Python deep_merge_dict Examples

Example #1

    def _create_modalities(self):
        """ Creates source and target modalities.

        Returns: A tuple `(input_modality, target_modality)`.
        """
        input_modality_params = deep_merge_dict(
            self.params["modality.params"],
            self.params["modality.source.params"])
        input_modality = Modality(
            params=input_modality_params,
            mode=self.mode,
            vocab_size=self._vocab_source.vocab_size,
            body_input_depth=self.params["embedding.dim.source"],
            name="input_symbol_modality",
            verbose=self.verbose)
        target_modality_params = deep_merge_dict(
            self.params["modality.params"],
            self.params["modality.target.params"])
        target_modality = Modality(
            params=target_modality_params,
            mode=self.mode,
            vocab_size=self._vocab_target.vocab_size,
            body_input_depth=self.params["embedding.dim.target"],
            name="target_symbol_modality",
            verbose=self.verbose)
        return input_modality, target_modality

Example #2

File: eval.py Project: 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()

Example #3

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()

Example #4

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()

Example #5

File: sequence_to_sequence.py Project: KIngpon/NJUNMT-tf

    def _create_modalities(self):
        """ Creates source and target modalities.

        Returns: A tuple `(input_modality, target_modality)`.
        """
        input_modality_params = deep_merge_dict(
            self.params["modality.params"], self.params["modality.source.params"])
        input_modality = Modality(
            params=input_modality_params,
            mode=self.mode,
            vocab_size=self._vocab_source.vocab_size,
            body_input_depth=self.params["embedding.dim.source"],
            name="input_symbol_modality",
            verbose=self.verbose)
        target_modality_params = deep_merge_dict(
            self.params["modality.params"], self.params["modality.target.params"])
        target_modality = Modality(
            params=target_modality_params,
            mode=self.mode,
            vocab_size=self._vocab_target.vocab_size,
            body_input_depth=self.params["embedding.dim.target"],
            name="target_symbol_modality",
            verbose=self.verbose)
        return input_modality, target_modality

Example #6

File: infer.py Project: 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()

Example #7

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()