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https://github.com/ruvnet/RuView
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feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152)
WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
@@ -267,6 +267,64 @@ Findings:
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says capacity *hurts* cross-subject, so the compact end may generalize no
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worse, but that is a hypothesis, not a measurement.
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### Compact-variant edge artifacts (MEASURED, 2026-06-11)
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Edge pipeline for the **tiny** checkpoint (56,290 params), same machinery and
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protocol as the full-model edge rows above (this Windows box, torch
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2.12.0+cpu, onnxruntime 1.26.0; dynamic-batch opset-17 TorchScript export;
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static QDQ **Percentile(99.99) conv-only** int8 calibrated on **512**
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corruption-free TRAIN-split windows; accuracy on the identical 10k-window
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seed-42 clean test subset; latency = median ms/window over 3 interleaved
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reps, with the full-model fp32/int8 sessions interleaved as same-session
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references). Script: `tiny_edge_bench.py`; raw:
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`results/edge_optimization.json` (`tiny_variant`). Torch-vs-ORT parity on the
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stored fixture input: **max abs diff 1.5e-7 — PASS** (< 1e-4). The tiny fp32
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subset PCK@20 (94.11%) matches the full clean-test sweep figure (94.11%)
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exactly, so the subset remains representative.
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Two forced deviations, both recorded in the JSON:
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1. **Adaptive-pool export rewrite.** tiny's derived stride schedule
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`[2,1,1,1]` leaves feature width 16, and the TorchScript exporter rejects
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`AdaptiveAvgPool2d((15,1))` when 15 is not a factor of the input height
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(the full model never hit this — its width was exactly 15). Since the
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pool over a fixed-size map is a fixed linear operator, the export wrapper
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replaces it with `mean(-1)` (W axis, a factor) + a constant averaging
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matmul using PyTorch's exact bin rule; the parity check (vs the original
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torch model with the real pool) proves exactness.
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2. **Calibration count 512, not "~500"**: ORT 1.26's histogram collector
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`np.asarray()`'s the per-batch maxima, so the calibration count must be a
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multiple of the 64-window calibration batch or the ragged last batch
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crashes it (the earlier static-PTQ run dodged this by using exactly 512).
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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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| full ONNX fp32 (same-session ref) | 8.97 MB | 2.27 | 1.42 | 96.68% | 99.15% | 0.00936 |
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| full static QDQ Percentile conv-only (same-session ref) | 2.53 MB | 5.53 | 3.82 | 96.61% | 99.16% | 0.01031 |
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| **tiny ONNX fp32** | **0.295 MB** | **0.66** | **0.24** | **94.11%** | 99.37% | 0.01253 |
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| tiny static QDQ Percentile conv-only | 0.248 MB | 0.85 | 1.03 | 92.68% | 99.33% | 0.01491 |
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(tiny torch `.pth` checkpoint for reference: 0.34 MB on disk; 56,290 fp32
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params ≈ 225 KB of weights.)
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Findings:
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- **The smallest deployable WiFlow-class model is the tiny ONNX fp32
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artifact: ~295 KB on disk, 0.66 ms/window batch-1 CPU (~1,500 windows/s),
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94.1% PCK@20** — 30× smaller and ~3.4× faster (in-session) than the full
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ONNX fp32 model for −2.6 pt PCK@20.
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- **int8 is a bad trade at this scale.** Static QDQ conv-only — the recipe
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that cost the full model only 0.07 pt — costs tiny **−1.43 pt** PCK@20
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(94.11 → 92.68%) and +19% MPJPE, saves only 47 KB (−16%; QDQ scales and
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the fp32 BN/attention glue are proportionally larger in a small graph),
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and is *slower* than tiny fp32 (0.85 vs 0.66 ms b1; 1.03 vs 0.24 ms b64 —
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QDQ kernel overhead dominates when the convs are this small). A 56k-param
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model has little redundancy left to absorb weight+activation rounding.
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- Deployment guidance, compact edition: ship tiny as **ONNX fp32** — at
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295 KB the int8 size saving solves no real constraint and costs accuracy
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and speed. If ~250 KB vs ~295 KB ever matters, weight-only quantization
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would be the thing to try next, not QDQ.
