def load_eval_config(output_path: Text) -> config.EvalConfig:
"""Loads eval config."""
path = os.path.join(output_path, _EVAL_CONFIG_FILE)
if tf.io.gfile.exists(path):
with tf.io.gfile.GFile(path, 'r') as f:
pb = json_format.Parse(f.read(), config_pb2.EvalConfigAndVersion())
_check_version(pb.version, output_path)
return pb.eval_config
else:
# Legacy suppport (to be removed in future).
# The previous version did not include file extension.
path = os.path.splitext(path)[0]
serialized_record = six.next(
tf.compat.v1.python_io.tf_record_iterator(path))
final_dict = pickle.loads(serialized_record)
_check_version(final_dict, output_path)
old_config = final_dict['eval_config']
slicing_specs = None
if old_config.slice_spec:
slicing_specs = [s.to_proto() for s in old_config.slice_spec]
options = config.Options()
options.compute_confidence_intervals.value = (
old_config.compute_confidence_intervals)
options.k_anonymization_count.value = old_config.k_anonymization_count
return config.EvalConfig(
input_data_specs=[
config.InputDataSpec(location=old_config.data_location)
],
model_specs=[config.ModelSpec(location=old_config.model_location)],
output_data_specs=[
config.OutputDataSpec(default_location=output_path)
],
slicing_specs=slicing_specs,
options=options)
def _serialize_eval_config(eval_config: config.EvalConfig) -> Text:
return json_format.MessageToJson(
config_pb2.EvalConfigAndVersion(eval_config=eval_config,
version=tfma_version.VERSION_STRING))