def table_inference():
prep_data()
testing_server = start_zk_server()
generate_model()
stream_env = StreamExecutionEnvironment.get_execution_environment()
table_env = TableEnvironment.get_table_environment(stream_env)
stream_env.set_parallelism(2)
test_data_path = "file://" + get_root_path(
) + "/examples/target/data/test/"
paths = [test_data_path + "0.tfrecords", test_data_path + "1.tfrecords"]
src_row_type = RowType([StringType(), IntegerType()],
["image_raw", "label"])
table_src = TFRTableSource(
paths=paths,
epochs=1,
out_row_type=src_row_type,
converters=[ScalarConverter.FIRST, ScalarConverter.ONE_HOT])
input_table = table_src.register_table(table_env=table_env)
builder = TableSchema.Builder()
builder.column(name='label_org',
data_type=IntegerType()).column(name='predict_label',
data_type=IntegerType())
output_schema = builder.build()
output_table = tensorflow_on_flink_table.inference(
num_worker=2,
func=mnist_table_inference.map_fun,
properties=build_props('0'),
stream_env=stream_env,
table_env=table_env,
input_table=input_table,
output_schema=output_schema)
output_table.write_to_sink(LogTableStreamSink())
table_env.generate_stream_graph()
stream_env.execute()
testing_server.stop()
def table_java_inference():
prep_data()
testing_server = start_zk_server()
generate_model()
stream_env = StreamExecutionEnvironment.get_execution_environment()
table_env = TableEnvironment.get_table_environment(stream_env)
stream_env.set_parallelism(2)
train_data_path = "file://" + get_root_path(
) + "/examples/target/data/test/"
paths = [train_data_path + "0.tfrecords", train_data_path + "1.tfrecords"]
src_row_type = RowType([StringType(), IntegerType()],
["image_raw", "label"])
table_src = TFRTableSource(
paths=paths,
epochs=1,
out_row_type=src_row_type,
converters=[ScalarConverter.FIRST, ScalarConverter.ONE_HOT])
tfr_tbl_name = "tfr_input_table"
table_env.register_table_source(tfr_tbl_name, table_src)
ext_func_name = "tfr_extract"
table_env.register_function(ext_func_name, java_inference_extract_func())
out_cols = 'image,org_label'
in_cols = ','.join(src_row_type.fields_names)
extracted = table_env.sql_query(
'select {} from {}, LATERAL TABLE({}({})) as T({})'.format(
out_cols, tfr_tbl_name, ext_func_name, in_cols, out_cols))
builder = TableSchema.Builder()
builder.column(name='real_label',
data_type=LongType()).column(name='predicted_label',
data_type=LongType())
output_schema = builder.build()
props = build_props('0')
props[TF_INFERENCE_EXPORT_PATH] = export_path
props[TF_INFERENCE_INPUT_TENSOR_NAMES] = 'image'
props[TF_INFERENCE_OUTPUT_TENSOR_NAMES] = 'prediction'
props[TF_INFERENCE_OUTPUT_ROW_FIELDS] = ','.join(
['org_label', 'prediction'])
output_table = tensorflow_on_flink_table.inference(
num_worker=2,
properties=props,
stream_env=stream_env,
table_env=table_env,
input_table=extracted,
output_schema=output_schema)
output_table.write_to_sink(LogInferAccSink())
table_env.generate_stream_graph()
stream_env.execute()
testing_server.stop()
