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- 标注：AI 预标注直写 labels_json（去候选确认两阶段）；重叠去重（minIoU）；全量标注按钮 - 训练：脚本迁移入 server/training/（Go 化 prepare_yolo/analyze_rfdetr，保留 train_server.py）；tflite 产物自检并入训练流程（check_tflite） - 数据目录/权重不进 git；.gitignore 迁移至仓库根
314 lines
12 KiB
Python
314 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""训练任务脚本（Go 后端 --task-json 驱动，产物契约见 server/common/training_runner.go）。
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用法：
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python train_server.py --task-json tasks/<taskId>.json
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任务参数（Go 侧写入，字段相对训练机 workdir）：
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workdir 训练机工作目录（脚本 / yolov8n.pt / venv 所在），启动即 chdir
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yolo 训练集目录（含 dataset.yaml），相对 workdir
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imgsz 训练/导出分辨率（默认 704，与端侧推理对齐）
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epochs 训练轮数
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batch 批大小
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device GPU 编号或 cpu
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project 训练输出目录（相对 workdir，末尾自动拼 name）
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log_file 每 epoch 一行 JSON 的进度日志（相对 workdir）
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result_file 结束结果 JSON（相对 workdir）
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artifact_zip 产物打包（best.pt + results.csv + 曲线，相对 workdir）
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产物契约：
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log_file {"epoch":1,"total":150,"metrics":{"metrics/mAP50(B)":0.87,...}}
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result_file {"metrics":{...},"names":["pheasant","suspect"],"best_tflite":"runs/tasks/<id>/weights/best.tflite",
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"tflite_check":{"ok":true,"reason":"","inputs":[...],"outputs":[...]}}
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result_file 存在 = 训练完成；异常时写 {"error":"..."}，Go 侧据以置失败并展示原因。
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tflite_check.ok=false（产物 shape 异常）时 Go 侧置训练失败并带出 reason。
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"""
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import argparse
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import json
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import math
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import os
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import struct
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import sys
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import traceback
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import zipfile
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from pathlib import Path
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# 抗尺度漂移（与人工训练基线一致）：随机缩放输入 0.5~1.5x
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MULTI_SCALE = 0.5
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# 早停耐心（连续 N 轮无提升即停）
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PATIENCE = 30
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def on_fit_epoch_end(trainer):
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"""ultralytics 回调：每 epoch 结束写一行进度 JSON（行缓冲，进程被杀不丢行）。"""
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metrics = {}
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for k, v in (trainer.metrics or {}).items():
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if isinstance(v, (int, float)) and math.isfinite(v):
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metrics[k] = round(float(v), 5)
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if _LOG is None:
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return
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total = getattr(trainer, "epochs", None) or getattr(trainer, "total_epochs", 0) or 0
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_LOG.write(json.dumps({
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"epoch": trainer.epoch + 1,
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"total": total,
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"metrics": metrics,
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}, ensure_ascii=False) + "\n")
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def register_callback():
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from ultralytics.utils.callbacks import callbacks
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callbacks["on_fit_epoch_end"].append(on_fit_epoch_end)
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def write_result(result_file, payload):
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Path(result_file).parent.mkdir(parents=True, exist_ok=True)
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# tmp + rename 原子写，避免 Go 侧读到半截文件
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tmp = result_file + ".tmp"
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with open(tmp, "w", encoding="utf-8") as f:
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json.dump(payload, f, ensure_ascii=False, indent=2)
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os.replace(tmp, result_file)
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def read_names(yolo_dir):
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"""从 data.yaml 读类别名（有序列表，ultralytics 依赖 pyyaml）"""
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try:
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import yaml
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with open(Path(yolo_dir) / "dataset.yaml", encoding="utf-8") as f:
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cfg = yaml.safe_load(f)
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names = cfg.get("names") or {}
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if isinstance(names, dict):
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return [names[k] for k in sorted(names, key=lambda k: int(k))]
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return list(names)
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except Exception:
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return []
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def find_best_tflite(save_dir):
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"""定位导出的 tflite（ultralytics 各版本产物位置不一）：
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优先 fp32 best.tflite（与人工基线一致），再 float32/int8，最后兜底全局搜。"""
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weights = save_dir / "weights"
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for name in ("best.tflite", "best_float32.tflite", "best_int8.tflite"):
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p = weights / name
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if p.exists():
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return p
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candidates = sorted(weights.rglob("*.tflite"))
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if candidates:
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return candidates[0]
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return None
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# TFLite flatbuffer 张量类型（tensorflow/lite/schema/schema.fbs，字段顺序敏感）
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TENSOR_TYPE = {0: "FLOAT32", 1: "FLOAT16", 2: "INT32", 3: "UINT8", 4: "INT64",
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5: "STRING", 6: "BOOL", 7: "INT16", 8: "COMPLEX64", 9: "INT8",