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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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@@ -626,5 +626,147 @@
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"description": "seed-42 file-level 70/15/15 test split, corrupted windows excluded, seed-42 random subset (same as quantize_bench/eval_ort_accuracy)",
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"subset_size": 10000
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}
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},
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"tiny_variant": {
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"env": {
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"torch": "2.12.0+cpu",
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"onnxruntime": "1.26.0",
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"platform": "Windows-11-10.0.26200-SP0",
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"num_threads": 16,
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"checkpoint": "results\\tiny_best.pth",
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"checkpoint_size_bytes": 340555,
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"params": 56290,
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"variant_config": {
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"tcn": [
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68,
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56,
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44,
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32
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],
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"conv": [
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2,
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4,
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8,
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16
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],
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"attn_groups": 2,
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"groups_mode": "depthwise",
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"input_pw_groups": 4
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}
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},
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"export": {
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"mode": "dynamic-batch",
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"exporter": "torchscript",
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"opset": 17,
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"file": "tiny_fp32_dynamic.onnx",
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"size_bytes": 295279,
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"size_mb": 0.295279,
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"verified_batches": [
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1,
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2,
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64
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],
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"note": "AdaptiveAvgPool2d((15,1)) replaced at export by an exact mean(-1) + constant averaging matmul (final_width 16 is not a multiple of 15, which the TorchScript exporter rejects); exactness proven by the parity check vs the original torch model"
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},
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"parity": {
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"fixture": "results/parity_fixture.npz input (batch 2, seed 42); reference output recomputed with the tiny torch model",
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"max_abs_diff_vs_torch": 1.4901161193847656e-07,
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"pass_lt_1e-4": true
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},
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"int8_static_percentile_conv": {
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"file": "tiny_int8_static_percentile_conv.onnx",
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"size_bytes": 248278,
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"size_mb": 0.248278,
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"calibration": {
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"method": "percentile",
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"percentile": 99.99,
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"windows": 512,
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"scope": "conv-only TRAIN-split corruption-free",
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"seconds": 1.5347836017608643
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},
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"per_channel": true,
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"activation_type": "QInt8",
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"weight_type": "QInt8",
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"max_abs_diff_vs_fp32_fixture": 0.018491357564926147
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},
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"latency": {
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"note": "3 interleaved repetitions per variant, median ms/window; full-model sessions are same-session references",
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"tiny_onnx_fp32": {
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"batch1_reps": [
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0.6312500008789357,
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0.6834500018157996,
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0.6595999984710943
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],
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"batch64_reps": [
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0.37747578119251557,
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0.24196640623586063,
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0.2314671875183194
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],
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"batch1_ms_per_window_median": 0.6595999984710943,
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"batch64_ms_per_window_median": 0.24196640623586063
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},
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"tiny_onnx_int8_static_percentile_conv": {
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"batch1_reps": [
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0.7988500001374632,
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0.9382499993080273,
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0.8451000030618161
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],
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"batch64_reps": [
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0.9211476562995813,
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1.3045390625165965,
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1.026230468767153
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],
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"batch1_ms_per_window_median": 0.8451000030618161,
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"batch64_ms_per_window_median": 1.026230468767153
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},
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"full_onnx_fp32_reference": {
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"batch1_reps": [
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2.267249998112675,
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2.80170000041835,
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2.132149998942623
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],
