How to use the asteroid.utils.prepare_parser_from_dict function in asteroid

with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
        json.dump(checkpoint.best_k_models, f, indent=0)


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

train_model_part(conf, train_part='filterbank')
            filterbank = load_best_filterbank_if_available(conf)
    train_model_part(conf, train_part='separator',
                     pretrained_filterbank=filterbank)


if __name__ == '__main__':
    import yaml
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
        json.dump(checkpoint.best_k_models, f, indent=0)


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

to_save = system.model.serialize()
    to_save.update(train_set.get_infos())
    torch.save(to_save, os.path.join(exp_dir, 'best_model.pth'))


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
        json.dump(checkpoint.best_k_models, f, indent=0)


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
        json.dump(checkpoint.best_k_models, f, indent=0)


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

# Equation (4) from Chimera paper.
        tot = self.alpha * dc_loss.mean() + (1 - self.alpha) * src_pit_loss
        # Return unweighted losses as well for logging.
        loss_dict = dict(dc_loss=dc_loss.mean(),
                         pit_loss=src_pit_loss)
        return tot, loss_dict


if __name__ == '__main__':
    import yaml
    from pprint import pprint
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    pprint(arg_dic)
    main(arg_dic)

to_save = system.model.serialize()
    to_save.update(train_set.get_infos())
    torch.save(to_save, os.path.join(exp_dir, 'best_model.pth'))


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
        json.dump(checkpoint.best_k_models, f, indent=0)


if __name__ == '__main__':
    import yaml
    from pprint import pprint as print
    from asteroid.utils import prepare_parser_from_dict, parse_args_as_dict

    # We start with opening the config file conf.yml as a dictionary from
    # which we can create parsers. Each top level key in the dictionary defined
    # by the YAML file creates a group in the parser.
    with open('local/conf.yml') as f:
        def_conf = yaml.safe_load(f)
    parser = prepare_parser_from_dict(def_conf, parser=parser)
    # Arguments are then parsed into a hierarchical dictionary (instead of
    # flat, as returned by argparse) to facilitate calls to the different
    # asteroid methods (see in main).
    # plain_args is the direct output of parser.parse_args() and contains all
    # the attributes in an non-hierarchical structure. It can be useful to also
    # have it so we included it here but it is not used.
    arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
    print(arg_dic)
    main(arg_dic)

def main(conf):
    experiment.run(config_updates=dict(
        json_path=conf['main_args']['json_path'],
        **conf['mm_config'])
    )


if __name__ == '__main__':
    if dlp_mpi.IS_MASTER:
        # We start with opening the config file conf.yml as a dictionary from
        # which we can create parsers. Each top level key in the dictionary defined
        # by the YAML file creates a group in the parser.
        with open('local/conf.yml') as f:
            def_conf = yaml.safe_load(f)
        parser = prepare_parser_from_dict(def_conf, parser=parser)
        # Arguments are then parsed into a hierarchical dictionary (instead of
        # flat, as returned by argparse) to falicitate calls to the different
        # asteroid methods (see in main).
        # plain_args is the direct output of parser.parse_args() and contains all
        # the attributes in an non-hierarchical structure. It can be useful to also
        # have it so we included it here but it is not used.
        arg_dict, plain_args = parse_args_as_dict(
            parser, return_plain_args=True)
    else:
        arg_dict = None
    arg_dict = dlp_mpi.bcast(arg_dict, root=dlp_mpi.MASTER)
    main(arg_dict)