ai-train_platform/train_worker_yolo.py

import sys
import json
import os
import torch
from ultralytics import YOLO

class MockModelConfigDAO:
    def __init__(self, db_config):
        self.db_config = db_config

    def insert_train_pid(self, task_id, train_pid):
        print(f"Inserted training PID {train_pid} for task {task_id}")

def train_model(dataset_dir, weight_name="best_segmentation_model.pt", config_overrides=None):
    """
    训练模型并保存权重
    """
    try:
        current_pid = os.getpid()
        print(f"Starting model training in process {current_pid} with dataset: {dataset_dir}")

        # 默认配置（可通过参数覆盖）
        DEFAULT_CONFIG = {
            "model": "pt/yolo11s-seg.pt",
            "pretrained": True,
            "data": os.path.join(dataset_dir, "data.yaml"),
            "project": "UAVid_Segmentation",
            "name": "v1.5_official",
            "epochs": 10,
            "batch_size": 8,
            "img_size": 640,
            "workers": 0,  # 禁用多进程数据加载
            "optimizer": "SGD",
            "lr0": 0.01,
            "lrf": 0.01,
            "momentum": 0.9,
            "weight_decay": 0.0005,
            "augment": True,
            "hyp": {
                "mosaic": 0.5,
                "copy_paste": 0.2,
                "mixup": 0.15,
            },
        }

        config = DEFAULT_CONFIG.copy()
        if config_overrides:
            config.update(config_overrides)

        print(f"Training config: {config}")

        # 检查数据配置文件
        data_path = config["data"]
        if not os.path.exists(data_path):
            raise FileNotFoundError(f"Data configuration file not found: {data_path}")

        # 初始化模型
        model = YOLO(config["model"])
        print(f"Model initialized with: {config["model"]}")

        # 开始训练
        results = model.train(
            data=config["data"],
            project=config["project"],
            name=config["name"],
            epochs=config["epochs"],
            batch=config["batch_size"],
            imgsz=config["img_size"],
            workers=config["workers"],
            optimizer=config["optimizer"],
            lr0=config["lr0"],
            lrf=config["lrf"],
            momentum=config["momentum"],
            weight_decay=config["weight_decay"],
            augment=config["augment"],
            device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
        )
        print(f"Training completed successfully in process {current_pid}")

        # 验证模型
        metrics = model.val()
        print(f"Validation mAP: {metrics.box:.2f} (box), {metrics.seg:.2f} (mask)")

        # 保存最佳模型
        try:
            if hasattr(results, 'best') and results.best:
                best_model_path = results.best
                if os.path.exists(best_model_path):
                    import shutil
                    shutil.copy2(best_model_path, weight_name)
                    print(f"Best model saved to: {os.path.abspath(weight_name)}")
                else:
                    torch.save(model.state_dict(), weight_name)
                    print(f"Best model path not found, saved state dict to: {weight_name}")
            else:
                torch.save(model.state_dict(), weight_name)
                print(f"Saved model state dict to: {weight_name}")
        except Exception as e:
            print(f"Warning: Failed to save best model: {e}")
            torch.save(model.state_dict(), weight_name)
            print(f"Fallback: Saved model state dict to: {weight_name}")

        return True

    except Exception as e:
        print(f"Model training failed in process {os.getpid()}: {e}")
        raise

def main():
    if len(sys.argv) != 2:
        print("Usage: python -c '<script>' <config_file>")
        sys.exit(1)

    config_file = sys.argv[1]

    try:
        with open(config_file, 'r', encoding='utf-8') as f:
            config = json.load(f)

        # 提取配置
        dataset_dir = config['dataset_dir']
        pt_name = config['pt_name']
        config_overrides = config['config_overrides']
        db_config = config['db_config']
        task_id = config['task_id']

        # 获取当前进程ID
        pid = os.getpid()
        print(f"Training process started for task {task_id} with PID {pid}")

        # 记录PID到数据库
        try:
            from middleware.query_model import ModelConfigDAO
            dao = ModelConfigDAO(db_config)
        except ImportError:
            dao = MockModelConfigDAO(db_config)

        dao.insert_train_pid(task_id, train_pid=pid)

        # 执行训练
        success = train_model(dataset_dir, pt_name, config_overrides)

        if success:
            print(f"Training completed successfully for task {task_id}")
            sys.exit(0)
        else:
            print(f"Training failed for task {task_id}")
            sys.exit(1)

    except Exception as e:
        print(f"Training error: {e}", file=sys.stderr)
        import traceback
        traceback.print_exc()
        sys.exit(1)

if __name__ == "__main__":
    main()