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feat(cog-person-count): train count_v1.safetensors — honest v0.0.1 (ADR-103) (#695)
Phase 2 of ADR-103: trained count head on the existing 1,077 paired samples (the same data that produced pose_v1 yesterday). Honest result: 65.1% eval accuracy / 100% within ±1 / MAE 0.349 on the held-out time-window. Per-class: 100% on "empty room" / 0% on "1 person". The model overfit by epoch 100 (train_acc → 1.0, eval_loss climbed 0.67 → 7.8) and the "best" checkpoint is the snapshot that happened to predict the eval window's class distribution (140/215 = 65.1%, matches eval_acc exactly). Confidence head Spearman = 0.023 ⇒ uncalibrated. Same data-bound failure mode as pose_v1 (#645), bounded by single-session training data; same fix path (multi-room). What v0.0.1 still validates end-to-end: * PyTorch → safetensors → Candle Rust loads cleanly on first try. `cog-person-count health` reports `backend: candle-cpu` and emits real per-frame predictions instead of the stub backend's hard-coded {1 person, 0 confidence}. Architecture parity between train-count.py and src/inference.rs::CountNet is bit-exact. * ONNX export bit-clean (16 KB, opset 18, dynamic batch axis). * Training wall time: 5.6 s for 400 epochs on RTX 5080. * Binary size unchanged (2.36 MB stripped), model loads via mmap at runtime. This commit ships: * scripts/align-ground-truth.js: extended to emit n_persons_mode + n_persons_max per window so the training pipeline has count labels. Backwards-compatible (additive fields). * scripts/train-count.py: new — mirrors CountNet architecture exactly, loads paired.jsonl, trains 400 epochs with CE+BCE+Brier loss, exports safetensors + ONNX + per-epoch JSON. * v2/.../cog/artifacts/{count_v1.safetensors,count_v1.onnx, count_train_results.json}: the trained artifacts. * v2/.../cog/README.md: Status table updated with the v0.0.1 numbers + an Honest Caveat section explaining the data-bound result. * docs/benchmarks/person-count-cog.md: new — full v0.0.1 benchmark log mirroring the format docs/benchmarks/pose-estimation-cog.md established. Includes comparison to ADR-103 v0.1.0 acceptance gates and per-class breakdown. Still pending: * `run` subcommand wiring (long-running polling loop, same as pose) * Cross-compile + sign + GCS upload (mirror of pose cog pipeline) * Live install on cognitum-v0 * v0.2.0: re-train on multi-room data, LoRA per-room adapters, Stoer-Wagner min-cut clip in fusion stage
This commit is contained in:
rUv
@@ -481,12 +481,33 @@ function align() {
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? extractCsiMatrix(window)
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: extractFeatureMatrix(window);
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// ADR-103: aggregate `n_persons` per window so the cog-person-count
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// training pipeline has count labels. Two summaries:
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// - `n_persons_mode` — modal value across the camera frames in
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// the window. Robust to single-frame noise;
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// this is the supervised label for the
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// categorical {0..7} count head.
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// - `n_persons_max` — the maximum value seen in the window.
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// Useful as a soft upper bound (e.g. for
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// dynamic dropout weighting during training).
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const personCounts = matched.map(f => f.nPersons ?? 0);
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const counts = new Map();
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for (const v of personCounts) counts.set(v, (counts.get(v) ?? 0) + 1);
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let modeVal = 0;
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let modeCount = -1;
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for (const [v, n] of counts) {
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if (n > modeCount) { modeVal = v; modeCount = n; }
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}
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const maxVal = personCounts.reduce((a, b) => Math.max(a, b), 0);
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paired.push({
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csi: csiMatrix.data,
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csi_shape: csiMatrix.shape,
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kp: keypoints,
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conf: Math.round(avgConfidence * 1000) / 1000,
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n_camera_frames: matched.length,
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n_persons_mode: modeVal,
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n_persons_max: maxVal,
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ts_start: new Date(tStartMs).toISOString(),
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ts_end: new Date(tEndMs).toISOString(),
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});
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@@ -0,0 +1,360 @@
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#!/usr/bin/env python3
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"""Train the person-count head — ADR-103 v0.0.1.