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"batch64_reps": [
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1.3050578124875756,
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1.4244992187855132,
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1.8014164062947202
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],
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"batch1_ms_per_window_median": 2.267249998112675,
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"batch64_ms_per_window_median": 1.4244992187855132
|
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},
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"full_onnx_int8_static_percentile_conv_reference": {
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"batch1_reps": [
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5.529599999135826,
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4.768399998283712,
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6.215800000063609
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],
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"batch64_reps": [
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3.815724218725336,
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3.1025562500417436,
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4.333318749957016
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],
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"batch1_ms_per_window_median": 5.529599999135826,
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"batch64_ms_per_window_median": 3.815724218725336
|
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}
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},
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"accuracy_subset": {
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"description": "seed-42 file-level 70/15/15 test split, corrupted windows excluded, seed-42 random subset (same as quantize_bench/eval_ort_accuracy/static_ptq_bench)",
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"subset_size": 10000
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},
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"accuracy": {
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"tiny_onnx_fp32": {
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"samples": 10000,
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"pck@20": 0.941106667804718,
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"pck@50": 0.99369333152771,
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"mpjpe": 0.012527281279861927,
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"wall_seconds": 10.927234888076782
|
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},
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"tiny_onnx_int8_static_percentile_conv": {
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"samples": 10000,
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"pck@20": 0.9268133331298828,
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"pck@50": 0.9932933319091797,
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"mpjpe": 0.014906252065300942,
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"wall_seconds": 12.320892333984375
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}
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}
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}
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}
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@@ -0,0 +1,305 @@
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"""ADR-152 efficiency-sweep follow-up: edge pipeline for the TINY compact
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WiFlow-STD variant (56,290 params, results/tiny_best.pth, trained overnight
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2026-06-10/11 -- see RESULTS.md "Efficiency sweep").
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Headline question: what does the smallest deployable WiFlow-class model look
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like (KB + ms + PCK)? Reuses the onnx_bench.py / static_ptq_bench.py
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machinery on the tiny checkpoint:
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1. Load tiny_best.pth with remote/sweep/model_compact.py
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(depthwise TCN groups, input_pw_groups=4, conv [2,4,8,16], attn groups 2).
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2. Export ONNX: dynamic batch, opset 17, TorchScript exporter (dynamo=False)
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-- same recipe that worked for the full model; verified at batch 1/2/64.
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One forced deviation: tiny's stride schedule [2,1,1,1] leaves final_width
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16, and the TorchScript exporter cannot export AdaptiveAvgPool2d((15,1))
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when 15 is not a factor of the input height (the full model never hit
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this -- its width was exactly 15). The adaptive pool over a fixed-size
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feature map is a fixed linear map, so the export wrapper replaces it with
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an exact matmul equivalent (PyTorch adaptive-pool bin semantics:
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bin i averages rows floor(i*H/K)..ceil((i+1)*H/K)); the W axis (20->1,
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a factor) becomes mean(-1). Exactness is proven by the parity check
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below, which compares against the ORIGINAL torch model with the real
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AdaptiveAvgPool2d.
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3. Torch-vs-ORT parity on the stored fixture input
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(results/parity_fixture.npz, batch 2, seed 42 -- same 540x20 input layout;
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reference output recomputed with the tiny torch model). PASS < 1e-4.
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4. Static QDQ conv-only int8 (quant_pre_process + quantize_static,
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per-channel QInt8 weights+activations, Percentile(99.99) calibration on
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512 corruption-free TRAIN-split windows -- the winning recipe and
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calibration count from static_ptq_bench.py. 512, not "about 500":
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ORT 1.26's histogram collector np.asarray()'s the per-batch maxima, so
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the calibration count must be a multiple of the batch size 64 or the
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ragged last batch crashes it).
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5. Disk size + CPU latency b1/b64 (3 interleaved reps, median ms/window)
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for tiny fp32 + tiny int8, with the full-model ONNX fp32 + static-int8
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sessions interleaved as same-session references.
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6. Accuracy (PCK@20/50 + MPJPE) on the identical 10k-window seed-42
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corruption-free test subset for tiny fp32 + tiny int8.
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Usage:
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PYTHONUTF8=1 .venv/Scripts/python.exe tiny_edge_bench.py \
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[--data-dir <preprocessed_csi_data>] [--subset 10000] [--calib 500]
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Writes/merges into results/edge_optimization.json under key "tiny_variant".
