Python Trainer.from_params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 from_params [as 别名]
def train_model(params: Params,
                serialization_dir: str,
                file_friendly_logging: bool = False,
                recover: 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.
    """
    prepare_environment(params)

    create_serialization_dir(params, serialization_dir, recover)
    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)

    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("Creating a vocabulary using %s data.", ", ".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))
    vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))

    model = Model.from_params(vocab, params.pop('model'))
    iterator = DataIterator.from_params(params.pop("iterator"))
    iterator.index_with(vocab)

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

    trainer_params = params.pop("trainer")
    trainer = Trainer.from_params(model,
                                  serialization_dir,
                                  iterator,
                                  train_data,
                                  validation_data,
                                  trainer_params)

    evaluate_on_test = params.pop_bool("evaluate_on_test", False)
    params.assert_empty('base train command')

    try:
        metrics = trainer.train()
    except KeyboardInterrupt:
        # if we have completed an epoch, try to create a model archive.
        if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
            logging.info("Training interrupted by the user. Attempting to create "
                         "a model archive using the current best epoch weights.")
            archive_model(serialization_dir, files_to_archive=params.files_to_archive)
        raise

    # Now tar up results
    archive_model(serialization_dir, files_to_archive=params.files_to_archive)

    if test_data and evaluate_on_test:
        test_metrics = evaluate(model, test_data, iterator, cuda_device=trainer._cuda_devices[0])  # pylint: disable=protected-access
        for key, value in test_metrics.items():
            metrics["test_" + key] = value

    elif test_data:
        logger.info("To evaluate on the test set after training, pass the "
                    "'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")

    metrics_json = json.dumps(metrics, indent=2)
    with open(os.path.join(serialization_dir, "metrics.json"), "w") as metrics_file:
        metrics_file.write(metrics_json)
    logger.info("Metrics: %s", metrics_json)

    return model

# 需要导入模块: from allennlp.training.trainer import Trainer [as 别名]
# 或者: from allennlp.training.trainer.Trainer import from_params [as 别名]
def fine_tune_model(model: Model,
                    params: Params,
                    serialization_dir: str,
                    file_friendly_logging: bool = False) -> 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.
    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)
    os.makedirs(serialization_dir)
    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. "
                       "Vocabulary from the saved model will be extended with current data.")

    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("Extending model vocabulary using %s data.", ", ".join(datasets_for_vocab_creation))
    vocab = model.vocab
    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(vocab)

    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 = Trainer.from_params(model,
                                  serialization_dir,
                                  iterator,
                                  train_data,
                                  validation_data,
                                  trainer_params)

    evaluate_on_test = params.pop_bool("evaluate_on_test", False)
    params.assert_empty('base train command')
    try:
        metrics = trainer.train()
    except KeyboardInterrupt:
        # if we have completed an epoch, try to create a model archive.
        if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
            logging.info("Fine-tuning interrupted by the user. Attempting to create "
                         "a model archive using the current best epoch weights.")
            archive_model(serialization_dir, files_to_archive=params.files_to_archive)
#.........这里部分代码省略.........