Add YOLOv8 support

README.md

@@ -8,6 +8,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * YOLOX support
 * YOLOv6 support
 * Dynamic batch-size
+* PP-YOLOE+ support
 
 ### Improvements on this repository
 
@@ -22,11 +23,12 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * New documentation for multiple models
 * YOLOv5 support
 * YOLOR support
-* **GPU YOLO Decoder** [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
+* GPU YOLO Decoder [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
-* **PP-YOLOE support**
+* PP-YOLOE support
-* **YOLOv7 support**
+* YOLOv7 support
-* **Optimized NMS** [#142](https://github.com/marcoslucianops/DeepStream-Yolo/issues/142)
+* Optimized NMS [#142](https://github.com/marcoslucianops/DeepStream-Yolo/issues/142)
-* **Models benchmarks**
+* Models benchmarks
+* **YOLOv8 support**
 
 ##
 
@@ -44,6 +46,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * [YOLOR usage](docs/YOLOR.md)
 * [PP-YOLOE usage](docs/PPYOLOE.md)
 * [YOLOv7 usage](docs/YOLOv7.md)
+* [YOLOv8 usage](docs/YOLOv8.md)
 * [Using your custom model](docs/customModels.md)
 * [Multiple YOLO GIEs](docs/multipleGIEs.md)
 
@@ -108,6 +111,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * [YOLOR](https://github.com/WongKinYiu/yolor)
 * [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
 * [YOLOv7](https://github.com/WongKinYiu/yolov7)
+* [YOLOv8](https://github.com/ultralytics/ultralytics)
 * [MobileNet-YOLO](https://github.com/dog-qiuqiu/MobileNet-Yolo)
 * [YOLO-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest)
 
@@ -131,7 +135,7 @@ sample = 1920x1080 video
 - Eval
 
 ```
-nms-iou-threshold = 0.6 (Darknet) / 0.65 (PyTorch) / 0.7 (Paddle)
+nms-iou-threshold = 0.6 (Darknet and YOLOv8) / 0.65 (YOLOR, YOLOv5 and YOLOv7) / 0.7 (Paddle)
 pre-cluster-threshold = 0.001
 topk = 300
 ```

config_infer_primary.txt

@@ -16,6 +16,7 @@ process-mode=1
 network-type=0
 cluster-mode=2
 maintain-aspect-ratio=0
+symmetric-padding=1
 parse-bbox-func-name=NvDsInferParseYolo
 custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet

config_infer_primary_yoloV5.txt

@@ -16,10 +16,10 @@ process-mode=1
 network-type=0
 cluster-mode=2
 maintain-aspect-ratio=1
+symmetric-padding=1
 parse-bbox-func-name=NvDsInferParseYolo
 custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet
-symmetric-padding=1
 
 [class-attrs-all]
 nms-iou-threshold=0.45

config_infer_primary_yoloV7.txt

@@ -16,6 +16,7 @@ process-mode=1
 network-type=0
 cluster-mode=2
 maintain-aspect-ratio=1
+symmetric-padding=1
 parse-bbox-func-name=NvDsInferParseYolo
 custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet

config_infer_primary_yoloV8.txt

@@ -0,0 +1,27 @@
+[property]
+gpu-id=0
+net-scale-factor=0.0039215697906911373
+model-color-format=0
+custom-network-config=yolov8s.cfg
+model-file=yolov8s.wts
+model-engine-file=model_b1_gpu0_fp32.engine
+#int8-calib-file=calib.table
+labelfile-path=labels.txt
+batch-size=1
+network-mode=0
+num-detected-classes=80
+interval=0
+gie-unique-id=1
+process-mode=1
+network-type=0
+cluster-mode=2
+maintain-aspect-ratio=1
+symmetric-padding=1
+parse-bbox-func-name=NvDsInferParseYolo
+custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
+engine-create-func-name=NvDsInferYoloCudaEngineGet
+
+[class-attrs-all]
+nms-iou-threshold=0.45
+pre-cluster-threshold=0.25
+topk=300

config_infer_primary_yolor.txt

@@ -16,6 +16,7 @@ process-mode=1
 network-type=0
 cluster-mode=2
 maintain-aspect-ratio=1
+symmetric-padding=1
 parse-bbox-func-name=NvDsInferParseYolo
 custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet

docs/YOLOv8.md

@@ -0,0 +1,139 @@
+# YOLOv8 usage
+
+**NOTE**: The yaml file is not required.
+
+* [Convert model](#convert-model)
+* [Compile the lib](#compile-the-lib)
+* [Edit the config_infer_primary_yoloV8 file](#edit-the-config_infer_primary_yolov8-file)
+* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
+* [Testing the model](#testing-the-model)
+
+##
+
+### Convert model
+
+#### 1. Download the YOLOv8 repo and install the requirements
+
+```
+git clone https://github.com/ultralytics/ultralytics.git
+cd ultralytics
+pip3 install -r requirements.txt
+```
+
+**NOTE**: It is recommended to use Python virtualenv.
+
+#### 2. Copy conversor
+
+Copy the `gen_wts_yoloV8.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder.
+
+#### 3. Download the model
+
+Download the `pt` file from [YOLOv8](https://github.com/ultralytics/assets/releases/) releases (example for YOLOv8s)
+
+```
+wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt
+```
+
+**NOTE**: You can use your custom model, but it is important to keep the YOLO model reference (`yolov8_`) in you `cfg` and `weights`/`wts` filenames to generate the engine correctly.
+
+#### 4. Convert model
+
+Generate the `cfg` and `wts` files (example for YOLOv8s)
+
+```
+python3 gen_wts_yoloV8.py -w yolov8s.pt
+```
+
+**NOTE**: To change the inference size (defaut: 640)
+
+```
+-s SIZE
+--size SIZE
+-s HEIGHT WIDTH
+--size HEIGHT WIDTH
+```
+
+Example for 1280
+
+```
+-s 1280
+```
+
+or
+
+```
+-s 1280 1280
+```
+
+#### 5. Copy generated files
+
+Copy the generated `cfg` and `wts` files to the `DeepStream-Yolo` folder.
+
+##
+
+### Compile the lib
+
+Open the `DeepStream-Yolo` folder and compile the lib
+
+* DeepStream 6.1.1 on x86 platform
+
+  ```
+  CUDA_VER=11.7 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.1 on x86 platform
+
+  ```
+  CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.0.1 / 6.0 on x86 platform
+
+  ```
+  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.1.1 / 6.1 on Jetson platform
+
+  ```
+  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.0.1 / 6.0 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+##
+
+### Edit the config_infer_primary_yoloV8 file
+
+Edit the `config_infer_primary_yoloV8.txt` file according to your model (example for YOLOv8s)
+
+```
+[property]
+...
+custom-network-config=yolov8s.cfg
+model-file=yolov8s.wts
+...
+```
+
+##
+
+### Edit the deepstream_app_config file
+
+```
+...
+[primary-gie]
+...
+config-file=config_infer_primary_yoloV8.txt
+```
+
+##
+
+### Testing the model
+
+```
+deepstream-app -c deepstream_app_config.txt
+```

nvdsinfer_custom_impl_Yolo/calibrator.cpp

@@ -4,20 +4,21 @@
  */
 
 #include "calibrator.h"
+
 #include <fstream>
 #include <iterator>
 
-namespace nvinfer1
+Int8EntropyCalibrator2::Int8EntropyCalibrator2(const int& batchsize, const int& channels, const int& height,
-{
+    const int& width, const int& letterbox, const std::string& imgPath,
-    Int8EntropyCalibrator2::Int8EntropyCalibrator2(const int &batchsize, const int &channels, const int &height, const int &width, const int &letterbox, const std::string &imgPath,
+    const std::string& calibTablePath) : batchSize(batchsize), inputC(channels), inputH(height), inputW(width),
-        const std::string &calibTablePath):batchSize(batchsize), inputC(channels), inputH(height), inputW(width), letterBox(letterbox), calibTablePath(calibTablePath), imageIndex(0)
+    letterBox(letterbox), calibTablePath(calibTablePath), imageIndex(0)
 {
   inputCount = batchsize * channels * height * width;
   std::fstream f(imgPath);
-        if (f.is_open())
+  if (f.is_open()) {
-        {
       std::string temp;
-            while (std::getline(f, temp)) imgPaths.push_back(temp);
+      while (std::getline(f, temp))
+        imgPaths.push_back(temp);
   }
   batchData = new float[inputCount];
   CUDA_CHECK(cudaMalloc(&deviceInput, inputCount * sizeof(float)));
@@ -30,28 +31,29 @@ namespace nvinfer1
     delete[] batchData;
 }
 
