diff --git a/docs/benchmarks/person-count-cog.md b/docs/benchmarks/person-count-cog.md new file mode 100644 index 00000000..f24fec0a --- /dev/null +++ b/docs/benchmarks/person-count-cog.md @@ -0,0 +1,83 @@ +# `cog-person-count` — Benchmark Log + +Append-only log of every published count_v1 training run per ADR-103. New runs add a section; never overwrite history. + +## v0.0.1 — first measured run (2026-05-21) + +### Setup + +| Component | Value | +|-----------|-------| +| Training host | `ruvultra` (Ubuntu, x86_64, RTX 5080) | +| Backend | PyTorch 2.12 + CUDA | +| Data | `data/paired/wiflow-p7-1779210883.paired.jsonl` — 1,077 paired samples, single 30-min session, label distribution `{0: 533, 1: 544}` | +| Train/eval split | 80/20 stratified on `ts_start` (held-out tail of the recording) | +| Architecture | Conv1d encoder (56→64→128→128, dilations 1/2/4) + Linear(128→64→8) count head + Linear(128→32→1) confidence head — bit-identical to `v2/crates/cog-person-count/src/inference.rs::CountNet` | +| Loss | `cross_entropy(count) + 0.3·BCE(conf) + 0.1·Brier(conf)` with per-class weighting | +| Optimizer | AdamW, lr 1e-3, cosine warm restarts (T_0=50) | +| Z-score normalisation | per-subcarrier on train statistics, applied to eval | +| Epochs | 400 | +| Wall time | **5.6 s** | + +### Accuracy (held-out 215-sample tail of the 30-min recording) + +| Metric | Value | +|--------|-------| +| Best eval accuracy | **65.1%** | +| Final eval accuracy | 65.1% | +| Within ±1 | **100%** (labels are all in `{0, 1}`, predictions trivially within ±1) | +| MAE | 0.349 persons | +| Class 0 ("empty") accuracy | **100%** (140 samples) | +| Class 1 ("1 person") accuracy | **0%** (75 samples) | +| Confidence↔correctness Spearman | 0.023 | + +### Honest read + +The model overfit hard. By epoch 100 train_acc reached 1.0 and eval_loss climbed from 0.67 → 7.8. The "best" checkpoint (epoch ~2-3) is the snapshot that happened to predict mostly class-0 across eval, which matches the held-out window's class distribution (140/215 = 65.1%) — i.e. it learned the **distribution of the tail of the recording**, not a real empty-vs-occupied classifier. + +Why: the training data is one continuous 30-minute solo recording. The held-out tail captures a stretch where the operator stepped away from the desk for stretches at a time, so the eval set is class-0-heavy and the model finds a degenerate "always predict 0" minimum that gets the eval distribution exactly right. Class 1 accuracy = 0 is the smoking gun. + +Same data-bound failure mode as `pose_v1` (#645). Same fix path: multi-room paired recordings. + +### What v0.0.1 still validates + +- **Pipeline correctness end-to-end.** The Rust cog loaded the PyTorch-trained safetensors successfully on first try (`backend: candle-cpu` reported by `cog-person-count health`), confirming the architecture in `src/inference.rs` is byte-compatible with `train-count.py`. +- **ONNX parity.** 16 KB ONNX, exports cleanly under opset 18 with dynamic batch axis. +- **Fast iteration loop.** 5.6 s end-to-end training means we can sweep hyperparameters or retrain on new data in seconds, not hours. +- **Cog binary size.** Same 2.36 MB stripped release binary (no change — model loads at runtime via mmap'd safetensors). + +### Comparison to ADR-103 v0.1.0 targets + +| Gate | Target | Today | Status | +|------|--------|-------|--------| +| Day-0 same-room accuracy within ±1 | ≥ 80% | 100% (trivially — labels span {0,1}) | met | +| Cross-room accuracy within ±1 | ≥ 60% | Not measured (no cross-room data) | deferred to v0.2.0 | +| MAE | ≤ 0.6 | 0.349 | met | +| Per-frame confidence reflects accuracy (Spearman) | r ≥ 0.5 | 0.023 | **NOT MET** | +| Inference latency on Pi 5 | < 5 ms / frame | Not yet measured (cross-compile pending) | deferred | +| Binary size on GCS | ≤ 4 MB | 2.36 MB | met | + +The accuracy ones look "met" only because the labels collapse to {0, 1} and "within ±1" with 8 classes is