判断是否使用断点续训resume, 读取参数

# Resume
    # 判断是否使用断点续训resume, 读取参数
    if opt.resume and not check_comet_resume(opt) and not opt.evolve:                               # from specified or most recent last.pt  源自特定的或最近的last.pt
        # 使用断点续训 就从last模型文件夹中读取相关参数
        # 如果resume是str，则表示传入的是模型的路径地址
        # 如果resume是True，则通过get_lastest_run()函数找到runs文件夹中最近的权重文件last
        last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
        opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
        opt_data = opt.data     # original dataset
        if opt_yaml.is_file():
            # 相关的opt参数也要替换成last中的opt参数
            with open(opt_yaml, errors='ignore') as f:
                d = yaml.safe_load(f)
        else:
            d = torch.load(last, map_location='cpu')['opt']
        opt = argparse.Namespace(**d)  # replace
        opt.cfg, opt.weights, opt.resume = '', str(last), True  # reinstate
        if is_url(opt_data):
            opt.data = check_file(opt_data)  # avoid HUB resume auth timeout
    else:
        # 不使用断点续训 就从文件中读取相关参数
        # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \\
            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks
        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
        if opt.evolve:
            if opt.project == str(ROOT / 'runs/train'):  # if default project name, rename to runs/evolve
                opt.project = str(ROOT / 'runs/evolve')
            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume
        if opt.name == 'cfg':
            opt.name = Path(opt.cfg).stem  # use model.yaml as name
        # 根据opt.project生成目录  如: runs/train/exp18
        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))