Fix logger error in DeepStream 6.0 / 6.0.1 + Change output classes format + Fixes

docs/DAMOYOLO.md

@@ -43,30 +43,6 @@ Generate the ONNX model file (example for DAMO-YOLO-S*)
 python3 export_damoyolo.py -w damoyolo_tinynasL25_S_477.pth -c configs/damoyolo_tinynasL25_S.py --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 11 or lower. The default opset is 11.
-
-```
---opset 11
-```
-
 **NOTE**: To change the inference size (defaut: 640)
 
 ```
@@ -88,6 +64,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 11 or lower. The default opset is 11.
+
+```
+--opset 11
+```
+
 #### 5. Copy generated files
 
 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.

docs/PPYOLOE.md

@@ -41,13 +41,13 @@ pip3 install onnx onnxsim onnxruntime
 python3 export_ppyoloe.py -w ppyoloe_plus_crn_s_80e_coco.pdparams -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
 
 ```
 --simplify
 ```
 
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
 
 ```
 --dynamic

docs/YOLONAS.md

@@ -46,30 +46,6 @@ Generate the ONNX model file (example for YOLO-NAS S)
 python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 14.
-
-```
---opset 12
-```
-
 **NOTE**: Model names
 
 ```
@@ -88,6 +64,18 @@ or
 -m yolo_nas_l
 ```
 
+**NOTE**: Number of classes (example for 80 classes)
+
+```
+-n 80
+```
+
+or
+
+```
+--classes 80
+```
+
 **NOTE**: To change the inference size (defaut: 640)
 
 ```
@@ -109,6 +97,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 14.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated file
 
 Copy the generated ONNX model file to the `DeepStream-Yolo` folder.

docs/YOLOR.md

@@ -55,37 +55,13 @@ Generate the ONNX model file
   python3 export_yolor.py -w yolor-p6.pt --dynamic
   ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 12.
-
-```
---opset 12
-```
-
 **NOTE**: To convert a P6 model
 
 ```
 --p6
 ```
 
-**NOTE**: To change the inference size (defaut: 640)
+**NOTE**: To change the inference size (defaut: 640 / 1280 for `--p6` models)
 
 ```
 -s SIZE
@@ -106,6 +82,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 12.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated files
 
 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder

docs/YOLOX.md

@@ -46,13 +46,13 @@ Generate the ONNX model file (example for YOLOX-s)
 python3 export_yolox.py -w yolox_s.pth -c exps/default/yolox_s.py --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
 
 ```
 --simplify
 ```
 
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
 
 ```
 --dynamic

docs/YOLOv5.md

@@ -47,37 +47,13 @@ Generate the ONNX model file (example for YOLOv5s)
 python3 export_yoloV5.py -w yolov5s.pt --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 17.
-
-```
---opset 12
-```
-
 **NOTE**: To convert a P6 model
 
 ```
 --p6
 ```
 
-**NOTE**: To change the inference size (defaut: 640)
+**NOTE**: To change the inference size (defaut: 640 / 1280 for `--p6` models)
 
 ```
 -s SIZE
@@ -98,6 +74,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 17.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated files
 
 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.

docs/YOLOv6.md

@@ -47,37 +47,13 @@ Generate the ONNX model file (example for YOLOv6-S 4.0)
 python3 export_yoloV6.py -w yolov6s.pt --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 13.
-
-```
---opset 12
-```
-
 **NOTE**: To convert a P6 model
 
 ```
 --p6
 ```
 
-**NOTE**: To change the inference size (defaut: 640)
+**NOTE**: To change the inference size (defaut: 640 / 1280 for `--p6` models)
 
 ```
 -s SIZE
@@ -98,6 +74,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 13.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated file
 
 Copy the generated ONNX model file to the `DeepStream-Yolo` folder.

docs/YOLOv7.md

@@ -49,37 +49,13 @@ Generate the ONNX model file (example for YOLOv7)
 python3 export_yoloV7.py -w yolov7.pt --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 12.
-
-```
---opset 12
-```
-
 **NOTE**: To convert a P6 model
 
 ```
 --p6
 ```
 
-**NOTE**: To change the inference size (defaut: 640)
+**NOTE**: To change the inference size (defaut: 640 / 1280 for `--p6` models)
 
 ```
 -s SIZE
@@ -100,6 +76,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 12.
+
+```
+--opset 12
+```
+
 #### 6. Copy generated files
 
