test/machine_learning/inference: add test cases for legacy path

Added test cases for legacy path of inference engine, which uses
user-given model information instead of ones from json file.

As for this, this patch has a little bit code refactoring which
uses parameterized test instead of fixture one of google test
to decide API path in runtime - json or legacy.

This patch enlarges exsiting test coverage from 119 to 132 test cases.

[==========] 132 tests from 6 test suites ran. (49021 ms total)
[  PASSED  ] 132 tests.

Change-Id: I9829725aad8037cbe5a82d50e7790a3e7a6bfe6b
Signed-off-by: Inki Dae <inki.dae@samsung.com>

7 files changed, 341 insertions, 39 deletions

diff --git a/test/testsuites/machine_learning/inference/test_face_detection.cpp b/test/testsuites/machine_learning/inference/test_face_detection.cpp
index ebf37e5d..376a7173 100644
--- a/test/testsuites/machine_learning/inference/test_face_detection.cpp
+++ b/test/testsuites/machine_learning/inference/test_face_detection.cpp
@@ -27,6 +27,7 @@ public:
 	{
 		ASSERT_EQ(mv_inference_configure(infer, engine_cfg),
 				  MEDIA_VISION_ERROR_NONE);
+
 		ASSERT_EQ(mv_inference_prepare(infer), MEDIA_VISION_ERROR_NONE);
 		ASSERT_EQ(MediaVision::Common::ImageHelper::loadImageToSource(
 						  IMG_FACE, mv_source),
@@ -37,9 +38,42 @@ public:
 	}
 };
 
-TEST_F(TestFaceDetection, CPU_TFLITE_MobilenetV1_SSD)
+TEST_P(TestFaceDetection, CPU_TFLITE_MobilenetV1_SSD)
 {
 	engine_config_hosted_cpu_tflite(engine_cfg,
-									FD_TFLITE_WEIGHT_MOBILENET_V1_SSD_300_PATH);
+									FD_TFLITE_WEIGHT_MOBILENET_V1_SSD_300_PATH, _use_json_parser);
+	if (!_use_json_parser) {
+		const char *inputNodeName = "normalized_input_image_tensor";
+		const char *outputNodeName[] = { "TFLite_Detection_PostProcess",
+											"TFLite_Detection_PostProcess:1",
+											"TFLite_Detection_PostProcess:2",
+											"TFLite_Detection_PostProcess:3" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.3),
+					MEDIA_VISION_ERROR_NONE);
+
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+																outputNodeName, 4), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceFace();
-}
\ No newline at end of file
+}
+
+INSTANTIATE_TEST_CASE_P(Prefix, TestFaceDetection,
+						::testing::Values(
+							ParamTypeOne(false),
+							ParamTypeOne(true)
+						)
+);
\ No newline at end of file
diff --git a/test/testsuites/machine_learning/inference/test_face_landmark_detection.cpp b/test/testsuites/machine_learning/inference/test_face_landmark_detection.cpp
index 6d4ada9c..e186c6fe 100644
--- a/test/testsuites/machine_learning/inference/test_face_landmark_detection.cpp
+++ b/test/testsuites/machine_learning/inference/test_face_landmark_detection.cpp
@@ -37,7 +37,7 @@ public:
 	}
 };
 
-TEST_F(TestFaceLandmarkDetection, CPU_OPENCV_CAFFE_CNNCASCADE)
+TEST_P(TestFaceLandmarkDetection, CPU_OPENCV_CAFFE_CNNCASCADE)
 {
 	const char *inputNodeName = "data";
 	const char *outputNodeName[] = { "Sigmoid_fc2" };
@@ -85,4 +85,11 @@ TEST_F(TestFaceLandmarkDetection, CPU_OPENCV_CAFFE_CNNCASCADE)
 					  outputNodeName, 1),
 			  MEDIA_VISION_ERROR_NONE);
 	inferenceFaceLandmark();
-}
\ No newline at end of file
+}
+
+INSTANTIATE_TEST_CASE_P(Prefix, TestFaceLandmarkDetection,
+						::testing::Values(
+							ParamTypeOne(false),
+							ParamTypeOne(true)
+						)
+);
\ No newline at end of file
diff --git a/test/testsuites/machine_learning/inference/test_image_classification.cpp b/test/testsuites/machine_learning/inference/test_image_classification.cpp
index 0aab5947..cdda3f63 100644
--- a/test/testsuites/machine_learning/inference/test_image_classification.cpp
+++ b/test/testsuites/machine_learning/inference/test_image_classification.cpp
@@ -63,58 +63,232 @@ public:
 	}
 };
 
