Drop Yolo CellParm

Yolo object detection not use CellParm anymore

Change-Id: Id9842754634b6f134b02f417c96ea24f5fc69096
Signed-off-by: Kwanghoon Son <k.son@samsung.com>

5 files changed, 0 insertions, 116 deletions

diff --git a/mv_machine_learning/inference/include/DecodeInfo.h b/mv_machine_learning/inference/include/DecodeInfo.h
index 67c7e326..6a38e54e 100644
--- a/mv_machine_learning/inference/include/DecodeInfo.h
+++ b/mv_machine_learning/inference/include/DecodeInfo.h
@@ -60,16 +60,6 @@ struct AnchorParam
 	std::map<std::string, inference_score_type_e> supportedCellType;
 	std::vector<std::vector<double> > vxScales;
 	std::vector<std::vector<double> > vyScales;
-	int numAnchorsPerCell;
-};
-
-struct CellParam
-{
-	int numScales;
-	std::vector<int> scales;
-	int offsetScales;
-	inference_score_type_e type;
-	std::map<std::string, inference_score_type_e> supportedCellType;
 };
 
 struct NMSParam
@@ -105,17 +95,12 @@ struct DecodeInfo
 {
 	AnchorParam anchorParam;
 	std::vector<cv::Rect2f> anchorBoxes;
-	CellParam cellParam;
 	NMSParam nmsParam;
 	RotateParam rotParam;
 	RoiOptionParam roiOptParam;
 
 	DecodeInfo()
 	{
-		cellParam.type = INFERENCE_SCORE_TYPE_NORMAL;
-		cellParam.supportedCellType.insert({ "NORMAL", INFERENCE_SCORE_TYPE_NORMAL });
-		cellParam.supportedCellType.insert({ "SIGMOID", INFERENCE_SCORE_TYPE_SIGMOID });
-
 		nmsParam.mode = INFERENCE_BOX_NMS_TYPE_NONE;
 		nmsParam.iouThreshold = 0.2f;
 		nmsParam.supportedBoxNmsTypes.insert({ "STANDARD", INFERENCE_BOX_NMS_TYPE_STANDARD });
@@ -155,13 +140,6 @@ struct DecodeInfo
 	float GetAnchorHscale();
 	float CalculateScale(float min, float max, int index, int maxStride);
 
-	// Cell param
-	int ParseCellParam(JsonObject *root);
-	std::vector<int> &GetCellScalesAll();
-	int GetCellNumScales();
-	int GetCellOffsetScales();
-	inference_score_type_e GetCellType();
-
 	// Nms param
 	int ParseNms(JsonObject *root);
 	int GetNmsMode();
diff --git a/mv_machine_learning/inference/src/BoxInfo.cpp b/mv_machine_learning/inference/src/BoxInfo.cpp
index 5091db22..43f055d1 100644
--- a/mv_machine_learning/inference/src/BoxInfo.cpp
+++ b/mv_machine_learning/inference/src/BoxInfo.cpp
@@ -179,12 +179,6 @@ int BoxInfo::ParseDecodeInfo(JsonObject *root)
 			LOGE("Fail to ParseAnchorParam[%d]", ret);
 			return ret;
 		}
-	} else if (json_object_has_member(cObject, "cell")) {
-		ret = GetDecodeInfo().ParseCellParam(cObject);
-		if (ret != MEDIA_VISION_ERROR_NONE) {
-			LOGE("Fail to ParseCellParam[%d]", ret);
-			return ret;
-		}
 	} else {
 		LOGE("anchor is mandatory. Invalid metadata");
 		LOGI("LEAVE");
diff --git a/mv_machine_learning/inference/src/DecodeInfo.cpp b/mv_machine_learning/inference/src/DecodeInfo.cpp
index e49245a0..f33f0627 100644
--- a/mv_machine_learning/inference/src/DecodeInfo.cpp
+++ b/mv_machine_learning/inference/src/DecodeInfo.cpp
@@ -73,8 +73,6 @@ int DecodeInfo::ParseAnchorParam(JsonObject *root)
 			LOGI("aspectRatio: %.4f", aspectRatio);
 		}
 	} else if (anchorParam.mode == 1) { // Yolo
-		anchorParam.numAnchorsPerCell = static_cast<int>(json_object_get_int_member(object, "num_anchors"));
-
 		anchorParam.offsetAnchors = static_cast<int>(json_object_get_int_member(object, "offset_anchors"));
 		JsonArray *xScales = json_object_get_array_member(object, "x_scales");
 		JsonArray *yScales = json_object_get_array_member(object, "y_scales");
@@ -127,51 +125,6 @@ int DecodeInfo::ParseAnchorParam(JsonObject *root)
 	return MEDIA_VISION_ERROR_NONE;
 }
 
