fix(impact): pass the IMPACT models their statistics flat and in itk-impact's order - #193
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Included review availability: 7 reviews are currently available. Your included PR review attempts over the past 7 days set your current allowance at 10 reviews per hour. 📝 WalkthroughWalkthroughThe change corrects impact statistic ordering and single-sample shape handling. Batched scoring now processes each sample with its matching attributes and mask. Regression tests cover these behaviors. Fireants uses inherited preprocessing. ChangesImpact statistics handling
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to The updated scoring path preserves compatible per-sample statistics handling, with no actionable merge-blocking risk identified. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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In `@konfai/metric/measure/impact.py`:
- Around line 221-230: Update ImpactFeatureModel.inputs and its TorchScript/MIND
consumers so batched attributes preserve each sample’s (B, 4)
ImageMin/ImageMax/ImageMean/ImageStd statistics during normalization; do not
trigger batch-wide fallback for B > 1. Ensure single-sample behavior remains
compatible and add a regression test covering batched normalization.
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…impact's order ImpactFeatureModel.inputs built stats as [[min, mean, max, std]]. The TorchScript models read them only when stats.numel() == 4, as a flat [min, max, mean, std] (the itk-impact contract): - a single sample gave a [1, 4] row, numel 4 too, so stats[0] was a 4-vector subtracted from the image and MIND failed with "size of tensor a (32) must match the size of tensor b (4)"; - min, mean, max, std made MIND and SAM divide by (mean - min) and TS standardise around the max. A batch failed the same test the other way: a [B, 4] block is not 4 values either, so every sample was normalized by the batch's own min/max (MIND) or mean/std (the MRI TS models), which is another case's intensities. slice_losses now scores one sample at a time, as itk-impact does and as _pca_project already did, so a batch scores exactly as its samples scored alone. It is the same work in more kernels: measured 5 % (B=2) to 17 % (B=4) of the model's time on an RTX PRO 5000. impact-reg's _ImpactCore.preprocessing flattened the row, but nothing has called preprocessing since the measures moved to ImpactFeatureModel.inputs, and its super() no longer exists: removed.
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Problem
ImpactFeatureModel.inputsbuilt the intensity statistics as[[min, mean, max, std]]. The IMPACT TorchScript models readstatsonly when it holds exactly 4 values, and expect it flat, in itk-impact's order[min, max, mean, std](itkImpactModelConfigurationDetail.h). Otherwise they compute the statistics fromxthemselves.Read out of the shipped models (
model.normalize.code):(x - stats[0]) / (stats[1] - stats[0])(x - stats[2]) / stats[3]statsThree bugs followed:
[1, 4]row also hasnumel() == 4, so MIND and TS took the branch that readsstats.stats[0]was then a 4-vector subtracted from the image, and MIND failed withsize of tensor a (32) must match the size of tensor b (4). This broke every FireANTs + IMPACT/MIND run.(mean - min)instead of(max - min), and TS MRI standardised around the max instead of the mean.[B, 4]block is not 4 values either, so every sample of a batch was normalised by the batch's min/max or mean/std, which is another case's intensities. SAM is worse: its gate isstats.size(0) == 4, so a batch of exactly 4 passed it and took a whole row as the minimum.impact-reg already had a fix:
_ImpactCore.preprocessingflattened the row. It became dead code when the measures moved toImpactFeatureModel.inputs. Nothing calls it any more, and thesuper().preprocessingit calls no longer exists.Change
inputstakes one sample's attribute and returns itk-impact's flat order.slice_lossesscores one sample at a time, as itk-impact does and as_pca_projectalready did for the same reason. Same work in more kernels: +5 % (B=2) to +17 % (B=4) of the model's time, measured on an RTX PRO 5000.Checked against the real MIND model, on a batch pairing a CT-scale volume with a
[0, 1]one: the batch now scores0.287980, exactly the mean of the two scored alone, where it scored0.144727before.Behaviour change
A single sample used to crash before it could read the wrong order, so nothing that worked is changed there. A batch of B > 1 now scores differently: it used to be normalised batch-wide, it is now normalised per sample. Runs whose samples share an intensity scale barely move; a mixed batch moves a lot (2x above, deliberately extreme).
Summary by CodeRabbit
Bug Fixes
Tests