-    int Int8EntropyCalibrator2::getBatchSize() const noexcept
+int
+Int8EntropyCalibrator2::getBatchSize() const noexcept
 {
   return batchSize;
 }
 
-    bool Int8EntropyCalibrator2::getBatch(void **bindings, const char **names, int nbBindings) noexcept
+bool
+Int8EntropyCalibrator2::getBatch(void** bindings, const char** names, int nbBindings) noexcept
 {
   if (imageIndex + batchSize > uint(imgPaths.size()))
     return false;
 
   float* ptr = batchData;
-        for (size_t j = imageIndex; j < imageIndex + batchSize; ++j)
+  for (size_t i = imageIndex; i < imageIndex + batchSize; ++i) {
-        {
+    cv::Mat img = cv::imread(imgPaths[i], cv::IMREAD_COLOR);
-            cv::Mat img = cv::imread(imgPaths[j], cv::IMREAD_COLOR);
     std::vector<float> inputData = prepareImage(img, inputC, inputH, inputW, letterBox);
 
     int len = (int) (inputData.size());
     memcpy(ptr, inputData.data(), len * sizeof(float));
 
     ptr += inputData.size();
-            std::cout << "Load image: " << imgPaths[j] << std::endl;
+    std::cout << "Load image: " << imgPaths[i] << std::endl;
-            std::cout << "Progress: " << (j + 1)*100. / imgPaths.size() << "%" << std::endl;
+    std::cout << "Progress: " << (i + 1)*100. / imgPaths.size() << "%" << std::endl;
   }
   imageIndex += batchSize;
   CUDA_CHECK(cudaMemcpy(deviceInput, batchData, inputCount * sizeof(float), cudaMemcpyHostToDevice));
@@ -59,84 +61,73 @@ namespace nvinfer1
   return true;
 }
 
-    const void* Int8EntropyCalibrator2::readCalibrationCache(std::size_t &length) noexcept
+const void*
+Int8EntropyCalibrator2::readCalibrationCache(std::size_t &length) noexcept
 {
   calibrationCache.clear();
   std::ifstream input(calibTablePath, std::ios::binary);
   input >> std::noskipws;
   if (readCache && input.good())
-        {
+    std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(calibrationCache));
-            std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(),
-                std::back_inserter(calibrationCache));
-        }
   length = calibrationCache.size();
   return length ? calibrationCache.data() : nullptr;
 }
 
-    void Int8EntropyCalibrator2::writeCalibrationCache(const void* cache, std::size_t length) noexcept
+void
+Int8EntropyCalibrator2::writeCalibrationCache(const void* cache, std::size_t length) noexcept
 {
   std::ofstream output(calibTablePath, std::ios::binary);
   output.write(reinterpret_cast<const char*>(cache), length);
 }
-}
 
-std::vector<float> prepareImage(cv::Mat& img, int input_c, int input_h, int input_w, int letter_box)
+std::vector<float>
+prepareImage(cv::Mat& img, int input_c, int input_h, int input_w, int letter_box)
 {
   cv::Mat out;
   int image_w = img.cols;
   int image_h = img.rows;
-    if (image_w != input_w || image_h != input_h)
+  if (image_w != input_w || image_h != input_h) {
-    {
+    if (letter_box == 1) {
-        if (letter_box == 1)
-        {
       float ratio_w = (float) image_w / (float) input_w;
       float ratio_h = (float) image_h / (float) input_h;
-            if (ratio_w > ratio_h)
+      if (ratio_w > ratio_h) {
-            {
         int new_width = input_w * ratio_h;
         int x = (image_w - new_width) / 2;
         cv::Rect roi(abs(x), 0, new_width, image_h);
         out = img(roi);
       }
-            else if (ratio_w < ratio_h)
+      else if (ratio_w < ratio_h) {
-            {
         int new_height = input_h * ratio_w;
         int y = (image_h - new_height) / 2;
         cv::Rect roi(0, abs(y), image_w, new_height);
         out = img(roi);
       }
-            else {
+      else
         out = img;
-            }
       cv::resize(out, out, cv::Size(input_w, input_h), 0, 0, cv::INTER_CUBIC);
     }
-        else
+    else {
-        {
       cv::resize(img, out, cv::Size(input_w, input_h), 0, 0, cv::INTER_CUBIC);
     }
     cv::cvtColor(out, out, cv::COLOR_BGR2RGB);
   }
   else
-    {
     cv::cvtColor(img, out, cv::COLOR_BGR2RGB);
-    }
+
   if (input_c == 3)
-    {
     out.convertTo(out, CV_32FC3, 1.0 / 255.0);
-    }
   else
-    {
     out.convertTo(out, CV_32FC1, 1.0 / 255.0);
-    }
+
   std::vector<cv::Mat> input_channels(input_c);
   cv::split(out, input_channels);
   std::vector<float> result(input_h * input_w * input_c);
   auto data = result.data();
   int channelLength = input_h * input_w;
-    for (int i = 0; i < input_c; ++i)
+  for (int i = 0; i < input_c; ++i) {
-    {
     memcpy(data, input_channels[i].data, channelLength * sizeof(float));
     data += channelLength;
   }
+
   return result;
 }

nvdsinfer_custom_impl_Yolo/calibrator.h

@@ -6,38 +6,32 @@
 #ifndef CALIBRATOR_H
 #define CALIBRATOR_H
 
-#include "opencv2/opencv.hpp"
-#include "cuda_runtime.h"
-#include "NvInfer.h"
 #include <vector>
-#include <string>
 
-#ifndef CUDA_CHECK
+#include "NvInfer.h"
-#define CUDA_CHECK(callstr)                                                                    \
+#include "opencv2/opencv.hpp"
-    {                                                                                          \
+
-        cudaError_t error_code = callstr;                                                      \
+#define CUDA_CHECK(status) {                                                                                               \
-        if (error_code != cudaSuccess) {                                                       \
+  if (status != 0) {                                                                                                       \
-            std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
+    std::cout << "CUDA failure: " << cudaGetErrorString(status) << " in file " << __FILE__  << " at line "  << __LINE__ << \
-            assert(0);                                                                         \
+        std::endl;                                                                                                         \
+    abort();                                                                                                               \
   }                                                                                                                        \
 }
-#endif
 
-namespace nvinfer1 {
 class Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2 {
   public:
-        Int8EntropyCalibrator2(const int &batchsize,
+    Int8EntropyCalibrator2(const int& batchsize, const int& channels, const int& height, const int& width,
-                             const int &channels,
+        const int& letterbox, const std::string& imgPath, const std::string& calibTablePath);
-                             const int &height,
-                             const int &width,
-                             const int &letterbox,
-                             const std::string &imgPath,
-                             const std::string &calibTablePath);
 
     virtual ~Int8EntropyCalibrator2();
+
     int getBatchSize() const noexcept override;
+
     bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override;
+
     const void* readCalibrationCache(std::size_t& length) noexcept override;
+
     void writeCalibrationCache(const void* cache, size_t length) noexcept override;
 
   private:
@@ -55,7 +49,6 @@ namespace nvinfer1 {
     bool readCache;
     std::vector<char> calibrationCache;
 };
-}
 
 std::vector<float> prepareImage(cv::Mat& img, int input_c, int input_h, int input_w, int letter_box);
 

nvdsinfer_custom_impl_Yolo/layers/activation_layer.cpp

@@ -5,118 +5,107 @@
 
 #include "activation_layer.h"
 
