from utils import os_utils as os from application.settings import * from . import schemas, models, crud from utils.websocket_server import room_manager from apps.business.project import models as proj_models, crud as proj_crud import yaml import asyncio import subprocess from redis.asyncio import Redis from sqlalchemy.ext.asyncio import AsyncSession async def before_train(proj_info: proj_models.ProjectInfo, db: AsyncSession): """ yolov5执行训练任务 :param proj_info: 项目信息 :param db: 数据库session :return: """ proj_dal = proj_crud.ProjectInfoDal(db) img_dal = proj_crud.ProjectImageDal(db) label_dal = proj_crud.ProjectLabelDal(db) # 先查询两个图片列表 project_images_train = img_dal.get_data( v_where=[proj_models.ProjectImage.project_id == proj_info.id, proj_models.ProjectImage.img_type == 'train']) project_images_val = img_dal.get_data( v_where=[proj_models.ProjectImage.project_id == proj_info.id, proj_models.ProjectImage.img_type == 'val']) # 得到训练版本 version_path = 'v' + str(proj_info.train_version + 1) # 创建训练的根目录 train_path = os.create_folder(datasets_url, proj_info.project_no, version_path) # 查询项目所属标签，返回两个 id，name一一对应的数组 label_id_list, label_name_list = label_dal.get_label_for_train(proj_info.id) # 创建图片的的两个文件夹 img_path_train = os.create_folder(train_path, 'images', 'train') img_path_val = os.create_folder(train_path, 'images', 'val') # 创建标签的两个文件夹 label_path_train = os.create_folder(train_path, 'labels', 'train') label_path_val = os.create_folder(train_path, 'labels', 'val') # 在根目录下创建yaml文件 yaml_file = os.file_path(train_path, proj_info.project_no + '.yaml') yaml_data = { 'path': train_path, 'train': 'images/train', 'val': 'images/val', 'test': None, 'names': {i: name for i, name in enumerate(label_name_list)} } with open(yaml_file, 'w', encoding='utf-8') as file: yaml.dump(yaml_data, file, allow_unicode=True, default_flow_style=False) # 开始循环复制图片和生成label.txt # 先操作train operate_img_label(project_images_train, img_path_train, label_path_train, db, label_id_list) # 再操作val operate_img_label(project_images_val, img_path_val, label_path_val, db, label_id_list) # 开始执行异步训练 data = yaml_file project = os.file_path(runs_url, proj_info.project_no) name = version_path return data, project, name async def operate_img_label( img_list: list[proj_models.ProjectImgLabel], img_path: str, label_path: str, db: AsyncSession, label_id_list: []): """ 生成图片和标签内容 :param label_id_list: :param db: 数据库session :param img_list: :param img_path: :param label_path: :return: """ for i in range(len(img_list)): image = img_list[i] # 先复制图片，并把图片改名，不改后缀 file_name = 'image' + str(i) os.copy_and_rename_file(image.image_url, img_path, file_name) # 查询这张图片的label信息然后生成这张照片的txt文件 img_label_list = await proj_crud.ProjectImgLabelDal(db).get_img_label_list(image.id) label_txt_path = os.file_path(label_path, file_name + '.txt') with open(label_txt_path, 'w', encoding='utf-8') as file: for image_label in img_label_list: index = label_id_list.index(image_label.label_id) file.write(str(index) + ' ' + image_label.mark_center_x + ' ' + image_label.mark_center_y + ' ' + image_label.mark_width + ' ' + image_label.mark_height + '\n') async def run_event_loop( data: str, project: str, name: str, train_in: schemas.ProjectTrainIn, project_id: int, db: AsyncSession): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # 运行异步函数 loop.run_until_complete(run_commend(data, project, name, train_in.epochs, train_in.patience, train_in.weights_id, project_id, db)) # 可选: 关闭循环 loop.close() async def run_commend( data: str, project: str, name: str, epochs: int, patience: int, weights: str, project_id: int, db: AsyncSession, rd: Redis): """ 执行训练 :param data: 训练数据集 :param project: 训练结果的项目目录 :param name: 实验名称 :param epochs: 训练轮数 :param patience: 早停耐心值 :param weights: 权重文件 :param project_id: 项目id :param db: 数据库session :param rd: redis连接 :return: """ yolo_path = os.file_path(yolo_url, 'train.py') room = 'train_' + str(project_id) await room_manager.send_to_room(room, f"AiCheckV2.0: 模型训练开始，请稍等。。。\n") commend = ["python", '-u', yolo_path, "--data=" + data, "--project=" + project, "--name=" + name, "--epochs=" + str(epochs), "--batch-size=8", "--exist-ok", "--patience=" + str(patience)] # 增加权重文件，在之前训练的基础上重新巡逻 if weights != '' and weights is not None: train_info = await crud.ProjectTrainDal(db).get_data(data_id=int(weights)) if train_info is not None: commend.append("--weights=" + train_info.best_pt) is_gpu = rd.get('is_gpu') # 判断是否存在cuda版本 if is_gpu == 'True': commend.append("--device=0") # 启动子进程 with subprocess.Popen( commend, bufsize=1, # bufsize=0时，为不缓存；bufsize=1时，按行缓存；bufsize为其他正整数时，为按照近似该正整数的字节数缓存 shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, # 这里可以显示yolov5训练过程中出现的进度条等信息 text=True, # 缓存内容为文本，避免后续编码显示问题 encoding='utf-8', ) as process: while process.poll() is None: line = process.stdout.readline() process.stdout.flush() # 刷新缓存，防止缓存过多造成卡死 if line != '\n' and '0%' not in line: await room_manager.send_to_room(room, line + '\n') # 等待进程结束并获取返回码 return_code = process.wait() if return_code != 0: await room_manager.send_to_room(room, 'error') else: await room_manager.send_to_room(room, 'success') # 然后保存版本训练信息 train = models.ProjectTrain() train.project_id = project_id train.train_version = name train_url = os.file_path(project, name) train.train_url = train_url train.train_data = data bast_pt_path = os.file_path(train_url, 'weights', 'best.pt') last_pt_path = os.file_path(train_url, 'weights', 'last.pt') train.best_pt = bast_pt_path train.last_pt = last_pt_path if weights is not None and weights != '': train.weights_id = weights train.weights_name = train_info.train_version train.patience = patience train.epochs = epochs await crud.ProjectTrainDal(db).create_data(data=train)