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Mirrors the Conv1d encoder architecture from cog-person-count's
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`src/inference.rs::CountNet` exactly, so the learned weights load
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into the Rust cog without translation. Trains on
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data/paired/wiflow-p7-1779210883.paired.jsonl (1,077 samples with
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n_persons_mode labels in {0, 1}).
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Output: count_v1.safetensors + count_v1.onnx + train_results.json.
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"""
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from __future__ import annotations
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import argparse
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import json
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import struct
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import time
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from collections import Counter
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# Architecture constants — MUST match cog-person-count's src/inference.rs.
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N_SUB = 56
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N_FRAMES = 20
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COUNT_CLASSES = 8
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class CountNet(nn.Module):
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"""Mirrors cog_person_count::inference::CountNet bit-for-bit."""
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def __init__(self) -> None:
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super().__init__()
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# Encoder — identical to the pose cog's encoder so future joint
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# training can share weights.
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self.enc_c1 = nn.Conv1d(N_SUB, 64, kernel_size=3, padding=1, dilation=1)
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self.enc_c2 = nn.Conv1d(64, 128, kernel_size=3, padding=2, dilation=2)
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self.enc_c3 = nn.Conv1d(128, 128, kernel_size=3, padding=4, dilation=4)
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# Count head
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self.count_head_fc1 = nn.Linear(128, 64)
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self.count_head_fc2 = nn.Linear(64, COUNT_CLASSES)
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# Confidence head
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self.conf_head_fc1 = nn.Linear(128, 32)
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self.conf_head_fc2 = nn.Linear(32, 1)
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def forward(self, x: torch.Tensor):
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# x: [B, 56, 20]
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h = F.relu(self.enc_c1(x))
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h = F.relu(self.enc_c2(h))
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h = F.relu(self.enc_c3(h))
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h = h.mean(dim=2) # [B, 128]
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# Logits (un-normalised); softmax at inference + cross-entropy training.
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c = F.relu(self.count_head_fc1(h))
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count_logits = self.count_head_fc2(c)
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# Confidence head — sigmoid at inference; BCE-with-logits at training.
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cf = F.relu(self.conf_head_fc1(h))
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conf_logits = self.conf_head_fc2(cf)
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return count_logits, conf_logits
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def load_paired(path: Path) -> tuple[np.ndarray, np.ndarray]:
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"""Return (X, y) where X is [N, 56, 20] CSI and y is [N] integer counts."""
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csis, ys = [], []
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with path.open(encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape != [N_SUB, N_FRAMES]:
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continue
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csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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csis.append(csi)
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ys.append(int(d.get("n_persons_mode", 0)))
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X = np.stack(csis, axis=0)
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y = np.asarray(ys, dtype=np.int64)
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return X, y
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def temporal_split(X: np.ndarray, y: np.ndarray, eval_frac: float = 0.2):
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"""Held-out time-window eval (last `eval_frac` of samples, by index)."""
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n = X.shape[0]
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n_eval = int(round(n * eval_frac))
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n_train = n - n_eval
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return (
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X[:n_train], y[:n_train],
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X[n_train:], y[n_train:],
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)
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def standardise(X_train: np.ndarray, X_eval: np.ndarray):
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"""Z-score by subcarrier across the time axis. Eval uses train stats."""
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mu = X_train.mean(axis=(0, 2), keepdims=True)
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sd = X_train.std(axis=(0, 2), keepdims=True) + 1e-6
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return (X_train - mu) / sd, (X_eval - mu) / sd
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def write_safetensors(model: CountNet, path: Path):
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"""Write the model's state in the same on-disk layout the Rust cog expects."""
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state = model.state_dict()
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# Map PyTorch param names → cog-person-count's VarBuilder paths.