-nvinfer1::ITensor* activationLayer(
+#include <cassert>
-    int layerIdx,
+#include <iostream>
-    std::string activation,
+
-    nvinfer1::ITensor* input,
+nvinfer1::ITensor*
-    nvinfer1::INetworkDefinition* network)
+activationLayer(int layerIdx, std::string activation, nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network,
+    std::string layerName)
 {
   nvinfer1::ITensor* output;
 
   if (activation == "linear")
-    {
     output = input;
-    }
+  else if (activation == "relu") {
-    else if (activation == "relu")
-    {
     nvinfer1::IActivationLayer* relu = network->addActivation(*input, nvinfer1::ActivationType::kRELU);
     assert(relu != nullptr);
-        std::string reluLayerName = "relu_" + std::to_string(layerIdx);
+    std::string reluLayerName = "relu_" + layerName + std::to_string(layerIdx);
     relu->setName(reluLayerName.c_str());
     output = relu->getOutput(0);
   }
-    else if (activation == "sigmoid" || activation == "logistic")
+  else if (activation == "sigmoid" || activation == "logistic") {
-    {
     nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
     assert(sigmoid != nullptr);
-        std::string sigmoidLayerName = "sigmoid_" + std::to_string(layerIdx);
+    std::string sigmoidLayerName = "sigmoid_" + layerName + std::to_string(layerIdx);
     sigmoid->setName(sigmoidLayerName.c_str());
     output = sigmoid->getOutput(0);
   }
-    else if (activation == "tanh")
+  else if (activation == "tanh") {
-    {
     nvinfer1::IActivationLayer* tanh = network->addActivation(*input, nvinfer1::ActivationType::kTANH);
     assert(tanh != nullptr);
-        std::string tanhLayerName = "tanh_" + std::to_string(layerIdx);
+    std::string tanhLayerName = "tanh_" + layerName + std::to_string(layerIdx);
     tanh->setName(tanhLayerName.c_str());
     output = tanh->getOutput(0);
   }
-    else if (activation == "leaky")
+  else if (activation == "leaky") {
-    {
     nvinfer1::IActivationLayer* leaky = network->addActivation(*input, nvinfer1::ActivationType::kLEAKY_RELU);
     assert(leaky != nullptr);
-        std::string leakyLayerName = "leaky_" + std::to_string(layerIdx);
+    std::string leakyLayerName = "leaky_" + layerName + std::to_string(layerIdx);
     leaky->setName(leakyLayerName.c_str());
     leaky->setAlpha(0.1);
     output = leaky->getOutput(0);
   }
-    else if (activation == "softplus")
+  else if (activation == "softplus") {
-    {
     nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
     assert(softplus != nullptr);
-        std::string softplusLayerName = "softplus_" + std::to_string(layerIdx);
+    std::string softplusLayerName = "softplus_" + layerName + std::to_string(layerIdx);
     softplus->setName(softplusLayerName.c_str());
     output = softplus->getOutput(0);
   }
-    else if (activation == "mish")
+  else if (activation == "mish") {
-    {
     nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
     assert(softplus != nullptr);
-        std::string softplusLayerName = "softplus_" + std::to_string(layerIdx);
+    std::string softplusLayerName = "softplus_" + layerName + std::to_string(layerIdx);
     softplus->setName(softplusLayerName.c_str());
     nvinfer1::IActivationLayer* tanh = network->addActivation(*softplus->getOutput(0), nvinfer1::ActivationType::kTANH);
     assert(tanh != nullptr);
-        std::string tanhLayerName = "tanh_" + std::to_string(layerIdx);
+    std::string tanhLayerName = "tanh_" + layerName + std::to_string(layerIdx);
     tanh->setName(tanhLayerName.c_str());
-        nvinfer1::IElementWiseLayer* mish
+    nvinfer1::IElementWiseLayer* mish = network->addElementWise(*input, *tanh->getOutput(0),
-            = network->addElementWise(*input, *tanh->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
+        nvinfer1::ElementWiseOperation::kPROD);
     assert(mish != nullptr);
-        std::string mishLayerName = "mish_" + std::to_string(layerIdx);
+    std::string mishLayerName = "mish_" + layerName + std::to_string(layerIdx);
     mish->setName(mishLayerName.c_str());
     output = mish->getOutput(0);
   }
-    else if (activation == "silu" || activation == "swish")
+  else if (activation == "silu" || activation == "swish") {
-    {
     nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
     assert(sigmoid != nullptr);
-        std::string sigmoidLayerName = "sigmoid_" + std::to_string(layerIdx);
+    std::string sigmoidLayerName = "sigmoid_" + layerName + std::to_string(layerIdx);
     sigmoid->setName(sigmoidLayerName.c_str());
-        nvinfer1::IElementWiseLayer* silu
+    nvinfer1::IElementWiseLayer* silu = network->addElementWise(*input, *sigmoid->getOutput(0),
-            = network->addElementWise(*input, *sigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
+        nvinfer1::ElementWiseOperation::kPROD);
     assert(silu != nullptr);
-        std::string siluLayerName = "silu_" + std::to_string(layerIdx);
+    std::string siluLayerName = "silu_" + layerName + std::to_string(layerIdx);
     silu->setName(siluLayerName.c_str());
     output = silu->getOutput(0);
   }
-    else if (activation == "hardsigmoid")
+  else if (activation == "hardsigmoid") {
-    {
     nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
     assert(hardsigmoid != nullptr);
-        std::string hardsigmoidLayerName = "hardsigmoid_" + std::to_string(layerIdx);
+    std::string hardsigmoidLayerName = "hardsigmoid_" + layerName + std::to_string(layerIdx);
     hardsigmoid->setName(hardsigmoidLayerName.c_str());
     hardsigmoid->setAlpha(1.0 / 6.0);
     hardsigmoid->setBeta(0.5);
     output = hardsigmoid->getOutput(0);
   }
-    else if (activation == "hardswish")
+  else if (activation == "hardswish") {
-    {
     nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
     assert(hardsigmoid != nullptr);
-        std::string hardsigmoidLayerName = "hardsigmoid_" + std::to_string(layerIdx);
+    std::string hardsigmoidLayerName = "hardsigmoid_" + layerName + std::to_string(layerIdx);
     hardsigmoid->setName(hardsigmoidLayerName.c_str());
     hardsigmoid->setAlpha(1.0 / 6.0);
     hardsigmoid->setBeta(0.5);
-        nvinfer1::IElementWiseLayer* hardswish
+    nvinfer1::IElementWiseLayer* hardswish = network->addElementWise(*input, *hardsigmoid->getOutput(0),
-            = network->addElementWise(*input, *hardsigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
+        nvinfer1::ElementWiseOperation::kPROD);
     assert(hardswish != nullptr);
-        std::string hardswishLayerName = "hardswish_" + std::to_string(layerIdx);
+    std::string hardswishLayerName = "hardswish_" + layerName + std::to_string(layerIdx);
     hardswish->setName(hardswishLayerName.c_str());
     output = hardswish->getOutput(0);
   }
-    else
+  else {
-    {
     std::cerr << "Activation not supported: " << activation << std::endl;
-        std::abort();
+    assert(0);
   }
   return output;
 }

nvdsinfer_custom_impl_Yolo/layers/activation_layer.h

@@ -6,15 +6,11 @@
 #ifndef __ACTIVATION_LAYER_H__
 #define __ACTIVATION_LAYER_H__
 
-#include <cassert>
+#include <string>
-#include <iostream>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* activationLayer(
+nvinfer1::ITensor* activationLayer(int layerIdx, std::string activation, nvinfer1::ITensor* input,
-    int layerIdx,
+    nvinfer1::INetworkDefinition* network, std::string layerName = "");
-    std::string activation,
-    nvinfer1::ITensor* input,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/batchnorm_layer.cpp

@@ -3,18 +3,14 @@
  * https://www.github.com/marcoslucianops
  */
 
-#include <math.h>
 #include "batchnorm_layer.h"
 
-nvinfer1::ITensor* batchnormLayer(
+#include <cassert>
-    int layerIdx,
+#include <math.h>
-    std::map<std::string, std::string>& block,
+
-    std::vector<float>& weights,
+nvinfer1::ITensor*
-    std::vector<nvinfer1::Weights>& trtWeights,
+batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int& weightPtr,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
-    std::string weightsType,
-    float eps,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
@@ -30,50 +26,40 @@ nvinfer1::ITensor* batchnormLayer(
   std::vector<float> bnRunningMean;
   std::vector<float> bnRunningVar;
 
-    if (weightsType == "weights")
+  if (weightsType == "weights") {
-    {
+    for (int i = 0; i < filters; ++i) {
-        for (int i = 0; i < filters; ++i)
-        {
       bnBiases.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnWeights.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnRunningMean.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
-            weightPtr++;
+      ++weightPtr;
     }
   }
-    else
+  else {
-    {
+    for (int i = 0; i < filters; ++i) {
-        for (int i = 0; i < filters; ++i)
-        {
       bnWeights.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnBiases.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnRunningMean.push_back(weights[weightPtr]);
-            weightPtr++;
+      ++weightPtr;
     }
-        for (int i = 0; i < filters; ++i)
+    for (int i = 0; i < filters; ++i) {
-        {
       bnRunningVar.push_back(sqrt(weights[weightPtr] + eps));
-            weightPtr++;
+      ++weightPtr;
     }
   }
 

nvdsinfer_custom_impl_Yolo/layers/batchnorm_layer.h

@@ -13,15 +13,8 @@
 
 #include "activation_layer.h"
 
