Method export_var

ppq/parser/tengine_exporter.py:154–188

(self, variable: Variable)

        return op_proto

    def export_var(self, variable: Variable) -> onnx.TensorProto:
        shape = variable.shape
        dtype = variable.dtype.value

        if dtype is None:
            ppq_warning(
                f"Data type of Variable {variable.name} is not correctly traced, "
                "ppq will export it as fp32 variable to onnx."
            )
            dtype = DataType.FP32.value

        if not variable.is_parameter:
            tensor_proto = helper.make_tensor_value_info(
                name=variable.name,
                # PPQ data type has exact same eunm value with onnx.
                elem_type=dtype,
                shape=shape,
            )
        else:
            value = variable.value
            if isinstance(value, torch.Tensor):
                if value.numel() == 0:
                    value = []
                elif value.ndim >= 1:
                    value = convert_any_to_numpy(variable.value).flatten()
                elif value.ndim == 0:
                    value = [
                        value.item(),
                    ]  # it is fine for onnx, cause shape for this value will be []
            else:
                value = value  # value is python primary type.
            tensor_proto = helper.make_tensor(
                name=variable.name, data_type=dtype, dims=shape, vals=value
            )
        return tensor_proto

    def export(self, file_path: str, graph: BaseGraph, config_path: str = None):
        # during export we will remove all boundary operations from graph.