trivially satisfied. The **confidence calibration is the real failure** for v0.0.1 — Spearman 0.023 means the confidence head is essentially random noise. That's also bounded by data scarcity; multi-session training should sharpen it. + +### Artifacts + +- `v2/crates/cog-person-count/cog/artifacts/count_v1.safetensors` — 392 KB +- `v2/crates/cog-person-count/cog/artifacts/count_v1.onnx` — 16 KB +- `v2/crates/cog-person-count/cog/artifacts/count_train_results.json` — full per-epoch loss curve + hyperparameters + per-class breakdown + +### Reproducibility + +```bash +# On any host with PyTorch + CUDA (cargo path not needed for training): +scp data/paired/wiflow-p7-1779210883.paired.jsonl :/tmp/ +scp scripts/train-count.py :/tmp/ +ssh "cd /tmp && python3 train-count.py --paired wiflow-p7-1779210883.paired.jsonl --epochs 400" +``` + +Loads in the Rust cog with no translation step (safetensors layout matches `cog-person-count::inference::CountNet` exactly): + +```bash +cp count_v1.safetensors v2/crates/cog-person-count/cog/artifacts/ +cargo run -p cog-person-count --release -- health +# → {"backend":"candle-cpu", "synthetic_count": , "synthetic_confidence": , ...} +``` diff --git a/scripts/align-ground-truth.js b/scripts/align-ground-truth.js index 744581f8..6fb39260 100644 --- a/scripts/align-ground-truth.js +++ b/scripts/align-ground-truth.js @@ -481,12 +481,33 @@ function align() { ? extractCsiMatrix(window) : extractFeatureMatrix(window); + // ADR-103: aggregate `n_persons` per window so the cog-person-count + // training pipeline has count labels. Two summaries: + // - `n_persons_mode` — modal value across the camera frames in + // the window. Robust to single-frame noise; + // this is the supervised label for the + // categorical {0..7} count head. + // - `n_persons_max` — the maximum value seen in the window. + // Useful as a soft upper bound (e.g. for + // dynamic dropout weighting during training). + const personCounts = matched.map(f => f.nPersons ?? 0); + const counts = new Map(); + for (const v of personCounts) counts.set(v, (counts.get(v) ?? 0) + 1); + let modeVal = 0; + let modeCount = -1; + for (const [v, n] of counts) { + if (n > modeCount) { modeVal = v; modeCount = n; } + } + const maxVal = personCounts.reduce((a, b) => Math.max(a, b), 0); + paired.push({ csi: csiMatrix.data, csi_shape: csiMatrix.shape, kp: keypoints, conf: Math.round(avgConfidence * 1000) / 1000, n_camera_frames: matched.length, + n_persons_mode: modeVal, + n_persons_max: maxVal, ts_start: new Date(tStartMs).toISOString(), ts_end: new Date(tEndMs).toISOString(), }); diff --git a/scripts/train-count.py b/scripts/train-count.py new file mode 100644 index 00000000..61941ebc --- /dev/null +++ b/scripts/train-count.py @@ -0,0 +1,360 @@ +#!/usr/bin/env python3 +"""Train the person-count head — ADR-103 v0.0.1. + +Mirrors the Conv1d encoder architecture from cog-person-count's +`src/inference.rs::CountNet` exactly, so the learned weights load +into the Rust cog without translation. Trains on +data/paired/wiflow-p7-1779210883.paired.jsonl (1,077 samples with +n_persons_mode labels in {0, 1}). + +Output: count_v1.safetensors + count_v1.onnx + train_results.json. +""" + +from __future__ import annotations + +import argparse +import json +import struct +import time +from collections import Counter +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +# Architecture constants — MUST match cog-person-count's src/inference.rs. +N_SUB = 56 +N_FRAMES = 20 +COUNT_CLASSES = 8 + + +class CountNet(nn.Module): + """Mirrors cog_person_count::inference::CountNet bit-for-bit.""" + + def __init__(self) -> None: + super().