def run()

in python/dlr/dlr_model.py [0:0]

• 19 lines of code
• 11 McCabe index (conditional complexity)

    def run(self, input_values):
        """
        Run inference with given input(s)

        Parameters
        ----------
        input_values : a single :py:class:`numpy.ndarray` or a dictionary
            For decision tree models, provide a single :py:class:`numpy.ndarray`
            to indicate a single input, as decision trees always accept only one
            input.

            For deep learning models, provide a dictionary where keys are input
            names (of type :py:class:`str`) and values are input tensors (of type
            :py:class:`numpy.ndarray`). Deep learning models allow more than one
            input, so each input must have a unique name.

        Returns
        -------
        out : :py:class:`numpy.ndarray`
            Prediction result
        """
        out = []
        # set input(s)
        if isinstance(input_values, (np.ndarray, np.generic)):
            # Treelite model or single input tvm/treelite model.
            # Treelite has a dummy input name 'data'.
            if self.input_names:
                self._set_input(self.input_names[0], input_values)
        elif isinstance(input_values, dict):
            # TVM model
            for key, value in input_values.items():
                if (self.input_names and key not in self.input_names) and \
                   (self.weight_names and key not in self.weight_names):
                    raise ValueError("%s is not a valid input name." % key)
                self._set_input(key, value)
        else:
            raise ValueError("input_values must be of type dict (tvm model) " +
                             "or a np.ndarray/generic (representing treelite models)")
        # run model
        self._run()
        # get output
        for i in range(self.num_outputs):
            ith_out = self._get_output(i)
            out.append(ith_out)
        return out