Fix batched_nms for ndarray inputs

[aymuos15]

Description

batched_nms is documented to accept an Nx4/Nx6 torch tensor or ndarray, but it computed boxes_for_nms = boxes + offsets[:, None] using the original boxes argument instead of the converted tensor boxes_t. Since offsets is a torch tensor derived from boxes_t, passing an ndarray added a numpy array to a torch tensor and raised TypeError, breaking batched_nms for all ndarray inputs.

The offset is now added to the converted tensor: boxes_for_nms = boxes_t + offsets[:, None]. Everything else downstream already operates on boxes_t, and the result is converted back to the input type, so the torch path is unchanged.

Types of changes

• Non-breaking change (fix or new feature that would not break existing functionality).
• 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.

[coderabbitai]

📝 Walkthrough

Walkthrough

The PR fixes a tensor consistency issue in batched_nms by computing the class-offset adjusted boxes from the already-converted float tensor boxes_t instead of the original input. This ensures the NMS path operates uniformly on tensor representation rather than mixing container types. A parameterized test is added to validate the function across multiple backends.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 33.33% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title clearly summarizes the main fix: resolving batched_nms function to work with ndarray inputs.
Description check	✅ Passed	Description explains the bug, root cause, and solution. Required sections present: description, types of changes. New tests added checkbox marked.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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🧹 Nitpick comments (1)

tests/data/test_box_utils.py (1)

273-281: 💤 Low value

Consider adding a docstring.

Per coding guidelines, docstrings should describe the test's purpose, parameters, and expected behavior. While existing tests in this file lack docstrings, adding one would improve maintainability.

📝 Suggested docstring

 class TestBatchedNms(unittest.TestCase):
     `@parameterized.expand`(TEST_NDARRAYS)
     def test_batched_nms_backend(self, p):
+        """Test batched_nms with different array backends (numpy/torch).
+        
+        Args:
+            p: Array constructor from TEST_NDARRAYS.
+            
+        Validates that batched_nms correctly handles ndarray inputs and 
+        performs class-aware NMS, keeping boxes [0, 2] from overlapping 
+        class-0 boxes and a separate class-1 box.
+        """
         boxes = p(np.array([[0, 0, 10, 10], [1, 1, 11, 11], [100, 100, 110, 110]], dtype=np.float32))

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/data/test_box_utils.py` around lines 273 - 281, Add a concise docstring
to the TestBatchedNms.test_batched_nms_backend test explaining its purpose,
inputs and expected outcome: state that the test constructs three boxes and
corresponding scores/labels, calls batched_nms(boxes, scores, labels,
nms_thresh=0.5) and asserts the kept indices equal [0, 2]; place this docstring
immediately under the def test_batched_nms_backend(...) line so readers can
quickly understand the test intent and expected behavior.

Source: Coding guidelines

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Nitpick comments:
In `@tests/data/test_box_utils.py`:
- Around line 273-281: Add a concise docstring to the
TestBatchedNms.test_batched_nms_backend test explaining its purpose, inputs and
expected outcome: state that the test constructs three boxes and corresponding
scores/labels, calls batched_nms(boxes, scores, labels, nms_thresh=0.5) and
asserts the kept indices equal [0, 2]; place this docstring immediately under
the def test_batched_nms_backend(...) line so readers can quickly understand the
test intent and expected behavior.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

Run ID: 96d1ae07-6c7b-426f-8946-a23c0a6acdd3

📥 Commits

Reviewing files that changed from the base of the PR and between 2a7d0cf and ee66c9e.

📒 Files selected for processing (2)

• monai/data/box_utils.py
• tests/data/test_box_utils.py