Hark
2a0a2e55ad
- M1 数据生成器：采样/老师/距离下界认证/时间律/统计/一致性门禁；确定性老师（碰撞次数计时 + resetSession）、pos 任务冻结 J6、--consensus 邻域共识过滤、Dr 类。 - 全流程代码与运行手册：数据打包/可微运动学与对拍/MeanFlow+WTA 模型/闭环仿真/APG/挖错循环/ONNX 导出；webapp 策略运行时 ik-policy.js、?policy=1 接入、tests/policy_bench.cjs；ik-solver 增 pairList/clusterExport/criticalPairs/pairGap/leverTable。
52 lines
3.4 KiB
Bash
Executable File
52 lines
3.4 KiB
Bash
Executable File
#!/usr/bin/env bash
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# 一条龙：bash ml/pipeline.sh <阶段> ；阶段按顺序：
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# setup 安装 Python/Node 依赖（torch 需先按 CUDA 版本装好）
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# kin 导出运动学/碰撞模型 + JS↔PyTorch 对拍（必须 PASS）
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# gate1 老师一致性门禁（决定生成时是否加 --consensus 4）
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# gen 生成 5 万条老师轨迹（NPROC 进程；CONSENSUS=1 时加共识过滤）
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# prep 打包训练样本
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# train 预训练（先 2000 步摸吞吐，再正式训练）
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# m5 MeanFlow 头门禁：贴立柱集关安全层穿柱率 ≤ 2%（不过 → 用 configs/wta.json 重训）
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# apg APG 首轮微调
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# mine 挖错循环（3–6 轮，含门禁回滚）
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# export 导出 ONNX（fp16）到 webapp/models/
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# bench 部署形态评测（Node，策略 + 安全层）
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# all 依次执行 kin → bench
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set -e
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cd "$(dirname "$0")/.."
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NPROC=${NPROC:-$(nproc)}
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N_TASKS=${N_TASKS:-50000}
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CFG=${CFG:-ml/configs/base.json}
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stage=${1:-help}
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run() { echo "== $*"; "$@"; }
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case $stage in
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setup) run pip install -r ml/requirements.txt; (cd ml && npm install --no-audit --no-fund) ;;
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kin) run node ml/prep/export_kin.cjs; run node ml/eval/dump_parity.cjs 2000; run python -m ml.eval.check_parity ;;
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gate1) mkdir -p ml/data; run node ml/gen/consistency.cjs 300 4242 | tee ml/data/consistency.json ;;
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gen) per=$(( (N_TASKS + NPROC - 1) / NPROC ))
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if [ -z "${CONSENSUS:-}" ] && [ -f ml/data/consistency.json ]; then
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CONSENSUS=$(python -c "import json;print(1 if json.load(open('ml/data/consistency.json'))['consistentByGateRule']<0.9 else 0)")
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echo "一致性门禁结果 → CONSENSUS=$CONSENSUS"
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fi
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extra=""; [ "${CONSENSUS:-0}" = "1" ] && extra="--consensus 4"
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run bash ml/gen/run_parallel.sh $per $NPROC 1 "$extra" ;;
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prep) run bash ml/prep/run_prep.sh $NPROC ;;
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train) run python -m ml.train --samples ml/data/samples --out ml/runs/bench --steps 2000 --no-eval --config $CFG
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run python -m ml.train --samples ml/data/samples --out ml/runs/base --config $CFG ;;
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m5) [ -f ml/runs/nc_gate.jsonl ] || run python -m ml.mining.mine_round --make-gate 1000 --seed 4321 --mix 0,0,1,0 --name nc_gate.jsonl --out ml/runs
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run python -m ml.sim.closed_loop --ckpt ml/runs/base/best.pt --tasks ml/runs/nc_gate.jsonl --n 1000 --out ml/runs/m5_gate.json
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python - <<'PY'
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import json; r = json.load(open('ml/runs/m5_gate.json'))
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print('贴立柱集（关安全层）穿柱率 %.2f%%，到达率 %.2f%%' % (100 * r['collision_rate'], 100 * r['success']))
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print('M5 门禁：' + ('通过' if r['collision_rate'] <= 0.02 else '不通过 → CFG=ml/configs/wta.json bash ml/pipeline.sh train'))
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PY
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;;
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apg) run python -m ml.apg.apg_finetune --init ml/runs/base/best.pt --samples ml/data/samples --out ml/runs/apg; cp ml/runs/apg/best.pt ml/runs/current.pt ;;
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mine) run bash ml/mining/loop.sh ${ROUNDS:-6} ${APG_STEPS:-1000} ${MINE_N:-50000} ;;
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export) ck=ml/runs/current.pt; [ -f $ck ] || ck=ml/runs/base/best.pt
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run python -m ml.export_onnx --ckpt $ck --out webapp/models/traj_policy.onnx --fp16 ;;
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bench) run node tests/policy_bench.cjs webapp/models/traj_policy.onnx nc,route --nc1000 --out ml/runs/policy_bench.json ;;
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all) for s in kin gate1 gen prep train m5 apg mine export bench; do bash ml/pipeline.sh $s; done ;;
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*) sed -n '2,16p' "$0" ;;
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esac
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