c151bdb819
The cargo-class push deliberately excluded the neural/semantic work to avoid reverting origin; origin never independently changed neural, so this brings it forward cleanly and wires it up. - neural/mod.rs declares the full decoder family; rans.rs gains the categorical decoder it needs (glc/mlicpp/elic/hyperprior/int_synth/ cmic/dcvc/snac + the JPEG-AI entropy/codestream/σ/value/filter port). - runtime.rs uses the shared deterministic ort_session loader. - Restores the semantic/generative path (semantic.rs, min_sim/sim_metric) behind the `semantic` feature, with the bins/tests that construct ModelRequirement updated accordingly. - Re-adds byte-exact-conformance and the tools/ export/encode scripts. Green together: wai-rs all features (75 lib + integration tests) + clippy, byte-exact-conformance (8), wai-web wasm, wai-spz-gen. Co-Authored-By: Claude Opus 4.8 <[email protected]>
74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
"""Export sink artifacts for the `wai.neural.elic` checkerboard context codec
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decoded by `wai-rs/src/neural/context.rs`.
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context.rs needs (next to the registered model path):
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hyper_synthesis.onnx z_hat -> [1, 2*Cy, Sy, Sy] (scales ‖ means, base)
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context.onnx y_anchors -> [1, 2*Cy, Sy, Sy] (refined params for non-anchors)
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synthesis.onnx y_hat -> [1, 3, H, W]
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hyper_cdfs_q<q>.json factorized z CDF (EntropyBottleneck)
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gauss_cdfs_q<q>.json { scale_table, cdf[], cdf_length[], offset[] } (GaussianConditional)
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It wraps an ELIC-style checkpoint exposing submodules `h_s` (hyper synthesis),
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`context` (the checkerboard context net: anchor-filled latent → params), `g_s`
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(synthesis), `entropy_bottleneck`, `gaussian_conditional`. CDF extraction is
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identical to the hyperprior tool (the entropy coders are the same).
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Run (needs torch + onnxruntime; bring your own ELIC checkpoint):
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python tools/wai_elic_export.py --ckpt elic.pt --quality 3 --size 256 \
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--out-dir wai-web/demo/models/elic
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import torch
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# Reuse the CDF extractors from the hyperprior tool (same entropy coders).
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from wai_mbt2018_mean_export import factorized_cdf_blob, gaussian_cdf_blob # noqa: E402
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def export_onnx(module, dummy, path: Path, in_name, out_name, opset):
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torch.onnx.export(
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module, (dummy,), path.as_posix(),
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input_names=[in_name], output_names=[out_name],
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dynamic_axes={in_name: {2: "h", 3: "w"}, out_name: {2: "h", 3: "w"}},
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opset_version=opset, do_constant_folding=True, dynamo=False)
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--ckpt", required=True, help="ELIC checkpoint exposing h_s/context/g_s + entropy coders")
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ap.add_argument("--quality", type=int, default=3)
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ap.add_argument("--size", type=int, default=256)
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ap.add_argument("--out-dir", required=True)
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ap.add_argument("--opset", type=int, default=17)
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args = ap.parse_args()
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out = Path(args.out_dir); out.mkdir(parents=True, exist_ok=True)
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q = args.quality
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net = torch.load(args.ckpt, map_location="cpu", weights_only=False).eval()
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net.update(force=True)
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with torch.no_grad():
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y = net.g_a(torch.zeros(1, 3, args.size, args.size))
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z = net.h_a(y)
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_, M, Sy, _ = y.shape
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_, N, Sz, _ = z.shape
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print(f" y[1,{M},{Sy},{Sy}] z[1,{N},{Sz},{Sz}]")
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export_onnx(net.h_s, torch.zeros(1, N, Sz, Sz), out / "hyper_synthesis.onnx", "z", "params", args.opset)
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export_onnx(net.context, torch.zeros(1, M, Sy, Sy), out / "context.onnx", "y", "params", args.opset)
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export_onnx(net.g_s, torch.zeros(1, M, Sy, Sy), out / "synthesis.onnx", "y", "image", args.opset)
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(out / f"hyper_cdfs_q{q}.json").write_text(
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json.dumps(factorized_cdf_blob(net.entropy_bottleneck, q), separators=(",", ":")))
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(out / f"gauss_cdfs_q{q}.json").write_text(
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json.dumps(gaussian_cdf_blob(net.gaussian_conditional, q), separators=(",", ":")))
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print(f"wrote 3 ONNX + 2 CDF tables → {out}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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