-nvinfer1::ITensor* batchnormLayer(
+nvinfer1::ITensor* batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int layerIdx,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
-    std::map<std::string, std::string>& block,
-    std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
-    int& weightPtr,
-    std::string weightsType,
-    float eps,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/c2f_layer.cpp

@@ -0,0 +1,82 @@
+/*
+ * Created by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#include "c2f_layer.h"
+
+#include <cassert>
+
+#include "convolutional_layer.h"
+
+nvinfer1::ITensor*
+c2fLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
+    nvinfer1::INetworkDefinition* network)
+{
+  nvinfer1::ITensor* output;
+
+  assert(block.at("type") == "c2f");
+  assert(block.find("n") != block.end());
+  assert(block.find("shortcut") != block.end());
+  assert(block.find("filters") != block.end());
+
+  int n = std::stoi(block.at("n"));
+  bool shortcut = (block.at("shortcut") == "1");
+  int filters = std::stoi(block.at("filters"));
+
+  nvinfer1::Dims inputDims = input->getDimensions();
+
+  nvinfer1::ISliceLayer* sliceLt = network->addSlice(*input,nvinfer1::Dims{3, {0, 0, 0}},
+      nvinfer1::Dims{3, {inputDims.d[0] / 2, inputDims.d[1], inputDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
+  assert(sliceLt != nullptr);
+  std::string sliceLtLayerName = "slice_lt_" + std::to_string(layerIdx);
+  sliceLt->setName(sliceLtLayerName.c_str());
+  nvinfer1::ITensor* lt = sliceLt->getOutput(0);
+
+  nvinfer1::ISliceLayer* sliceRb = network->addSlice(*input,nvinfer1::Dims{3, {inputDims.d[0] / 2, 0, 0}},
+      nvinfer1::Dims{3, {inputDims.d[0] / 2, inputDims.d[1], inputDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
+  assert(sliceRb != nullptr);
+  std::string sliceRbLayerName = "slice_rb_" + std::to_string(layerIdx);
+  sliceRb->setName(sliceRbLayerName.c_str());
+  nvinfer1::ITensor* rb = sliceRb->getOutput(0);
+
+  std::vector<nvinfer1::ITensor*> concatInputs;
+  concatInputs.push_back(lt);
+  concatInputs.push_back(rb);
+  output = rb;
+
+  for (int i = 0; i < n; ++i) {
+    std::string cv1MlayerName = "c2f_1_" + std::to_string(i + 1) + "_";
+    nvinfer1::ITensor* cv1M = convolutionalLayer(layerIdx, block, weights, trtWeights, weightPtr, weightsType, filters, eps,
+        output, network, cv1MlayerName);
+    assert(cv1M != nullptr);
+
+    std::string cv2MlayerName = "c2f_2_" + std::to_string(i + 1) + "_";
+    nvinfer1::ITensor* cv2M = convolutionalLayer(layerIdx, block, weights, trtWeights, weightPtr, weightsType, filters, eps,
+        cv1M, network, cv2MlayerName);
+    assert(cv2M != nullptr);
+
+    if (shortcut) {
+      nvinfer1::IElementWiseLayer* ew = network->addElementWise(*rb, *cv2M, nvinfer1::ElementWiseOperation::kSUM);
+      assert(ew != nullptr);
+      std::string ewLayerName = "shortcut_c2f_" + std::to_string(i + 1) + "_" + std::to_string(layerIdx);
+      ew->setName(ewLayerName.c_str());
+      output = ew->getOutput(0);
+      concatInputs.push_back(output);
+    }
+    else {
+      output = cv2M;
+      concatInputs.push_back(output);
+    }
+  }
+
+  nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
+  assert(concat != nullptr);
+  std::string concatLayerName = "route_" + std::to_string(layerIdx);
+  concat->setName(concatLayerName.c_str());
+  concat->setAxis(0);
+  output = concat->getOutput(0);
+
+  return output;
+}

nvdsinfer_custom_impl_Yolo/layers/c2f_layer.h

@@ -0,0 +1,18 @@
+/*
+ * Created by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#ifndef __C2F_LAYER_H__
+#define __C2F_LAYER_H__
+
+#include <map>
+#include <vector>
+
+#include "NvInfer.h"
+
+nvinfer1::ITensor* c2fLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
+    nvinfer1::INetworkDefinition* network);
+
+#endif

nvdsinfer_custom_impl_Yolo/layers/channels_layer.cpp

@@ -5,28 +5,27 @@
 
 #include "channels_layer.h"
 
-nvinfer1::ITensor* channelsLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    nvinfer1::ITensor* input,
+channelsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    nvinfer1::ITensor* implicitTensor,
+    nvinfer1::ITensor* implicitTensor, nvinfer1::INetworkDefinition* network)
-    nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
   assert(block.at("type") == "shift_channels" || block.at("type") == "control_channels");
 
   if (block.at("type") == "shift_channels") {
-        nvinfer1::IElementWiseLayer* shift
+    nvinfer1::IElementWiseLayer* shift = network->addElementWise(*input, *implicitTensor,
-            = network->addElementWise(*input, *implicitTensor, nvinfer1::ElementWiseOperation::kSUM);
+        nvinfer1::ElementWiseOperation::kSUM);
     assert(shift != nullptr);
     std::string shiftLayerName = "shift_channels_" + std::to_string(layerIdx);
     shift->setName(shiftLayerName.c_str());
     output = shift->getOutput(0);
   }
   else if (block.at("type") == "control_channels") {
-        nvinfer1::IElementWiseLayer* control
+    nvinfer1::IElementWiseLayer* control = network->addElementWise(*input, *implicitTensor,
-            = network->addElementWise(*input, *implicitTensor, nvinfer1::ElementWiseOperation::kPROD);
+        nvinfer1::ElementWiseOperation::kPROD);
     assert(control != nullptr);
     std::string controlLayerName = "control_channels_" + std::to_string(layerIdx);
     control->setName(controlLayerName.c_str());

nvdsinfer_custom_impl_Yolo/layers/channels_layer.h

@@ -7,15 +7,10 @@
 #define __CHANNELS_LAYER_H__
 
 #include <map>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* channelsLayer(
+nvinfer1::ITensor* channelsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
+    nvinfer1::ITensor* implicitTensor, nvinfer1::INetworkDefinition* network);
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
-    nvinfer1::ITensor* implicitTensor,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/cls_layer.cpp

@@ -5,10 +5,10 @@
 
 #include "cls_layer.h"
 
-nvinfer1::ITensor* clsLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    nvinfer1::ITensor* input,
+clsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;

nvdsinfer_custom_impl_Yolo/layers/cls_layer.h

@@ -7,14 +7,10 @@
 #define __CLS_LAYER_H__
 
 #include <map>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* clsLayer(
+nvinfer1::ITensor* clsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp

@@ -3,24 +3,19 @@
  * https://www.github.com/marcoslucianops
  */
 
-#include <math.h>
 #include "convolutional_layer.h"
 
-nvinfer1::ITensor* convolutionalLayer(
+#include <cassert>
-    int layerIdx,
+#include <math.h>
-    std::map<std::string, std::string>& block,
+
-    std::vector<float>& weights,
+nvinfer1::ITensor*
-    std::vector<nvinfer1::Weights>& trtWeights,
+convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int& weightPtr,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, int& inputChannels, float eps,
-    std::string weightsType,
+    nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network, std::string layerName)
-    int& inputChannels,
-    float eps,
-    nvinfer1::ITensor* input,
-    nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
-    assert(block.at("type") == "convolutional");
+  assert(block.at("type") == "convolutional" || block.at("type") == "c2f");
   assert(block.find("filters") != block.end());
   assert(block.find("pad") != block.end());
   assert(block.find("size") != block.end());
@@ -34,8 +29,7 @@ nvinfer1::ITensor* convolutionalLayer(
   int bias = filters;
 
   bool batchNormalize = false;
-    if (block.find("batch_normalize") != block.end())
+  if (block.find("batch_normalize") != block.end()) {
-    {
     bias = 0;
     batchNormalize = (block.at("batch_normalize") == "1");
   }
@@ -61,57 +55,47 @@ nvinfer1::ITensor* convolutionalLayer(
   nvinfer1::Weights convWt {nvinfer1::DataType::kFLOAT, nullptr, size};
   nvinfer1::Weights convBias {nvinfer1::DataType::kFLOAT, nullptr, bias};
 
