From: Kwanghoon Son <k.son@samsung.com>
Date: Mon, 12 Aug 2024 04:01:49 +0000 (+0000)
Subject: Add dynamic tensor mode
X-Git-Tag: accepted/tizen/unified/20240820.163517^0
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=025a79b2c1b07c77be133dc67f528f8e0fcf9e9e;p=platform%2Fcore%2Fmultimedia%2Finference-engine-tflite.git

Add dynamic tensor mode

If dynamic tensor, memcpy buffer.

Change-Id: I6d4bb96bc12e286ab3da15f07c3fc3e8900c519d
Signed-off-by: Kwanghoon Son <k.son@samsung.com>
---

diff --git a/src/inference_engine_tflite.cpp b/src/inference_engine_tflite.cpp
index edb27c2..ee52348 100644
--- a/src/inference_engine_tflite.cpp
+++ b/src/inference_engine_tflite.cpp
@@ -143,6 +143,14 @@ namespace TFLiteImpl
 			return INFERENCE_ENGINE_ERROR_OUT_OF_MEMORY;
 		}
 
+		// find dynamic tensor
+		mIsDynamicTensorMode = false;
+		for (size_t i = 0; i < mInterpreter->subgraphs_size(); i++)
+			if (mInterpreter->subgraph(i)->HasDynamicTensors()) {
+				LOGI("Dynamic tensor mode is enabled");
+				mIsDynamicTensorMode = true;
+			}
+
 		return ret;
 	}
 
@@ -154,6 +162,8 @@ namespace TFLiteImpl
 		if (mInputLayers.empty()) {
 			SetInterpreterInfo();
 		}
+		if (mIsDynamicTensorMode)
+			return INFERENCE_ENGINE_ERROR_NONE;
 
 		for (auto& layer : mInputLayers) {
 			size_t size = 1;
@@ -190,6 +200,9 @@ namespace TFLiteImpl
 			SetInterpreterInfo();
 		}
 
+		if (mIsDynamicTensorMode)
+			return INFERENCE_ENGINE_ERROR_NONE;
+
 		for (auto& layer : mOutputLayers) {
 			inference_engine_tensor_buffer buffer;
 			size_t size = 1;
@@ -346,6 +359,29 @@ namespace TFLiteImpl
 			std::map<std::string, inference_engine_tensor_buffer> &output_buffers)
 	{
 		LOGI("ENTER");
+		if (mIsDynamicTensorMode)
+			for (auto &input_buffer : input_buffers) {
+				void *pBuff;
+				switch (mInterpreter->tensor(mInputLayerId[input_buffer.first])->type) {
+				case kTfLiteUInt8:
+					LOGI("type is kTfLiteUInt8");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<uint8_t>(mInputLayerId[input_buffer.first]));
+					break;
+				case kTfLiteInt64:
+					LOGI("type is kTfLiteInt64");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<int64_t>(mInputLayerId[input_buffer.first]));
+					break;
+				case kTfLiteFloat32:
+					LOGI("type is kTfLiteFloat32");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<float>(mInputLayerId[input_buffer.first]));
+					break;
+				default:
+					LOGE("Not supported");
+					return INFERENCE_ENGINE_ERROR_NOT_SUPPORTED_FORMAT;
+				}
+				memcpy(pBuff, input_buffer.second.buffer, input_buffer.second.size);
+			}
+
 		TfLiteStatus status = mInterpreter->Invoke();
 
 		if (status != kTfLiteOk) {
@@ -353,6 +389,29 @@ namespace TFLiteImpl
 			return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
 		}
 
+		if (mIsDynamicTensorMode)
+			for (auto &output_buffer : output_buffers) {
+				void *pBuff;
+				switch (mInterpreter->tensor(mOutputLayerId[output_buffer.first])->type) {
+				case kTfLiteUInt8:
+					LOGI("type is kTfLiteUInt8");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<uint8_t>(mOutputLayerId[output_buffer.first]));
+					break;
+				case kTfLiteInt64:
+					LOGI("type is kTfLiteInt64");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<int64_t>(mOutputLayerId[output_buffer.first]));
+					break;
+				case kTfLiteFloat32:
+					LOGI("type is kTfLiteFloat32");
+					pBuff = static_cast<void *>(mInterpreter->typed_tensor<float>(mOutputLayerId[output_buffer.first]));
+					break;
+				default:
+					LOGE("Not supported");
+					return INFERENCE_ENGINE_ERROR_NOT_SUPPORTED_FORMAT;
+				}
+				memcpy(output_buffer.second.buffer, pBuff, output_buffer.second.size);
+			}
+
 		LOGI("LEAVE");
 		return INFERENCE_ENGINE_ERROR_NONE;
 	}
diff --git a/src/inference_engine_tflite_private.h b/src/inference_engine_tflite_private.h
index 8009022..1f366a6 100644
--- a/src/inference_engine_tflite_private.h
+++ b/src/inference_engine_tflite_private.h
@@ -105,6 +105,7 @@ namespace TFLiteImpl
 		int mTargetTypes { INFERENCE_TARGET_NONE };
 
 		TfLiteDelegate *mDelegate {};
+		bool mIsDynamicTensorMode;
 	};
 
 } /* InferenceEngineImpl */