Python cfg.merge_from_list方法代码示例

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
def adaptive_config_change(name, old, new):
    if old == new:
        return
    print('    {:<20} {} --> {}'.format(name, old, new))
    cfg.merge_from_list([name, new])

# ################################################################################################# 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
def main():
    parser=argparse.ArgumentParser()
    # parser.add_argument('-b', '--batch-size', type=int, default=128)
    parser.add_argument('-p', '--process', type=int, default=1)
    parser.add_argument('-r', '--reset', action='store_true')
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    parser.add_argument("--master_addr", default="127.0.0.1", type=str,
                        help="Master node (rank 0)'s address, should be either "
                             "the IP address or the hostname of node 0, for "
                             "single node multi-proc training, the "
                             "--master_addr can simply be 127.0.0.1")

    parser.add_argument("--master_port", default=29500, type=int,
                        help="Master node (rank 0)'s free port that needs to "
                             "be used for communciation during distributed "
                             "training")
    args=parser.parse_args()

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

    ngpus_per_node = torch.cuda.device_count()
    os.environ["WORLD_SIZE"] = str(ngpus_per_node)
    os.environ["MASTER_ADDR"] = args.master_addr
    os.environ["MASTER_PORT"] = str(args.master_port)

    cfg.SOLVER.IMS_PER_BATCH = 8
    cfg.SOLVER.MAX_ITER = 88888888
    cfg.TEST.IMS_PER_BATCH = ngpus_per_node
    cfg.OUTPUT_DIR = config.log_dir
    cfg.freeze()

    train_server = TestServer(ngpus_per_node)
    train_server.run(args.process, reset_pipe=args.reset) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
    parser.add_argument(
        "--config-file",
        default="/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
        metavar="FILE",
        help="path to config file",
    )

    parser.add_argument(
        "--checkpoint", default="", metavar="FILE", help="path to checkpoint file"
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

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

    if distributed:
        torch.cuda.set_device(args.local_rank)
        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()

    save_dir = ""
    logger = setup_logger("maskrcnn_benchmark", save_dir, args.local_rank)
    logger.info(cfg)

    model = build_detection_model(cfg)
    model.to(cfg.MODEL.DEVICE)

    load_from_checkpoint(cfg, model, args.checkpoint)

    data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)

    iou_types = ("bbox",)
    if cfg.MODEL.MASK_ON:
        iou_types = iou_types + ("segm",)
    inference(model, data_loader_val, iou_types=iou_types, box_only=cfg.MODEL.RPN_ONLY, device=cfg.MODEL.DEVICE) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import merge_from_list [as 别名]
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)