-    if (weightsType == "weights")
+  if (weightsType == "weights") {
-    {
+    if (batchNormalize == false) {
-        if (batchNormalize == false)
-        {
       float* val;
       if (bias != 0) {
         val = new float[filters];
-                for (int i = 0; i < filters; ++i)
+        for (int i = 0; i < filters; ++i) {
-                {
             val[i] = weights[weightPtr];
-                    weightPtr++;
+            ++weightPtr;
         }
         convBias.values = val;
         trtWeights.push_back(convBias);
       }
       val = new float[size];
-            for (int i = 0; i < size; ++i)
+      for (int i = 0; i < size; ++i) {
-            {
           val[i] = weights[weightPtr];
-                weightPtr++;
+          ++weightPtr;
       }
       convWt.values = val;
       trtWeights.push_back(convWt);
     }
-        else
+    else {
-        {
+      for (int i = 0; i < filters; ++i) {
-            for (int i = 0; i < filters; ++i)
-            {
         bnBiases.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnWeights.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnRunningMean.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
-                weightPtr++;
+        ++weightPtr;
       }
       float* val = new float[size];
-            for (int i = 0; i < size; ++i)
+      for (int i = 0; i < size; ++i) {
-            {
         val[i] = weights[weightPtr];
-                weightPtr++;
+        ++weightPtr;
       }
       convWt.values = val;
       trtWeights.push_back(convWt);
@@ -119,57 +103,47 @@ nvinfer1::ITensor* convolutionalLayer(
         trtWeights.push_back(convBias);
     }
   }
-    else
+  else {
-    {
+    if (batchNormalize == false) {
-        if (batchNormalize == false)
-        {
       float* val = new float[size];
-            for (int i = 0; i < size; ++i)
+      for (int i = 0; i < size; ++i) {
-            {
         val[i] = weights[weightPtr];
-                weightPtr++;
+        ++weightPtr;
       }
       convWt.values = val;
       trtWeights.push_back(convWt);
       if (bias != 0) {
         val = new float[filters];
-                for (int i = 0; i < filters; ++i)
+        for (int i = 0; i < filters; ++i) {
-                {
           val[i] = weights[weightPtr];
-                    weightPtr++;
+          ++weightPtr;
         }
         convBias.values = val;
         trtWeights.push_back(convBias);
       }
     }
-        else
+    else {
-        {
       float* val = new float[size];
-            for (int i = 0; i < size; ++i)
+      for (int i = 0; i < size; ++i) {
-            {
         val[i] = weights[weightPtr];
-                weightPtr++;
+        ++weightPtr;
       }
       convWt.values = val;
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnWeights.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnBiases.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnRunningMean.push_back(weights[weightPtr]);
-                weightPtr++;
+        ++weightPtr;
       }
-            for (int i = 0; i < filters; ++i)
+      for (int i = 0; i < filters; ++i) {
-            {
         bnRunningVar.push_back(sqrt(weights[weightPtr] + eps));
-                weightPtr++;
+        ++weightPtr;
       }
       trtWeights.push_back(convWt);
       if (bias != 0)
@@ -177,10 +151,10 @@ nvinfer1::ITensor* convolutionalLayer(
     }
   }
 
-    nvinfer1::IConvolutionLayer* conv
+  nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*input, filters, nvinfer1::Dims{2, {kernelSize, kernelSize}},
-        = network->addConvolutionNd(*input, filters, nvinfer1::Dims{2, {kernelSize, kernelSize}}, convWt, convBias);
+      convWt, convBias);
   assert(conv != nullptr);
-    std::string convLayerName = "conv_" + std::to_string(layerIdx);
+  std::string convLayerName = "conv_" + layerName + std::to_string(layerIdx);
   conv->setName(convLayerName.c_str());
   conv->setStrideNd(nvinfer1::Dims{2, {stride, stride}});
   conv->setPaddingNd(nvinfer1::Dims{2, {pad, pad}});
@@ -190,8 +164,7 @@ nvinfer1::ITensor* convolutionalLayer(
 
   output = conv->getOutput(0);
 
-    if (batchNormalize == true)
+  if (batchNormalize == true) {
-    {
     size = filters;
     nvinfer1::Weights shift {nvinfer1::DataType::kFLOAT, nullptr, size};
     nvinfer1::Weights scale {nvinfer1::DataType::kFLOAT, nullptr, size};
@@ -214,12 +187,12 @@ nvinfer1::ITensor* convolutionalLayer(
 
     nvinfer1::IScaleLayer* batchnorm = network->addScale(*output, nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
     assert(batchnorm != nullptr);
-        std::string batchnormLayerName = "batchnorm_" + std::to_string(layerIdx);
+    std::string batchnormLayerName = "batchnorm_" + layerName + std::to_string(layerIdx);
     batchnorm->setName(batchnormLayerName.c_str());
     output = batchnorm->getOutput(0);
   }
 
-    output = activationLayer(layerIdx, activation, output, network);
+  output = activationLayer(layerIdx, activation, output, network, layerName);
   assert(output != nullptr);
 
   return output;

nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.h

@@ -13,16 +13,8 @@
 
 #include "activation_layer.h"
 
-nvinfer1::ITensor* convolutionalLayer(
+nvinfer1::ITensor* convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int layerIdx,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, int& inputChannels, float eps,
-    std::map<std::string, std::string>& block,
+    nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network, std::string layerName = "");
-    std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
-    int& weightPtr,
-    std::string weightsType,
-    int& inputChannels,
-    float eps,
-    nvinfer1::ITensor* input,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/detect_v8_layer.cpp