-int DecodeInfo::ParseCellParam(JsonObject *root)
-{
-	JsonObject *object = json_object_get_object_member(root, "cell");
-
-	cellParam.numScales = static_cast<int>(json_object_get_int_member(object, "num_scales"));
-
-	JsonArray *array = json_object_get_array_member(object, "scales");
-	unsigned int elements2 = json_array_get_length(array);
-	for (unsigned int elem2 = 0; elem2 < elements2; ++elem2) {
-		auto scale = static_cast<int>(json_array_get_int_element(array, elem2));
-		cellParam.scales.push_back(scale);
-		LOGI("scale: %d", scale);
-	}
-
-	cellParam.offsetScales = static_cast<int>(json_object_get_int_member(object, "offset_scales"));
-	try {
-		cellParam.type = GetSupportedType(object, "type", cellParam.supportedCellType);
-	} catch (const std::exception &e) {
-		LOGE("Invalid %s", e.what());
-		return MEDIA_VISION_ERROR_INVALID_OPERATION;
-	}
-
-	return MEDIA_VISION_ERROR_NONE;
-}
-
-std::vector<int> &DecodeInfo::GetCellScalesAll()
-{
-	return cellParam.scales;
-}
-
-int DecodeInfo::GetCellNumScales()
-{
-	return cellParam.numScales;
-}
-
-int DecodeInfo::GetCellOffsetScales()
-{
-	return cellParam.offsetScales;
-}
-
-inference_score_type_e DecodeInfo::GetCellType()
-{
-	return cellParam.type;
-}
-
 float DecodeInfo::CalculateScale(float min, float max, int index, int maxStride)
 {
 	return min + (max - min) * 1.0 * index / (maxStride - 1.0f);
diff --git a/mv_machine_learning/inference/src/Inference.cpp b/mv_machine_learning/inference/src/Inference.cpp
index 41a446de..9f3b25cf 100644
--- a/mv_machine_learning/inference/src/Inference.cpp
+++ b/mv_machine_learning/inference/src/Inference.cpp
@@ -1073,8 +1073,6 @@ int Inference::GetObjectDetectionResults(ObjectDetectionResults *results)
 				return MEDIA_VISION_ERROR_INVALID_OPERATION;
 			}
 			numberOfObjects = mOutputLayerProperty.layers[outputMeta.GetScoreName()].shape[scoreIndexes[0]];
-		} else if (outputMeta.GetBoxDecodingType() == INFERENCE_BOX_DECODING_TYPE_YOLO_ANCHOR) {
-			numberOfObjects = boxOffset / outputMeta.GetBoxDecodeInfo().GetCellNumScales() - 5;
 		}
 
 		ObjectDecoder objDecoder(mOutputTensorBuffers, outputMeta, boxOffset,
diff --git a/mv_machine_learning/inference/src/ObjectDecoder.cpp b/mv_machine_learning/inference/src/ObjectDecoder.cpp
index c617f2de..06e8653e 100644
--- a/mv_machine_learning/inference/src/ObjectDecoder.cpp
+++ b/mv_machine_learning/inference/src/ObjectDecoder.cpp
@@ -80,13 +80,6 @@ Box ObjectDecoder::decodeBox(int idx, float score, int label, int offset)
 	float cHeight =
 			mTensorBuffer.getValue<float>(mMeta.GetBoxName(), idx * mBoxOffset + offset + mMeta.GetBoxOrder()[3]);
 
-	if (mMeta.GetBoxDecodeInfo().GetCellType() == INFERENCE_SCORE_TYPE_SIGMOID) {
-		cx = PostProcess::sigmoid(cx);
-		cy = PostProcess::sigmoid(cy);
-		cWidth = PostProcess::sigmoid(cWidth);
-		cHeight = PostProcess::sigmoid(cHeight);
-	}
-
 	LOGI("cx:%.2f, cy:%.2f, cW:%.2f, cH:%.2f", cx, cy, cWidth, cHeight);
 	// convert type to ORIGIN_CENTER if ORIGIN_LEFTTOP
 	if (mMeta.GetBoxType() == INFERENCE_BOX_TYPE_ORIGIN_LEFTTOP) {
@@ -148,15 +141,6 @@ int ObjectDecoder::decode()
 	int ret = MEDIA_VISION_ERROR_NONE;
 	int totalIdx = mNumberOfOjects;
 
-	if (mMeta.GetBoxDecodingType() == INFERENCE_BOX_DECODING_TYPE_YOLO_ANCHOR) {
-		totalIdx = 0;
-		for (auto &scale : mMeta.GetBoxDecodeInfo().GetCellScalesAll()) {
-			totalIdx += (static_cast<int>(mScaleW) / scale * static_cast<int>(mScaleH) / scale) *
-						mMeta.GetBoxDecodeInfo().GetCellNumScales() / mMeta.GetBoxDecodeInfo().GetCellOffsetScales();
-		}
-		boxList.reserve(mNumberOfOjects);
-	}
-
 	for (int idx = 0; idx < totalIdx; ++idx) {
 		if (mMeta.GetBoxDecodingType() == INFERENCE_BOX_DECODING_TYPE_BYPASS) {
 			float score = decodeScore(idx);
@@ -183,29 +167,6 @@ int ObjectDecoder::decode()
 			boxList.push_back(boxes);
 		} else { // INFERENCE_BOX_DECODING_TYPE_YOLO_ANCHOR
 			int cellIdx = idx * mBoxOffset;
-			for (int j = 0; j < mMeta.GetBoxDecodeInfo().GetCellOffsetScales(); ++j) {
-				float score = decodeScore(cellIdx + (mNumberOfOjects + 5) * j + 4);
-				if (score <= 0.0f) {
-					continue;
-				}
-				LOGI("score[%d]: %.2f", j, score);
-				// need to check the score
-				float topObjScore = 0.0f;
-				int topObjIdx = 0;
-				for (int objIdx_ = 0; objIdx_ < mNumberOfOjects; ++objIdx_) {
-					float objScore_ = decodeScore(cellIdx + (mNumberOfOjects + 5) * j + 5 + objIdx_);
-					if (objScore_ > topObjScore) {
-						topObjScore = objScore_;
-						topObjIdx = objIdx_;
-					}
-				}
-
-				if (topObjScore < mMeta.GetScoreThreshold())
-					continue;
-
-				Box box = decodeBox(idx, topObjScore, topObjIdx, (mNumberOfOjects + 5) * j);
-				boxes.push_back(box);
-			}
 		}
 	}