48becebcab
8a7492b23 moved ort to 2.0.0-rc.15 and gave ort_session.rs the environment rc.15 asks every session builder for; five test files build sessions of their own (jpegai_e2e and the four calibration tests) and still called `Session::builder()` with none. They build only with `neural` (and `legacy_int`), which no push-triggered job builds, and a checked list cut at ten lines hid them. rc.15 allows one environment per process, and these tests call into wai in the same process, so they take wai's: `ort_session::environment` is public as `wai::ort_environment` (a new path, compatible). All five build and run under neural,semantic,legacy_int; without their JPEG AI fixtures they return early, as before. jpegai_decode_path builds and passes under its features. clippy reports nothing in the files this touches. Changelog: none Co-Authored-By: Claude Opus 5.5 <[email protected]>
66 lines
3.3 KiB
Rust
66 lines
3.3 KiB
Rust
//! Calibration: integer `jpegai_synthesis::decode_hyper` vs the float reference —
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//! run the SAME hyper-latent `z` through `hyper_decoder.onnx` (ort) and through
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//! `decode_hyper`, compare the 640-channel entropy params `p`. The output is not
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//! pixels but fixed-point SGMM params, so we use a scale-invariant SNR
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//! (20·log10(rms(signal)/rms(error))) plus max abs error. Needs `neural` (ort).
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//!
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//! env: WAI_JPEGAI_HYPERDEC = path to common_modules/hyper_decoder.onnx,
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//! WAI_JPEGAI_HYPERDEC_WEIGHTS = path to the exported 16-bit weights JSON.
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#![cfg(all(feature = "neural", feature = "legacy_int"))]
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use ort::session::{builder::GraphOptimizationLevel, Session};
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use ort::value::Value;
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use wai::jpegai_synthesis::{decode_hyper, Weights};
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fn ort_run(path: &str, x: Vec<f32>, c: usize, h: usize, w: usize) -> (Vec<f32>, Vec<usize>) {
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let mut s = Session::builder(wai::ort_environment().unwrap()).unwrap()
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.with_intra_threads(1).unwrap().with_inter_threads(1).unwrap()
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.with_optimization_level(GraphOptimizationLevel::Level3).unwrap()
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.commit_from_file(path).unwrap();
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let name = s.inputs().first().unwrap().name().to_string();
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let arr = ndarray::Array4::from_shape_vec((1, c, h, w), x).unwrap();
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let v = Value::from_array(arr).unwrap();
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let out = s.run(ort::inputs![name.as_str() => v]).unwrap();
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let view = out[0].try_extract_array::<f32>().unwrap();
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(view.iter().copied().collect(), view.shape().to_vec())
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}
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#[test]
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fn hyper_decoder_int_vs_float_snr() {
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let onnx = match std::env::var("WAI_JPEGAI_HYPERDEC") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_HYPERDEC to hyper_decoder.onnx"); return; }
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};
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let wjson = match std::env::var("WAI_JPEGAI_HYPERDEC_WEIGHTS") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_HYPERDEC_WEIGHTS to the weights JSON"); return; }
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};
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let (c, h, w) = (160usize, 8usize, 8usize);
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let n = c * h * w;
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// integer-valued hyper-latent (entropy-decoded symbols), small magnitude
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let lf: Vec<f32> = (0..n).map(|i| ((i % 11) as f32) - 5.0).collect();
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let (pf, shape) = ort_run(&onnx, lf.clone(), c, h, w);
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let (fmn, fmx) = pf.iter().fold((f32::MAX, f32::MIN), |(a, b), &v| (a.min(v), b.max(v)));
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eprintln!("float hyper out shape = {:?}, len {}, range [{:.3}, {:.3}]", shape, pf.len(), fmn, fmx);
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let li: Vec<i64> = lf.iter().map(|&x| (x * 65536.0).round() as i64).collect();
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let v: serde_json::Value = serde_json::from_slice(&std::fs::read(&wjson).unwrap()).unwrap();
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let wt = Weights::from_json(&v).unwrap();
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let (pi, ph, pw) = decode_hyper(&li, c, h, w, &wt);
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eprintln!("int hyper out = {} vals at [{},{}]", pi.len(), ph, pw);
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assert_eq!(pi.len(), pf.len(), "output size mismatch (640×2h×2w expected)");
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let (mut maxd, mut se, mut sig) = (0f64, 0f64, 0f64);
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for i in 0..pi.len() {
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let f = pf[i] as f64;
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let q = pi[i] as f64 / 65536.0; // dequantize Q16
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let d = (q - f).abs();
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maxd = maxd.max(d);
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se += d * d;
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sig += f * f;
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}
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let snr = 10.0 * (sig / se).log10();
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eprintln!("INT vs FLOAT hyper_decoder: max|Δ| = {:.4} SNR = {:.2} dB", maxd, snr);
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assert!(snr > 45.0, "hyper_decoder SNR {snr:.2} dB too low (expected high — pure conv chain)");
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}
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