e420065a8

📥 Commits

Reviewing files that changed from the base of the PR and between 0743b3e and 67d24d6.

📒 Files selected for processing (1)

• monai/deploy/operators/monet_bundle_inference_operator.py

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Do not silently drop non-MetaTensor modalities from kwargs.

At Line 92–94, only MetaTensor values are added to multimodal_data; all other provided modalities are ignored without error. That can produce incomplete inference inputs.

Proposed fix

         if len(kwargs) > 0:
             multimodal_data = {"image": data}
             for key in kwargs.keys():
                 if isinstance(kwargs[key], MetaTensor):
                     multimodal_data[key] = ResampleToMatch(mode="bilinear")(kwargs[key], img_dst=data)
+                else:
+                    multimodal_data[key] = kwargs[key]
             data = ConcatItemsd(keys=list(multimodal_data.keys()), name="image")(multimodal_data)["image"]

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@monai/deploy/operators/monet_bundle_inference_operator.py` around lines 89 -
94, The current loop in the inference operator only adds kwargs entries when
isinstance(..., MetaTensor), silently dropping others; update the handling in
the method (where multimodal_data, ResampleToMatch, and ConcatItemsd are used)
to validate kwargs: iterate items in kwargs and for each key either resample and
add it to multimodal_data if it's a MetaTensor, or raise a clear
TypeError/ValueError that includes the offending key name and its actual type so
callers know they passed an unsupported modality type (do not silently ignore
non-MetaTensor values).

[chezhia]

I think this can be ignored

[coderabbitai]

@chezhia, understood — I'll defer to your judgment on this one. Acknowledged and moving on!

[chezhia]

@SimoneBendazzoli93 It looks like the DCO (Developer Certificate of Origin) check is failing. To fix this, please ensure all your commits are signed off.

You can do this by amending your previous commits using:
git commit --amend --signoff

Or, if you have multiple commits, you can perform an interactive rebase:
git rebase -i main --signoff

Then, force-push the changes to the branch. This is required for the PR to be mergedr

[sonarqubecloud]

Quality Gate failed

Failed conditions
C Reliability Rating on New Code (required ≥ A)

See analysis details on SonarQube Cloud

Catch issues before they fail your Quality Gate with our IDE extension SonarQube for IDE

[chezhia]

A couple of items identified by copilot needs review from author. Accepted a few minor suggestions.

[chezhia]

This suggestion from copilot needs review - If the parent class is initializing the model properly, we can accept the suggestion, but I am not sure if get_bundle_config is needed to apply any config patch.

[chezhia]

@SimoneBendazzoli93 - please review this suggestion above

[chezhia]

I think this can be ignored