 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.

docs/YOLOv8.md

@@ -46,30 +46,6 @@ Generate the ONNX model file (example for YOLOv8s)
 python3 export_yoloV8.py -w yolov8s.pt --dynamic
 ```
 
-**NOTE**: To simplify the ONNX model (DeepStream >= 6)
-
-```
---simplify
-```
-
-**NOTE**: To use dynamic batch-size (DeepStream >= 6)
-
-```
---dynamic
-```
-
-**NOTE**: To use implicit batch-size (example for batch-size = 4)
-
-```
---batch 4
-```
-
-**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 16.
-
-```
---opset 12
-```
-
 **NOTE**: To change the inference size (defaut: 640)
 
 ```
@@ -91,6 +67,30 @@ or
 -s 1280 1280
 ```
 
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use implicit batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 16.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated files
 
 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.

nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp

@@ -73,14 +73,14 @@ addBBoxProposal(const float bx1, const float by1, const float bx2, const float b
 }
 
 static std::vector<NvDsInferParseObjectInfo>
-decodeTensorYolo(const float* boxes, const float* scores, const int* classes, const uint& outputSize, const uint& netW,
+decodeTensorYolo(const float* boxes, const float* scores, const float* classes, const uint& outputSize, const uint& netW,
     const uint& netH, const std::vector<float>& preclusterThreshold)
 {
   std::vector<NvDsInferParseObjectInfo> binfo;
 
   for (uint b = 0; b < outputSize; ++b) {
     float maxProb = scores[b];
-    int maxIndex = classes[b];
+    int maxIndex = (int) classes[b];
 
     if (maxProb < preclusterThreshold[maxIndex])
       continue;
@@ -102,14 +102,14 @@ decodeTensorYolo(const float* boxes, const float* scores, const int* classes, co
 }
 
 static std::vector<NvDsInferParseObjectInfo>
-decodeTensorYoloE(const float* boxes, const float* scores, const int* classes, const uint& outputSize, const uint& netW,
+decodeTensorYoloE(const float* boxes, const float* scores, const float* classes, const uint& outputSize, const uint& netW,
     const uint& netH, const std::vector<float>& preclusterThreshold)
 {
   std::vector<NvDsInferParseObjectInfo> binfo;
 
   for (uint b = 0; b < outputSize; ++b) {
     float maxProb = scores[b];
-    int maxIndex = classes[b];
+    int maxIndex = (int) classes[b];
 
     if (maxProb < preclusterThreshold[maxIndex])
       continue;
@@ -136,26 +136,14 @@ NvDsInferParseCustomYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo
 
   std::vector<NvDsInferParseObjectInfo> objects;
 
-  NvDsInferLayerInfo* boxes;
-  NvDsInferLayerInfo* scores;
-  NvDsInferLayerInfo* classes;
+  const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
+  const NvDsInferLayerInfo& scores = outputLayersInfo[1];
+  const NvDsInferLayerInfo& classes = outputLayersInfo[2];
 
-  for (uint i = 0; i < 3; ++i) {
-    if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
-      classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else if (outputLayersInfo[i].inferDims.d[1] == 4) {
-      boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else {
-      scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-  }
+  const uint outputSize = boxes.inferDims.d[0];
 
-  const uint outputSize = boxes->inferDims.d[0];
-
-  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (boxes->buffer),
-      (const float*) (scores->buffer), (const int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height,
+  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (boxes.buffer),
+      (const float*) (scores.buffer), (const float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height,
       detectionParams.perClassPreclusterThreshold);
 
   objects.insert(objects.end(), outObjs.begin(), outObjs.end());
@@ -176,26 +164,14 @@ NvDsInferParseCustomYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInf
 
   std::vector<NvDsInferParseObjectInfo> objects;
 
-  NvDsInferLayerInfo* boxes;
-  NvDsInferLayerInfo* scores;
-  NvDsInferLayerInfo* classes;
+  const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
+  const NvDsInferLayerInfo& scores = outputLayersInfo[1];
+  const NvDsInferLayerInfo& classes = outputLayersInfo[2];
 
-  for (uint i = 0; i < 3; ++i) {
-    if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
-      classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else if (outputLayersInfo[i].inferDims.d[1] == 4) {
-      boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else {