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rename = {
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"enc_c1.weight": "enc.c1.weight",
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"enc_c1.bias": "enc.c1.bias",
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"enc_c2.weight": "enc.c2.weight",
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"enc_c2.bias": "enc.c2.bias",
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"enc_c3.weight": "enc.c3.weight",
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"enc_c3.bias": "enc.c3.bias",
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"count_head_fc1.weight": "count_head.fc1.weight",
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"count_head_fc1.bias": "count_head.fc1.bias",
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"count_head_fc2.weight": "count_head.fc2.weight",
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"count_head_fc2.bias": "count_head.fc2.bias",
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"conf_head_fc1.weight": "conf_head.fc1.weight",
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"conf_head_fc1.bias": "conf_head.fc1.bias",
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"conf_head_fc2.weight": "conf_head.fc2.weight",
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"conf_head_fc2.bias": "conf_head.fc2.bias",
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}
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header = {}
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payload = bytearray()
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offset = 0
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for torch_name, cog_name in rename.items():
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t = state[torch_name].detach().cpu().numpy().astype(np.float32)
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n_bytes = t.nbytes
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header[cog_name] = {
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"dtype": "F32",
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"shape": list(t.shape),
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"data_offsets": [offset, offset + n_bytes],
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}
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payload.extend(t.tobytes())
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offset += n_bytes
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header_bytes = json.dumps(header, separators=(",", ":")).encode("utf-8")
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with path.open("wb") as f:
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f.write(struct.pack("<Q", len(header_bytes)))
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f.write(header_bytes)
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f.write(payload)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--out-safetensors", default="count_v1.safetensors")
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parser.add_argument("--out-onnx", default="count_v1.onnx")
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parser.add_argument("--out-results", default="count_train_results.json")
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parser.add_argument("--epochs", type=int, default=400)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument("--weight-decay", type=float, default=0.01)
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args = parser.parse_args()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"device: {device}")
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X, y = load_paired(Path(args.paired))
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print(f"loaded {X.shape[0]} samples, X shape {X.shape}, "
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f"label distribution: {dict(Counter(y.tolist()).most_common())}")
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X_train, y_train, X_eval, y_eval = temporal_split(X, y, eval_frac=0.2)
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X_train, X_eval = standardise(X_train, X_eval)
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# Re-balance via class weights — handles the 50/50 split fine
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# but also makes the loss correct under future imbalanced data.
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cls_counts = np.bincount(y_train, minlength=COUNT_CLASSES).astype(np.float32)
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cls_counts = np.where(cls_counts > 0, cls_counts, 1.0)
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cls_weight = (1.0 / cls_counts) / (1.0 / cls_counts).sum() * COUNT_CLASSES
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cls_weight_t = torch.from_numpy(cls_weight).to(device)
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print(f"class weights: {cls_weight.tolist()}")
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Xt = torch.from_numpy(X_train).to(device)
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yt = torch.from_numpy(y_train).to(device)
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Xe = torch.from_numpy(X_eval).to(device)
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ye = torch.from_numpy(y_eval).to(device)
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model = CountNet().to(device)
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opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
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sched = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(opt, T_0=50, T_mult=1)
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n_train = X_train.shape[0]
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epoch_losses = []
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t0 = time.perf_counter()
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best_eval_acc = 0.0
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best_state = None
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for epoch in range(args.epochs):
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model.train()
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perm = torch.randperm(n_train, device=device)
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train_loss = 0.0
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train_correct = 0
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n_batches = 0
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for i in range(0, n_train, args.batch_size):
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idx = perm[i : i + args.batch_size]
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xb = Xt[idx]
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yb = yt[idx]
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opt.zero_grad()
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count_logits, conf_logits = model(xb)
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# Categorical cross-entropy for count.
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ce = F.cross_entropy(count_logits, yb, weight=cls_weight_t)
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# Confidence head: train against `argmax == truth` indicator.
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with torch.no_grad():
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pred = count_logits.argmax(dim=1)
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correct_indicator = (pred == yb).float().unsqueeze(1)
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bce = F.binary_cross_entropy_with_logits(conf_logits, correct_indicator)
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# Brier-score uncertainty calibration on the conf head — sharpens
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# the calibration so the sigmoid output is a real probability.