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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 platform
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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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sys.path.insert(0, os.path.join(HERE, "remote", "sweep"))
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# quantize_bench sets up upstream imports + the np.load mmap patch
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from quantize_bench import build_test_subset # noqa: E402
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from eval_ort_accuracy import evaluate_ort # noqa: E402
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from static_ptq_bench import ( # noqa: E402
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build_calibration_windows,
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interleaved_latency,
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make_reader,
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ort_session,
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)
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from model_compact import CompactWiFlowPoseModel, describe # noqa: E402
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TINY_CKPT = os.path.join(RESULTS, "tiny_best.pth")
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TINY_FP32_ONNX = os.path.join(RESULTS, "tiny_fp32_dynamic.onnx")
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TINY_PREPROC_ONNX = os.path.join(RESULTS, "tiny_fp32_preproc.onnx")
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TINY_INT8_ONNX = os.path.join(RESULTS, "tiny_int8_static_percentile_conv.onnx")
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FULL_FP32_ONNX = os.path.join(RESULTS, "retrained_fp32_dynamic.onnx")
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FULL_INT8_ONNX = os.path.join(RESULTS, "retrained_int8_static_percentile_conv.onnx")
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# Exact tiny config from remote/sweep/run_sweep.py VARIANTS (measured 56,290
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# params, clean-test PCK@20 94.11% -- results/efficiency_sweep.jsonl).
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TINY = dict(tcn=[68, 56, 44, 32], conv=[2, 4, 8, 16], attn_groups=2,
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groups_mode="depthwise", input_pw_groups=4)
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def load_tiny_model():
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model = CompactWiFlowPoseModel(
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tcn_channels=TINY["tcn"], conv_channels=TINY["conv"],
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attn_groups=TINY["attn_groups"], groups_mode=TINY["groups_mode"],
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input_pw_groups=TINY["input_pw_groups"], dropout=0.5)
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state = torch.load(TINY_CKPT, map_location="cpu", weights_only=True)
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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 adaptive_pool_matrix(h_in, h_out):
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"""Exact AdaptiveAvgPool1d as a (h_out, h_in) averaging matrix, using
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PyTorch's bin rule: bin i covers rows floor(i*h_in/h_out) ..
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ceil((i+1)*h_in/h_out)."""
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w = torch.zeros(h_out, h_in)
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for i in range(h_out):
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s = (i * h_in) // h_out
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e = -((-(i + 1) * h_in) // h_out) # ceil division
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w[i, s:e] = 1.0 / (e - s)
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return w
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class ExportWrapper(torch.nn.Module):
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"""CompactWiFlowPoseModel forward with the AdaptiveAvgPool2d((K,1))
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replaced by an exact fixed linear map (mean over the factor W axis, then
|
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a constant averaging matmul over the non-factor H axis) so the
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TorchScript ONNX exporter accepts it. Bit-equivalent up to float
|
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round-off; proven by the parity check against the original model."""
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def __init__(self, m, num_keypoints=15):
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super().__init__()
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self.m = m
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self.register_buffer(
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"pool_w_t", adaptive_pool_matrix(m.final_width, num_keypoints).t())
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def forward(self, x):
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m = self.m
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x = m.tcn(x)
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x = x.transpose(1, 2).unsqueeze(1)
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x = m.up(x)
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for block in m.residual_blocks:
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x = block(x)
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x = x.permute(0, 1, 3, 2)
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x = m.attention(x)
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x = m.decoder(x) # [B, 2, H=final_width, T=20]
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x = x.mean(-1) # W-axis pool (20 -> 1, a factor)
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x = x.matmul(self.pool_w_t) # exact adaptive H pool: [B, 2, K]
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return x.transpose(1, 2) # [B, K, 2]
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def export_onnx(model):
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"""Dynamic-batch TorchScript export (the recipe that worked for the full
|
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model in onnx_bench.py), verified at batch 1/2/64. Uses ExportWrapper
|
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(see docstring) because final_width 16 is not a multiple of 15."""