@@ -0,0 +1,196 @@
+/*
+ * Created by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#include "detect_v8_layer.h"
+
+#include <cassert>
+
+nvinfer1::ITensor*
+detectV8Layer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
+    nvinfer1::INetworkDefinition* network)
+{
+  nvinfer1::ITensor* output;
+
+  assert(block.at("type") == "detect_v8");
+  assert(block.find("num") != block.end());
+  assert(block.find("classes") != block.end());
+
+  int num = std::stoi(block.at("num"));
+  int classes = std::stoi(block.at("classes"));
+  int reg_max = num / 4;
+
+  nvinfer1::Dims inputDims = input->getDimensions();
+
+  nvinfer1::ISliceLayer* sliceBox = network->addSlice(*input, nvinfer1::Dims{2, {0, 0}},
+      nvinfer1::Dims{2, {num, inputDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
+  assert(sliceBox != nullptr);
+  std::string sliceBoxLayerName = "slice_box_" + std::to_string(layerIdx);
+  sliceBox->setName(sliceBoxLayerName.c_str());
+  nvinfer1::ITensor* box = sliceBox->getOutput(0);
+
+  nvinfer1::ISliceLayer* sliceCls = network->addSlice(*input, nvinfer1::Dims{2, {num, 0}},
+      nvinfer1::Dims{2, {classes, inputDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
+  assert(sliceCls != nullptr);
+  std::string sliceClsLayerName = "slice_cls_" + std::to_string(layerIdx);
+  sliceCls->setName(sliceClsLayerName.c_str());
+  nvinfer1::ITensor* cls = sliceCls->getOutput(0);
+
+  nvinfer1::IShuffleLayer* shuffle1Box = network->addShuffle(*box);
+  assert(shuffle1Box != nullptr);
+  std::string shuffle1BoxLayerName = "shuffle1_box_" + std::to_string(layerIdx);
+  shuffle1Box->setName(shuffle1BoxLayerName.c_str());
+  nvinfer1::Dims reshape1Dims = {3, {4, reg_max, inputDims.d[1]}};
+  shuffle1Box->setReshapeDimensions(reshape1Dims);
+  nvinfer1::Permutation permutation1;
+  permutation1.order[0] = 1;
+  permutation1.order[1] = 0;
+  permutation1.order[2] = 2;
+  shuffle1Box->setSecondTranspose(permutation1);
+  box = shuffle1Box->getOutput(0);
+
+  nvinfer1::ISoftMaxLayer* softmax = network->addSoftMax(*box);
+  assert(softmax != nullptr);
+  std::string softmaxLayerName = "softmax_box_" + std::to_string(layerIdx);
+  softmax->setName(softmaxLayerName.c_str());
+  softmax->setAxes(1 << 0);
+  box = softmax->getOutput(0);
+
+  nvinfer1::Weights dflWt {nvinfer1::DataType::kFLOAT, nullptr, reg_max};
+
+  float* val = new float[reg_max];
+  for (int i = 0; i < reg_max; ++i) {
+    val[i] = i;
+  }
+  dflWt.values = val;
+
+  nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*box, 1, nvinfer1::Dims{2, {1, 1}}, dflWt,
+      nvinfer1::Weights{});
+  assert(conv != nullptr);
+  std::string convLayerName = "conv_box_" + std::to_string(layerIdx);
+  conv->setName(convLayerName.c_str());
+  conv->setStrideNd(nvinfer1::Dims{2, {1, 1}});
+  conv->setPaddingNd(nvinfer1::Dims{2, {0, 0}});
+  box = conv->getOutput(0);
+
+  nvinfer1::IShuffleLayer* shuffle2Box = network->addShuffle(*box);
+  assert(shuffle2Box != nullptr);
+  std::string shuffle2BoxLayerName = "shuffle2_box_" + std::to_string(layerIdx);
+  shuffle2Box->setName(shuffle2BoxLayerName.c_str());
+  nvinfer1::Dims reshape2Dims = {2, {4, inputDims.d[1]}};
+  shuffle2Box->setReshapeDimensions(reshape2Dims);
+  box = shuffle2Box->getOutput(0);
+
+  nvinfer1::Dims shuffle2BoxDims = box->getDimensions();
+
+  nvinfer1::ISliceLayer* sliceLtBox = network->addSlice(*box, nvinfer1::Dims{2, {0, 0}},
+      nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
+  assert(sliceLtBox != nullptr);
+  std::string sliceLtBoxLayerName = "slice_lt_box_" + std::to_string(layerIdx);
+  sliceLtBox->setName(sliceLtBoxLayerName.c_str());
+  nvinfer1::ITensor* lt = sliceLtBox->getOutput(0);
+
+  nvinfer1::ISliceLayer* sliceRbBox = network->addSlice(*box, nvinfer1::Dims{2, {2, 0}},
+      nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
+  assert(sliceRbBox != nullptr);
+  std::string sliceRbBoxLayerName = "slice_rb_box_" + std::to_string(layerIdx);
+  sliceRbBox->setName(sliceRbBoxLayerName.c_str());
+  nvinfer1::ITensor* rb = sliceRbBox->getOutput(0);
+
+  int channels = 2 * shuffle2BoxDims.d[1];
+  nvinfer1::Weights anchorPointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
+  val = new float[channels];
+  for (int i = 0; i < channels; ++i) {
+    val[i] = weights[weightPtr];
+    ++weightPtr;
+  }
+  anchorPointsWt.values = val;
+  trtWeights.push_back(anchorPointsWt);
+
+  nvinfer1::IConstantLayer* anchorPoints = network->addConstant(nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}},
+      anchorPointsWt);
+  assert(anchorPoints != nullptr);
+  std::string anchorPointsLayerName = "anchor_points_" + std::to_string(layerIdx);
+  anchorPoints->setName(anchorPointsLayerName.c_str());
+  nvinfer1::ITensor* anchorPointsTensor = anchorPoints->getOutput(0);
+
+  nvinfer1::IElementWiseLayer* x1y1 = network->addElementWise(*anchorPointsTensor, *lt,
+      nvinfer1::ElementWiseOperation::kSUB);
+  assert(x1y1 != nullptr);
+  std::string x1y1LayerName = "x1y1_" + std::to_string(layerIdx);
+  x1y1->setName(x1y1LayerName.c_str());
+  nvinfer1::ITensor* x1y1Tensor = x1y1->getOutput(0);
+
+  nvinfer1::IElementWiseLayer* x2y2 = network->addElementWise(*rb, *anchorPointsTensor,
+      nvinfer1::ElementWiseOperation::kSUM);
+  assert(x2y2 != nullptr);
+  std::string x2y2LayerName = "x2y2_" + std::to_string(layerIdx);
+  x2y2->setName(x2y2LayerName.c_str());
+  nvinfer1::ITensor* x2y2Tensor = x2y2->getOutput(0);
+
+  std::vector<nvinfer1::ITensor*> concatBoxInputs;
+  concatBoxInputs.push_back(x1y1Tensor);
+  concatBoxInputs.push_back(x2y2Tensor);
+
+  nvinfer1::IConcatenationLayer* concatBox = network->addConcatenation(concatBoxInputs.data(), concatBoxInputs.size());
+  assert(concatBox != nullptr);
+  std::string concatBoxLayerName = "concat_box_" + std::to_string(layerIdx);
+  concatBox->setName(concatBoxLayerName.c_str());
+  concatBox->setAxis(0);
+  box = concatBox->getOutput(0);
+
+  channels = shuffle2BoxDims.d[1];
+  nvinfer1::Weights stridePointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
+  val = new float[channels];
+  for (int i = 0; i < channels; ++i) {
+    val[i] = weights[weightPtr];
+    ++weightPtr;
+  }
+  stridePointsWt.values = val;
+  trtWeights.push_back(stridePointsWt);
+
+  nvinfer1::IConstantLayer* stridePoints = network->addConstant(nvinfer1::Dims{2, {1, shuffle2BoxDims.d[1]}},
+      stridePointsWt);
+  assert(stridePoints != nullptr);
+  std::string stridePointsLayerName = "stride_points_" + std::to_string(layerIdx);
+  stridePoints->setName(stridePointsLayerName.c_str());
+  nvinfer1::ITensor* stridePointsTensor = stridePoints->getOutput(0);
+
+  nvinfer1::IElementWiseLayer* pred = network->addElementWise(*box, *stridePointsTensor,
+      nvinfer1::ElementWiseOperation::kPROD);
+  assert(pred != nullptr);
+  std::string predLayerName = "pred_" + std::to_string(layerIdx);
+  pred->setName(predLayerName.c_str());
+  box = pred->getOutput(0);
+
+  nvinfer1::IActivationLayer* sigmoid = network->addActivation(*cls, nvinfer1::ActivationType::kSIGMOID);
+  assert(sigmoid != nullptr);
+  std::string sigmoidLayerName = "sigmoid_cls_" + std::to_string(layerIdx);
+  sigmoid->setName(sigmoidLayerName.c_str());
+  cls = sigmoid->getOutput(0);
+
+  std::vector<nvinfer1::ITensor*> concatInputs;
+  concatInputs.push_back(box);
+  concatInputs.push_back(cls);
+
+  nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
+  assert(concat != nullptr);
+  std::string concatLayerName = "concat_" + std::to_string(layerIdx);
+  concat->setName(concatLayerName.c_str());
+  concat->setAxis(0);
+  output = concat->getOutput(0);
+
+  nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*output);
+  assert(shuffle != nullptr);
+  std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
+  shuffle->setName(shuffleLayerName.c_str());
+  nvinfer1::Permutation permutation2;
+  permutation2.order[0] = 1;
+  permutation2.order[1] = 0;
+  shuffle->setFirstTranspose(permutation2);
+  output = shuffle->getOutput(0);
+
+  return output;
+}

nvdsinfer_custom_impl_Yolo/layers/detect_v8_layer.h

@@ -0,0 +1,18 @@
+/*
+ * Created by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#ifndef __DETECT_V8_LAYER_H__
+#define __DETECT_V8_LAYER_H__
+
+#include <map>
+#include <vector>
+
+#include "NvInfer.h"
+
+nvinfer1::ITensor* detectV8Layer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
+    nvinfer1::INetworkDefinition* network);
+
+#endif

nvdsinfer_custom_impl_Yolo/layers/implicit_layer.cpp

@@ -5,13 +5,11 @@
 
 #include "implicit_layer.h"
 
-nvinfer1::ITensor* implicitLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    std::vector<float>& weights,
+implicitLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::INetworkDefinition* network)
-    int& weightPtr,
-    nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
@@ -23,10 +21,9 @@ nvinfer1::ITensor* implicitLayer(
   nvinfer1::Weights convWt {nvinfer1::DataType::kFLOAT, nullptr, filters};
 
   float* val = new float[filters];
-    for (int i = 0; i < filters; ++i)
+  for (int i = 0; i < filters; ++i) {
-    {
     val[i] = weights[weightPtr];
-        weightPtr++;
+    ++weightPtr;
   }
   convWt.values = val;
   trtWeights.push_back(convWt);

nvdsinfer_custom_impl_Yolo/layers/implicit_layer.h

@@ -8,16 +8,10 @@
 
 #include <map>
 #include <vector>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* implicitLayer(
+nvinfer1::ITensor* implicitLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int layerIdx,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::INetworkDefinition* network);
-    std::map<std::string, std::string>& block,
-    std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
-    int& weightPtr,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/pooling_layer.cpp

@@ -5,52 +5,49 @@
 
 #include "pooling_layer.h"
 
