def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str, debug_info_str=None, enable_mlir_converter=False): """Convert `input_data_str` according to model and toco parameters. Unless you know what you are doing consider using the more friendly `tf.compat.v1.lite.toco_convert`. Args: model_flags_str: Serialized proto describing model properties, see `toco/model_flags.proto`. toco_flags_str: Serialized proto describing conversion properties, see `toco/toco_flags.proto`. input_data_str: Input data in serialized form (e.g. a graphdef is common, or it can be hlo text or proto) debug_info_str: Serialized `GraphDebugInfo` proto describing logging information. (default None) enable_mlir_converter: Enables MLIR-based conversion instead of the default TOCO conversion. (default False) Returns: Converted model in serialized form (e.g. a TFLITE model is common). Raises: ConverterError: When conversion fails in TFLiteConverter, usually due to ops not being supported. RuntimeError: When conversion fails, an exception is raised with the error message embedded. """ # Historically, TOCO conversion failures would trigger a crash, so we would # attempt to run the converter out-of-process. The MLIR conversion pipeline # surfaces errors instead, and can be safely run in-process. if enable_mlir_converter or not _toco_from_proto_bin: try: model_str = wrap_toco.wrapped_toco_convert(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter) return model_str except Exception as e: converter_error = ConverterError(str(e)) for error_data in _metrics_wrapper.retrieve_collected_errors(): converter_error.append_error(error_data) raise converter_error return _run_toco_binary(model_flags_str, toco_flags_str, input_data_str, debug_info_str)
def test_basic_retrieve_collected_errors_empty(self): errors = metrics_wrapper.retrieve_collected_errors() self.assertEmpty(errors)