mirror of
https://github.com/ruvnet/RuView
synced 2026-07-19 16:53:18 +00:00
feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction
Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
@@ -16,3 +16,4 @@ downloads/
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*.safetensors
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results/parity_fixture.json
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__pycache__/
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*.onnx
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@@ -104,7 +104,113 @@ checkpoint REFUTED; dataset/code require repairs)**. RuView docs may cite
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ESP32 numbers (different hardware, 5 subjects, in-domain random split,
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15 keypoints).
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## Edge optimization (measured)
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ADR-152 "optimize beyond SOTA" track, 2026-06-10, this Windows box (Windows 11,
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16 torch threads, torch 2.12.0+cpu, onnxruntime 1.26.0). Subject: the retrained
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checkpoint `results/retrained_best_pose_model.pth` (2,225,042 fp32 params).
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Scripts: `quantize_bench.py`, `onnx_bench.py`, `eval_ort_accuracy.py`.
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Raw numbers: `results/edge_optimization.json`.
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Accuracy is on a **10,000-window seed-42 random subset** of the corruption-free
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test split (same seed-42 file-level 70/15/15 split as `eval_repro.py`; 54,000
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test windows, 1,440 corrupted excluded via `results/nan_windows_mask.npy` |
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`results/big_windows_mask.npy`, leaving 52,560; subset drawn with
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`np.random.default_rng(42)`). The fp32 subset PCK@20 (96.68%) matches the full
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clean-test figure (96.61%), so the subset is representative.
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Latency is CPU ms/window, median of repeated runs, 3 interleaved repetitions
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per variant (medians below; run-to-run spread on this box is large, roughly
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±20-40% at batch 1 — reps are in the JSON).
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| Variant | Disk size | Batch 1 (ms/win) | Batch 64 (ms/win) | PCK@20 | PCK@50 | MPJPE |
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|---|---|---|---|---|---|---|
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| torch fp32 (baseline) | 9.07 MB | 11.0 | 2.27 | 96.68% | 99.15% | 0.00936 |
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| torch fp16 (`.half()`) | **4.58 MB** | 24.3 | 2.42 | 96.68% | 99.15% | 0.00946 |
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| torch int8 dynamic | 9.07 MB (unchanged) | 15.6 | 2.06 | 96.68% (identical) | 99.15% | 0.00936 |
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| ONNX fp32 (onnxruntime) | 8.97 MB | **3.2** | **2.0** | 96.68% | 99.15% | 0.00936 |
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| ONNX int8 (ORT dynamic, supplementary) | **2.44 MB** | 6.5 | 5.8 | 96.52% | 99.15% | 0.01108 |
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Findings:
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- **torch dynamic INT8 quantizes nothing on this model.** The architecture has
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**zero `nn.Linear` layers** — it is entirely Conv1d (21) + Conv2d (22) +
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BatchNorm. `torch.ao.quantization.quantize_dynamic` (requested over
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`{Linear, Conv1d, Conv2d}`) converted **0 modules / 0.0% of params**: dynamic
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quantization only has kernels for Linear/RNN-family modules and silently
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skips convolutions. The "int8" model is bit-identical to fp32 (same outputs,
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same 9.07 MB). Conv quantization would require static (PTQ) quantization
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with calibration — out of scope here; the ORT dynamic path below is the
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honest int8 datapoint.
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- **fp16 halves size for free accuracy-wise** (PCK@20 −0.005 pt, MPJPE
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+0.0001) but is *slower* on CPU at batch 1 (~2.2×) — torch CPU fp16 conv
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kernels are emulated. fp16 is a storage/transport format here, not a CPU
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runtime win.
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- **ONNX Runtime is the real batch-1 latency win: ~3.4× faster than torch**
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(3.2 vs 11.0 ms/window) at identical accuracy (parity 2.4e-7).
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### Verdict on the paper's "~2.2 MB int8" claim
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**Plausible but not free, and unreachable by the obvious PyTorch route.**
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2,225,042 params × 1 byte ≈ 2.2 MB assumes *every* parameter quantizes.
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PyTorch dynamic quantization — the one-liner most readers would reach for —
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yields **9.07 MB (0% quantized)** because the model has no Linear layers.
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ONNX Runtime dynamic quantization, which does have int8 conv weight support,
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gets **2.44 MB** (close to the claim; the overhead is BatchNorm params/buffers
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and quantization scales kept in fp32) at a measurable accuracy cost:
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PCK@20 96.68 → 96.52% (−0.16 pt) and MPJPE 0.00936 → 0.01108 (+18%), and
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~2× slower inference than ONNX fp32 (ConvInteger kernels). The paper does not
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state a method or an int8 accuracy; treat "2.2 MB" as a weight-arithmetic
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estimate, achievable in practice only via conv-capable quantization toolchains
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and with a small accuracy penalty.
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### ONNX export status
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**Works.** Exported via the TorchScript exporter (`dynamo=False`), opset 17,
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with a dynamic batch axis — `results/retrained_fp32_dynamic.onnx` (8.97 MB),
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verified to run at batch 1/2/64. The axial attention's
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`view(N*W, C, H)` reshape traced correctly (sizes recorded as graph ops, not
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baked constants). The dynamo exporter also captures the graph but crashed on
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this box writing a ✅ to a cp1252 console (cosmetic Windows encoding issue, not
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a model blocker). Parity vs torch on the stored fixture
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(`results/parity_fixture.npz`, batch 2, seed 42): **max abs diff 2.4e-7 —
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PASS** (< 1e-4). ORT-quantized int8 model: `results/retrained_int8_ort_dynamic.onnx`.