-      scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-  }
+  const uint outputSize = boxes.inferDims.d[0];
 
-  const uint outputSize = boxes->inferDims.d[0];
-
-  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (boxes->buffer),
-      (const float*) (scores->buffer), (const int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height,
+  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (boxes.buffer),
+      (const float*) (scores.buffer), (const float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height,
       detectionParams.perClassPreclusterThreshold);
 
   objects.insert(objects.end(), outObjs.begin(), outObjs.end());

nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu

@@ -37,7 +37,7 @@ extern "C" bool
 NvDsInferParseYoloECuda(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
     NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList);
 
-__global__ void decodeTensorYoloCuda(NvDsInferParseObjectInfo *binfo, float* boxes, float* scores, int* classes,
+__global__ void decodeTensorYoloCuda(NvDsInferParseObjectInfo *binfo, float* boxes, float* scores, float* classes,
     int outputSize, int netW, int netH, float minPreclusterThreshold)
 {
   int x_id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -46,7 +46,7 @@ __global__ void decodeTensorYoloCuda(NvDsInferParseObjectInfo *binfo, float* box
     return;
 
   float maxProb = scores[x_id];
-  int maxIndex = classes[x_id];
+  int maxIndex = (int) classes[x_id];
 
   if (maxProb < minPreclusterThreshold) {
     binfo[x_id].detectionConfidence = 0.0;
@@ -76,7 +76,7 @@ __global__ void decodeTensorYoloCuda(NvDsInferParseObjectInfo *binfo, float* box
   binfo[x_id].classId = maxIndex;
 }
 
-__global__ void decodeTensorYoloECuda(NvDsInferParseObjectInfo *binfo, float* boxes, float* scores, int* classes,
+__global__ void decodeTensorYoloECuda(NvDsInferParseObjectInfo *binfo, float* boxes, float* scores, float* classes,
     int outputSize, int netW, int netH, float minPreclusterThreshold)
 {
   int x_id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -85,7 +85,7 @@ __global__ void decodeTensorYoloECuda(NvDsInferParseObjectInfo *binfo, float* bo
     return;
 
   float maxProb = scores[x_id];
-  int maxIndex = classes[x_id];
+  int maxIndex = (int) classes[x_id];
 
   if (maxProb < minPreclusterThreshold) {
     binfo[x_id].detectionConfidence = 0.0;
@@ -119,23 +119,11 @@ static bool NvDsInferParseCustomYoloCuda(std::vector<NvDsInferLayerInfo> const&
     return false;
   }
 
-  NvDsInferLayerInfo* boxes;
-  NvDsInferLayerInfo* scores;
-  NvDsInferLayerInfo* classes;
+  const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
+  const NvDsInferLayerInfo& scores = outputLayersInfo[1];
+  const NvDsInferLayerInfo& classes = outputLayersInfo[2];
 
-  for (uint i = 0; i < 3; ++i) {
-    if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
-      classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else if (outputLayersInfo[i].inferDims.d[1] == 4) {
-      boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else {
-      scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-  }
-
-  const int outputSize = boxes->inferDims.d[0];
+  const int outputSize = boxes.inferDims.d[0];
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
@@ -146,8 +134,8 @@ static bool NvDsInferParseCustomYoloCuda(std::vector<NvDsInferLayerInfo> const&
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensorYoloCuda<<<number_of_blocks, threads_per_block>>>(
-      thrust::raw_pointer_cast(objects.data()), (float*) (boxes->buffer), (float*) (scores->buffer),
-      (int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height, minPreclusterThreshold);
+      thrust::raw_pointer_cast(objects.data()), (float*) (boxes.buffer), (float*) (scores.buffer),
+      (float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height, minPreclusterThreshold);
 
   objectList.resize(outputSize);
   thrust::copy(objects.begin(), objects.end(), objectList.begin());
@@ -164,23 +152,11 @@ static bool NvDsInferParseCustomYoloECuda(std::vector<NvDsInferLayerInfo> const&
     return false;
   }
 
-  NvDsInferLayerInfo* boxes;
-  NvDsInferLayerInfo* scores;
-  NvDsInferLayerInfo* classes;
+  const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
+  const NvDsInferLayerInfo& scores = outputLayersInfo[1];
+  const NvDsInferLayerInfo& classes = outputLayersInfo[2];
 
-  for (uint i = 0; i < 3; ++i) {
-    if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
