📦 WP-090 — feat(predict): segmentation inference¶
Engineering log · Phase 9 — Inference and generalization
WP-090 — a crash the evaluator could never reach¶
decode_instance_masks raises on a zero-length instance axis: F.interpolate rejects a [1, 0, 16, 16] tensor outright. The function has been in the tree since WP-053a and no test caught it, because from the evaluator it is unreachable — both decoders always hand it a fixed 300 rows, padding included, so the axis is never empty. Predict decodes only the survivors, and an image with nothing above the confidence threshold has none.
The general shape is worth keeping: a function whose only caller pads to a fixed length has never been tested at its degenerate input, and the untested case arrives with the second caller rather than with the first. Guarded at the call site here, with the crash pinned by a test on both decode paths; the shared function itself is left for its own package.
Two things this package had to get right that a shape check cannot see. The masks are assembled while the boxes are still on the letterboxed canvas, because that is the frame the A11 box crop is defined in — cropping after the inverse letterbox leaves every box right and every mask quietly wrong. And the mask path is not decoder-independent: the one-to-one coefficients are what the E2E decode reads and the dense ones are what the suppression path reads, so pairing either decoder's rows with the other branch's coefficients yields a plausible mask of a different object. The test plants different prototypes per branch, so a path reading the wrong one returns a mask on the other half of the picture and fails rather than passing with a mask nobody looks at.
For the report, COCO RLE through faster_coco_eval.mask.encode — already a declared dependency, and already the encoder every segm_ statistic is measured through, so a predicted mask on disk and a scored mask are the same object in the same format. Rejected: a polygon contour needs a tracer and is lossy on masks with holes, which the box crop routinely produces; a sidecar .npz splits one prediction across two files, so a report can be archived into silently describing masks that are gone; mask-derived scalars answer a smaller question than the caller asked.