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## Measurement (b): BLOCKED-ON-DATA (attempted 2026-06-10)
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The fine-tune-on-ESP32 measurement stopped at dataset characterization, per the
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pre-registered stop rule (<2,000 paired windows). Findings (MEASURED):
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- **Only one trainable paired dataset exists**: `ruvultra:~/work/cog-pose-train/paired.jsonl`
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— 1,077 windows (one subject, one room, one 29.9-min session, single node;
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CSI [56, 20]; 17 COCO keypoints, MediaPipe confidence mean 0.44 — only 264
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windows pass ADR-079's own conf>0.5 training filter). Prior measured attempts
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on this exact set: 0–3% torso-PCK@20 (temporal splits, three independent
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pipelines). Fine-tuning a 2.23M-param model on ~860 train windows would
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measure memorization, not transfer.
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- **The April session behind the old "92.9% PCK@20" claim is lost** (345
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samples, 35 subcarriers; raw CSI gone from ruvzen/ruvultra/cognitum-v0; only
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a 69-sample predictions+GT holdout survives at `models/wiflow-real/eval-holdout.jsonl`).
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- **Forensic recheck of that holdout RETRACTS the 92.9% figure**: the trainer's
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`pck()` used an absolute 0.2 image-unit threshold (not torso-normalized) and
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the model output a **constant pose** (pred std 0.0000 across 69 near-static
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frames; a mean predictor scores 100% under the same protocol). The
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torso-normalized PCK@20 on the same holdout is 19.1%. This corroborates the
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2026-05-11 audit retraction (CHANGELOG, PR #535); stale doc citations were
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removed 2026-06-10 (user-guide, readme-details, ADR-152 §2.1.3). The §2.2
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no-citation rule now applies to ADR-079 accuracy claims.
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Unblock criteria: a paired collection session of ≥2k windows (≈35+ min at the
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observed stride; multi-pose, conf>0.5, ideally with the §2.1.3 two-checkerboard
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calibration), plus a re-baselined our-pipeline number under torso-PCK@20 on the
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same split. WiFlow-STD assets stand ready on ruvultra (`~/wiflow-std-bench/`).
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Also worth investigating: ADR-079's protocol predicts ~9k windows per 30 min;
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the May session under-delivered ~8× (aligner drop rate?).
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## Pending
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- (b) fine-tune on our ESP32 17-keypoint eval set.
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- (c) our internal WiFlow on their dataset (15-keypoint subset mapping).
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- (b) fine-tune on our ESP32 17-keypoint eval set — **BLOCKED-ON-DATA**, see above.
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- (c) our internal WiFlow on their dataset (15-keypoint subset mapping) — also
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affected: there is currently no validated internal pose model to compare
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(the 92.9% artifact is retracted; the MM-Fi SOTA models in ADR-150 §3 are a
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different input domain).
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@@ -0,0 +1,91 @@
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"""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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import time
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import numpy as np
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import torch
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HERE = os.path.dirname(os.path.abspath(__file__))
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RESULTS = os.path.join(HERE, "results")
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sys.path.insert(0, HERE)
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from quantize_bench import build_test_subset # noqa: E402 (sets up upstream imports)
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sys.path.insert(0, os.path.join(HERE, "upstream"))
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from utils.metrics import calculate_mpjpe, calculate_pck # noqa: E402
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def evaluate_ort(sess, loader, label):
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inp = sess.get_inputs()[0].name
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totals = {0.2: 0.0, 0.5: 0.0}
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total_mpe, n = 0.0, 0
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t0 = time.time()
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for batch_idx, (bx, by) in enumerate(loader):
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out = torch.from_numpy(sess.run(None, {inp: bx.numpy()})[0])
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pck = calculate_pck(out, by, thresholds=[0.2, 0.5])
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mpe = calculate_mpjpe(out, by)
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bs = by.size(0)
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total_mpe += mpe * bs
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for t in totals:
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totals[t] += pck[t] * bs
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n += bs
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if batch_idx % 50 == 0:
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print(f" [{label}] batch {batch_idx}: n={n} "
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f"pck20={totals[0.2]/n:.4f} mpjpe={total_mpe/n:.4f} "
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f"({time.time()-t0:.0f}s)", flush=True)
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return {"samples": n, "pck@20": totals[0.2] / n, "pck@50": totals[0.5] / n,
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"mpjpe": total_mpe / n, "wall_seconds": time.time() - t0}
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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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@@ -0,0 +1,228 @@
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"""ADR-152 edge optimization: ONNX export + onnxruntime CPU benchmark for the
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retrained WiFlow-STD checkpoint.
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- Exports fp32 to ONNX. The axial attention reshapes with python ints taken
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from tensor.size() (view(N*W, C, H)), so a traced graph bakes the batch
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size; we first try a dynamic-batch export and verify it actually works at
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batch sizes 1/2/64 -- if not, we fall back to fixed-batch exports.
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- Verifies output parity vs torch on the stored fixture
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(results/parity_fixture.npz, batch 2, seed 42): max abs diff < 1e-4.
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- Measures onnxruntime CPU latency at batch 1 and 64 (median of N runs).
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- Supplementary: onnxruntime dynamic int8 quantization of the exported model
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(weight size datapoint for the paper's "~2.2 MB int8" claim).
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Usage:
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.venv/Scripts/python.exe onnx_bench.py
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Writes/merges into results/edge_optimization.json under key "onnx".
