wai-rs: jpegai_icci::IcciNet — the eICCI filter, float-exact vs the ONNX #15
2026-07-15
wai-rs: jpegai_icci::IcciNet — the eICCI filter, float-exact vs the ONNX
Completes eICCI, the last unported JPEG-AI decode stage: three planes in, three refined planes out (wavelet transform included). The shipped ONNX is much smaller than the reference Python class implies — trust the export, not icci_models.py: class says nf=64, nbY=8, nbUV=8 ONNX is nf=48, nbY=2, nbUV=4 (145,265 params, 21 tensors) and the exporter has already folded/deduped nearly everything: the luma stem's BN is folded into onnx::Conv_8299; every conv bias is zero (hence deduped to one tensor); every block scale equals scaleY=0.1 (also deduped); BNUV is γ=1,β=0,μ=0,σ²=1, i.e. x/√(1+ε), folded into conv_firstUV here. IcciNet also drops a redundancy: the reference's process_U and process_V each rebuild the same chroma features, so it runs that trunk twice. The trunk is evaluated once and fed to both heads — identical output, half the chroma work. Validated against the ONNX itself (planes → planes, ort): max|Δ| = 0.0 / 1e-7 / 0.0 for Y/U/V. Float, not quantised: the net is float in the reference, and IEEE-754 with a fixed accumulation order is deterministic across machines — so the port is byte-identical and carries no PTQ error. Co-Authored-By: Claude Opus 4.8 <[email protected]>