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)