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"""
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import json
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import os
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import platform
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import statistics
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import sys
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import time
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import traceback
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import numpy as np
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import torch
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HERE = os.path.dirname(os.path.abspath(__file__))
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UPSTREAM = os.path.join(HERE, "upstream")
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RESULTS = os.path.join(HERE, "results")
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sys.path.insert(0, UPSTREAM)
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import types # noqa: E402
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_models_pkg = types.ModuleType("models")
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_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
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sys.modules["models"] = _models_pkg
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from models.pose_model import WiFlowPoseModel # noqa: E402
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CHECKPOINT = os.path.join(RESULTS, "retrained_best_pose_model.pth")
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OUT_JSON = os.path.join(RESULTS, "edge_optimization.json")
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def load_fp32_model():
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state = torch.load(CHECKPOINT, map_location="cpu", weights_only=True)
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renames = {"att.": "attention.", "final_conv.": "decoder."}
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state = {next((new + k[len(old):] for old, new in renames.items()
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if k.startswith(old)), k): v
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for k, v in state.items()}
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model = WiFlowPoseModel(dropout=0.5)
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model.load_state_dict(state, strict=True)
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model.eval()
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return model
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def try_export(model, path, batch, dynamic, opset=17):
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"""Returns (ok, exporter_used, error)."""
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x = torch.rand(batch, 540, 20)
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attempts = []
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if dynamic:
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attempts.append(("dynamo", dict(dynamo=True,
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dynamic_shapes={"x": {0: "batch"}})))
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attempts.append(("torchscript", dict(dynamo=False,
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dynamic_axes={"input": {0: "batch"},
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"output": {0: "batch"}})))
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else:
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attempts.append(("torchscript", dict(dynamo=False)))
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attempts.append(("dynamo", dict(dynamo=True)))
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last_err = None
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for name, kw in attempts:
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try:
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with torch.no_grad():
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torch.onnx.export(model, (x,), path, opset_version=opset,
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input_names=["input"], output_names=["output"],
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**kw)
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return True, name, None
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except Exception as e: # noqa: BLE001
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last_err = f"{name}: {type(e).__name__}: {e}"
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traceback.print_exc()
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return False, None, last_err
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def ort_session(path):
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import onnxruntime as ort
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return ort.InferenceSession(path, providers=["CPUExecutionProvider"])
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|
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def ort_run(sess, x):
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inp = sess.get_inputs()[0].name
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return sess.run(None, {inp: x})[0]
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|
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def bench_ort(sess, batch, n_runs):
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rng = np.random.default_rng(123)
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x = rng.random((batch, 540, 20), dtype=np.float32)
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for _ in range(max(5, n_runs // 10)):
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ort_run(sess, x)
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times = []
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for _ in range(n_runs):
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t0 = time.perf_counter()
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ort_run(sess, x)
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times.append(time.perf_counter() - t0)
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med = statistics.median(times)
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return {
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"batch_size": batch,
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"runs": n_runs,
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"median_ms_per_batch": med * 1e3,
|
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"median_ms_per_window": med * 1e3 / batch,
|
||||
"windows_per_second": batch / med,
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}
|
||||
|
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|
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def main():
|
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import onnxruntime
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model = load_fp32_model()
|
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results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"onnxruntime": onnxruntime.__version__,
|
||||
"platform": platform.platform(),
|
||||
},
|
||||
}
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|
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fixture = np.load(os.path.join(RESULTS, "parity_fixture.npz"))