-nvinfer1::ITensor* poolingLayer(
+#include <cassert>
-    int layerIdx,
+#include <iostream>
-    std::map<std::string, std::string>& block,
+
-    nvinfer1::ITensor* input,
+nvinfer1::ITensor*
+poolingLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
   assert(block.at("type") == "maxpool" || block.at("type") == "avgpool");
 
-    if (block.at("type") == "maxpool")
+  if (block.at("type") == "maxpool") {
-    {
     assert(block.find("size") != block.end());
     assert(block.find("stride") != block.end());
 
     int size = std::stoi(block.at("size"));
     int stride = std::stoi(block.at("stride"));
 
-        nvinfer1::IPoolingLayer* maxpool
+    nvinfer1::IPoolingLayer* maxpool = network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX,
-            = network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX, nvinfer1::Dims{2, {size, size}});
+        nvinfer1::Dims{2, {size, size}});
     assert(maxpool != nullptr);
     std::string maxpoolLayerName = "maxpool_" + std::to_string(layerIdx);
     maxpool->setName(maxpoolLayerName.c_str());
     maxpool->setStrideNd(nvinfer1::Dims{2, {stride, stride}});
     maxpool->setPaddingNd(nvinfer1::Dims{2, {(size - 1) / 2, (size - 1) / 2}});
-        if (size == 2 && stride == 1)
+    if (size == 2 && stride == 1) {
-        {
       maxpool->setPrePadding(nvinfer1::Dims{2, {0, 0}});
       maxpool->setPostPadding(nvinfer1::Dims{2, {1, 1}});
     }
     output = maxpool->getOutput(0);
   }
-    else if (block.at("type") == "avgpool")
+  else if (block.at("type") == "avgpool") {
-    {
     nvinfer1::Dims inputDims = input->getDimensions();
-        nvinfer1::IPoolingLayer* avgpool = network->addPoolingNd(
+    nvinfer1::IPoolingLayer* avgpool = network->addPoolingNd(*input, nvinfer1::PoolingType::kAVERAGE,
-            *input, nvinfer1::PoolingType::kAVERAGE, nvinfer1::Dims{2, {inputDims.d[1], inputDims.d[2]}});
+        nvinfer1::Dims{2, {inputDims.d[1], inputDims.d[2]}});
     assert(avgpool != nullptr);
     std::string avgpoolLayerName = "avgpool_" + std::to_string(layerIdx);
     avgpool->setName(avgpoolLayerName.c_str());
     output = avgpool->getOutput(0);
   }
-    else
+  else {
-    {
     std::cerr << "Pooling not supported: " << block.at("type") << std::endl;
-        std::abort();
+    assert(0);
   }
 
   return output;

nvdsinfer_custom_impl_Yolo/layers/pooling_layer.h

@@ -7,15 +7,10 @@
 #define __POOLING_LAYER_H__
 
 #include <map>
-#include <cassert>
-#include <iostream>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* poolingLayer(
+nvinfer1::ITensor* poolingLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/reduce_layer.cpp

@@ -5,10 +5,8 @@
 
 #include "reduce_layer.h"
 
-nvinfer1::ITensor* reduceLayer(
+nvinfer1::ITensor*
-    int layerIdx,
+reduceLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
@@ -26,14 +24,12 @@ nvinfer1::ITensor* reduceLayer(
   std::string strAxes = block.at("axes");
   std::vector<int32_t> axes;
   size_t lastPos = 0, pos = 0;
-    while ((pos = strAxes.find(',', lastPos)) != std::string::npos)
+  while ((pos = strAxes.find(',', lastPos)) != std::string::npos) {
-    {
     int vL = std::stoi(trim(strAxes.substr(lastPos, pos - lastPos)));
     axes.push_back(vL);
     lastPos = pos + 1;
   }
-    if (lastPos < strAxes.length())
+  if (lastPos < strAxes.length()) {
-    {
     std::string lastV = trim(strAxes.substr(lastPos));
     if (!lastV.empty())
       axes.push_back(std::stoi(lastV));

nvdsinfer_custom_impl_Yolo/layers/reduce_layer.h

@@ -6,13 +6,9 @@
 #ifndef __REDUCE_LAYER_H__
 #define __REDUCE_LAYER_H__
 
-#include "NvInfer.h"
 #include "../utils.h"
 
-nvinfer1::ITensor* reduceLayer(
+nvinfer1::ITensor* reduceLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/reg_layer.cpp

@@ -5,13 +5,11 @@
 
 #include "reg_layer.h"
 
-nvinfer1::ITensor* regLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    std::vector<float>& weights,
+regLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
-    int& weightPtr,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
@@ -29,15 +27,15 @@ nvinfer1::ITensor* regLayer(
   output = shuffle->getOutput(0);
   nvinfer1::Dims shuffleDims = output->getDimensions();
 
-    nvinfer1::ISliceLayer* sliceLt = network->addSlice(
+  nvinfer1::ISliceLayer* sliceLt = network->addSlice(*output, nvinfer1::Dims{2, {0, 0}},
-        *output, nvinfer1::Dims{2, {0, 0}}, nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
+      nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
   assert(sliceLt != nullptr);
   std::string sliceLtLayerName = "slice_lt_" + std::to_string(layerIdx);
   sliceLt->setName(sliceLtLayerName.c_str());
   nvinfer1::ITensor* lt = sliceLt->getOutput(0);
 
-    nvinfer1::ISliceLayer* sliceRb = network->addSlice(
+  nvinfer1::ISliceLayer* sliceRb = network->addSlice(*output, nvinfer1::Dims{2, {0, 2}},
-        *output, nvinfer1::Dims{2, {0, 2}}, nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
+      nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
   assert(sliceRb != nullptr);
   std::string sliceRbLayerName = "slice_rb_" + std::to_string(layerIdx);
   sliceRb->setName(sliceRbLayerName.c_str());
@@ -46,10 +44,9 @@ nvinfer1::ITensor* regLayer(
   int channels = shuffleDims.d[0] * 2;
   nvinfer1::Weights anchorPointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
   float* val = new float[channels];
-    for (int i = 0; i < channels; ++i)
+  for (int i = 0; i < channels; ++i) {
-    {
     val[i] = weights[weightPtr];
-        weightPtr++;
+    ++weightPtr;
   }
   anchorPointsWt.values = val;
   trtWeights.push_back(anchorPointsWt);
@@ -60,15 +57,15 @@ nvinfer1::ITensor* regLayer(
   anchorPoints->setName(anchorPointsLayerName.c_str());
   nvinfer1::ITensor* anchorPointsTensor = anchorPoints->getOutput(0);
 
-    nvinfer1::IElementWiseLayer* x1y1
+  nvinfer1::IElementWiseLayer* x1y1 = network->addElementWise(*anchorPointsTensor, *lt,
-        = network->addElementWise(*anchorPointsTensor, *lt, nvinfer1::ElementWiseOperation::kSUB);
+      nvinfer1::ElementWiseOperation::kSUB);
   assert(x1y1 != nullptr);
   std::string x1y1LayerName = "x1y1_" + std::to_string(layerIdx);
   x1y1->setName(x1y1LayerName.c_str());
   nvinfer1::ITensor* x1y1Tensor = x1y1->getOutput(0);
 
-    nvinfer1::IElementWiseLayer* x2y2
+  nvinfer1::IElementWiseLayer* x2y2 = network->addElementWise(*rb, *anchorPointsTensor,
-        = network->addElementWise(*rb, *anchorPointsTensor, nvinfer1::ElementWiseOperation::kSUM);
+      nvinfer1::ElementWiseOperation::kSUM);
   assert(x2y2 != nullptr);
   std::string x2y2LayerName = "x2y2_" + std::to_string(layerIdx);
   x2y2->setName(x2y2LayerName.c_str());
@@ -88,10 +85,9 @@ nvinfer1::ITensor* regLayer(
   channels = shuffleDims.d[0];
   nvinfer1::Weights stridePointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
   val = new float[channels];
-    for (int i = 0; i < channels; ++i)
+  for (int i = 0; i < channels; ++i) {
-    {
       val[i] = weights[weightPtr];
-        weightPtr++;
+      ++weightPtr;
   }
   stridePointsWt.values = val;
   trtWeights.push_back(stridePointsWt);
@@ -102,8 +98,8 @@ nvinfer1::ITensor* regLayer(
   stridePoints->setName(stridePointsLayerName.c_str());
   nvinfer1::ITensor* stridePointsTensor = stridePoints->getOutput(0);
 