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wrapper = ExportWrapper(model).eval()
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x = torch.rand(2, 540, 20)
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with torch.no_grad():
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torch.onnx.export(
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wrapper, (x,), TINY_FP32_ONNX, opset_version=17,
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input_names=["input"], output_names=["output"], dynamo=False,
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dynamic_axes={"input": {0: "batch"}, "output": {0: "batch"}})
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sess = ort_session(TINY_FP32_ONNX)
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inp = sess.get_inputs()[0].name
|
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for b in (1, 2, 64):
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y = sess.run(None, {inp: np.zeros((b, 540, 20), dtype=np.float32)})[0]
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assert y.shape == (b, 15, 2), y.shape
|
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return {
|
||||
"mode": "dynamic-batch", "exporter": "torchscript", "opset": 17,
|
||||
"file": os.path.basename(TINY_FP32_ONNX),
|
||||
"size_bytes": os.path.getsize(TINY_FP32_ONNX),
|
||||
"size_mb": os.path.getsize(TINY_FP32_ONNX) / 1e6,
|
||||
"verified_batches": [1, 2, 64],
|
||||
"note": "AdaptiveAvgPool2d((15,1)) replaced at export by an exact "
|
||||
"mean(-1) + constant averaging matmul (final_width 16 is not "
|
||||
"a multiple of 15, which the TorchScript exporter rejects); "
|
||||
"exactness proven by the parity check vs the original torch "
|
||||
"model",
|
||||
}
|
||||
|
||||
|
||||
def quantize_tiny(calib_windows):
|
||||
"""quant_pre_process + static QDQ conv-only Percentile(99.99) int8 --
|
||||
the winning recipe from static_ptq_bench.py."""
|
||||
from onnxruntime.quantization import (CalibrationMethod, QuantFormat,
|
||||
QuantType, quantize_static)
|
||||
from onnxruntime.quantization.shape_inference import quant_pre_process
|
||||
|
||||
quant_pre_process(TINY_FP32_ONNX, TINY_PREPROC_ONNX)
|
||||
t0 = time.time()
|
||||
quantize_static(
|
||||
TINY_PREPROC_ONNX, TINY_INT8_ONNX, make_reader(calib_windows),
|
||||
quant_format=QuantFormat.QDQ,
|
||||
op_types_to_quantize=["Conv"],
|
||||
per_channel=True,
|
||||
activation_type=QuantType.QInt8,
|
||||
weight_type=QuantType.QInt8,
|
||||
calibrate_method=CalibrationMethod.Percentile,
|
||||
extra_options={"CalibPercentile": 99.99},
|
||||
)
|
||||
return {
|
||||
"file": os.path.basename(TINY_INT8_ONNX),
|
||||
"size_bytes": os.path.getsize(TINY_INT8_ONNX),
|
||||
"size_mb": os.path.getsize(TINY_INT8_ONNX) / 1e6,
|
||||
"calibration": {"method": "percentile", "percentile": 99.99,
|
||||
"windows": int(len(calib_windows)),
|
||||
"scope": "conv-only TRAIN-split corruption-free",
|
||||
"seconds": time.time() - t0},
|
||||
"per_channel": True,
|
||||
"activation_type": "QInt8",
|
||||
"weight_type": "QInt8",
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
import onnxruntime
|
||||
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("--calib", type=int, default=512,
|
||||
help="calibration windows; must be a multiple of the "
|
||||
"64-window calibration batch (ORT histogram "
|
||||
"collector rejects ragged batches)")
|
||||
parser.add_argument("--skip-accuracy", action="store_true")
|
||||
parser.add_argument("--out", default=os.path.join(RESULTS, "edge_optimization.json"))
|
||||
args = parser.parse_args()
|
||||
|
||||
model = load_tiny_model()
|
||||
info = describe(model)
|
||||
print(f"tiny model: {info['params']:,} params, tcn_groups={info['tcn_groups_per_block']}, "