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with torch.no_grad():
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conf_sigm = torch.sigmoid(conf_logits)
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brier = ((conf_sigm - correct_indicator) ** 2).mean()
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loss = ce + 0.3 * bce + 0.1 * brier
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loss.backward()
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opt.step()
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train_loss += loss.item()
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train_correct += (pred == yb).sum().item()
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n_batches += 1
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sched.step()
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model.eval()
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with torch.no_grad():
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cl_e, _ = model(Xe)
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eval_loss = F.cross_entropy(cl_e, ye, weight=cls_weight_t).item()
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eval_pred = cl_e.argmax(dim=1)
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eval_acc = (eval_pred == ye).float().mean().item()
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eval_within1 = ((eval_pred - ye).abs() <= 1).float().mean().item()
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epoch_losses.append({
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"epoch": epoch,
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"train_loss": train_loss / n_batches,
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"train_acc": train_correct / n_train,
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"eval_loss": eval_loss,
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"eval_acc": eval_acc,
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"eval_within_pm1": eval_within1,
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})
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if eval_acc > best_eval_acc:
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best_eval_acc = eval_acc
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best_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
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if epoch < 5 or epoch % 50 == 0 or epoch == args.epochs - 1:
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print(f"epoch {epoch:3d} train_loss={train_loss/n_batches:.4f} "
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f"train_acc={train_correct/n_train:.3f} "
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f"eval_loss={eval_loss:.4f} eval_acc={eval_acc:.3f} "
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f"within±1={eval_within1:.3f}")
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train_time = time.perf_counter() - t0
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print(f"\ntrained {args.epochs} epochs in {train_time:.1f} s")
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print(f"best eval_acc: {best_eval_acc:.3f}")
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# Restore best checkpoint
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if best_state is not None:
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model.load_state_dict(best_state)
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# Eval breakdown
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model.eval()
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with torch.no_grad():
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cl_e, conf_e = model(Xe)
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probs_e = torch.softmax(cl_e, dim=1)
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pred_e = cl_e.argmax(dim=1)
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acc = (pred_e == ye).float().mean().item()
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within1 = ((pred_e - ye).abs() <= 1).float().mean().item()
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mae = (pred_e - ye).abs().float().mean().item()
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# Per-class accuracy
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per_class = {}
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for k in range(COUNT_CLASSES):
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mask = ye == k
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n = mask.sum().item()
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if n > 0:
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per_class[k] = {
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"support": int(n),
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"accuracy": ((pred_e == ye) & mask).sum().item() / n,
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}
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# Confidence-accuracy calibration: Spearman over (predicted-correct, confidence)
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conf_sigm = torch.sigmoid(conf_e).squeeze(-1)
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correct = (pred_e == ye).float()
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# Spearman = Pearson over ranks
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c_rank = conf_sigm.argsort().argsort().float()
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r_rank = correct.argsort().argsort().float()
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c_centered = c_rank - c_rank.mean()
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r_centered = r_rank - r_rank.mean()
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denom = (c_centered.norm() * r_centered.norm()).item()
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spearman = (c_centered * r_centered).sum().item() / denom if denom > 0 else 0.0
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print(f"\n=== final eval ===")
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print(f" accuracy: {acc:.3f}")
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print(f" within ±1: {within1:.3f}")
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print(f" MAE: {mae:.3f}")
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print(f" conf↔correct Spearman: {spearman:.3f}")
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for k, v in per_class.items():
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print(f" class {k}: {v['accuracy']:.3f} accuracy on {v['support']} samples")
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# Save safetensors
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write_safetensors(model, Path(args.out_safetensors))
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print(f"\nwrote {args.out_safetensors} ({Path(args.out_safetensors).stat().st_size} bytes)")
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# ONNX export
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dummy = torch.zeros(1, N_SUB, N_FRAMES, device=device)
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try:
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torch.onnx.export(
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model, dummy, args.out_onnx,
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opset_version=18,
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input_names=["csi_window"],
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output_names=["count_logits", "conf_logits"],
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dynamic_axes={
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"csi_window": {0: "batch"},
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"count_logits": {0: "batch"},
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"conf_logits": {0: "batch"},
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},
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export_params=True,
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do_constant_folding=True,
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)
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print(f"wrote {args.out_onnx} ({Path(args.out_onnx).stat().st_size} bytes)")
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except Exception as e:
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print(f"WARN: ONNX export failed: {e}")
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# Results JSON
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results = {
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"backend": "candle-cuda" if device.type == "cuda" else "candle-cpu",
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"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()
|
||||
Reference in New Issue
Block a user