From: Inki Dae <inki.dae@samsung.com>
Date: Mon, 2 Jan 2023 01:16:08 +0000 (+0900)
Subject: fix a bug to incorrect tensor info
X-Git-Tag: accepted/tizen/7.0/unified/20230920.022050~3
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=5f06cc191f36ac3a14b84e64fe587228a4f91b1a;p=platform%2Fcore%2Fmultimedia%2Finference-engine-mlapi.git

fix a bug to incorrect tensor info

[Version] : 0.4.7
[Issue type] : bug fix

Fixed a bug that incorrect tensor info is used by
destroy previous tensor handle of mlapi before creating a new one.

In case of face recognition scenario, output tensor info
can be updated in runtime because new class can be added
while training - adding a new class allows to change output
tensor size.

Change-Id: Idb66bf213de8316b95f7e1697693bbf162e8c7a7
Signed-off-by: Inki Dae <inki.dae@samsung.com>
---

diff --git a/packaging/inference-engine-mlapi.spec b/packaging/inference-engine-mlapi.spec
index a1cd8c1..b3aa670 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:    0.4.6
+Version:    0.4.7
 Release:    0
 Group:      Multimedia/Libraries
 License:    Apache-2.0
diff --git a/src/inference_engine_mlapi.cpp b/src/inference_engine_mlapi.cpp
index 645a303..90ee0c8 100644
--- a/src/inference_engine_mlapi.cpp
+++ b/src/inference_engine_mlapi.cpp
@@ -501,13 +501,17 @@ 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 (ret != ML_ERROR_NONE) {
-				LOGE("Failed to request ml_tensors_data_create(%d).", ret);
-				return INFERENCE_ENGINE_ERROR_INVALID_OPERATION;
-			}
+		// In runtime, output tensor info can be updated.
+		// So make sure to destroy previous handle. I.e., face recognition framework.
+		if (mOutputDataHandle) {
+			ml_tensors_data_destroy(mOutputDataHandle);
+			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;
 		}
 
 		// TODO. Cache tensor info and reduce function call in UpdateTensorsInfo()