study: correctness harness for Triton preproc vs F.interpolate

[aseembits93]

Summary

• Adds a set of isolated correctness harnesses under development/stream_interface/ for comparing the Triton preproc kernel against cv2.resize (the production numpy-input preproc path) and F.interpolate (the tensor-input path, also used at training time). Ends in a full COCO val2017 study covering all three variants.
• Reorders arithmetic in triton_preprocess.py to match PyTorch's op order exactly ((x/255 - mean)/std). Mathematically equivalent to the prior fused form but differs at the LSB; halves per-pixel drift from 9.5e-7 → 4.8e-7 (single fp32 ULP) against F.interpolate.

Reference path: cv2.resize is the production preproc that Triton is replacing, so it is the baseline below. F.interpolate is reported alongside as a secondary reference because RF-DETR is trained against it — i.e. it's the bilinear kernel the model has actually seen.

Harnesses added

• correctness_{dump,check}.py — InferencePipeline-based driver that toggles RFDETR_USE_TRITON_PREPROC or RFDETR_TRITON_FULLPOSTPROC. Uses greedy IoU-0.5 pairing so det-count shifts don't cascade into false mismatches.
• correctness_direct_{dump,check}.py — drives RFDetrForInstanceSegmentationTRT directly via AutoModel; numpy input routes to cv2.resize, tensor input routes to F.interpolate.
• correctness_preproc_only_{dump,check}.py — isolates the preproc change: both runs call forward() + post_process() identically; only the function filling the (1,3,H,W) fp32 input tensor differs. Fresh allocation each call so neither inherits the fast-path's in-place-into-TRT-input trick.
• coco_preproc_only_{dump,check}.py — preproc-only study over all 5000 COCO val2017 images. --preproc {cv2,ref,triton} selects the variant; the driver runs one dump per variant (default cv2,ref,triton), pair-diffs each non-triton variant against triton, and prints a side-by-side mAP table with deltas vs the cv2 baseline.
• preproc_parity_probe.py — pixel-level fp32 diff of the preprocessed tensor against both cv2.resize and F.interpolate.

Findings (COCO val2017, rfdetr-seg-nano, 5000 images, ~281k dets @ conf 0.05)

Preproc tensor parity (512×512 probe, single frame):

pair	max abs diff	mean abs diff	notes
triton vs cv2.resize	1.3e-2	3.9e-3	essentially the PyTorch-vs-OpenCV bilinear gap
triton vs F.interpolate	4.8e-7	4.7e-8	~½ fp32 ULP — Triton tracks the training-time kernel
F.interpolate vs cv2.resize	1.3e-2	3.9e-3	the kernel RF-DETR was trained against has always been ~1.3e-2 off from cv2

Detection diff (conf ≥ 0.4):

	cv2 vs triton	ref (F.interp) vs triton
matched pairs	25879 / 26464 (97.8 %)	26038 / 26464 (98.4 %)
unmatched A / B	516 / 585	413 / 426
mean Δconf	7.5e-3	5.9e-3
p95 Δconf	3.0e-2	2.3e-2
max Δconf	0.441	0.418
mask-md5 mismatches	94.5 %	91.0 %

COCO mAP (cv2 = baseline):

metric	cv2	ref (F.interp)	triton	Δref	Δtriton
bbox AP 50:95	0.4753	0.4763	0.4757	+0.0010	+0.0004
bbox AP 50	0.6604	0.6609	0.6610	+0.0005	+0.0006
bbox AP 75	0.5132	0.5147	0.5133	+0.0016	+0.0001
bbox AP S	0.2431	0.2399	0.2407	−0.0032	−0.0023
bbox AP M	0.5282	0.5295	0.5283	+0.0013	+0.0001
bbox AP L	0.7049	0.7071	0.7054	+0.0022	+0.0006
segm AP 50:95	0.3955	0.3959	0.3957	+0.0004	+0.0002
segm AP 50	0.6141	0.6141	0.6141	+0.0001	+0.0000
segm AP 75	0.4188	0.4192	0.4187	+0.0004	−0.0001

(Δ = variant − cv2.)

Takeaway: Triton ties or beats the cv2 production baseline on every aggregate mAP metric except segm AP 75 (−0.0001) and bbox AP S (−0.0023, roughly the run-to-run fp16 TRT noise floor). Triton is also bit-identical (½ ULP) to the F.interpolate kernel the model was trained against, which cv2 has always been ~1.3e-2 off from. Bit-exact parity with cv2 is not reachable (different bilinear tap pattern, different rounding), but eval-equivalence on COCO is demonstrated.

Test plan

• Run coco_preproc_only_check.py with all three preproc variants on COCO val2017 (5000 images each) — three dumps completed on Tesla T4, ~12 im/s per variant.
• Verify preproc_parity_probe.py reports ≤ 1 fp32 ULP between Triton and F.interpolate, and quantifies the cv2 gap.
• Verify mAP Δ vs cv2 stays within ±0.002 for every bbox/segm aggregate (50:95, 50, 75) — holds.
• Reviewer: confirm the reorder in triton_preprocess.py is acceptable (one fused mul-add → three separate ops; perf impact on the kernel hot path should be minimal).

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