perf(RandCropBoxByPosNegLabeld): avoid N-fold mask allocation in randomize

[aymuos15]

Description

RandCropBoxByPosNegLabeld.randomize builds a foreground/background mask when precomputed indices are not available. The previous implementation called convert_box_to_mask for each box (creating an N-channel stacked mask) then reduced with np.amax to a single channel, allocating an (N_boxes, H, W, D) int16 intermediate that grows linearly with the number of boxes.

The fix paints directly into a single (1, H, W, D) union mask by iterating over each extended box and setting mask[0, box_slices] = 1.

Changes

monai/apps/detection/transforms/dictionary.py

1. Remove the now-unused import of convert_box_to_mask from monai.apps.detection.transforms.box_ops.

2. Replace the convert_box_to_mask + np.amax(...)[0:1] block in randomize() with direct slicing into a pre-allocated zero mask.

Performance

Benchmarked old (convert_box_to_mask + np.amax) vs new (direct slicing) implementations on synthetic 3D volumes with random boxes (spatial_size=(64,64,64), num_samples=4, pos=1, neg=1, whole_box=True).

System specs

• CPU: 12th Gen Intel Core i7-12800H (14 cores / 20 threads, up to 4.8 GHz)
• RAM: 30 GiB
• OS: Ubuntu 22.04.5 LTS, kernel 6.8.0-124-generic
• Python 3.10.12, NumPy 2.2.6, PyTorch 2.13.0+cu130

randomize() only

image_size	n_boxes	old (ms)	new (ms)	speedup
(96,96,96)	5	2.471	1.331	1.86x
(96,96,96)	20	17.998	1.921	9.37x
(160,160,160)	5	22.931	15.241	1.50x
(160,160,160)	20	78.651	6.563	11.98x
(256,256,128)	5	62.477	26.386	2.37x
(256,256,128)	20	174.831	24.427	7.16x

Full __call__()

image_size	n_boxes	old (ms)	new (ms)	speedup
(96,96,96)	5	6.416	4.823	1.33x
(96,96,96)	20	21.745	5.078	4.28x
(160,160,160)	5	22.820	13.778	1.66x
(160,160,160)	20	85.259	14.000	6.09x
(256,256,128)	5	76.623	38.455	1.99x
(256,256,128)	20	188.849	36.955	5.11x

Types of changes

• 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.

[coderabbitai]

📝 Walkthrough

Walkthrough

RandCropBoxByPosNegLabeld.randomize now constructs a zero-valued mask, marks each extended-box region directly, and passes it through map_binary_to_indices to derive foreground and background indices. The unused convert_box_to_mask import was removed.

Estimated code review effort: 2 (Simple) | ~10 minutes

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% 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
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.
Title check	✅ Passed	The title matches the main change: optimizing RandCropBoxByPosNegLabeld.randomize by avoiding repeated mask allocation.
Description check	✅ Passed	The description covers the change, motivation, benchmarks, and change type checklist, matching the template well.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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[coderabbitai]

🧹 Nitpick comments (1)

monai/apps/detection/transforms/dictionary.py (1)

1157-1163: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low value

Consider iterable unpacking per RUF005.

Ruff flags two concatenation patterns that could use unpacking for clarity.

♻️ Optional refactor

-            mask_img = np.zeros((1,) + tuple(image_size), dtype=np.int16)
+            mask_img = np.zeros((1, *tuple(image_size)), dtype=np.int16)

-                slicing = (0,) + tuple(
-                    slice(extended_boxes_np[b, d], extended_boxes_np[b, d + spatial_dims]) for d in range(spatial_dims)
-                )
+                slicing = (0, *tuple(
+                    slice(extended_boxes_np[b, d], extended_boxes_np[b, d + spatial_dims]) for d in range(spatial_dims)
+                ))

🤖 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 `@monai/apps/detection/transforms/dictionary.py` around lines 1157 - 1163,
Update the tuple construction in the mask-building loop to use iterable
unpacking instead of concatenating the leading `(0,)` tuple with the generated
slice tuple, resolving the RUF005 warning while preserving the existing
`mask_img` indexing behavior.

Source: Linters/SAST tools

🤖 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 `@monai/apps/detection/transforms/dictionary.py`:
- Around line 1157-1163: Update the tuple construction in the mask-building loop
to use iterable unpacking instead of concatenating the leading `(0,)` tuple with
the generated slice tuple, resolving the RUF005 warning while preserving the
existing `mask_img` indexing behavior.

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ℹ️ Review info

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📥 Commits

Reviewing files that changed from the base of the PR and between fd12fdc and 916ede3.

📒 Files selected for processing (1)

• monai/apps/detection/transforms/dictionary.py