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fx, fy = fixture["input"], fixture["output"] # (2,540,20) -> (2,15,2)
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# ---- export: dynamic batch first, fall back to fixed --------------------
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dyn_path = os.path.join(RESULTS, "retrained_fp32_dynamic.onnx")
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ok, exporter, err = try_export(model, dyn_path, batch=2, dynamic=True)
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dynamic_works = False
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if ok:
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||||
# verify the dynamic graph really runs at other batch sizes
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try:
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sess = ort_session(dyn_path)
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for b in (1, 2, 64):
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y = ort_run(sess, np.zeros((b, 540, 20), dtype=np.float32))
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assert y.shape == (b, 15, 2), y.shape
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dynamic_works = True
|
||||
except Exception as e: # noqa: BLE001
|
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print(f"dynamic-batch model does not generalize: {e}")
|
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|
||||
sessions = {}
|
||||
if dynamic_works:
|
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results["export"] = {"mode": "dynamic-batch", "exporter": exporter,
|
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"file": os.path.basename(dyn_path),
|
||||
"size_mb": os.path.getsize(dyn_path) / 1e6}
|
||||
sess = ort_session(dyn_path)
|
||||
sessions = {1: sess, 2: sess, 64: sess}
|
||||
print(f"dynamic-batch export OK via {exporter}")
|
||||
else:
|
||||
results["export"] = {"mode": "fixed-batch", "fallback_reason": err,
|
||||
"files": {}}
|
||||
for b in (1, 2, 64):
|
||||
p = os.path.join(RESULTS, f"retrained_fp32_b{b}.onnx")
|
||||
ok, exporter, err = try_export(model, p, batch=b, dynamic=False)
|
||||
if not ok:
|
||||
results["export"]["files"][str(b)] = {"error": err}
|
||||
print(f"EXPORT FAILED at batch {b}: {err}")
|
||||
continue
|
||||
results["export"]["files"][str(b)] = {
|
||||
"exporter": exporter, "file": os.path.basename(p),
|
||||
"size_mb": os.path.getsize(p) / 1e6}
|
||||
sessions[b] = ort_session(p)
|
||||
print(f"fixed-batch {b} export OK via {exporter}")
|
||||
|
||||
# ---- parity vs torch on the fixture -------------------------------------
|
||||
if 2 in sessions:
|
||||
y_ort = ort_run(sessions[2], fx)
|
||||
with torch.no_grad():
|
||||
y_torch = model(torch.from_numpy(fx)).numpy()
|
||||
results["parity"] = {
|
||||
"fixture": "results/parity_fixture.npz (batch 2, seed 42)",
|
||||
"max_abs_diff_vs_stored_fixture": float(np.abs(y_ort - fy).max()),
|
||||
"max_abs_diff_vs_torch_now": float(np.abs(y_ort - y_torch).max()),
|
||||
"pass_lt_1e-4": bool(np.abs(y_ort - y_torch).max() < 1e-4),
|
||||
}
|
||||
print("parity:", json.dumps(results["parity"], indent=2))
|
||||
|
||||
# ---- latency -------------------------------------------------------------
|
||||
results["latency"] = {}
|
||||
if 1 in sessions:
|
||||
results["latency"]["batch1"] = bench_ort(sessions[1], 1, 100)
|
||||
print(f"ORT batch 1: {results['latency']['batch1']['median_ms_per_window']:.2f} ms/window")
|
||||
if 64 in sessions:
|
||||
results["latency"]["batch64"] = bench_ort(sessions[64], 64, 30)
|
||||
print(f"ORT batch 64: {results['latency']['batch64']['median_ms_per_window']:.3f} ms/window")
|
||||
|
||||
# ---- supplementary: ORT dynamic int8 (size datapoint for the 2.2MB claim)
|
||||
src = (dyn_path if dynamic_works
|
||||
else os.path.join(RESULTS, "retrained_fp32_b1.onnx"))
|
||||
if os.path.exists(src):
|
||||
try:
|
||||
from onnxruntime.quantization import QuantType, quantize_dynamic
|
||||
q_path = os.path.join(RESULTS, "retrained_int8_ort_dynamic.onnx")
|
||||
quantize_dynamic(src, q_path, weight_type=QuantType.QInt8)
|
||||
entry = {"file": os.path.basename(q_path),
|
||||
"size_mb": os.path.getsize(q_path) / 1e6}
|
||||
try:
|
||||
qs = ort_session(q_path)
|
||||
yq = ort_run(qs, fx[:1] if not dynamic_works else fx)
|
||||
ref = fy[:1] if not dynamic_works else fy
|
||||
entry["runs"] = True
|
||||
entry["max_abs_diff_vs_fp32_fixture"] = float(np.abs(yq - ref).max())
|
||||
except Exception as e: # noqa: BLE001
|
||||
entry["runs"] = False
|
||||
entry["run_error"] = f"{type(e).__name__}: {e}"
|
||||
results["ort_int8_dynamic_supplementary"] = entry
|
||||
print("ORT int8:", json.dumps(entry, indent=2))
|
||||
except Exception as e: # noqa: BLE001
|
||||
results["ort_int8_dynamic_supplementary"] = {
|
||||
"error": f"{type(e).__name__}: {e}"}
|
||||
|
||||
merged = {}
|
||||
if os.path.exists(OUT_JSON):
|
||||
with open(OUT_JSON) as f:
|
||||
merged = json.load(f)
|
||||
merged["onnx"] = results
|
||||
with open(OUT_JSON, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"wrote {OUT_JSON}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,290 @@
|
||||
"""ADR-152 "optimize beyond SOTA": edge-optimization benchmark for the
|
||||
retrained WiFlow-STD checkpoint (results/retrained_best_pose_model.pth,
|
||||
~96% PCK@20, fp32 params 2,225,042).
|
||||
|
||||
Measures, for fp32 / fp16 / dynamic-int8 torch variants:
|
||||
(a) serialized state_dict size on disk,
|
||||
(b) CPU inference latency per window at batch 1 and batch 64
|
||||
(median of repeated runs, this Windows box),
|
||||
(c) accuracy (PCK@20/50 + MPJPE, upstream metrics) on a corruption-free
|
||||
random subset of the seed-42 file-level 70/15/15 test split
|
||||
(same split as eval_repro.py; corrupted windows 487-499 excluded via
|
||||
results/nan_windows_mask.npy | results/big_windows_mask.npy).
|
||||
|
||||
Also verifies the paper's "~2.2 MB int8" size claim: reports which layer
|
||||
types torch dynamic quantization actually converts (the model contains NO
|
||||
nn.Linear -- it is Conv1d/Conv2d/BatchNorm only) and the real on-disk size.
|
||||
|
||||
Usage:
|
||||
.venv/Scripts/python.exe quantize_bench.py \
|
||||
--data-dir C:/Users/ruv/.cache/kagglehub/datasets/kaka2434/wiflow-dataset/versions/1/preprocessed_csi_data \
|
||||
[--subset 10000] [--skip-accuracy]
|
||||
|
||||
Writes/merges into results/edge_optimization.json under key "torch".
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
UPSTREAM = os.path.join(HERE, "upstream")
|
||||
RESULTS = os.path.join(HERE, "results")
|
||||
sys.path.insert(0, UPSTREAM)
|
||||
|
||||
# Upstream models/__init__.py is broken as published (imports a name tcn.py
|
||||
# does not define); register a stub package so it never executes.
|
||||
import types # noqa: E402
|
||||
|
||||
_models_pkg = types.ModuleType("models")
|
||||
_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
|
||||
sys.modules["models"] = _models_pkg
|
||||
|
||||
import dataset as upstream_dataset # noqa: E402
|
||||
from dataset import ( # noqa: E402
|
||||
PreprocessedCSIKeypointsDataset,
|
||||
create_preprocessed_train_val_test_loaders,
|
||||
)
|
||||
from models.pose_model import WiFlowPoseModel # noqa: E402
|
||||
from utils.metrics import calculate_mpjpe, calculate_pck # noqa: E402
|
||||
|
||||
CHECKPOINT = os.path.join(RESULTS, "retrained_best_pose_model.pth")
