Python Trainer.list_available方法代码示例

# 需要导入模块: from allennlp.training.trainer import Trainer [as 别名]
# 或者: from allennlp.training.trainer.Trainer import list_available [as 别名]
def train_model(params: Params,
                serialization_dir: str,
                file_friendly_logging: bool = False,
                recover: bool = False,
                force: bool = False) -> Model:
    """
    Trains the model specified in the given :class:`Params` object, using the data and training
    parameters also specified in that object, and saves the results in ``serialization_dir``.

    Parameters
    ----------
    params : ``Params``
        A parameter object specifying an AllenNLP Experiment.
    serialization_dir : ``str``
        The directory in which to save results and logs.
    file_friendly_logging : ``bool``, optional (default=False)
        If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
        down tqdm's output to only once every 10 seconds.
    recover : ``bool``, optional (default=False)
        If ``True``, we will try to recover a training run from an existing serialization
        directory.  This is only intended for use when something actually crashed during the middle
        of a run.  For continuing training a model on new data, see the ``fine-tune`` command.

    Returns
    -------
    best_model: ``Model``
        The model with the best epoch weights.
    """
    prepare_environment(params)

    create_serialization_dir(params, serialization_dir, recover, force)
    prepare_global_logging(serialization_dir, file_friendly_logging)

    cuda_device = params.params.get('trainer').get('cuda_device', -1)
    if isinstance(cuda_device, list):
        for device in cuda_device:
            check_for_gpu(device)
    else:
        check_for_gpu(cuda_device)

    params.to_file(os.path.join(serialization_dir, CONFIG_NAME))

    all_datasets = datasets_from_params(params)
    datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets))

    for dataset in datasets_for_vocab_creation:
        if dataset not in all_datasets:
            raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}")

    logger.info("From dataset instances, %s will be considered for vocabulary creation.",
                ", ".join(datasets_for_vocab_creation))
    vocab = Vocabulary.from_params(
            params.pop("vocabulary", {}),
            (instance for key, dataset in all_datasets.items()
             for instance in dataset
             if key in datasets_for_vocab_creation)
    )

    model = Model.from_params(vocab=vocab, params=params.pop('model'))

    # Initializing the model can have side effect of expanding the vocabulary
    vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))

    iterator = DataIterator.from_params(params.pop("iterator"))
    iterator.index_with(vocab)
    validation_iterator_params = params.pop("validation_iterator", None)
    if validation_iterator_params:
        validation_iterator = DataIterator.from_params(validation_iterator_params)
        validation_iterator.index_with(vocab)
    else:
        validation_iterator = None

    train_data = all_datasets['train']
    validation_data = all_datasets.get('validation')
    test_data = all_datasets.get('test')

    trainer_params = params.pop("trainer")
    no_grad_regexes = trainer_params.pop("no_grad", ())
    for name, parameter in model.named_parameters():
        if any(re.search(regex, name) for regex in no_grad_regexes):
            parameter.requires_grad_(False)

    frozen_parameter_names, tunable_parameter_names = \
                   get_frozen_and_tunable_parameter_names(model)
    logger.info("Following parameters are Frozen  (without gradient):")
    for name in frozen_parameter_names:
        logger.info(name)
    logger.info("Following parameters are Tunable (with gradient):")
    for name in tunable_parameter_names:
        logger.info(name)

    trainer_choice = trainer_params.pop_choice("type",
                                               Trainer.list_available(),
                                               default_to_first_choice=True)
    trainer = Trainer.by_name(trainer_choice).from_params(model=model,
                                                          serialization_dir=serialization_dir,
                                                          iterator=iterator,
                                                          train_data=train_data,
                                                          validation_data=validation_data,
                                                          params=trainer_params,
#.........这里部分代码省略.........

# 需要导入模块: from allennlp.training.trainer import Trainer [as 别名]
# 或者: from allennlp.training.trainer.Trainer import list_available [as 别名]
def fine_tune_model(model: Model,
                    params: Params,
                    serialization_dir: str,
                    extend_vocab: bool = False,
                    file_friendly_logging: bool = False,
                    batch_weight_key: str = "") -> Model:
    """
    Fine tunes the given model, using a set of parameters that is largely identical to those used
    for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
    if it is present (as we are already given a ``Model`` here).

    The main difference between the logic done here and the logic done in ``train_model`` is that
    here we do not worry about vocabulary construction or creating the model object.  Everything
    else is the same.

    Parameters
    ----------
    archive : ``Archive``
        A saved model archive that is the result of running the ``train`` command.
    train_data_path : ``str``
        Path to the training data to use for fine-tuning.
    serialization_dir : ``str``
        The directory in which to save results and logs.
    validation_data_path : ``str``, optional
        Path to the validation data to use while fine-tuning.
    extend_vocab: ``bool``, optional (default=False)
        If ``True``, we use the new instances to extend your vocabulary.
    file_friendly_logging : ``bool``, optional (default=False)
        If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
        down tqdm's output to only once every 10 seconds.
    """
    prepare_environment(params)
    if os.path.exists(serialization_dir) and os.listdir(serialization_dir):
        raise ConfigurationError(f"Serialization directory ({serialization_dir}) "
                                 f"already exists and is not empty.")

    os.makedirs(serialization_dir, exist_ok=True)
    prepare_global_logging(serialization_dir, file_friendly_logging)

    serialization_params = deepcopy(params).as_dict(quiet=True)
    with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
        json.dump(serialization_params, param_file, indent=4)

    if params.pop('model', None):
        logger.warning("You passed parameters for the model in your configuration file, but we "
                       "are ignoring them, using instead the model parameters in the archive.")

    vocabulary_params = params.pop('vocabulary', {})
    if vocabulary_params.get('directory_path', None):
        logger.warning("You passed `directory_path` in parameters for the vocabulary in "
                       "your configuration file, but it will be ignored. ")

    all_datasets = datasets_from_params(params)
    vocab = model.vocab

    if extend_vocab:
        datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets))

        for dataset in datasets_for_vocab_creation:
            if dataset not in all_datasets:
                raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}")

        logger.info("Extending model vocabulary using %s data.", ", ".join(datasets_for_vocab_creation))
        vocab.extend_from_instances(vocabulary_params,
                                    (instance for key, dataset in all_datasets.items()
                                     for instance in dataset
                                     if key in datasets_for_vocab_creation))

    vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))

    iterator = DataIterator.from_params(params.pop("iterator"))
    iterator.index_with(model.vocab)
    validation_iterator_params = params.pop("validation_iterator", None)
    if validation_iterator_params:
        validation_iterator = DataIterator.from_params(validation_iterator_params)
        validation_iterator.index_with(vocab)
    else:
        validation_iterator = None

    train_data = all_datasets['train']
    validation_data = all_datasets.get('validation')
    test_data = all_datasets.get('test')

    trainer_params = params.pop("trainer")
    no_grad_regexes = trainer_params.pop("no_grad", ())
    for name, parameter in model.named_parameters():
        if any(re.search(regex, name) for regex in no_grad_regexes):
            parameter.requires_grad_(False)

    frozen_parameter_names, tunable_parameter_names = \
                   get_frozen_and_tunable_parameter_names(model)
    logger.info("Following parameters are Frozen  (without gradient):")
    for name in frozen_parameter_names:
        logger.info(name)
    logger.info("Following parameters are Tunable (with gradient):")
    for name in tunable_parameter_names:
        logger.info(name)

    trainer_choice = trainer_params.pop_choice("type",
                                               Trainer.list_available(),
#.........这里部分代码省略.........