Python Params.get方法代码示例

# 需要导入模块: from allennlp.common.params import Params [as 别名]
# 或者: from allennlp.common.params.Params import get [as 别名]
    def _load(cls,
              config: Params,
              serialization_dir: str,
              weights_file: str = None,
              cuda_device: int = -1) -> 'Model':
        """
        Ensembles don't have vocabularies or weights of their own, so they override _load.
        """
        model_params = config.get('model')

        # The experiment config tells us how to _train_ a model, including where to get pre-trained
        # embeddings from.  We're now _loading_ the model, so those embeddings will already be
        # stored in our weights.  We don't need any pretrained weight file anymore, and we don't
        # want the code to look for it, so we remove it from the parameters here.
        remove_pretrained_embedding_params(model_params)
        model = Model.from_params(vocab=None, params=model_params)

        # Force model to cpu or gpu, as appropriate, to make sure that the embeddings are
        # in sync with the weights
        if cuda_device >= 0:
            model.cuda(cuda_device)
        else:
            model.cpu()

        return model

# 需要导入模块: from allennlp.common.params import Params [as 别名]
# 或者: from allennlp.common.params.Params import get [as 别名]
    def _load(cls,
              config: Params,
              serialization_dir: str,
              weights_file: str = None,
              cuda_device: int = -1) -> 'Model':
        """
        Instantiates an already-trained model, based on the experiment
        configuration and some optional overrides.
        """
        weights_file = weights_file or os.path.join(serialization_dir, _DEFAULT_WEIGHTS)

        # Load vocabulary from file
        vocab_dir = os.path.join(serialization_dir, 'vocabulary')
        vocab = Vocabulary.from_files(vocab_dir)

        model_params = config.get('model')

        # The experiment config tells us how to _train_ a model, including where to get pre-trained
        # embeddings from.  We're now _loading_ the model, so those embeddings will already be
        # stored in our weights.  We don't need any pretrained weight file anymore, and we don't
        # want the code to look for it, so we remove it from the parameters here.
        remove_pretrained_embedding_params(model_params)
        model = Model.from_params(vocab=vocab, params=model_params)
        model_state = torch.load(weights_file, map_location=util.device_mapping(cuda_device))
        model.load_state_dict(model_state)

        # Force model to cpu or gpu, as appropriate, to make sure that the embeddings are
        # in sync with the weights
        if cuda_device >= 0:
            model.cuda(cuda_device)
        else:
            model.cpu()

        return model

# 需要导入模块: from allennlp.common.params import Params [as 别名]
# 或者: from allennlp.common.params.Params import get [as 别名]
    def load(cls,
             config: Params,
             serialization_dir: str,
             weights_file: str = None,
             cuda_device: int = -1) -> 'Model':
        """
        Instantiates an already-trained model, based on the experiment
        configuration and some optional overrides.

        Parameters
        ----------
        config: Params
            The configuration that was used to train the model. It should definitely
            have a `model` section, and should probably have a `trainer` section
            as well.
        serialization_dir: str = None
            The directory containing the serialized weights, parameters, and vocabulary
            of the model.
        weights_file: str = None
            By default we load the weights from `best.th` in the serialization
            directory, but you can override that value here.
        cuda_device: int = -1
            By default we load the model on the CPU, but if you want to load it
            for GPU usage you can specify the id of your GPU here


        Returns
        -------
        model: Model
            The model specified in the configuration, loaded with the serialized
            vocabulary and the trained weights.
        """
        weights_file = weights_file or os.path.join(serialization_dir, _DEFAULT_WEIGHTS)

        # Load vocabulary from file
        vocab_dir = os.path.join(serialization_dir, 'vocabulary')
        vocab = Vocabulary.from_files(vocab_dir)

        model_params = config.get('model')

        # The experiment config tells us how to _train_ a model, including where to get pre-trained
        # embeddings from.  We're now _loading_ the model, so those embeddings will already be
        # stored in our weights.  We don't need any pretrained weight file anymore, and we don't
        # want the code to look for it, so we remove it from the parameters here.
        _remove_pretrained_embedding_params(model_params)
        model = Model.from_params(vocab, model_params)
        model_state = torch.load(weights_file, map_location=util.device_mapping(cuda_device))
        model.load_state_dict(model_state)

        # Force model to cpu or gpu, as appropriate, to make sure that the embeddings are
        # in sync with the weights
        if cuda_device >= 0:
            model.cuda(cuda_device)
        else:
            model.cpu()

        return model