-TEST_F(TestImageClassification, CPU_TFLITE_MobilenetV1)
+TEST_P(TestImageClassification, CPU_TFLITE_MobilenetV1)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_MOBILENET_V1_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH, _use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "input";
+		const char *outputNodeName[] = { "MobilenetV1/Predictions/Reshape_1" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_MobilenetV2)
+TEST_P(TestImageClassification, CPU_TFLITE_MobilenetV2)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_MOBILENET_V2_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH, _use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "input";
+		const char *outputNodeName[] = { "MobilenetV2/Predictions/Reshape_1" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.01),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_Densenet)
+TEST_P(TestImageClassification, CPU_TFLITE_Densenet)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_DENSENET_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH, _use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "Placeholder";
+		const char *outputNodeName[] = { "softmax_tensor" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 255.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_Nasnet)
+TEST_P(TestImageClassification, CPU_TFLITE_Nasnet)
 {
 	engine_config_hosted_cpu_tflite_user_model(engine_cfg,
 											   IC_TFLITE_WEIGHT_NASNET_224_PATH,
-											   IC_LABEL_MOBILENET_V1_224_PATH);
+											   IC_LABEL_MOBILENET_V1_224_PATH,
+											   _use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "input";
+		const char *outputNodeName[] = { "final_layer/predictions" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_MNasnet)
+TEST_P(TestImageClassification, CPU_TFLITE_MNasnet)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_MNASNET_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH,
+			_use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "input";
+		const char *outputNodeName[] = { "output" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 57.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_Squeezenet)
+TEST_P(TestImageClassification, CPU_TFLITE_Squeezenet)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_SQUEEZENET_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH,
+			_use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "Placeholder";
+		const char *outputNodeName[] = { "softmax_tensor" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
 }
 
-TEST_F(TestImageClassification, CPU_TFLITE_QUANT_MobilenetV1)
+TEST_P(TestImageClassification, CPU_TFLITE_QUANT_MobilenetV1)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, IC_TFLITE_WEIGHT_QUANT_MOBILENET_V1_224_PATH,
-			IC_LABEL_MOBILENET_V1_224_PATH);
+			IC_LABEL_MOBILENET_V1_224_PATH,
+			_use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "input";
+		const char *outputNodeName[] = { "MobilenetV1/Predictions/Reshape_1" };
+
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_DATA_TYPE, MV_INFERENCE_DATA_UINT8),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 1.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.0),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 224),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+															  outputNodeName, 1), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceBanana();
-}
\ No newline at end of file
+}
+
+INSTANTIATE_TEST_CASE_P(Prefix, TestImageClassification,
+						::testing::Values(
+							ParamTypeOne(false),
+							ParamTypeOne(true)
+						)
+);
\ No newline at end of file
diff --git a/test/testsuites/machine_learning/inference/test_inference_helper.cpp b/test/testsuites/machine_learning/inference/test_inference_helper.cpp
index 1c88689f..81a0380b 100644
--- a/test/testsuites/machine_learning/inference/test_inference_helper.cpp
+++ b/test/testsuites/machine_learning/inference/test_inference_helper.cpp
@@ -16,19 +16,23 @@ TestInference::~TestInference()
 }
 
 void engine_config_hosted_cpu_tflite(mv_engine_config_h handle,
-									 const char *tf_weight)
+									 const char *tf_weight,
+									 const bool use_json_parser)
 {
 	EXPECT_EQ(mv_engine_config_set_string_attribute(
 					  handle, MV_INFERENCE_MODEL_WEIGHT_FILE_PATH, tf_weight),
 			  MEDIA_VISION_ERROR_NONE);
 
