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https://github.com/ruvnet/RuView
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ruv
b9e9a1b5fd
wiflow_std: min_feature_width (default 15) replaces the keypoints->stride coupling — for_keypoints(17) now provably builds the trained [2,2,2,2] graph and pools 15->17, matching the validated Python protocol (pinned by tests); param_count() total on invalid configs; random_mask returns Result and rejects non-finite/out-of-range ratios; trainer checkpoints switched to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11). ieee80211bf: SBP proxy now re-triggers instances and relays reports via Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via their existing path); missed_instances reset on success = consecutive semantics; SessionTable gains a guarded SBP entry point + unknown-id drop counter; initiator-role sessions reject inbound setup/SBP requests (RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp outside Idle return InvalidStateForCommand; SBP validation unified through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping. events.rs split out to honor the 500-line cap. calibration/cli: enrollment geometry now actually reaches trained banks — both production call sites attach .with_geometry; --geometry flag on train-room and POST /enroll/geometry + train-body geometry on calibrate-serve give production a recording surface; geometry-free banks log the ADR-152 §2.1.2 note. benchmarks: corruption masks committed as ground truth (unregenerable after in-place cleaning; verified bit-identical regeneration from the pristine copy) + generate_corruption_masks.py producer; _bench_common.py dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20 re-verified equal to the last digit); remote scripts get the mmap patch; tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer executes (and overwrote) the export — artifact restored byte-exact. Workspace: 2,963 tests passed, 0 failed; Python proof PASS. Co-Authored-By: claude-flow <ruv@ruv.net>
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
"""ADR-152 edge optimization: accuracy of the ONNX fp32 and ORT-dynamic-int8
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models on the same corruption-free 10k test subset used by quantize_bench.py.
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The torch dynamic-int8 path quantizes nothing (no nn.Linear in the model), so
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the only real int8 datapoint for the paper's "~2.2 MB int8" claim is the
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onnxruntime dynamically quantized model -- this script measures what that
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quantization costs in PCK/MPJPE.
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Usage:
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.venv/Scripts/python.exe eval_ort_accuracy.py \
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--data-dir <preprocessed_csi_data> [--subset 10000]
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Writes/merges into results/edge_optimization.json under key "onnx_accuracy".
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"""
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import argparse
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import json
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import os
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import sys
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HERE = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, HERE)
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from _bench_common import RESULTS, evaluate # noqa: E402
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from quantize_bench import build_test_subset # noqa: E402 (sets up upstream imports)
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def evaluate_ort(sess, loader, label):
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"""ORT-session evaluation via the canonical _bench_common.evaluate loop."""
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return evaluate(sess, loader, label=label)
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def main():
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import onnxruntime as ort
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-dir", default=os.path.join(
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os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
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"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
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parser.add_argument("--subset", type=int, default=10000)
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parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
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args = parser.parse_args()
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loader, _n_clean = build_test_subset(args.data_dir, args.subset)
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results = {}
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for label, fname in (("onnx_fp32", "retrained_fp32_dynamic.onnx"),
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("onnx_int8_ort_dynamic", "retrained_int8_ort_dynamic.onnx")):
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path = os.path.join(RESULTS, fname)
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if not os.path.exists(path):
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results[label] = {"error": f"{fname} not found; run onnx_bench.py first"}
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continue
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sess = ort.InferenceSession(path, providers=["CPUExecutionProvider"])
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print(f"=== accuracy: {label} ({fname}) ===")
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results[label] = evaluate_ort(sess, loader, label)
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print(json.dumps(results[label], indent=2))
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merged = {}
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if os.path.exists(args.out):
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with open(args.out) as f:
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merged = json.load(f)
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merged["onnx_accuracy"] = results
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with open(args.out, "w") as f:
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json.dump(merged, f, indent=2)
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print(f"wrote {args.out}")
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if __name__ == "__main__":
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main()
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