drop code duplicationsubmit/tizen/20220812.070529tizen_devel

[Version] : 0.4.5-0
[Issue type] : code cleanup

Drop the code duplication for cleanup.

Change-Id: Ic41991cb335b12bd21a6caba159b5b2bc5881576
Signed-off-by: Inki Dae <inki.dae@samsung.com>

3 files changed, 43 insertions, 72 deletions

diff --git a/packaging/inference-engine-mlapi.spec b/packaging/inference-engine-mlapi.spec
index 1bf2368..d37f2c9 100644
--- a/packaging/inference-engine-mlapi.spec
+++ b/packaging/inference-engine-mlapi.spec
@@ -1,6 +1,6 @@
 Name:       inference-engine-mlapi
 Summary:    ML Single API backend of NNStreamer for MediaVision
-Version:    0.4.4
+Version:    0.4.5
 Release:    0
 Group:      Multimedia/Libraries
 License:    Apache-2.0
diff --git a/src/inference_engine_mlapi.cpp b/src/inference_engine_mlapi.cpp
index b0ee2f5..b23ffe3 100644
--- a/src/inference_engine_mlapi.cpp
+++ b/src/inference_engine_mlapi.cpp
@@ -387,54 +387,29 @@ namespace MLAPIImpl
 		return err;
 	}
 
-	int InferenceMLAPI::GetInputTensorBuffers(
-			std::map<std::string, inference_engine_tensor_buffer> &buffers)
+	int InferenceMLAPI::GetTensorInfo(std::map<std::string, int>& designated_layers,
+									  std::map<std::string, inference_engine_tensor_buffer> &buffers,
+									  ml_tensors_data_h& dataHandle, ml_tensors_info_h& infoHandle)
 	{
-		LOGI("ENTER");
-
-		// TODO. Implement this function according to a given ML Single API backend properly.
-
-		// ML Single API will always provide internal tensor buffers so
-		// get the tensor buffers back to Mediavision framework so that
-		// Mediavision framework doesn't allocate the tensor buffers internally.
-
-		buffers.clear();
-
-		int ret = INFERENCE_ENGINE_ERROR_NONE;
-
-		// TODO. Below is test code, should we allocate new buffer for every inference?
-		if (mInputDataHandle == NULL) {
-			ret = ml_tensors_data_create(mInputInfoHandle, &mInputDataHandle);
-			if (ret != ML_ERROR_NONE) {
-				LOGE("Failed to request ml_tensors_data_create(%d).", ret);
-				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
-			}
-		}
-
-		// TODO. Cache tensor info and reduce function call in UpdateTensorsInfo()
-		for (auto& input : mDesignated_inputs) {
+		for (auto& layer : designated_layers) {
 			inference_engine_tensor_buffer in_buffer;
 			ml_tensor_type_e in_type;
 
-			ret = ml_tensors_data_get_tensor_data(mInputDataHandle, input.second, &in_buffer.buffer, &in_buffer.size);
+			int ret = ml_tensors_data_get_tensor_data(dataHandle, layer.second, &in_buffer.buffer, &in_buffer.size);
 			if (ret != ML_ERROR_NONE) {
 				LOGE("Failed to request ml_tensors_data_get_tensor_data(%d).", ret);
-				ml_tensors_data_destroy(mInputDataHandle);
-
 				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
 			}
 
 			LOGE("buffer = %p, size = %zu\n", in_buffer.buffer, in_buffer.size);
 
-			ret = ml_tensors_info_get_tensor_type(mInputInfoHandle, input.second, &in_type);
+			ret = ml_tensors_info_get_tensor_type(infoHandle, layer.second, &in_type);
 			if (ret != ML_ERROR_NONE) {
 				LOGE("Failed to request ml_tensors_info_get_tensor_type(%d).", ret);
-				ml_tensors_data_destroy(mInputDataHandle);
-
 				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
 			}
 
-			LOGI("input tensor type = %d", in_type);
+			LOGI("tensor type = %d", in_type);
 
 			int type = 0;
 
@@ -442,28 +417,24 @@ namespace MLAPIImpl
 				type = ConvertTensorTypeToInternal(in_type);
 			} catch (const std::invalid_argument& ex) {
 				LOGE("Error (%s) (%d)", ex.what(), in_type);
-				ml_tensors_data_destroy(mInputDataHandle);
-
 				return INFERENCE_ENGINE_ERROR_INVALID_PARAMETER;
 			}
 
 			in_buffer.data_type = static_cast<inference_tensor_data_type_e>(type);
 			in_buffer.owner_is_backend = 1;
 
-			buffers.insert(std::make_pair(input.first, in_buffer));
+			buffers.insert(std::make_pair(layer.first, in_buffer));
 		}
 
-		LOGI("LEAVE");
-
 		return INFERENCE_ENGINE_ERROR_NONE;
 	}
 
-	int InferenceMLAPI::GetOutputTensorBuffers(
+	int InferenceMLAPI::GetInputTensorBuffers(
 			std::map<std::string, inference_engine_tensor_buffer> &buffers)
 	{
 		LOGI("ENTER");
 
-		// TODO. Need to check if model file loading is done.
+		// TODO. Implement this function according to a given ML Single API backend properly.
 