-      classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else if (outputLayersInfo[i].inferDims.d[1] == 4) {
-      boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-    else {
-      scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
-    }
-  }
-
-  const int outputSize = boxes->inferDims.d[0];
+  const int outputSize = boxes.inferDims.d[0];
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
@@ -191,8 +167,8 @@ static bool NvDsInferParseCustomYoloECuda(std::vector<NvDsInferLayerInfo> const&
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensorYoloECuda<<<number_of_blocks, threads_per_block>>>(
-      thrust::raw_pointer_cast(objects.data()), (float*) (boxes->buffer), (float*) (scores->buffer),
-      (int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height, minPreclusterThreshold);
+      thrust::raw_pointer_cast(objects.data()), (float*) (boxes.buffer), (float*) (scores.buffer),
+      (float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height, minPreclusterThreshold);
 
   objectList.resize(outputSize);
   thrust::copy(objects.begin(), objects.end(), objectList.begin());

nvdsinfer_custom_impl_Yolo/yolo.cpp

@@ -76,7 +76,7 @@ Yolo::createEngine(nvinfer1::IBuilder* builder)
 
   if (m_NetworkType == "onnx") {
 
-#if NV_TENSORRT_MAJOR >= 8
+#if NV_TENSORRT_MAJOR >= 8 && NV_TENSORRT_MINOR > 0
     parser = nvonnxparser::createParser(*network, *builder->getLogger());
 #else
     parser = nvonnxparser::createParser(*network, logger);

nvdsinfer_custom_impl_Yolo/yolo.h

@@ -45,7 +45,9 @@
 #define INT int32_t
 #else
 #define INT int
+#endif
 
+#if NV_TENSORRT_MAJOR < 8 || (NV_TENSORRT_MAJOR == 8 && NV_TENSORRT_MINOR == 0)
 static class Logger : public nvinfer1::ILogger {
   void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override {
     if (severity <= nvinfer1::ILogger::Severity::kWARNING)

utils/export_damoyolo.py

@@ -18,6 +18,7 @@ class DeepStreamOutput(nn.Module):
     def forward(self, x):
         boxes = x[1]
         scores, classes = torch.max(x[0], 2, keepdim=True)
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_ppyoloe.py

@@ -19,7 +19,7 @@ class DeepStreamOutput(nn.Layer):
         boxes = x['bbox']
         x['bbox_num'] = x['bbox_num'].transpose([0, 2, 1])
         scores = paddle.max(x['bbox_num'], 2, keepdim=True)
-        classes = paddle.argmax(x['bbox_num'], 2, keepdim=True)
+        classes = paddle.cast(paddle.argmax(x['bbox_num'], 2, keepdim=True), dtype='float32')
         return boxes, scores, classes
 
 

utils/export_yoloV5.py

@@ -20,6 +20,7 @@ class DeepStreamOutput(nn.Module):
         objectness = x[:, :, 4:5]
         scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
         scores *= objectness
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yoloV6.py

@@ -24,6 +24,7 @@ class DeepStreamOutput(nn.Module):
         objectness = x[:, :, 4:5]
         scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
         scores *= objectness
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yoloV7.py

@@ -20,6 +20,7 @@ class DeepStreamOutput(nn.Module):
         objectness = x[:, :, 4:5]
         scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
         scores *= objectness
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yoloV7_u6.py

@@ -18,6 +18,7 @@ class DeepStreamOutput(nn.Module):
         x = x.transpose(1, 2)
         boxes = x[:, :, :4]
         scores, classes = torch.max(x[:, :, 4:], 2, keepdim=True)
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yoloV8.py

@@ -19,6 +19,7 @@ class DeepStreamOutput(nn.Module):
         x = x.transpose(1, 2)
         boxes = x[:, :, :4]
         scores, classes = torch.max(x[:, :, 4:], 2, keepdim=True)
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yolonas.py

@@ -15,6 +15,7 @@ class DeepStreamOutput(nn.Module):
     def forward(self, x):
         boxes = x[0]
         scores, classes = torch.max(x[1], 2, keepdim=True)
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yolor.py

@@ -17,6 +17,7 @@ class DeepStreamOutput(nn.Module):
         objectness = x[:, :, 4:5]
         scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
         scores *= objectness
+        classes = classes.float()
         return boxes, scores, classes
 
 

utils/export_yolox.py

@@ -19,6 +19,7 @@ class DeepStreamOutput(nn.Module):
         objectness = x[:, :, 4:5]
         scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
         scores *= objectness
+        classes = classes.float()
         return boxes, scores, classes