def forward()

in source/neuropod/backends/python_bridge/_neuropod_native_bootstrap/executor.py [0:0]

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

    def forward(self, inputs):
        """
        Run inference using the specifed inputs.

        :param  inputs:     A dict mapping input names to values. This must match the input
                            spec in the neuropod config for the loaded model.
                            Ex: {'x1': np.array([5]), 'x2': np.array([6])}
                            *Note:* all the keys in this dict must be strings and all the
                            values must be numpy arrays

        :returns:   A dict mapping output names to values. All the keys
                    in this dict are strings and all the values are numpy arrays.
        """
        # Convert bytes to unicode
        for k, v in inputs.items():
            if v.dtype.type == np.bytes_:
                try:
                    inputs[k] = np.char.decode(v, encoding="UTF-8")
                except UnicodeDecodeError:
                    raise ValueError("Error in UTF-8 decoding: {}".format(v))

        out = self.model(**inputs)

        # Make sure everything is a numpy array
        for key, value in out.items():
            if not isinstance(value, np.ndarray):
                raise RuntimeError(
                    "All outputs must be numpy arrays! Output `{}` was of type `{}`".format(
                        key, type(value)
                    )
                )

        # Convert unicode to bytes
        for k, v in out.items():
            if v.dtype.type == np.unicode_:
                out[k] = np.char.encode(v, encoding="UTF-8")

        return out