|
||||
|
||||
# csi_windows.npy is ~13 GB; mmap large arrays instead of loading into RAM
|
||||
# (same trick as eval_repro.py).
|
||||
_np_load = np.load
|
||||
|
||||
|
||||
def _np_load_mmap(path, *a, **kw):
|
||||
if (isinstance(path, str) and path.endswith(".npy")
|
||||
and os.path.getsize(path) > 1 << 30 and "mmap_mode" not in kw):
|
||||
kw["mmap_mode"] = "r"
|
||||
return _np_load(path, *a, **kw)
|
||||
|
||||
|
||||
upstream_dataset.np.load = _np_load_mmap
|
||||
|
||||
|
||||
def load_fp32_model():
|
||||
state = torch.load(CHECKPOINT, map_location="cpu", weights_only=True)
|
||||
# legacy upstream names, harmless no-op on the retrained checkpoint
|
||||
renames = {"att.": "attention.", "final_conv.": "decoder."}
|
||||
state = {next((new + k[len(old):] for old, new in renames.items()
|
||||
if k.startswith(old)), k): v
|
||||
for k, v in state.items()}
|
||||
model = WiFlowPoseModel(dropout=0.5)
|
||||
model.load_state_dict(state, strict=True)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def state_dict_size_bytes(model, path):
|
||||
torch.save(model.state_dict(), path)
|
||||
return os.path.getsize(path)
|
||||
|
||||
|
||||
def bench_latency(model, batch_size, n_runs, dtype=torch.float32):
|
||||
gen = torch.Generator().manual_seed(123)
|
||||
x = torch.rand(batch_size, 540, 20, generator=gen).to(dtype)
|
||||
with torch.no_grad():
|
||||
for _ in range(max(5, n_runs // 10)): # warmup
|
||||
model(x)
|
||||
times = []
|
||||
for _ in range(n_runs):
|
||||
t0 = time.perf_counter()
|
||||
model(x)
|
||||
times.append(time.perf_counter() - t0)
|
||||
med = statistics.median(times)
|
||||
return {
|
||||
"batch_size": batch_size,
|
||||
"runs": n_runs,
|
||||
"median_ms_per_batch": med * 1e3,
|
||||
"median_ms_per_window": med * 1e3 / batch_size,
|
||||
"windows_per_second": batch_size / med,
|
||||
}
|
||||
|
||||
|
||||
def build_test_subset(data_dir, subset_size, batch_size=64):
|
||||
"""Seed-42 file-level 70/15/15 test split (exactly as eval_repro.py),
|
||||
minus corrupted windows, then a seed-42 random subset."""
|
||||
dataset = PreprocessedCSIKeypointsDataset(
|
||||
data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
|
||||
_tr, _va, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=batch_size, num_workers=0, random_seed=42)
|
||||
test_indices = np.asarray(test_loader.dataset.indices)
|
||||
|
||||
corrupted = (np.load(os.path.join(RESULTS, "nan_windows_mask.npy"))
|
||||
| np.load(os.path.join(RESULTS, "big_windows_mask.npy")))
|
||||
clean = test_indices[~corrupted[test_indices]]
|
||||
print(f"test split: {len(test_indices)} windows, "
|
||||
f"{len(test_indices) - len(clean)} corrupted excluded, "
|
||||
f"{len(clean)} clean")
|
||||
|
||||
if subset_size and subset_size < len(clean):
|
||||
rng = np.random.default_rng(42)
|
||||
clean = np.sort(rng.choice(clean, size=subset_size, replace=False))
|
||||
subset = torch.utils.data.Subset(dataset, clean.tolist())
|
||||
loader = DataLoader(subset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=0)
|
||||
return loader, len(clean)
|
||||
|
||||
|
||||
def evaluate(model, loader, dtype=torch.float32, label=""):
|
||||
totals = {0.2: 0.0, 0.5: 0.0}
|
||||
total_mpe, n = 0.0, 0
|
||||
t0 = time.time()
|
||||
with torch.no_grad():
|
||||
for batch_idx, (bx, by) in enumerate(loader):
|
||||
out = model(bx.to(dtype)).float()
|
||||
pck = calculate_pck(out, by, thresholds=[0.2, 0.5])
|
||||
mpe = calculate_mpjpe(out, by)
|
||||
bs = by.size(0)
|
||||
total_mpe += mpe * bs
|
||||
for t in totals:
|
||||
totals[t] += pck[t] * bs
|
||||
n += bs
|
||||
if batch_idx % 50 == 0:
|
||||
print(f" [{label}] batch {batch_idx}: n={n} "
|
||||
f"pck20={totals[0.2]/n:.4f} mpjpe={total_mpe/n:.4f} "
|
||||
f"({time.time()-t0:.0f}s)", flush=True)
|
||||
return {
|
||||
"samples": n,
|
||||
"pck@20": totals[0.2] / n,
|
||||
"pck@50": totals[0.5] / n,
|
||||
"mpjpe": total_mpe / n,
|
||||
"wall_seconds": time.time() - t0,
|
||||
}
|
||||
|
||||
|
||||
def quantize_int8_dynamic(fp32_model):
|
||||
"""torch.ao.quantization.quantize_dynamic on Linear/Conv where supported.
|
||||
Returns (model, report) where report documents what actually quantized."""
