From: Inki Dae <inki.dae@samsung.com>
Date: Thu, 4 Feb 2021 03:58:00 +0000 (+0900)
Subject: test: Do not return in error case
X-Git-Tag: submit/tizen/20210422.072212~8
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=686c017073d767d12a61f8430c360fc8c29e3c35;p=platform%2Fcore%2Fmultimedia%2Finference-engine-interface.git

test: Do not return in error case

Google test framework provides assertion macro so
we don't have to return in error case.
It must be enough with the assertion macro.

Change-Id: I9a8f1375e5ddfbe06a2803567a3eea8f11f1e74d
Signed-off-by: Inki Dae <inki.dae@samsung.com>
---

diff --git a/test/src/inference_engine_profiler.cpp b/test/src/inference_engine_profiler.cpp
index 96cb21f..41c83ec 100644
--- a/test/src/inference_engine_profiler.cpp
+++ b/test/src/inference_engine_profiler.cpp
@@ -136,16 +136,10 @@ TEST_P(InferenceEngineTfliteTest, Inference)
 									   .target_devices = target_devices };
 
 	auto engine = std::make_unique<InferenceEngineCommon>();
-	if (engine == nullptr) {
-		ASSERT_TRUE(engine);
-		return;
-	}
+	ASSERT_TRUE(engine);
 
 	int ret = engine->EnableProfiler(true);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	if (backend_type == INFERENCE_BACKEND_ONE)
 		backend_name = "one";
@@ -153,19 +147,13 @@ TEST_P(InferenceEngineTfliteTest, Inference)
 	ret = engine->DumpProfileToFile("profile_data_" + backend_name +
 									"_" + Target_Formats[target_devices] +
 									"_tflite_model.txt");
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->LoadConfigFile();
 	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->BindBackend(&config);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_capacity capacity;
 	ret = engine->GetBackendCapacity(&capacity);
@@ -176,10 +164,7 @@ TEST_P(InferenceEngineTfliteTest, Inference)
 
 	std::vector<std::string> models;
 	int model_type = GetModelInfo(model_paths, models);
-	if (model_type == -1) {
-		ASSERT_NE(model_type, -1);
-		return;
-	}
+	ASSERT_NE(model_type, -1);
 
 	inference_engine_layer_property input_property;
 	std::vector<std::string>::iterator iter;
@@ -197,10 +182,7 @@ TEST_P(InferenceEngineTfliteTest, Inference)
 	}
 
 	ret = engine->SetInputLayerProperty(input_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_layer_property output_property;
 
@@ -209,23 +191,14 @@ TEST_P(InferenceEngineTfliteTest, Inference)
 	}
 
 	ret = engine->SetOutputLayerProperty(output_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->Load(models, (inference_model_format_e) model_type);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	std::vector<inference_engine_tensor_buffer> inputs, outputs;
 	ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	// Copy input image tensor data from a given file to input tensor buffer.
 	for (int i = 0; i < (int) image_paths.size(); ++i) {
@@ -556,32 +529,20 @@ TEST_P(InferenceEngineCaffeTest, Inference)
 									   .target_devices = target_devices };
 
 	auto engine = std::make_unique<InferenceEngineCommon>();
-	if (engine == nullptr) {
-		ASSERT_TRUE(engine);
-		return;
-	}
+	ASSERT_TRUE(engine);
 
 	int ret = engine->EnableProfiler(true);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->DumpProfileToFile("profile_data_" + backend_name +
 									"_caffe_model.txt");
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->LoadConfigFile();
 	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->BindBackend(&config);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_capacity capacity;
 	ret = engine->GetBackendCapacity(&capacity);
@@ -592,10 +553,7 @@ TEST_P(InferenceEngineCaffeTest, Inference)
 
 	std::vector<std::string> models;
 	int model_type = GetModelInfo(model_paths, models);
-	if (model_type == -1) {
-		ASSERT_NE(model_type, -1);
-		return;
-	}
+	ASSERT_NE(model_type, -1);
 
 	inference_engine_layer_property input_property;
 	std::vector<std::string>::iterator iter;
@@ -613,10 +571,7 @@ TEST_P(InferenceEngineCaffeTest, Inference)
 	}
 
 	ret = engine->SetInputLayerProperty(input_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_layer_property output_property;
 
