Method _direct_initialize_inputs

caffe2/python/onnx/backend.py:634–642

(cls, inputs, initialized, ws, device_option)

    @classmethod
    def _direct_initialize_inputs(cls, inputs, initialized, ws, device_option):
        for value_info in inputs:
            if value_info.name in initialized:
                continue
            shape = list(d.dim_value for d in value_info.type.tensor_type.shape.dim)
            ws.FeedBlob(
                value_info.name,
                np.ones(shape, dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[value_info.type.tensor_type.elem_type]),
                device_option)

    @staticmethod
    def optimize_onnx(input, init=False, predict=False):