|
||||
qmodel = torch.ao.quantization.quantize_dynamic(
|
||||
fp32_model, {nn.Linear, nn.Conv1d, nn.Conv2d}, dtype=torch.qint8)
|
||||
|
||||
quantized, total_params, quant_params = [], 0, 0
|
||||
for name, mod in qmodel.named_modules():
|
||||
cls = type(mod).__module__ + "." + type(mod).__name__
|
||||
if "quantized" in cls:
|
||||
w = mod.weight() if callable(getattr(mod, "weight", None)) else None
|
||||
numel = w.numel() if w is not None else 0
|
||||
quant_params += numel
|
||||
quantized.append({"module": name, "class": cls, "params": numel})
|
||||
for p in fp32_model.parameters():
|
||||
total_params += p.numel()
|
||||
|
||||
n_linear = sum(isinstance(m, nn.Linear) for m in fp32_model.modules())
|
||||
n_conv1d = sum(isinstance(m, nn.Conv1d) for m in fp32_model.modules())
|
||||
n_conv2d = sum(isinstance(m, nn.Conv2d) for m in fp32_model.modules())
|
||||
report = {
|
||||
"eligible_module_counts": {
|
||||
"nn.Linear": n_linear, "nn.Conv1d": n_conv1d, "nn.Conv2d": n_conv2d},
|
||||
"modules_actually_quantized": quantized,
|
||||
"n_modules_quantized": len(quantized),
|
||||
"params_total": total_params,
|
||||
"params_quantized": quant_params,
|
||||
"params_quantized_fraction": quant_params / total_params,
|
||||
}
|
||||
return qmodel, report
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-dir", default=os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "kagglehub", "datasets", "kaka2434",
|
||||
"wiflow-dataset", "versions", "1", "preprocessed_csi_data"))
|
||||
parser.add_argument("--subset", type=int, default=10000)
|
||||
parser.add_argument("--runs-b1", type=int, default=100)
|
||||
parser.add_argument("--runs-b64", type=int, default=30)
|
||||
parser.add_argument("--skip-accuracy", action="store_true")
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
torch.manual_seed(42)
|
||||
results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"platform": platform.platform(),
|
||||
"processor": platform.processor(),
|
||||
"num_threads": torch.get_num_threads(),
|
||||
"checkpoint": os.path.relpath(CHECKPOINT, HERE),
|
||||
},
|
||||
"variants": {},
|
||||
}
|
||||
|
||||
# ---- build variants ---------------------------------------------------
|
||||
fp32 = load_fp32_model()
|
||||
n_params = sum(p.numel() for p in fp32.parameters())
|
||||
results["env"]["params"] = n_params
|
||||
print(f"fp32 model: {n_params:,} params")
|
||||
|
||||
fp16 = load_fp32_model().half()
|
||||
|
||||
int8, q_report = quantize_int8_dynamic(load_fp32_model())
|
||||
results["int8_dynamic_quant_report"] = q_report
|
||||
print(f"int8 dynamic: {q_report['n_modules_quantized']} modules quantized, "
|
||||
f"{q_report['params_quantized_fraction']*100:.1f}% of params")
|
||||
|
||||
variants = {
|
||||
"fp32": (fp32, torch.float32, "retrained_fp32_resaved.pth"),
|
||||
"fp16": (fp16, torch.float16, "retrained_fp16.pth"),
|
||||
"int8_dynamic": (int8, torch.float32, "retrained_int8_dynamic.pth"),
|
||||
}
|
||||
|
||||
# ---- (a) size + (b) latency -------------------------------------------
|
||||
for name, (model, dtype, fname) in variants.items():
|
||||
path = os.path.join(RESULTS, fname)
|
||||
size = state_dict_size_bytes(model, path)
|
||||
print(f"\n=== {name}: {size/1e6:.3f} MB on disk ({fname}) ===")
|
||||
lat1 = bench_latency(model, 1, args.runs_b1, dtype)
|
||||
lat64 = bench_latency(model, 64, args.runs_b64, dtype)
|
||||
print(f" batch 1: {lat1['median_ms_per_window']:.2f} ms/window "
|
||||
f"({lat1['windows_per_second']:.0f}/s)")
|
||||
print(f" batch 64: {lat64['median_ms_per_window']:.3f} ms/window "
|
||||
f"({lat64['windows_per_second']:.0f}/s)")
|
||||
results["variants"][name] = {
|
||||
"file": fname,
|
||||
"size_bytes": size,
|
||||
"size_mb": size / 1e6,
|
||||
"latency_batch1": lat1,
|
||||
"latency_batch64": lat64,
|
||||
}
|
||||
|
||||
# ---- (c) accuracy ------------------------------------------------------
|
||||
if not args.skip_accuracy:
|
||||
loader, n_clean = build_test_subset(args.data_dir, args.subset)
|
||||
results["accuracy_subset"] = {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted "
|
||||
"windows (files 487-499) excluded, seed-42 random "
|
||||
"subset",
|
||||
"subset_size": min(args.subset, n_clean) if args.subset else n_clean,
|
||||
"clean_test_total": n_clean,
|
||||
}
|
||||
for name, (model, dtype, _f) in variants.items():
|
||||
print(f"\n=== accuracy: {name} ===")
|
||||
results["variants"][name]["accuracy"] = evaluate(
|
||||
model, loader, dtype, label=name)
|
||||
print(json.dumps(results["variants"][name]["accuracy"], indent=2))
|
||||
|
||||
# ---- merge into edge_optimization.json ---------------------------------
|
||||
merged = {}
|
||||
if os.path.exists(args.out):
|
||||
with open(args.out) as f:
|
||||
merged = json.load(f)
|
||||
merged["torch"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"\nwrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"torch": {
|
||||
"env": {
|
||||
"torch": "2.12.0+cpu",
|
||||
"platform": "Windows-11-10.0.26200-SP0",
|
||||
"processor": "Intel64 Family 6 Model 197 Stepping 2, GenuineIntel",
|
||||
"num_threads": 16,
|
||||
"checkpoint": "results\\retrained_best_pose_model.pth",
|
||||
"params": 2225042
|
||||
},
|
||||
"variants": {
|
||||
"fp32": {
|
||||
"file": "retrained_fp32_resaved.pth",
|
||||
"size_bytes": 9068948,
|
||||
"size_mb": 9.068948,
|
||||
"latency_batch1": {
|
||||
"batch_size": 1,
|
||||
"runs": 100,
|
||||
"median_ms_per_batch": 24.903650000851485,
|
||||
"median_ms_per_window": 24.903650000851485,
|
||||
"windows_per_second": 40.15475642991324
|
||||
},
|
||||
"latency_batch64": {
|
||||
"batch_size": 64,
|
||||
"runs": 30,
|
||||
"median_ms_per_batch": 184.02919999789447,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
"fp16": {
|
||||
"file": "retrained_fp16.pth",
|
||||
"size_bytes": 4580332,
|
||||
"size_mb": 4.580332,
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
"latency_batch64": {
|
||||
"batch_size": 64,
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||||
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||||
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||||
},
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||||
"accuracy": {
|
||||
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|
||||
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||||
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||||
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||||
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||||
}
|
||||
},
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||||