@@ -625,23 +580,14 @@ TEST_P(InferenceEngineCaffeTest, Inference)
 	}
 
 	ret = engine->SetOutputLayerProperty(output_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->Load(models, (inference_model_format_e) model_type);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	std::vector<inference_engine_tensor_buffer> inputs, outputs;
 	ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	// Copy input image tensor data from a given file to input tensor buffer.
 	for (int i = 0; i < (int) image_paths.size(); ++i) {
@@ -756,32 +702,20 @@ TEST_P(InferenceEngineDldtTest, Inference)
 									   .target_devices = target_devices };
 
 	auto engine = std::make_unique<InferenceEngineCommon>();
-	if (engine == nullptr) {
-		ASSERT_TRUE(engine);
-		return;
-	}
+	ASSERT_TRUE(engine);
 
 	int ret = engine->EnableProfiler(true);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->DumpProfileToFile("profile_data_" + backend_name +
 									"_dldt_model.txt");
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->LoadConfigFile();
 	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->BindBackend(&config);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_capacity capacity;
 	ret = engine->GetBackendCapacity(&capacity);
@@ -792,10 +726,7 @@ TEST_P(InferenceEngineDldtTest, Inference)
 
 	std::vector<std::string> models;
 	int model_type = GetModelInfo(model_paths, models);
-	if (model_type == -1) {
-		ASSERT_NE(model_type, -1);
-		return;
-	}
+	ASSERT_NE(model_type, -1);
 
 	inference_engine_layer_property input_property;
 	std::vector<std::string>::iterator iter;
@@ -813,10 +744,7 @@ TEST_P(InferenceEngineDldtTest, Inference)
 	}
 
 	ret = engine->SetInputLayerProperty(input_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_layer_property output_property;
 
@@ -825,23 +753,14 @@ TEST_P(InferenceEngineDldtTest, Inference)
 	}
 
 	ret = engine->SetOutputLayerProperty(output_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->Load(models, (inference_model_format_e) model_type);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	std::vector<inference_engine_tensor_buffer> inputs, outputs;
 	ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	// Copy input image tensor data from a given file to input tensor buffer.
 	for (int i = 0; i < (int) image_paths.size(); ++i) {
diff --git a/test/src/inference_engine_tc.cpp b/test/src/inference_engine_tc.cpp
index b46b4e8..d5b5b92 100644
--- a/test/src/inference_engine_tc.cpp
+++ b/test/src/inference_engine_tc.cpp
@@ -244,10 +244,7 @@ TEST_P(InferenceEngineTestCase_G3, Load_P)
 
 	std::vector<std::string> models;
 	int model_type = GetModelInfo(model_paths, models);
-	if (model_type == -1) {
-		ASSERT_NE(model_type, -1);
-		return;
-	}
+	ASSERT_NE(model_type, -1);
 
 	ret = engine->Load(models, (inference_model_format_e) model_type);
 	EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
@@ -548,10 +545,7 @@ TEST_P(InferenceEngineTestCase_G6, Inference_P)
 	std::cout << "backend = " << backend_name << std::endl;
 
 	auto engine = std::make_unique<InferenceEngineCommon>();
-	if (engine == nullptr) {
-		ASSERT_TRUE(engine);
-		return;
-	}
+	ASSERT_TRUE(engine);
 
 	if (profiler > INFERENCE_ENGINE_PROFILER_OFF &&
 		profiler < INFERENCE_ENGINE_PROFILER_MAX) {
@@ -574,10 +568,7 @@ TEST_P(InferenceEngineTestCase_G6, Inference_P)
 
 	std::vector<std::string> models;
 	int model_type = GetModelInfo(model_paths, models);
-	if (model_type == -1) {
-		ASSERT_NE(model_type, -1);
-		return;
-	}
+	ASSERT_NE(model_type, -1);
 
 	inference_engine_layer_property input_property;
 	std::vector<std::string>::iterator iter;
@@ -595,10 +586,7 @@ TEST_P(InferenceEngineTestCase_G6, Inference_P)
 	}
 
 	ret = engine->SetInputLayerProperty(input_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	inference_engine_layer_property output_property;
 
@@ -607,23 +595,14 @@ TEST_P(InferenceEngineTestCase_G6, Inference_P)
 	}
 
 	ret = engine->SetOutputLayerProperty(output_property);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	ret = engine->Load(models, (inference_model_format_e) model_type);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	std::vector<inference_engine_tensor_buffer> inputs, outputs;
 	ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
-	if (ret != INFERENCE_ENGINE_ERROR_NONE) {
-		ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
-		return;
-	}
+	ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
 
 	// Copy input image tensor data from a given file to input tensor buffer.
 	for (int i = 0; i < (int) image_paths.size(); ++i) {