 		// ML Single API will always provide internal tensor buffers so
 		// get the tensor buffers back to Mediavision framework so that
@@ -474,8 +445,8 @@ namespace MLAPIImpl
 		int ret = INFERENCE_ENGINE_ERROR_NONE;
 
 		// TODO. Below is test code, should we allocate new buffer for every inference?
-		if (mOutputDataHandle == NULL) {
-			ret = ml_tensors_data_create(mOutputInfoHandle, &mOutputDataHandle);
+		if (mInputDataHandle == NULL) {
+			ret = ml_tensors_data_create(mInputInfoHandle, &mInputDataHandle);
 			if (ret != ML_ERROR_NONE) {
 				LOGE("Failed to request ml_tensors_data_create(%d).", ret);
 				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
@@ -483,47 +454,44 @@ namespace MLAPIImpl
 		}
 
 		// TODO. Cache tensor info and reduce function call in UpdateTensorsInfo()
-		for (auto& output : mDesignated_outputs) {
-			inference_engine_tensor_buffer out_buffer;
-			ml_tensor_type_e out_type;
-
-			ret = ml_tensors_data_get_tensor_data(mOutputDataHandle, output.second, &out_buffer.buffer, &out_buffer.size);
-			if (ret != ML_ERROR_NONE) {
-				LOGE("Failed to request ml_tensors_data_get_tensor_data(%d).", ret);
-				ml_tensors_data_destroy(mOutputDataHandle);
+		ret = GetTensorInfo(mDesignated_inputs, buffers, mInputDataHandle, mInputInfoHandle);
+		if (ret != INFERENCE_ENGINE_ERROR_NONE)
+			ml_tensors_data_destroy(mInputDataHandle);
 
-				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
-			}
+		LOGI("LEAVE");
 
-			LOGE("buffer = %p, size = %zu\n", out_buffer.buffer, out_buffer.size);
+		return INFERENCE_ENGINE_ERROR_NONE;
+	}
 
-			ret = ml_tensors_info_get_tensor_type(mOutputInfoHandle, output.second, &out_type);
-			if (ret != ML_ERROR_NONE) {
-				LOGE("Failed to request ml_tensors_info_get_tensor_type(%d).", ret);
-				ml_tensors_data_destroy(mOutputDataHandle);
+	int InferenceMLAPI::GetOutputTensorBuffers(
+			std::map<std::string, inference_engine_tensor_buffer> &buffers)
+	{
+		LOGI("ENTER");
 
-				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
-			}
+		// TODO. Need to check if model file loading is done.
 
-			LOGI("output tensor type = %d", out_type);
+		// ML Single API will always provide internal tensor buffers so
+		// get the tensor buffers back to Mediavision framework so that
+		// Mediavision framework doesn't allocate the tensor buffers internally.
 
-			int type = 0;
+		buffers.clear();
 
-			try {
-				type = ConvertTensorTypeToInternal(out_type);
-			} catch (const std::invalid_argument& ex) {
-				LOGE("Error (%s) (%d)", ex.what(), out_type);
-				ml_tensors_data_destroy(mOutputDataHandle);
+		int ret = INFERENCE_ENGINE_ERROR_NONE;
 
-				return INFERENCE_ENGINE_ERROR_INVALID_PARAMETER;
+		// TODO. Below is test code, should we allocate new buffer for every inference?
+		if (mOutputDataHandle == NULL) {
+			ret = ml_tensors_data_create(mOutputInfoHandle, &mOutputDataHandle);
+			if (ret != ML_ERROR_NONE) {
+				LOGE("Failed to request ml_tensors_data_create(%d).", ret);
+				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
 			}
-
-			out_buffer.data_type = static_cast<inference_tensor_data_type_e>(type);
-			out_buffer.owner_is_backend = 1;
-
-			buffers.insert(std::make_pair(output.first, out_buffer));
 		}
 
+		// TODO. Cache tensor info and reduce function call in UpdateTensorsInfo()
+		ret = GetTensorInfo(mDesignated_outputs, buffers, mOutputDataHandle, mOutputInfoHandle);
+		if (ret != INFERENCE_ENGINE_ERROR_NONE)
+			ml_tensors_data_destroy(mOutputDataHandle);
+
 		LOGI("LEAVE");
 
 		return INFERENCE_ENGINE_ERROR_NONE;
diff --git a/src/inference_engine_mlapi_private.h b/src/inference_engine_mlapi_private.h
index be74a6c..60d2570 100644
--- a/src/inference_engine_mlapi_private.h
+++ b/src/inference_engine_mlapi_private.h
@@ -91,6 +91,9 @@ namespace MLAPIImpl
 		std::tuple<ml_nnfw_type_e, ml_nnfw_hw_e> GetNNFWInfo();
 		std::string GetModelPath(const std::vector<std::string>& model_paths);
 		const char *GetCustomProp();
+		int GetTensorInfo(std::map<std::string, int>& designated_layers,
+						  std::map<std::string, inference_engine_tensor_buffer> &buffers,
+						  ml_tensors_data_h& dataHandle, ml_tensors_info_h& infoHandle);
 
 		int mPluginType;
 		int mTargetDevice;