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10: "FLOAT64", 11: "COMPLEX128", 12: "UINT64", 13: "RESOURCE",
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14: "VARIANT", 15: "UINT32", 16: "UINT16", 17: "INT4",
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18: "BFLOAT16", 19: "FLOAT8_E4M3FN", 20: "FLOAT8_E4M3FNUZ",
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21: "FLOAT8_E5M2", 22: "FLOAT8_E5M2FNUZ"}
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class _FB:
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"""极简 flatbuffer 读取器（仅 Model/SubGraph/Tensor 表所需字段）"""
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def __init__(self, data):
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self.d = data
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def u32(self, pos): return struct.unpack_from("<I", self.d, pos)[0]
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def i32(self, pos): return struct.unpack_from("<i", self.d, pos)[0]
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def u16(self, pos): return struct.unpack_from("<H", self.d, pos)[0]
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def field(self, t, i):
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vto = self.i32(t)
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vt = t - vto
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vs = self.u16(vt)
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off = vt + 4 + 2 * i
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if off + 2 > vt + vs:
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return None
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f = self.u16(off)
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return None if f == 0 else t + f
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def deref(self, pos):
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return pos + self.u32(pos) if pos is not None else None
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def vec(self, pos):
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pos = self.deref(pos)
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if pos is None:
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return None
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return pos + 4, self.u32(pos)
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def vec_table(self, pos):
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start, n = self.vec(pos)
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return [start + i * 4 + self.u32(start + i * 4) for i in range(n)]
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def string(self, pos):
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pos = self.deref(pos)
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if pos is None:
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return None
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n = self.u32(pos)
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return self.d[pos + 4:pos + 4 + n].decode("utf-8", "replace")
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def int_vec(self, pos):
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start, n = self.vec(pos)
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return [self.i32(start + 4 * i) for i in range(n)]
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def check_tflite(path, imgsz):
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"""TFLite 产物自检（原 inspect_tflite.py，2026-08-26 并入）：
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输入恰 1 张且 4 维、元素总数 == imgsz²×3（NCHW/NHWC 皆可）、输出 ≥ 1 张且 batch 维 = 1。
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返回 {"ok","reason","inputs":[{"name","shape","type"}],"outputs":[...]}。"""
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try:
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with open(path, "rb") as f:
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fb = _FB(f.read())
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root = fb.u32(0) # Model 表
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subgraphs = fb.vec_table(fb.field(root, 2)) # Model.subgraphs [2]
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sg = subgraphs[0]
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tensors = fb.vec_table(fb.field(sg, 0)) # SubGraph.tensors [0]
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inputs = fb.int_vec(fb.field(sg, 1)) # SubGraph.inputs [1]
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outputs = fb.int_vec(fb.field(sg, 2)) # SubGraph.outputs [2]
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def describe(i):
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t = tensors[i]
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shape = fb.int_vec(fb.field(t, 0))
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# type 有默认值 FLOAT32(=0)：字段等于默认值时 flatbuffer 省略（vtable 偏移 0）
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ttype_f = fb.field(t, 1)
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ttype = fb.d[ttype_f] if ttype_f else 0
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return {"name": fb.string(fb.field(t, 3)) or "?",
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"shape": shape,
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"type": TENSOR_TYPE.get(ttype, ttype)}
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in_desc = [describe(i) for i in inputs]
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out_desc = [describe(i) for i in outputs]
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problems = []
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if len(in_desc) != 1:
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problems.append(f"输入张量 {len(in_desc)} 个（期望 1）")
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else:
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shape = in_desc[0]["shape"]
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n = 1
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for d in shape:
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n *= d
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if len(shape) != 4:
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problems.append(f"输入 shape {shape} 非 4 维")
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elif n != imgsz * imgsz * 3:
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problems.append(f"输入元素数 {n} ≠ imgsz²×3={imgsz * imgsz * 3}（shape {shape}，imgsz={imgsz}）")
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if not out_desc:
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problems.append("无输出张量")
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else:
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for o in out_desc:
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if o["shape"] and o["shape"][0] != 1:
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problems.append(f"输出 batch 维 ≠ 1: {o['shape']}")
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return {"ok": not problems, "reason": "；".join(problems),
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"inputs": in_desc, "outputs": out_desc}
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except Exception as e:
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return {"ok": False, "reason": f"解析失败: {e}", "inputs": [], "outputs": []}
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def build_artifact_zip(zip_path, save_dir):
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Path(zip_path).parent.mkdir(parents=True, exist_ok=True)
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with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
