From: Inki Dae <inki.dae@samsung.com>
Date: Tue, 7 Sep 2021 03:59:07 +0000 (+0900)
Subject: mv_machine_learning: introduce allocate function for TensorBuffer class
X-Git-Tag: submit/tizen/20210924.044804~9
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=a80a0852ed96593b8aa149f40e3dcb784d760058;p=platform%2Fcore%2Fapi%2Fmediavision.git

mv_machine_learning: introduce allocate function for TensorBuffer class

Added allocate function to allocate buffers for input and output tensors.
This patch drops code duplication - same code was used to allocate
input and output tensor buffers.

Change-Id: I0cfb46633a9a3fe74c734307ac9c15b5df064e63
Signed-off-by: Inki Dae <inki.dae@samsung.com>
---

diff --git a/mv_machine_learning/mv_inference/inference/include/TensorBuffer.h b/mv_machine_learning/mv_inference/inference/include/TensorBuffer.h
index 9054ec7b..de4beb31 100644
--- a/mv_machine_learning/mv_inference/inference/include/TensorBuffer.h
+++ b/mv_machine_learning/mv_inference/inference/include/TensorBuffer.h
@@ -48,7 +48,9 @@ namespace inference
 
 		bool empty();
 		bool exist(std::string name);
-		void clear();
+		int allocate(inference_engine_tensor_buffer& tensor_buffer,
+					 const inference_engine_tensor_info& tensor_info);
+		void release();
 		size_t size();
 
 		IETensorBuffer& getAllTensorBuffer();
diff --git a/mv_machine_learning/mv_inference/inference/src/Inference.cpp b/mv_machine_learning/mv_inference/inference/src/Inference.cpp
index 835bc6fa..68db6054 100755
--- a/mv_machine_learning/mv_inference/inference/src/Inference.cpp
+++ b/mv_machine_learning/mv_inference/inference/src/Inference.cpp
@@ -637,11 +637,11 @@ namespace inference
 		LOGI("ENTER");
 
 		if (!mInputTensorBuffers.empty()) {
-			mInputTensorBuffers.clear();
+			mInputTensorBuffers.release();
 		}
 
 		if (!mOutputTensorBuffers.empty()) {
-			mOutputTensorBuffers.clear();
+			mOutputTensorBuffers.release();
 		}
 
 		LOGI("LEAVE");
@@ -685,34 +685,15 @@ namespace inference
 		// then allocate the buffers at here.
 		if (mInputTensorBuffers.empty()) {
 			for(auto& layer : mInputLayerProperty.layers) {
-				const inference_engine_tensor_info& tensor_info = layer.second;
 				inference_engine_tensor_buffer tensor_buffer;
-				if (tensor_info.data_type ==
-					INFERENCE_TENSOR_DATA_TYPE_FLOAT32) {
-					tensor_buffer.buffer = new float[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-						   INFERENCE_TENSOR_DATA_TYPE_UINT8) {
-					tensor_buffer.buffer = new unsigned char[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-						   INFERENCE_TENSOR_DATA_TYPE_UINT16) {
-					tensor_buffer.buffer = new unsigned short[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else {
-					LOGE("Invalid input tensor data type.");
-					return MEDIA_VISION_ERROR_INVALID_PARAMETER;
-				}
 
-				if (tensor_buffer.buffer == NULL) {
-					LOGE("Fail to allocate input tensor buffer.");
-					return MEDIA_VISION_ERROR_OUT_OF_MEMORY;
+				ret = mInputTensorBuffers.allocate(tensor_buffer, layer.second);
+				if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+					LOGE("Fail to allocate tensor buffer.");
+					mInputTensorBuffers.release();
+					return ret;
 				}
 
-				LOGI("Allocated input tensor buffer(size = %zu, data type = %d)",
-					 tensor_info.size, tensor_info.data_type);
-				tensor_buffer.owner_is_backend = 0;
-				tensor_buffer.data_type = tensor_info.data_type;
 				mInputTensorBuffers.setTensorBuffer(layer.first, tensor_buffer);
 			}
 		}
@@ -737,45 +718,15 @@ namespace inference
 		// then allocate the buffers at here.
 		if (mOutputTensorBuffers.empty()) {
 			for (auto& layer : mOutputLayerProperty.layers) {
-				const inference_engine_tensor_info& tensor_info = layer.second;
 				inference_engine_tensor_buffer tensor_buffer;
-				if (tensor_info.data_type ==
-					INFERENCE_TENSOR_DATA_TYPE_FLOAT32) {
-					tensor_buffer.buffer = new float[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-					INFERENCE_TENSOR_DATA_TYPE_INT64) {
-					tensor_buffer.buffer = new long long[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-					INFERENCE_TENSOR_DATA_TYPE_UINT32) {
-					tensor_buffer.buffer = new unsigned int[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-					INFERENCE_TENSOR_DATA_TYPE_UINT8) {
-					tensor_buffer.buffer = new char[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else if (tensor_info.data_type ==
-						   INFERENCE_TENSOR_DATA_TYPE_UINT16) {
-					tensor_buffer.buffer = new unsigned short[tensor_info.size];
-					tensor_buffer.size = tensor_info.size;
-				} else {
-					LOGE("Invalid output tensor data type.");
-					CleanupTensorBuffers();
-					return MEDIA_VISION_ERROR_INVALID_PARAMETER;
-				}
 