"int8_dynamic": {
|
||||
"file": "retrained_int8_dynamic.pth",
|
||||
"size_bytes": 9068948,
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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},
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||||
"accuracy": {
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||||
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||||
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||||
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||||
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}
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||||
}
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||||
},
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||||
"int8_dynamic_quant_report": {
|
||||
"eligible_module_counts": {
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||||
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||||
"nn.Conv1d": 21,
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||||
"nn.Conv2d": 22
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||||
},
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||||
"modules_actually_quantized": [],
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||||
"n_modules_quantized": 0,
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||||
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||||
"params_quantized_fraction": 0.0
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||||
},
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||||
"accuracy_subset": {
|
||||
"description": "seed-42 file-level 70/15/15 test split, corrupted windows (files 487-499) excluded, seed-42 random subset",
|
||||
"subset_size": 10000,
|
||||
"clean_test_total": 10000
|
||||
}
|
||||
},
|
||||
"onnx": {
|
||||
"env": {
|
||||
"torch": "2.12.0+cpu",
|
||||
"onnxruntime": "1.26.0",
|
||||
"platform": "Windows-11-10.0.26200-SP0"
|
||||
},
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||||
"export": {
|
||||
"mode": "dynamic-batch",
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||||
"exporter": "torchscript",
|
||||
"file": "retrained_fp32_dynamic.onnx",
|
||||
"size_mb": 8.971781
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},
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"parity": {
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"fixture": "results/parity_fixture.npz (batch 2, seed 42)",
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"max_abs_diff_vs_stored_fixture": 2.384185791015625e-07,
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"max_abs_diff_vs_torch_now": 2.384185791015625e-07,
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"pass_lt_1e-4": true
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||||
},
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||||
"latency": {
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||||
"batch1": {
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"batch_size": 1,
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"runs": 100,
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"median_ms_per_batch": 2.5410999987798277,
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"batch_size": 64,
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},
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"ort_int8_dynamic_supplementary": {
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"file": "retrained_int8_ort_dynamic.onnx",
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"size_mb": 2.438794,
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"runs": true,
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}
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},
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||||
"onnx_accuracy": {
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||||
"onnx_fp32": {
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"samples": 10000,
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"pck@20": 0.9668200004577636,
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},
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"onnx_int8_ort_dynamic": {
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}
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},
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"latency_controlled_rerun": {
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"note": "3 interleaved repetitions per variant, median ms/window; quiet box",
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"fp32": {
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"onnx_int8_ort_dynamic": {
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]
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||||
}
|
||||
}
|
||||
@@ -47,7 +47,7 @@ Adopt four changes, ordered by effort-vs-gain:
|
||||
|
||||
1. **Record transceiver geometry at enrollment.** `EnrollmentProtocol` gains an optional `NodeGeometry` record per node (position estimate, antenna orientation, inter-node distances where known). Stored alongside the room baseline in the bank; schema-versioned so existing banks remain readable.
|
||||
2. **Fuse geometry embeddings into specialist training.** Where a specialist head consumes the (future, ADR-150) backbone embedding, concatenate a small learned embedding of `NodeGeometry` — the PerceptAlign mechanism, transplanted to our per-room banks. Statistical specialists (current) ignore it; LoRA heads (ADR-151 P6) consume it.
|
||||
3. **Adopt the two-checkerboard alignment for the camera-supervised path (ADR-079).** When MediaPipe supervision is used, calibrate camera↔WiFi into one shared 3D frame before regression (<5 min, two checkerboards, a few photos). This is the direct defense against F1 for our 92.9%-PCK@20 pipeline.
|
||||
3. **Adopt the two-checkerboard alignment for the camera-supervised path (ADR-079).** When MediaPipe supervision is used, calibrate camera↔WiFi into one shared 3D frame before regression (<5 min, two checkerboards, a few photos). This is the direct defense against F1 for our camera-supervised pipeline. ~~92.9%-PCK@20~~ — *that figure was retracted during measurement (b) (2026-06-10): the surviving holdout shows a constant-output model under an absolute (non-torso) threshold on 69 near-static frames; mean predictor scores 100% under the same protocol. The §2.2 no-citation rule now applies to it.*
|
||||
4. **Evaluate on the PerceptAlign cross-domain dataset** (21 subjects / 7 layouts) as the MERIDIAN cross-layout benchmark — *gated on confirming its license and downloadability* (open question; repo per paper: github.com/Trymore-lab/PerceptAlign).