|
||||
f"strides={info['conv_strides']}, final_width={info['final_width']}")
|
||||
assert info["params"] == 56290, info["params"]
|
||||
|
||||
results = {
|
||||
"env": {
|
||||
"torch": torch.__version__,
|
||||
"onnxruntime": onnxruntime.__version__,
|
||||
"platform": platform.platform(),
|
||||
"num_threads": torch.get_num_threads(),
|
||||
"checkpoint": os.path.relpath(TINY_CKPT, HERE),
|
||||
"checkpoint_size_bytes": os.path.getsize(TINY_CKPT),
|
||||
"params": info["params"],
|
||||
"variant_config": TINY,
|
||||
},
|
||||
}
|
||||
|
||||
# ---- export + parity ----------------------------------------------------
|
||||
print("\n=== ONNX export (dynamic batch, opset 17, torchscript) ===")
|
||||
results["export"] = export_onnx(model)
|
||||
print(f" {results['export']['size_mb']:.3f} MB, batches {results['export']['verified_batches']} OK")
|
||||
|
||||
fixture = np.load(os.path.join(RESULTS, "parity_fixture.npz"))
|
||||
fx = fixture["input"] # (2, 540, 20), seed 42 -- same input layout as full model
|
||||
sess_fp32 = ort_session(TINY_FP32_ONNX)
|
||||
y_ort = sess_fp32.run(None, {sess_fp32.get_inputs()[0].name: fx})[0]
|
||||
with torch.no_grad():
|
||||
y_torch = model(torch.from_numpy(fx)).numpy()
|
||||
results["parity"] = {
|
||||
"fixture": "results/parity_fixture.npz input (batch 2, seed 42); "
|
||||
"reference output recomputed with the tiny torch model",
|
||||
"max_abs_diff_vs_torch": 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))
|
||||
assert results["parity"]["pass_lt_1e-4"], "torch-vs-ORT parity FAILED"
|
||||
|
||||
# ---- static PTQ int8 ------------------------------------------------------
|
||||
print(f"\n=== static QDQ int8 (Percentile conv-only, {args.calib} calib windows) ===")
|
||||
calib = build_calibration_windows(args.data_dir, args.calib)
|
||||
results["int8_static_percentile_conv"] = quantize_tiny(calib)
|
||||
print(f" {results['int8_static_percentile_conv']['size_mb']:.3f} MB")
|
||||
sess_int8 = ort_session(TINY_INT8_ONNX)
|
||||
yq = sess_int8.run(None, {sess_int8.get_inputs()[0].name: fx})[0]
|
||||
results["int8_static_percentile_conv"]["max_abs_diff_vs_fp32_fixture"] = float(
|
||||
np.abs(yq - y_torch).max())
|
||||
|
||||
# ---- latency (3 interleaved reps, full-model sessions as references) -----
|
||||
print("\n=== latency (3 interleaved reps) ===")
|
||||
lat_sessions = {
|
||||
"tiny_onnx_fp32": sess_fp32,
|
||||
"tiny_onnx_int8_static_percentile_conv": sess_int8,
|
||||
"full_onnx_fp32_reference": ort_session(FULL_FP32_ONNX),
|
||||
"full_onnx_int8_static_percentile_conv_reference": ort_session(FULL_INT8_ONNX),
|
||||
}
|
||||
results["latency"] = {
|
||||
"note": "3 interleaved repetitions per variant, median ms/window; "
|
||||
"full-model sessions are same-session references",
|
||||
**interleaved_latency(lat_sessions),
|
||||
}
|
||||
|
||||
# ---- accuracy on the standard 10k corruption-free test subset ------------
|
||||
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 excluded, seed-42 random subset (same as "
|
||||
"quantize_bench/eval_ort_accuracy/static_ptq_bench)",
|
||||
"subset_size": min(args.subset, n_clean) if args.subset else n_clean,
|
||||
}
|
||||
results["accuracy"] = {}
|
||||
for name, sess in (("tiny_onnx_fp32", sess_fp32),
|
||||
("tiny_onnx_int8_static_percentile_conv", sess_int8)):
|
||||
print(f"\n=== accuracy: {name} ===")
|
||||
results["accuracy"][name] = evaluate_ort(sess, loader, name)
|
||||
print(json.dumps(results["accuracy"][name], 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["tiny_variant"] = results
|
||||
with open(args.out, "w") as f:
|
||||
json.dump(merged, f, indent=2)
|
||||
print(f"\nwrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user