-	std::string meta_file_path = tf_weight;
-	meta_file_path = meta_file_path.substr(0, meta_file_path.find('.'));
-	meta_file_path += std::string(".json");
+	if (use_json_parser) {
+		std::string meta_file_path = tf_weight;
+		meta_file_path = meta_file_path.substr(0, meta_file_path.find('.'));
+		meta_file_path += std::string(".json");
+
+		EXPECT_EQ(mv_engine_config_set_string_attribute(
+						handle, MV_INFERENCE_MODEL_META_FILE_PATH , meta_file_path.c_str()),
+				MEDIA_VISION_ERROR_NONE);
+	}
 
-	EXPECT_EQ(mv_engine_config_set_string_attribute(
-					  handle, MV_INFERENCE_MODEL_META_FILE_PATH , meta_file_path.c_str()),
-			  MEDIA_VISION_ERROR_NONE);
 	EXPECT_EQ(mv_engine_config_set_int_attribute(handle,
 												 MV_INFERENCE_BACKEND_TYPE,
 												 MV_INFERENCE_BACKEND_TFLITE),
@@ -41,10 +45,11 @@ void engine_config_hosted_cpu_tflite(mv_engine_config_h handle,
 
 void engine_config_hosted_cpu_tflite_user_model(mv_engine_config_h handle,
 												const char *tf_weight,
-												const char *user_file)
+												const char *user_file,
+												const bool use_json_parser)
 {
-	engine_config_hosted_cpu_tflite(handle, tf_weight);
+	engine_config_hosted_cpu_tflite(handle, tf_weight, use_json_parser);
 	EXPECT_EQ(mv_engine_config_set_string_attribute(
 					  handle, MV_INFERENCE_MODEL_USER_FILE_PATH, user_file),
 			  MEDIA_VISION_ERROR_NONE);
-}
+}
\ No newline at end of file
diff --git a/test/testsuites/machine_learning/inference/test_inference_helper.hpp b/test/testsuites/machine_learning/inference/test_inference_helper.hpp
index 16bb4c62..a04fb000 100644
--- a/test/testsuites/machine_learning/inference/test_inference_helper.hpp
+++ b/test/testsuites/machine_learning/inference/test_inference_helper.hpp
@@ -3,8 +3,18 @@
 
 #include <mv_inference.h>
 
-class TestInference : public ::testing::Test
+typedef std::tuple<int> ParamTypeOne;
+
+class TestInference : public ::testing::TestWithParam<ParamTypeOne>
 {
+protected:
+	void SetUp() final
+	{
+		std::tie(_use_json_parser) = GetParam();
+	}
+
+	bool _use_json_parser;
+
 public:
 	TestInference();
 	virtual ~TestInference();
@@ -14,10 +24,12 @@ public:
 };
 
 void engine_config_hosted_cpu_tflite(mv_engine_config_h handle,
-									 const char *tf_weight);
+									 const char *tf_weight,
+									 const bool use_json_parser);
 
 void engine_config_hosted_cpu_tflite_user_model(mv_engine_config_h handle,
 												const char *tf_weight,
-												const char *user_file);
+												const char *user_file,
+												const bool use_json_parser);
 
 #endif //__TEST_INFERENCE_HELPER_HPP__
diff --git a/test/testsuites/machine_learning/inference/test_object_detection.cpp b/test/testsuites/machine_learning/inference/test_object_detection.cpp
index 8cea9f7b..b849c20b 100644
--- a/test/testsuites/machine_learning/inference/test_object_detection.cpp
+++ b/test/testsuites/machine_learning/inference/test_object_detection.cpp
@@ -46,10 +46,45 @@ public:
 	}
 };
 
