ModelScope/maas_lib/models/nlp/space/dialog_intent_model.py

from typing import Any, Dict, Optional

from maas_lib.trainers.nlp.space.trainers.intent_trainer import IntentTrainer
from maas_lib.utils.constant import Tasks
from ...base import Model, Tensor
from ...builder import MODELS
from .model.generator import Generator
from .model.model_base import ModelBase

__all__ = ['DialogIntentModel']


@MODELS.register_module(Tasks.dialog_intent, module_name=r'space-intent')
class DialogIntentModel(Model):

    def __init__(self, model_dir: str, *args, **kwargs):
        """initialize the test generation model from the `model_dir` path.

        Args:
            model_dir (str): the model path.
            model_cls (Optional[Any], optional): model loader, if None, use the
                default loader to load model weights, by default None.
        """

        super().__init__(model_dir, *args, **kwargs)
        self.model_dir = model_dir
        self.text_field = kwargs.pop('text_field')
        self.config = kwargs.pop('config')
        self.generator = Generator.create(self.config, reader=self.text_field)
        self.model = ModelBase.create(
            model_dir=model_dir,
            config=self.config,
            reader=self.text_field,
            generator=self.generator)

        def to_tensor(array):
            """
            numpy array -> tensor
            """
            import torch
            array = torch.tensor(array)
            return array.cuda() if self.config.use_gpu else array

        self.trainer = IntentTrainer(
            model=self.model,
            to_tensor=to_tensor,
            config=self.config,
            reader=self.text_field)
        self.trainer.load()

    def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
        """return the result by the model

        Args:
            input (Dict[str, Any]): the preprocessed data

        Returns:
            Dict[str, np.ndarray]: results
                Example:
                    {
                        'predictions': array([1]), # lable 0-negative 1-positive
                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
                    }
        """
        from numpy import array, float32
        import torch
        print('--forward--')
        result = self.trainer.forward(input)

        return result