|
||||
> **Gate resolved (2026-06-10, MEASURED by repo inspection):** repo exists, **MIT license**, dataset downloadable from HuggingFace (5 per-scene repos, raw CSI + separate vision keypoints; Intel 5300, 1TX×3RX×3 ant, 57 subcarriers — same order as ESP32 subcarrier counts; Scene3 ships 3 distinct layouts). Code present, no pretrained weights. Benchmark adoption unblocked; dataset-side license terms inherit HF dataset terms (not separately stated — check at download time).
|
||||
|
||||
|
||||
+15
-10
@@ -50,7 +50,7 @@ See [PR #405](https://github.com/ruvnet/RuView/pull/405) for full details.
|
||||
### What's New in v0.7.0
|
||||
|
||||
<details>
|
||||
<summary><strong>Camera Ground-Truth Training — 92.9% PCK@20</strong></summary>
|
||||
<summary><strong>Camera Ground-Truth Training</strong></summary>
|
||||
|
||||
**v0.7.0 adds camera-supervised pose training** using MediaPipe + real ESP32 CSI data:
|
||||
|
||||
@@ -76,15 +76,20 @@ node scripts/train-wiflow-supervised.js --data data/paired/*.jsonl --scale lite
|
||||
node scripts/eval-wiflow.js --model models/wiflow-real/wiflow-v1.json --data data/paired/*.jsonl
|
||||
```
|
||||
|
||||
**Result: 92.9% PCK@20** from a 5-minute data collection session with one ESP32-S3 and one webcam.
|
||||
> **Accuracy retraction (2026-06-10):** the "92.9% PCK@20" figure previously
|
||||
> shown here is retracted. A forensic recheck of the surviving eval holdout
|
||||
> (69 samples) found a constant-output model scored with an absolute
|
||||
> (non-torso-normalized) threshold on nearly-static frames — a protocol under
|
||||
> which a trivial mean-pose predictor scores 100%. Torso-normalized PCK@20 on
|
||||
> the same holdout is ~19% (from that degenerate predictor). No measured
|
||||
> camera-supervised PCK@20 is currently published (CHANGELOG, PR #535).
|
||||
|
||||
| Metric | Before (proxy) | After (camera-supervised) |
|
||||
|--------|----------------|--------------------------|
|
||||
| PCK@20 | 0% | **92.9%** |
|
||||
| Eval loss | 0.700 | **0.082** |
|
||||
| Bone constraint | N/A | **0.008** |
|
||||
| Training time | N/A | **19 minutes** |
|
||||
| Model size | N/A | **974 KB** |
|
||||
| Metric | Camera-supervised run (protocol retracted) |
|
||||
|--------|--------------------------------------------|
|
||||
| Eval loss | 0.082 |
|
||||
| Bone constraint | 0.008 |
|
||||
| Training time | 19 minutes |
|
||||
| Model size | 974 KB |
|
||||
|
||||
Pre-trained model: [HuggingFace ruv/ruview/wiflow-v1](https://huggingface.co/ruv/ruview)
|
||||
|
||||
@@ -868,7 +873,7 @@ Download a pre-built binary — no build toolchain needed:
|
||||
|
||||
| Release | What's included | Tag |
|
||||
|---------|-----------------|-----|
|
||||
| [v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0) | **Latest** — Camera-supervised WiFlow model (92.9% PCK@20), ground-truth training pipeline, ruvector optimizations | `v0.7.0` |
|
||||
| [v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0) | **Latest** — Camera-supervised WiFlow model (accuracy figure retracted 2026-06-10, see above), ground-truth training pipeline, ruvector optimizations | `v0.7.0` |
|
||||
| [v0.6.0](https://github.com/ruvnet/RuView/releases/tag/v0.6.0-esp32) | [Pre-trained models on HuggingFace](https://huggingface.co/ruv/ruview), 17 sensing apps, 51.6% contrastive improvement, 0.008ms inference | `v0.6.0-esp32` |
|
||||
| [v0.5.5](https://github.com/ruvnet/RuView/releases/tag/v0.5.5-esp32) | SNN + MinCut (#348 fix) + CNN spectrogram + WiFlow + multi-freq mesh + graph transformer | `v0.5.5-esp32` |
|
||||
| [v0.5.4](https://github.com/ruvnet/RuView/releases/tag/v0.5.4-esp32) | Cognitum Seed integration ([ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md)), 8-dim feature vectors, RVF store, witness chain, security hardening | `v0.5.4-esp32` |
|
||||
|
||||
+8
-1
@@ -1747,7 +1747,14 @@ See [ADR-071](adr/ADR-071-ruvllm-training-pipeline.md) and the [pretraining tuto
|
||||
|
||||
For significantly higher accuracy, use a webcam as a **temporary teacher** during training. The camera captures real 17-keypoint poses via MediaPipe, paired with simultaneous ESP32 CSI data. After training, the camera is no longer needed — the model runs on CSI only.
|
||||
|
||||
**Result: 92.9% PCK@20** from a 5-minute collection session.
|
||||
> **Accuracy note (2026-06-10):** the previously cited "92.9% PCK@20" figure is
|
||||
> retracted — a forensic recheck of the surviving eval holdout showed it came
|
||||
> from a constant-output model scored with an absolute (non-torso-normalized)
|
||||
> threshold on 69 nearly-static frames, a protocol under which a trivial
|
||||
> mean-pose predictor scores 100%. No measured camera-supervised PCK@20 is
|
||||
> currently published (see CHANGELOG, PR #535). Treat this workflow as a data
|
||||
> collection mechanism; accuracy claims will follow a ≥35-minute multi-pose
|
||||
> collection session evaluated with torso-normalized PCK.
|
||||
|
||||
### Requirements
|
||||
|
||||
|
||||
Reference in New Issue
Block a user