-				if (tensor_buffer.buffer == NULL) {
-					LOGE("Fail to allocate output tensor buffer.");
-					CleanupTensorBuffers();
-					return MEDIA_VISION_ERROR_OUT_OF_MEMORY;
+				ret = mInputTensorBuffers.allocate(tensor_buffer, layer.second);
+				if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+					LOGE("Fail to allocate tensor buffer.");
+					mInputTensorBuffers.release();
+					return ret;
 				}
 
-				LOGI("Allocated output tensor buffer(size = %zu, data type = %d)",
-					 tensor_info.size, tensor_info.data_type);
-
-				tensor_buffer.owner_is_backend = 0;
-				tensor_buffer.data_type = tensor_info.data_type;
 				mOutputTensorBuffers.setTensorBuffer(layer.first, tensor_buffer);
 			}
 		}
diff --git a/mv_machine_learning/mv_inference/inference/src/TensorBuffer.cpp b/mv_machine_learning/mv_inference/inference/src/TensorBuffer.cpp
index 6e4fc30c..383ba508 100644
--- a/mv_machine_learning/mv_inference/inference/src/TensorBuffer.cpp
+++ b/mv_machine_learning/mv_inference/inference/src/TensorBuffer.cpp
@@ -39,7 +39,44 @@ namespace inference
 		return getTensorBuffer(name) != nullptr;
 	}
 
-	void TensorBuffer::clear()
+	int TensorBuffer::allocate(inference_engine_tensor_buffer& tensor_buffer,
+							   const inference_engine_tensor_info& tensor_info)
+	{
+		if (tensor_info.data_type <= INFERENCE_TENSOR_DATA_TYPE_NONE ||
+				tensor_info.data_type >= INFERENCE_TENSOR_DATA_TYPE_MAX) {
+			LOGE("Invalid tensor data type.");
+			return MEDIA_VISION_ERROR_INVALID_PARAMETER;
+		}
+
+		try {
+			if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_FLOAT32)
+				tensor_buffer.buffer = new float[tensor_info.size];
+			else if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_INT64)
+				tensor_buffer.buffer = new long long[tensor_info.size];
+			else if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_UINT64)
+				tensor_buffer.buffer = new unsigned long long[tensor_info.size];
+			else if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_UINT32)
+				tensor_buffer.buffer = new unsigned int[tensor_info.size];
+			else if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_UINT8)
+				tensor_buffer.buffer = new char[tensor_info.size];
+			else if (tensor_info.data_type == INFERENCE_TENSOR_DATA_TYPE_UINT16)
+				tensor_buffer.buffer = new unsigned short[tensor_info.size];
+		}  catch (const std::bad_alloc& e) {
+			LOGE("Fail to allocate tensor buffer.(%s)", e.what());
+			return MEDIA_VISION_ERROR_OUT_OF_MEMORY;
+		}
+
+		tensor_buffer.size = tensor_info.size;
+
+		LOGI("Allocated tensor buffer(size = %zu, data type = %d)",
+				tensor_info.size, tensor_info.data_type);
+		tensor_buffer.owner_is_backend = 0;
+		tensor_buffer.data_type = tensor_info.data_type;
+
+		return MEDIA_VISION_ERROR_NONE;
+	}
+
+	void TensorBuffer::release()
 	{
 		for (auto& tensorBuffer : mTensorBuffer) {
 			auto& tBuffer = tensorBuffer.second;
@@ -85,8 +122,7 @@ namespace inference
 
 	bool TensorBuffer::setTensorBuffer(std::string name, inference_engine_tensor_buffer& buffer)
 	{
-		if (name.empty() ||
-			buffer.buffer == nullptr) {
+		if (name.empty() || buffer.buffer == nullptr) {
 			LOGE("Invalid parameters: %s, %p", name.c_str(), buffer.buffer);
 			return false;
 		}