Python collect_env.collect_env_info方法代碼示例

本文整理匯總了Python中maskrcnn_benchmark.utils.collect_env.collect_env_info方法的典型用法代碼示例。如果您正苦於以下問題：Python collect_env.collect_env_info方法的具體用法？Python collect_env.collect_env_info怎麽用？Python collect_env.collect_env_info使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類maskrcnn_benchmark.utils.collect_env的用法示例。

在下文中一共展示了collect_env.collect_env_info方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        run_test(cfg, model, args.distributed)

開發者ID:Res2Net，項目名稱:Res2Net-maskrcnn，代碼行數:62，代碼來源:train_net.py

示例2: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="../configs/e2e_r2cnn_R_50_FPN_1x.yaml",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        default=True,
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        run_test(cfg, model, args.distributed)

開發者ID:Xiangyu-CAS，項目名稱:R2CNN.pytorch，代碼行數:63，代碼來源:train_net.py

示例3: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="../configs/rrpn/e2e_rrpn_X_101_32x8d_FPN_1x_DOTA.yaml",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        test(cfg, model, args.distributed)

開發者ID:clw5180，項目名稱:remote_sensing_object_detection_2019，代碼行數:62，代碼來源:train_net.py

示例4: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="configs/free_anchor_R-50-FPN_8gpu_1x.yaml",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank % torch.cuda.device_count())
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        test(cfg, model, args.distributed)

開發者ID:zhangxiaosong18，項目名稱:FreeAnchor，代碼行數:61，代碼來源:train_net.py

示例5: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.deprecated.init_process_group(
            backend="nccl", init_method="env://"
        )

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        test(cfg, model, args.distributed)

開發者ID:chengyangfu，項目名稱:retinamask，代碼行數:61，代碼來源:train_net.py

示例6: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--skip-test",
        dest="skip_test",
        help="Do not test the final model",
        action="store_true",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        test(cfg, model, args.distributed)

開發者ID:mlperf，項目名稱:training，代碼行數:62，代碼來源:train_net.py

示例7: main

# 需要導入模塊: from maskrcnn_benchmark.utils import collect_env [as 別名]
# 或者: from maskrcnn_benchmark.utils.collect_env import collect_env_info [as 別名]
def main():  
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")  
    parser.add_argument(  
        "--config-file",  
        default="",  
        metavar="FILE",  
        help="path to config file",  
        type=str,  
    )  
    parser.add_argument("--local_rank", type=int, default=0)  
    parser.add_argument(  
        "--skip-test",  
        dest="skip_test",  
        help="Do not test the final model",  
        action="store_true",  
    )  
    parser.add_argument(  
        "opts",  
        help="Modify config options using the command-line",  
        default=None,  
        nargs=argparse.REMAINDER,  
    )  

    args = parser.parse_args()  

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1  
    args.distributed = num_gpus > 1  

    if args.distributed:  
        torch.cuda.set_device(args.local_rank)  
        torch.distributed.init_process_group(  
            backend="nccl", init_method="env://"  
        )  
        synchronize()  

    cfg.merge_from_file(args.config_file)  
    cfg.merge_from_list(args.opts)  
    cfg.freeze()  

    output_dir = cfg.OUTPUT_DIR  
    if output_dir:  
        mkdir(output_dir)  

    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())  
    logger.info("Using {} GPUs".format(num_gpus))  
    logger.info(args)  

    logger.info("Collecting env info (might take some time)")  
    logger.info("\n" + collect_env_info())  

    logger.info("Loaded configuration file {}".format(args.config_file))  
    with open(args.config_file, "r") as cf:  
        config_str = "\n" + cf.read()  
        logger.info(config_str)  
    logger.info("Running with config:\n{}".format(cfg))  

    logger.info("Arch Decoder: {}".format(cfg.SEARCH.DECODER.CONFIG))  
    model = train(cfg, args.local_rank, args.distributed)  

    if not args.skip_test:  
        run_test(cfg, model, args.distributed)

開發者ID:Lausannen，項目名稱:NAS-FCOS，代碼行數:63，代碼來源:train_net.py

注：本文中的maskrcnn_benchmark.utils.collect_env.collect_env_info方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。