def test_request_body_with_binary_data(self):
example = text_format.Parse(
"""
features {
feature { key: "x_bytes" value { bytes_list { value: ["ASa8asdf"] }}}
feature { key: "x" value { bytes_list { value: "JLK7ljk3" }}}
feature { key: "y" value { int64_list { value: [1, 2] }}}
feature { key: "z" value { float_list { value: [4.5, 5, 5.5] }}}
}
""", tf.train.Example())
inference_spec_type = model_spec_pb2.InferenceSpecType(
ai_platform_prediction_model_spec=model_spec_pb2.
AIPlatformPredictionModelSpec(project_id='test_project',
model_name='test_model',
version_name='test_version'))
remote_predict = run_inference._RemotePredictDoFn(
inference_spec_type, None)
result = list(remote_predict._prepare_instances([example]))
self.assertEqual(result, [
{
'x_bytes': {
'b64': 'QVNhOGFzZGY='
},
'x': 'JLK7ljk3',
'y': [1, 2],
'z': [4.5, 5, 5.5]
},
])
def test_request_serialized_example(self):
example = text_format.Parse(
"""
features {
feature { key: "x_bytes" value { bytes_list { value: ["ASa8asdf"] }}}
feature { key: "x" value { bytes_list { value: "JLK7ljk3" }}}
feature { key: "y" value { int64_list { value: [1, 2] }}}
}
""", tf.train.Example())
inference_spec_type = model_spec_pb2.InferenceSpecType(
ai_platform_prediction_model_spec=model_spec_pb2
.AIPlatformPredictionModelSpec(
project_id='test_project',
model_name='test_model',
version_name='test_version',
use_serialization_config=True))
remote_predict = run_inference._RemotePredictDoFn(inference_spec_type, None)
result = list(remote_predict._prepare_instances([example]))
self.assertEqual(result, [{
'b64': base64.b64encode(example.SerializeToString()).decode()
}])