From: heechul.jeon <heechul.jeon@samsung.com>
Date: Thu, 30 Jun 2022 07:15:52 +0000 (+0900)
Subject: InferenceTFLite: Remove code redundancy
X-Git-Tag: submit/tizen/20220824.063348~1
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=689f25360305adb5c00b0b78ac04477aefdda256;p=platform%2Fcore%2Fmultimedia%2Finference-engine-tflite.git

InferenceTFLite: Remove code redundancy

[Versin] 0.0.4
[Issue type] code cleanup

Change-Id: Ia6942cd730aedd74f5acbd98b75f6b4e1b7dabfa
Signed-off-by: heechul.jeon <heechul.jeon@samsung.com>
---

diff --git a/packaging/inference-engine-tflite.spec b/packaging/inference-engine-tflite.spec
index fb56e8c..8277bda 100644
--- a/packaging/inference-engine-tflite.spec
+++ b/packaging/inference-engine-tflite.spec
@@ -1,6 +1,6 @@
 Name:       inference-engine-tflite
 Summary:    Tensorflow-Lite based implementation of inference-engine-interface
-Version:    0.0.3
+Version:    0.0.4
 Release:    0
 Group:      Multimedia/Libraries
 License:    Apache-2.0
diff --git a/src/inference_engine_tflite.cpp b/src/inference_engine_tflite.cpp
index 36e671d..e78b971 100644
--- a/src/inference_engine_tflite.cpp
+++ b/src/inference_engine_tflite.cpp
@@ -366,81 +366,22 @@ namespace TFLiteImpl
 
 	int InferenceTFLite::SetInterpreterInfo()
 	{
+		int ret = INFERENCE_ENGINE_ERROR_NONE;
 		LOGI("ENTER");
+
 		if (mInputLayers.empty()) {
 			LOGI("mInputLayer is empty. layers and tensors that mInterpreter has will be returned.");
 
-			mInputLayers.clear();
-			for (auto& layer : mInputLayerId) {
-
-				std::vector<size_t> shape_nhwc;
-
-				for (int idx = 0;
-					 idx < mInterpreter->tensor(layer.second)->dims->size; idx++) {
-					shape_nhwc.push_back(
-							mInterpreter->tensor(layer.second)->dims->data[idx]);
-				}
-
-				inference_engine_tensor_info tensor_info {
-					shape_nhwc, INFERENCE_TENSOR_SHAPE_NHWC,
-					INFERENCE_TENSOR_DATA_TYPE_NONE, 1
-				};
-
-				if (mInterpreter->tensor(layer.second)->type == kTfLiteUInt8) {
-					LOGI("type is kTfLiteUInt8");
-					tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_UINT8;
-				} else if (mInterpreter->tensor(layer.second)->type ==
-						   kTfLiteFloat32) {
-					LOGI("type is kTfLiteFloat32");
-					tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
-				} else {
-					LOGE("Not supported");
-					return INFERENCE_ENGINE_ERROR_NOT_SUPPORTED_FORMAT;
-				}
-
-				for (auto& dim : tensor_info.shape) {
-					tensor_info.size *= dim;
-				}
-				mInputLayers.insert(std::make_pair(mInterpreter->tensor(layer.second)->name, tensor_info));
-			}
+			ret = FillLayer(mInputLayers, mInputLayerId);
+			if (ret != INFERENCE_ENGINE_ERROR_NONE)
+				return ret;
 		}
 
 		if (mOutputLayers.empty()) {
 			LOGI("mOutputLayers is empty. layers and tensors that mInterpreter has will be returned.");
-
-			mOutputLayers.clear();
-			for (auto& layer : mOutputLayerId) {
-
-				std::vector<size_t> shape_nhwc;
-
-				for (int idx = 0;
-					 idx < mInterpreter->tensor(layer.second)->dims->size; idx++) {
-					shape_nhwc.push_back(
-							mInterpreter->tensor(layer.second)->dims->data[idx]);
-				}
-
-				inference_engine_tensor_info tensor_info {
-					shape_nhwc, INFERENCE_TENSOR_SHAPE_NHWC,
-					INFERENCE_TENSOR_DATA_TYPE_NONE, 1
-				};
-
-				if (mInterpreter->tensor(layer.second)->type == kTfLiteUInt8) {
-					LOGI("type is kTfLiteUInt8");
-					tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_UINT8;
-				} else if (mInterpreter->tensor(layer.second)->type ==
-						   kTfLiteFloat32) {
-					LOGI("type is kTfLiteFloat32");
-					tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
-				} else {
-					LOGE("Not supported");
-					return INFERENCE_ENGINE_ERROR_NOT_SUPPORTED_FORMAT;
-				}
-
-				for (auto& dim : tensor_info.shape) {
-					tensor_info.size *= dim;
-				}
-				mOutputLayers.insert(std::make_pair(mInterpreter->tensor(layer.second)->name, tensor_info));
-			}
+			ret = FillLayer(mOutputLayers, mOutputLayerId);
+			if (ret != INFERENCE_ENGINE_ERROR_NONE)
+				return ret;
 		}
 		LOGI("LEAVE");
 		return INFERENCE_ENGINE_ERROR_NONE;
@@ -471,6 +412,49 @@ namespace TFLiteImpl
 		}
 	}
 
+	int InferenceTFLite::FillLayer(std::map<std::string, inference_engine_tensor_info>& layers,
+			std::map<std::string, int>& layerId)
+	{
+		layers.clear();
+		for (auto& layer : layerId) {
+
+			std::vector<size_t> shape_nhwc;
+
+			for (int idx = 0;
+					idx < mInterpreter->tensor(layer.second)->dims->size; idx++) {
+				shape_nhwc.push_back(
+						mInterpreter->tensor(layer.second)->dims->data[idx]);
+			}
+
+			inference_engine_tensor_info tensor_info {
+				shape_nhwc, INFERENCE_TENSOR_SHAPE_NHWC,
+				INFERENCE_TENSOR_DATA_TYPE_NONE, 1
+			};
+
+			switch (mInterpreter->tensor(layer.second)->type)
+			{
+			case kTfLiteUInt8:
+				LOGI("type is kTfLiteUInt8");
+				tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_UINT8;
+				break;
+			case kTfLiteFloat32:
+				LOGI("type is kTfLiteFloat32");
+				tensor_info.data_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+				break;
+			default:
+				LOGE("Not supported");
+				return INFERENCE_ENGINE_ERROR_NOT_SUPPORTED_FORMAT;
+			}
+
+			for (auto& dim : tensor_info.shape) {
+				tensor_info.size *= dim;
+			}
+			layers.insert(std::make_pair(mInterpreter->tensor(layer.second)->name, tensor_info));
+
+		}
+		return INFERENCE_ENGINE_ERROR_NONE;
+	}
+
 	extern "C"
 	{
 		class IInferenceEngineCommon *EngineCommonInit(void)
diff --git a/src/inference_engine_tflite_private.h b/src/inference_engine_tflite_private.h
index 33dd1f4..d491500 100644
--- a/src/inference_engine_tflite_private.h
+++ b/src/inference_engine_tflite_private.h
@@ -88,6 +88,8 @@ namespace TFLiteImpl
 		void FillLayerId(std::map<std::string, int>& layerId,
 				std::map<std::string, inference_engine_tensor_info>& layers,
 				const std::vector<int>& buffer);
+		int FillLayer(std::map<std::string, inference_engine_tensor_info>& layers,
+				std::map<std::string, int>& layerId);
 
 		std::unique_ptr<tflite::Interpreter> mInterpreter;
 		std::unique_ptr<tflite::FlatBufferModel> mFlatBuffModel;