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for p in (save_dir / "weights" / "best.pt", save_dir / "results.csv"):
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if p.exists():
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zf.write(p, arcname=p.name)
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# 曲线/混淆矩阵等绘图产物在 save_dir 根目录
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for p in sorted(save_dir.glob("*.png")) + sorted(save_dir.glob("*.jpg")):
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zf.write(p, arcname=p.name)
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def main():
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ap = argparse.ArgumentParser(description="observer 训练任务脚本（Go --task-json 驱动）")
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ap.add_argument("--task-json", required=True, help="任务参数 JSON 文件路径")
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args = ap.parse_args()
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with open(args.task_json, encoding="utf-8") as f:
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task = json.load(f)
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global _LOG
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base = os.path.abspath(task["workdir"])
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def task_path(key):
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"""任务参数里的路径全部相对 workdir，这里绝对化（chdir 失败也能定位）"""
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p = task[key]
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return p if os.path.isabs(p) else os.path.join(base, p)
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try:
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os.chdir(base)
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except Exception:
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pass # 目录不可用则后续训练必然失败，错误路径仍尽力写 result.json
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log_file = task_path("log_file")
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result_file = task_path("result_file")
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artifact_zip = task_path("artifact_zip")
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global _LOG
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_LOG = None
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try:
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Path(log_file).parent.mkdir(parents=True, exist_ok=True)
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_LOG = open(log_file, "a", encoding="utf-8", buffering=1)
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except Exception:
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pass # 日志不可写（如 workdir 缺失）不阻断错误路径，traceback 仍走 stderr
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imgsz = int(task.get("imgsz") or 704)
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epochs = int(task.get("epochs") or 150)
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batch = int(task.get("batch") or 16)
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device = task.get("device") or "0"
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data = os.path.join(task["yolo"], "dataset.yaml")
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names = read_names(task["yolo"])
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try:
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from ultralytics import YOLO
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register_callback()
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model = YOLO("yolov8n.pt")
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model.train(
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data=data, imgsz=imgsz, epochs=epochs,
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patience=PATIENCE, batch=batch, device=device, workers=4,
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# project 必须绝对路径，相对路径会被拼到默认 runs/detect 下造成双层嵌套
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project=task_path("project"), name="train",
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exist_ok=True, plots=True, multi_scale=MULTI_SCALE,
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)
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trainer = getattr(model, "trainer", None)
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if trainer is None:
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raise RuntimeError("训练完成但无法获取 trainer（save_dir 未知）")
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save_dir = Path(trainer.save_dir)
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best_tflite = find_best_tflite(save_dir)
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if best_tflite is None:
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model.export(format="tflite", imgsz=imgsz)
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best_tflite = find_best_tflite(save_dir)
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if best_tflite is None:
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raise RuntimeError("tflite 导出失败：weights 目录下未找到任何 .tflite 产物")
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# tflite 产物自检：shape 异常也写进 result.json（ok=false），Go 侧据此置失败
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tflite_check = check_tflite(str(best_tflite), imgsz)
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if not tflite_check["ok"]:
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print(f"[tflite-check] FAIL: {tflite_check['reason']}", file=sys.stderr)
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# 结果指标取 best 轮（发布的是 best.pt），缺省回退末轮
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metrics = getattr(trainer, "best_metrics", None) or trainer.metrics
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clean_metrics = {}
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for k, v in (metrics or {}).items():
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if isinstance(v, (int, float)) and math.isfinite(v):
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clean_metrics[k] = round(float(v), 5)
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build_artifact_zip(artifact_zip, save_dir)
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# best_tflite 相对 workdir，Go 侧按此路径拉取
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write_result(result_file, {
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"metrics": clean_metrics,
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"names": names,
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"best_tflite": os.path.relpath(best_tflite, base),
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"tflite_check": tflite_check,
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})
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except Exception:
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err = traceback.format_exc()
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if _LOG is not None:
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_LOG.write("\n" + err + "\n")
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try:
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write_result(result_file, {"error": err[-2000:]})
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except Exception:
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pass
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sys.exit(1)
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if __name__ == "__main__":
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main()
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