-TEST_F(TestObjectDetection, CPU_TFLITE_MobilenetV1_SSD)
+TEST_P(TestObjectDetection, CPU_TFLITE_MobilenetV1_SSD)
 {
 	engine_config_hosted_cpu_tflite_user_model(
 			engine_cfg, OD_TFLITE_WEIGHT_MOBILENET_V1_SSD_300_PATH,
-			OD_LABEL_MOBILENET_V1_SSD_300_PATH);
+			OD_LABEL_MOBILENET_V1_SSD_300_PATH,
+			_use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "normalized_input_image_tensor";
+		const char *outputNodeName[] = { "TFLite_Detection_PostProcess",
+											"TFLite_Detection_PostProcess:1",
+											"TFLite_Detection_PostProcess:2",
+											"TFLite_Detection_PostProcess:3" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.3),
+					MEDIA_VISION_ERROR_NONE);
+
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+																outputNodeName, 4), MEDIA_VISION_ERROR_NONE);
+	}
+
 	inferenceDog();
-}
\ No newline at end of file
+}
+
+INSTANTIATE_TEST_CASE_P(Prefix, TestObjectDetection,
+						::testing::Values(
+							ParamTypeOne(false),
+							ParamTypeOne(true)
+						)
+);
\ No newline at end of file
diff --git a/test/testsuites/machine_learning/inference/test_pose_landmark_detection.cpp b/test/testsuites/machine_learning/inference/test_pose_landmark_detection.cpp
index 033488c1..58c4b43a 100644
--- a/test/testsuites/machine_learning/inference/test_pose_landmark_detection.cpp
+++ b/test/testsuites/machine_learning/inference/test_pose_landmark_detection.cpp
@@ -37,9 +37,44 @@ public:
 	}
 };
 
-TEST_F(TestPoseLandmarkDetection, CPU_TFLITE_MobilenetV1)
+TEST_P(TestPoseLandmarkDetection, CPU_TFLITE_MobilenetV1)
 {
 	engine_config_hosted_cpu_tflite(
-			engine_cfg, PLD_TFLITE_WEIGHT_MOBILENET_V1_POSENET_257_PATH);
+			engine_cfg, PLD_TFLITE_WEIGHT_MOBILENET_V1_POSENET_257_PATH, _use_json_parser);
+
+	if (!_use_json_parser) {
+		const char *inputNodeName = "sub_2";
+		const char *outputNodeName[] = { "MobilenetV1/heatmap_2/BiasAdd",
+											"MobilenetV1/offset_2/BiasAdd",
+											"MobilenetV1/displacement_fwd_2/BiasAdd",
+											"MobilenetV1/displacement_bwd_2/BiasAdd" };
+
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_MEAN_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_MODEL_STD_VALUE, 127.5),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_double_attribute(engine_cfg, MV_INFERENCE_CONFIDENCE_THRESHOLD, 0.3),
+					MEDIA_VISION_ERROR_NONE);
+
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_WIDTH, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_HEIGHT, 300),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_int_attribute(engine_cfg, MV_INFERENCE_INPUT_TENSOR_CHANNELS, 3),
+					MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_string_attribute(engine_cfg, MV_INFERENCE_INPUT_NODE_NAME,
+														inputNodeName), MEDIA_VISION_ERROR_NONE);
+		ASSERT_EQ(mv_engine_config_set_array_string_attribute(engine_cfg, MV_INFERENCE_OUTPUT_NODE_NAMES,
+																outputNodeName, 4), MEDIA_VISION_ERROR_NONE);
+	}
+
+
 	inferencePoseLandmark();
-}
\ No newline at end of file
+}
+
+INSTANTIATE_TEST_CASE_P(Prefix, TestPoseLandmarkDetection,
+						::testing::Values(
+							ParamTypeOne(false),
+							ParamTypeOne(true)
+						)
+);
\ No newline at end of file