Add missing channel_wise parameter to RandScaleIntensityFixedMean

[engmohamedsalah]

Summary

• Adds the channel_wise parameter to RandScaleIntensityFixedMean and RandScaleIntensityFixedMeand, which was documented in docstrings but never implemented.
• When channel_wise=True, a separate random scale factor is generated per channel, and preserve_range/fixed_mean are applied per channel — following the existing pattern from RandScaleIntensity.
• Fixes docstring indentation for channel_wise in both array and dictionary transforms.

Fixes #8363

Changes

monai/transforms/intensity/array.py

• Added channel_wise parameter to RandScaleIntensityFixedMean.__init__
• Updated randomize() to generate per-channel factors when channel_wise=True
• Updated __call__ to apply per-channel scaling with individual random factors

monai/transforms/intensity/dictionary.py

• Added channel_wise parameter to RandScaleIntensityFixedMeand.__init__
• Updated __call__ to pass image data to randomize() (needed for channel count), following the pattern from RandScaleIntensityd

Tests

• Added test_channel_wise and test_channel_wise_preserve_range to test_rand_scale_intensity_fixed_mean.py
• Added test_channel_wise to test_rand_scale_intensity_fixed_meand.py

Test plan

• All existing tests pass (no regressions)
• New channel_wise tests pass for both array and dictionary transforms
• Verified per-channel mean preservation with fixed_mean=True
• Verified preserve_range clipping works per channel

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📥 Commits

Reviewing files that changed from the base of the PR and between a1f8023 and f996a93.

📒 Files selected for processing (4)

• monai/transforms/intensity/array.py
• monai/transforms/intensity/dictionary.py
• tests/transforms/test_rand_scale_intensity_fixed_mean.py
• tests/transforms/test_rand_scale_intensity_fixed_meand.py

🚧 Files skipped from review as they are similar to previous changes (1)

• tests/transforms/test_rand_scale_intensity_fixed_meand.py

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📝 Walkthrough

Walkthrough

Adds channel-wise support to RandScaleIntensityFixedMean and its dictionary wrapper. The array transform receives a new channel_wise bool parameter, forwards it to the internal scaler, randomizes per-channel factors when enabled, and applies per-channel scaling by iterating channels, stacking results, and preserving dtype. The dictionary transform propagates channel_wise, adjusts randomization to reuse a key-specific factor, and includes an early tensor-conversion path for empty-key cases. Tests were added to validate per-channel scaling and preserve_range behavior.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Additional notes

Implementation defaults channel_wise=False to preserve backward compatibility. Review should focus on correctness of per-channel factor generation, channel iteration/stacking, dtype/preserve_range handling, and the adjusted dictionary call paths.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 27.27% 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 accurately summarizes the main change: implementing the missing channel_wise parameter for RandScaleIntensityFixedMean.
Description check	✅ Passed	Description is comprehensive and well-structured, covering changes in multiple files, test additions, and linking to the issue. All key details are present.
Linked Issues check	✅ Passed	The PR fully addresses issue #8363 by implementing the documented but missing channel_wise parameter with per-channel factor generation and per-channel application of preserve_range and fixed_mean.
Out of Scope Changes check	✅ Passed	All changes are directly scoped to implementing channel_wise support across array and dictionary transforms plus corresponding tests, with no extraneous modifications.

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