Python mmdet.__version__() Examples

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus
    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    

    #os.environ["CUDA_VISIBLE_DEVICES"] = "0, 1, 2, 3, 4, 5, 6, 7"
    os.environ["CUDA_VISIBLE_DEVICES"] = "6, 7"


    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "7"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "2"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "0"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "6"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "5"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "3"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus
    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text,
            classes=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def collect_env():
    """Collect the information of the running environments."""
    env_info = {}
    env_info['sys.platform'] = sys.platform
    env_info['Python'] = sys.version.replace('\n', '')

    cuda_available = torch.cuda.is_available()
    env_info['CUDA available'] = cuda_available

    if cuda_available:
        from torch.utils.cpp_extension import CUDA_HOME
        env_info['CUDA_HOME'] = CUDA_HOME

        if CUDA_HOME is not None and osp.isdir(CUDA_HOME):
            try:
                nvcc = osp.join(CUDA_HOME, 'bin/nvcc')
                nvcc = subprocess.check_output(
                    f'"{nvcc}" -V | tail -n1', shell=True)
                nvcc = nvcc.decode('utf-8').strip()
            except subprocess.SubprocessError:
                nvcc = 'Not Available'
            env_info['NVCC'] = nvcc

        devices = defaultdict(list)
        for k in range(torch.cuda.device_count()):
            devices[torch.cuda.get_device_name(k)].append(str(k))
        for name, devids in devices.items():
            env_info['GPU ' + ','.join(devids)] = name

    gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
    gcc = gcc.decode('utf-8').strip()
    env_info['GCC'] = gcc

    env_info['PyTorch'] = torch.__version__
    env_info['PyTorch compiling details'] = torch.__config__.show()

    env_info['TorchVision'] = torchvision.__version__

    env_info['OpenCV'] = cv2.__version__

    env_info['MMCV'] = mmcv.__version__
    env_info['MMDetection'] = mmdet.__version__
    from mmdet.ops import get_compiler_version, get_compiling_cuda_version
    env_info['MMDetection Compiler'] = get_compiler_version()
    env_info['MMDetection CUDA Compiler'] = get_compiling_cuda_version()
    return env_info 

def main():
    args = parse_args()
    cfg = Config.fromfile(args.config)
    
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        if args.job_name is '':
            args.job_name = 'output'
        else:
            args.job_name = time.strftime("%Y%m%d-%H%M%S-") + args.job_name
        cfg.work_dir = osp.join(args.work_dir, args.job_name)
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        os.environ['MASTER_ADDR'] = 'localhost'
        os.environ['MASTER_PORT'] = '%d' % args.port
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    utils.create_work_dir(cfg.work_dir)
    logger = utils.get_root_logger(cfg.work_dir, cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))
    logger.info('Search args: \n'+str(args))
    logger.info('Search configs: \n'+str(cfg))

    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # set random seeds  
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)
    
    utils.set_data_path(args.data_path, cfg.data)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
    model.backbone.get_sub_obj_list(cfg.sub_obj, (1, 3,)+cfg.image_size_madds)

    if cfg.use_syncbn:
        model = utils.convert_sync_batchnorm(model)

    train_dataset, arch_dataset = build_divide_dataset(cfg.data, part_1_ratio=cfg.train_data_ratio)

    search_detector(model, 
                    (train_dataset, arch_dataset),
                    cfg,
                    distributed=distributed,
                    validate=args.validate,
                    logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from

    cfg.gpus = args.gpus
    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))
    logger.info('MMDetection Version: {}'.format(__version__))
    logger.info('Config: {}'.format(cfg.text))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            classes=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    if args.load_from is not None:
        cfg.load_from = args.load_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark 
    # 在图片输入尺度固定时开启，可以加速.一般都是关的，只有在固定尺度的网络如SSD512中才开启
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        # 创建工作目录存放训练文件，如果不键入，会自动按照py配置文件生成对应的目录
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:    
        # 断点继续训练的权值文件
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    # ipdb.set_trace(context=35)
    #  搭建模型
    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

     # 将训练配置传入
    train_dataset = build_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in checkpoints as meta data
        # 要注意的是，以前发布的模型是不存这个类别等信息的，
        # 用的默认COCO或者VOC参数，所以如果用以前训练好的模型检测时会提醒warning一下，无伤大雅
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)

    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES   # model的CLASSES属性本来没有的，但是python不用提前声明，再赋值的时候自动定义变量
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger)