Python load_from_config_path示例

示例#1

文件： eval.py 项目： KIngpon/NJUNMT-tf

def main(_argv):
    model_configs = maybe_load_yaml(DEFAULT_EVAL_CONFIGS)
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_eval_model_configs(model_configs, FLAGS)

    model_configs = deep_merge_dict(model_configs, ModelConfigs.load(FLAGS.model_dir))
    model_configs = update_eval_model_configs(model_configs, FLAGS)
    runner = EvalExperiment(model_configs=model_configs)

    runner.run()

示例#2

def main(_argv):
    model_configs = maybe_load_yaml(DEFAULT_EVAL_CONFIGS)
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_eval_model_configs(model_configs, FLAGS)

    model_configs = deep_merge_dict(model_configs,
                                    ModelConfigs.load(FLAGS.model_dir))
    model_configs = update_eval_model_configs(model_configs, FLAGS)
    runner = EvalExperiment(model_configs=model_configs)

    runner.run()

示例#3

def main(_argv):
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_configs_from_flags(model_configs, FLAGS,
                                              EVAL_ARGS.keys())

    model_configs = deep_merge_dict(model_configs,
                                    ModelConfigs.load(FLAGS.model_dir))
    model_configs = update_configs_from_flags(model_configs, FLAGS,
                                              EVAL_ARGS.keys())
    runner = EvalExperiment(model_configs=model_configs)
    runner.run()

示例#4

文件： infer.py 项目： KIngpon/NJUNMT-tf

def main(_argv):
    model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_infer_model_configs(model_configs, FLAGS)

    model_dirs = FLAGS.model_dir.strip().split(",")
    if len(model_dirs) == 1:
        model_configs = deep_merge_dict(model_configs, ModelConfigs.load(model_dirs[0]))
        model_configs = update_infer_model_configs(model_configs, FLAGS)
        runner = InferExperiment(model_configs=model_configs)
    else:
        runner = EnsembleExperiment(model_configs=model_configs, model_dirs=model_dirs,
                                    weight_scheme=FLAGS.weight_scheme)
    runner.run()

示例#5

def main(_argv):
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_configs_from_flags(model_configs, FLAGS,
                                              INFER_ARGS.keys())

    model_dirs = FLAGS.model_dir.strip().split(",")

    ip, port = FLAGS.server_address.split(":")
    addr = (ip, int(port))

    server = TranslateServer(addr, TranslateRequestHandler)
    server.init_experiment(model_configs=model_configs,
                           model_dirs=model_dirs,
                           weight_scheme=FLAGS.weight_scheme)
    server.serve_forever()

示例#6

def main(_argv):
    model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_infer_model_configs(model_configs, FLAGS)

    model_dirs = FLAGS.model_dir.strip().split(",")
    if len(model_dirs) == 1:
        model_configs = deep_merge_dict(model_configs,
                                        ModelConfigs.load(model_dirs[0]))
        runner = InferExperiment(model_configs=model_configs)
    else:
        runner = EnsembleExperiment(model_configs=model_configs,
                                    model_dirs=model_dirs,
                                    weight_scheme=FLAGS.weight_scheme)
    runner.run()

示例#7

文件： train.py 项目： zhengzx-nlp/NJUNMT-tf-server

def main(_argv):
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_configs_from_flags(model_configs, FLAGS, TRAIN_ARGS.keys())
    model_dir = model_configs["model_dir"]
    if not gfile.Exists(model_dir):
        gfile.MakeDirs(model_dir)

    if "CUDA_VISIBLE_DEVICES" not in os.environ.keys():
        raise OSError("need CUDA_VISIBLE_DEVICES environment variable")
    tf.logging.info("CUDA_VISIBLE_DEVICES={}".format(os.environ["CUDA_VISIBLE_DEVICES"]))

    training_runner = TrainingExperiment(
        model_configs=model_configs)

    training_runner.run()

示例#8

文件： train.py 项目： KIngpon/NJUNMT-tf

def main(_argv):
    model_configs = maybe_load_yaml(DEFAULT_TRAIN_CONFIGS)
    # load flags from config file
    model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
    # replace parameters in configs_file with tf FLAGS
    model_configs = update_train_model_configs(model_configs, FLAGS)
    model_dir = model_configs["model_dir"]
    if not gfile.Exists(model_dir):
        gfile.MakeDirs(model_dir)

    if "CUDA_VISIBLE_DEVICES" not in os.environ.keys():
        raise OSError("need CUDA_VISIBLE_DEVICES environment variable")
    tf.logging.info("CUDA_VISIBLE_DEVICES={}".format(os.environ["CUDA_VISIBLE_DEVICES"]))

    training_runner = TrainingExperiment(
        model_configs=model_configs)

    training_runner.run()