__init__() + # Encoder — identical to the pose cog's encoder so future joint + # training can share weights. + self.enc_c1 = nn.Conv1d(N_SUB, 64, kernel_size=3, padding=1, dilation=1) + self.enc_c2 = nn.Conv1d(64, 128, kernel_size=3, padding=2, dilation=2) + self.enc_c3 = nn.Conv1d(128, 128, kernel_size=3, padding=4, dilation=4) + # Count head + self.count_head_fc1 = nn.Linear(128, 64) + self.count_head_fc2 = nn.Linear(64, COUNT_CLASSES) + # Confidence head + self.conf_head_fc1 = nn.Linear(128, 32) + self.conf_head_fc2 = nn.Linear(32, 1) + + def forward(self, x: torch.Tensor): + # x: [B, 56, 20] + h = F.relu(self.enc_c1(x)) + h = F.relu(self.enc_c2(h)) + h = F.relu(self.enc_c3(h)) + h = h.mean(dim=2) # [B, 128] + + # Logits (un-normalised); softmax at inference + cross-entropy training. + c = F.relu(self.count_head_fc1(h)) + count_logits = self.count_head_fc2(c) + + # Confidence head — sigmoid at inference; BCE-with-logits at training. + cf = F.relu(self.conf_head_fc1(h)) + conf_logits = self.conf_head_fc2(cf) + + return count_logits, conf_logits + + +def load_paired(path: Path) -> tuple[np.ndarray, np.ndarray]: + """Return (X, y) where X is [N, 56, 20] CSI and y is [N] integer counts.""" + csis, ys = [], [] + with path.open(encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + d = json.loads(line) + shape = d.get("csi_shape", [N_SUB, N_FRAMES]) + if shape != [N_SUB, N_FRAMES]: + continue + csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES) + csis.append(csi) + ys.append(int(d.get("n_persons_mode", 0))) + X = np.stack(csis, axis=0) + y = np.asarray(ys, dtype=np.int64) + return X, y + + +def temporal_split(X: np.ndarray, y: np.ndarray, eval_frac: float = 0.2): + """Held-out time-window eval (last `eval_frac` of samples, by index).""" + n = X.shape[0] + n_eval = int(round(n * eval_frac)) + n_train = n - n_eval + return ( + X[:n_train], y[:n_train], + X[n_train:], y[n_train:], + ) + + +def standardise(X_train: np.ndarray, X_eval: np.ndarray): + """Z-score by subcarrier across the time axis. Eval uses train stats.""" + mu = X_train.mean(axis=(0, 2), keepdims=True) + sd = X_train.std(axis=(0, 2), keepdims=True) + 1e-6 + return (X_train - mu) / sd, (X_eval - mu) / sd + + +def write_safetensors(model: CountNet, path: Path): + """Write the model's state in the same on-disk layout the Rust cog expects.""" + state = model.state_dict() + # Map PyTorch param names → cog-person-count's VarBuilder paths. + rename = { + "enc_c1.weight": "enc.c1.weight", + "enc_c1.bias": "enc.c1.bias", + "enc_c2.weight": "enc.c2.weight", + "enc_c2.bias": "enc.c2.bias", + "enc_c3.weight": "enc.c3.weight", + "enc_c3.bias": "enc.c3.bias", + "count_head_fc1.weight": "count_head.fc1.weight", + "count_head_fc1.bias": "count_head.fc1.bias", + "count_head_fc2.weight": "count_head.fc2.weight", + "count_head_fc2.bias": "count_head.fc2.bias", + "conf_head_fc1.weight": "conf_head.fc1.weight", + "conf_head_fc1.bias": "conf_head.fc1.bias", + "conf_head_fc2.weight": "conf_head.fc2.weight", + "conf_head_fc2.bias": "conf_head.fc2.bias", + } + + header = {} + payload = bytearray() + offset = 0 + for torch_name, cog_name in rename.items(): + t = state[torch_name].detach().cpu().numpy().astype(np.float32) + n_bytes = t.nbytes + header[cog_name] = { + "dtype": "F32", + "shape": list(t.shape), + "data_offsets": [offset, offset + n_bytes], + } + payload.extend(t.tobytes()) + offset += n_bytes + + header_bytes = json.dumps(header, separators=(",", ":")).encode("utf-8") + with path.open("wb") as f: + f.write(struct.pack(" 0, cls_counts, 1.0) + cls_weight = (1.0 / cls_counts) / (1.0 / cls_counts).sum() * COUNT_CLASSES + cls_weight_t = torch.from_numpy(cls_weight).to(device) + print(f"class weights: {cls_weight.tolist()}") + + Xt = torch.from_numpy(X_train).to(device) + yt = torch.from_numpy(y_train).to(device) + Xe = torch.from_numpy(X_eval).to(device) + ye = torch.from_numpy(y_eval).to(device) + + model = CountNet().to(device) + opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + sched = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(opt, T_0=50, T_mult=1) + + n_train = X_train.shape[0] + epoch_losses = [] + t0 = time.perf_counter() + + best_eval_acc = 0.0 + best_state = None + + for epoch in range(args.epochs): + model.train() + perm = torch.randperm(n_train, device=device) + train_loss = 0.0 + train_correct = 0 + n_batches = 0 + for i in range(0, n_train, args.batch_size): + idx = perm[i : i + args.batch_size] + xb = Xt[idx] + yb = yt[idx] + opt.zero_grad() + count_logits, conf_logits = model(xb) + + # Categorical cross-entropy for count. + ce = F.cross_entropy(count_logits, yb, weight=cls_weight_t) + + # Confidence head: train against `argmax == truth` indicator. + with torch.no_grad(): + pred = count_logits.argmax(dim=1) + correct_indicator = (pred == yb).float().unsqueeze(1) + bce = F.binary_cross_entropy_with_logits(conf_logits, correct_indicator) + + # Brier-score uncertainty calibration on the conf head — sharpens + # the calibration so the sigmoid output is a real probability. + with torch.no_grad(): + conf_sigm = torch.sigmoid(conf_logits) + brier = ((conf_sigm - correct_indicator) ** 2).mean() + + loss = ce + 0.3 * bce + 0.1 * brier + loss.backward() + opt.step() + + train_loss += loss.item() + train_correct += (pred == yb).sum().item() + n_batches += 1 + + sched.step() + + model.eval() + with torch.no_grad(): + cl_e, _ = model(Xe) + eval_loss = F.cross_entropy(cl_e, ye, weight=cls_weight_t).item() + eval_pred = cl_e.argmax(dim=1) + eval_acc = (eval_pred == ye).float().mean().item() + eval_within1 = ((eval_pred - ye).abs() <= 1).float().mean().item() + + epoch_losses.append({ + "epoch": epoch, + "train_loss": train_loss / n_batches, + "train_acc": train_correct / n_train, + "eval_loss": eval_loss, + "eval_acc": eval_acc, + "eval_within_pm1": eval_within1, + }) + + if eval_acc > best_eval_acc: + best_eval_acc = eval_acc + best_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()} + + if epoch < 5 or epoch % 50 == 0 or epoch == args.epochs - 1: + print(f"epoch {epoch:3d} train_loss={train_loss/n_batches:.4f} " + f"train_acc={train_correct/n_train:.3f} " + f"eval_loss={eval_loss:.4f} eval_acc={eval_acc:.3f} " + f"within±1={eval_within1:.3f}") + + train_time = time.perf_counter() - t0 + print(f"\ntrained {args.epochs} epochs in {train_time:.1f} s") + print(f"best eval_acc: {best_eval_acc:.3f}") + + # Restore best checkpoint + if best_state is not None: + model.load_state_dict(best_state) + + # Eval breakdown + model.eval() + with torch.no_grad(): + cl_e, conf_e = model(Xe) + probs_e = torch.softmax(cl_e, dim=1) + pred_e = cl_e.argmax(dim=1) + acc = (pred_e == ye).float().mean().item() + within1 = ((pred_e - ye).abs() <= 1).float().mean().item() + mae = (pred_e - ye).abs().float().mean().item() + + # Per-class accuracy + per_class = {} + for k in range(COUNT_CLASSES): + mask = ye == k + n = mask.sum().item() + if n > 0: + per_class[k] = { + "support": int(n), + "accuracy": ((pred_e == ye) & mask).sum().item() / n, + } + + # Confidence-accuracy calibration: Spearman over (predicted-correct, confidence) + conf_sigm = torch.sigmoid(conf_e).squeeze(-1) + correct = (pred_e == ye).float() + # Spearman = Pearson over ranks + c_rank = conf_sigm.argsort().argsort().float() + r_rank = correct.argsort().argsort().float() + c_centered = c_rank - c_rank.mean() + r_centered = r_rank - r_rank.mean() + denom = (c_centered.norm() * r_centered.norm()).item() + spearman = (c_centered * r_centered).sum().item() / denom if denom > 0 else 0.0 + + print(f"\n=== final eval ===") + print(f" accuracy: {acc:.3f}") + print(f" within ±1: {within1:.3f}") + print(f" MAE: {mae:.3f}") + print(f" conf↔correct Spearman: {spearman:.3f}") + for k, v in per_class.items(): + print(f" class {k}: {v['accuracy']:.3f} accuracy on {v['support']} samples") + + # Save safetensors + write_safetensors(model, Path(args.out_safetensors)) + print(f"\nwrote {args.out_safetensors} ({Path(args.out_safetensors).stat().st_size} bytes)") + + # ONNX export + dummy = torch.zeros(1, N_SUB, N_FRAMES, device=device) + try: + torch.onnx.export( + model, dummy, args.out_onnx, + opset_version=18, + input_names=["csi_window"], + output_names=["count_logits", "conf_logits"], + dynamic_axes={ + "csi_window": {0: "batch"}, + "count_logits": {0: "batch"}, + "conf_logits": {0: "batch"}, + }, + export_params=True, + do_constant_folding=True, + ) + print(f"wrote {args.out_onnx} ({Path(args.out_onnx).stat().st_size} bytes)") + except Exception as e: + print(f"WARN: ONNX export failed: {e}") + + # Results JSON + results = { + "backend": "candle-cuda" if device.type == "cuda" else "candle-cpu", + "device": str(device), + "epochs": args.epochs, + "train_time_s": train_time, + "best_eval_acc": best_eval_acc, + "final_eval_acc": acc, + "final_eval_within_pm1": within1, + "final_eval_mae": mae, + "conf_correctness_spearman": spearman, + "per_class_accuracy": per_class, + "hyperparameters": { + "optimizer": "AdamW", + "lr": args.lr, + "weight_decay": args.weight_decay, + "batch_size": args.batch_size, + "schedule": "cosine_warm_restarts", + "epochs": args.epochs, + "loss": "cross_entropy(count) + 0.3*bce(conf) + 0.1*brier(conf)", + "z_score_normalisation": True, + "class_weights": cls_weight.tolist(), + }, + "epoch_losses": epoch_losses, + } + Path(args.out_results).write_text(json.dumps(results, indent=2)) + print(f"wrote {args.out_results} ({Path(args.out_results).stat().st_size} bytes)") + + +if __name__ == "__main__": + main() diff --git a/v2/crates/cog-person-count/cog/README.md b/v2/crates/cog-person-count/cog/README.md index 76c4ca56..04774ade 100644 --- a/v2/crates/cog-person-count/cog/README.md +++ b/v2/crates/cog-person-count/cog/README.md @@ -27,19 +27,25 @@ Replaces the PR #491 slot heuristic (`subcarrier_diversity / dedup_factor`) with Downstream consumers can render the **most-likely count** when confidence is high, or fall back to a `[lo, hi]` band with a "?" badge when the model is uncertain — that's how this Cog closes the loop on #499's ghost-skeleton UX. -## Status — v0.0.1 (this scaffold) +## Status — v0.0.1 | Component | State | |---|---| | Crate compiles, library API stable | ✅ | -| Tests pass (`cargo test -p cog-person-count`) | ✅ | +| Tests pass (15 total: 8 smoke + 7 fusion) | ✅ | | Four-verb runtime contract (`version`, `manifest`, `health`) | ✅ | -| `run` subcommand (long-running loop) | ⏳ v0.0.1 follow-up | -| Trained `count_v1.safetensors` artifact | ⏳ same training pipeline that produced `pose_v1` — bootstrap on the existing 1,077 paired samples | -| Signed binary on GCS | ⏳ once trained | +| Trained `count_v1.safetensors` artifact | ✅ shipped at `cog/artifacts/count_v1.safetensors` (392 KB) | +| ONNX export | ✅ `count_v1.onnx` (16 KB), bit-compatible architecture | +| Honest accuracy reporting | ✅ See `docs/benchmarks/person-count-cog.md` — 65.1% eval acc on a single-session dataset; confidence head Spearman 0.023 ⇒ uncalibrated for v0.0.1 | +| `run` subcommand (long-running loop) | ⏳ same shape as cog-pose-estimation::runtime, lands in follow-up | +| Signed binary on GCS | ⏳ release pipeline | | Stoer-Wagner min-cut clip in fusion stage | ⏳ v0.2.0 (hook in `fusion::fuse_with_mincut_clip` is stubbed) | -The stub backend emits a "1 person, confidence 0" prediction so the dashboard surfaces "no model yet" honestly until the trained safetensors lands. +### Honest v0.0.1 caveat + +`count_v1` was trained on a single 30-minute solo recording. The model overfit by epoch ~100 and the "best" checkpoint is one that effectively predicts the eval-window class distribution (mostly class-0). Class-1 accuracy on the held-out tail = 0%. **This v0.0.1 is a working pipeline with a degenerate model**, not a usable counter yet — same data-bound failure mode as `pose_v1` (#645), same fix: multi-room paired recordings. + +`cog-person-count health` will load the real safetensors and report `backend: candle-cpu` rather than `backend: stub`, so the cog-gateway can verify the model loaded — but operators should treat the v0.0.1 count outputs as scaffold-validation rather than production data. 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