-    nvinfer1::IElementWiseLayer* pred
+  nvinfer1::IElementWiseLayer* pred = network->addElementWise(*output, *stridePointsTensor,
-        = network->addElementWise(*output, *stridePointsTensor, nvinfer1::ElementWiseOperation::kPROD);
+      nvinfer1::ElementWiseOperation::kPROD);
   assert(pred != nullptr);
   std::string predLayerName = "pred_" + std::to_string(layerIdx);
   pred->setName(predLayerName.c_str());

nvdsinfer_custom_impl_Yolo/layers/reg_layer.h

@@ -8,17 +8,11 @@
 
 #include <map>
 #include <vector>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* regLayer(
+nvinfer1::ITensor* regLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
-    int layerIdx,
+    std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
-    std::map<std::string, std::string>& block,
-    std::vector<float>& weights,
-    std::vector<nvinfer1::Weights>& trtWeights,
-    int& weightPtr,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/reorg_layer.cpp

@@ -5,10 +5,11 @@
 
 #include "reorg_layer.h"
 
-nvinfer1::ITensor* reorgLayer(
+#include <vector>
-    int layerIdx,
+#include <cassert>
-    std::map<std::string, std::string>& block,
+
-    nvinfer1::ITensor* input,
+nvinfer1::ITensor*
+reorgLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
@@ -17,30 +18,26 @@ nvinfer1::ITensor* reorgLayer(
 
   nvinfer1::Dims inputDims = input->getDimensions();
 
-    nvinfer1::ISliceLayer *slice1 = network->addSlice(
+  nvinfer1::ISliceLayer *slice1 = network->addSlice(*input, nvinfer1::Dims{3, {0, 0, 0}},
-        *input, nvinfer1::Dims{3, {0, 0, 0}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
+      nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
-        nvinfer1::Dims{3, {1, 2, 2}});
   assert(slice1 != nullptr);
   std::string slice1LayerName = "slice1_" + std::to_string(layerIdx);
   slice1->setName(slice1LayerName.c_str());
 
-    nvinfer1::ISliceLayer *slice2 = network->addSlice(
+  nvinfer1::ISliceLayer *slice2 = network->addSlice(*input, nvinfer1::Dims{3, {0, 1, 0}},
-        *input, nvinfer1::Dims{3, {0, 1, 0}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
+      nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
-        nvinfer1::Dims{3, {1, 2, 2}});
   assert(slice2 != nullptr);
   std::string slice2LayerName = "slice2_" + std::to_string(layerIdx);
   slice2->setName(slice2LayerName.c_str());
 
-    nvinfer1::ISliceLayer *slice3 = network->addSlice(
+  nvinfer1::ISliceLayer *slice3 = network->addSlice(*input, nvinfer1::Dims{3, {0, 0, 1}},
-        *input, nvinfer1::Dims{3, {0, 0, 1}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
+      nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
-        nvinfer1::Dims{3, {1, 2, 2}});
   assert(slice3 != nullptr);
   std::string slice3LayerName = "slice3_" + std::to_string(layerIdx);
   slice3->setName(slice3LayerName.c_str());
 
-    nvinfer1::ISliceLayer *slice4 = network->addSlice(
+  nvinfer1::ISliceLayer *slice4 = network->addSlice(*input, nvinfer1::Dims{3, {0, 1, 1}},
-        *input, nvinfer1::Dims{3, {0, 1, 1}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
+      nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
-        nvinfer1::Dims{3, {1, 2, 2}});
   assert(slice4 != nullptr);
   std::string slice4LayerName = "slice4_" + std::to_string(layerIdx);
   slice4->setName(slice4LayerName.c_str());

nvdsinfer_custom_impl_Yolo/layers/reorg_layer.h

@@ -3,19 +3,14 @@
  * https://www.github.com/marcoslucianops
  */
 
-#ifndef __REORGV5_LAYER_H__
+#ifndef __REORG_LAYER_H__
-#define __REORGV5_LAYER_H__
+#define __REORG_LAYER_H__
 
 #include <map>
-#include <vector>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* reorgLayer(
+nvinfer1::ITensor* reorgLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/route_layer.cpp

@@ -5,12 +5,9 @@
 
 #include "route_layer.h"
 
-nvinfer1::ITensor* routeLayer(
+nvinfer1::ITensor*
-    int layerIdx,
+routeLayer(int layerIdx, std::string& layers, std::map<std::string, std::string>& block,
-    std::string& layers,
+    std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network)
-    std::map<std::string, std::string>& block,
-    std::vector<nvinfer1::ITensor*> tensorOutputs,
-    nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
@@ -20,22 +17,19 @@ nvinfer1::ITensor* routeLayer(
   std::string strLayers = block.at("layers");
   std::vector<int> idxLayers;
   size_t lastPos = 0, pos = 0;
-    while ((pos = strLayers.find(',', lastPos)) != std::string::npos)
+  while ((pos = strLayers.find(',', lastPos)) != std::string::npos) {
-    {
     int vL = std::stoi(trim(strLayers.substr(lastPos, pos - lastPos)));
     idxLayers.push_back(vL);
     lastPos = pos + 1;
   }
-    if (lastPos < strLayers.length())
+  if (lastPos < strLayers.length()) {
-    {
     std::string lastV = trim(strLayers.substr(lastPos));
     if (!lastV.empty())
       idxLayers.push_back(std::stoi(lastV));
   }
   assert (!idxLayers.empty());
   std::vector<nvinfer1::ITensor*> concatInputs;
-    for (uint i = 0; i < idxLayers.size(); ++i)
+  for (uint i = 0; i < idxLayers.size(); ++i) {
-    {
     if (idxLayers[i] < 0)
       idxLayers[i] = tensorOutputs.size() + idxLayers[i];
     assert (idxLayers[i] >= 0 && idxLayers[i] < (int)tensorOutputs.size());
@@ -62,15 +56,13 @@ nvinfer1::ITensor* routeLayer(
     output = concat->getOutput(0);
   }
 
-    if (block.find("groups") != block.end())
+  if (block.find("groups") != block.end()) {
-    {
     nvinfer1::Dims prevTensorDims = output->getDimensions();
     int groups = stoi(block.at("groups"));
     int group_id = stoi(block.at("group_id"));
     int startSlice = (prevTensorDims.d[0] / groups) * group_id;
     int channelSlice = (prevTensorDims.d[0] / groups);
-        nvinfer1::ISliceLayer* slice = network->addSlice(
+    nvinfer1::ISliceLayer* slice = network->addSlice(*output, nvinfer1::Dims{3, {startSlice, 0, 0}},
-            *output, nvinfer1::Dims{3, {startSlice, 0, 0}},
         nvinfer1::Dims{3, {channelSlice, prevTensorDims.d[1], prevTensorDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
     assert(slice != nullptr);
     std::string sliceLayerName = "slice_" + std::to_string(layerIdx);

nvdsinfer_custom_impl_Yolo/layers/route_layer.h

@@ -6,14 +6,9 @@
 #ifndef __ROUTE_LAYER_H__
 #define __ROUTE_LAYER_H__
 
-#include "NvInfer.h"
 #include "../utils.h"
 
-nvinfer1::ITensor* routeLayer(
+nvinfer1::ITensor* routeLayer(int layerIdx, std::string& layers, std::map<std::string, std::string>& block,
-    int layerIdx,
+    std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network);
-    std::string& layers,
-    std::map<std::string, std::string>& block,
-    std::vector<nvinfer1::ITensor*> tensorOutputs,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/shortcut_layer.cpp

@@ -5,15 +5,11 @@
 
 #include "shortcut_layer.h"
 
-nvinfer1::ITensor* shortcutLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::string mode,
+nvinfer1::ITensor*
-    std::string activation,
+shortcutLayer(int layerIdx, std::string mode, std::string activation, std::string inputVol, std::string shortcutVol,
-    std::string inputVol,
+    std::map<std::string, std::string>& block, nvinfer1::ITensor* input, nvinfer1::ITensor* shortcutInput,
-    std::string shortcutVol,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
-    nvinfer1::ITensor* shortcutInput,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
@@ -25,19 +21,16 @@ nvinfer1::ITensor* shortcutLayer(
   if (mode == "mul")
     operation = nvinfer1::ElementWiseOperation::kPROD;
 
-    if (mode == "add" && inputVol != shortcutVol)
+  if (mode == "add" && inputVol != shortcutVol) {
-    {
+    nvinfer1::ISliceLayer* slice = network->addSlice(*shortcutInput, nvinfer1::Dims{3, {0, 0, 0}}, input->getDimensions(),
-        nvinfer1::ISliceLayer* slice = network->addSlice(
+        nvinfer1::Dims{3, {1, 1, 1}});
-            *shortcutInput, nvinfer1::Dims{3, {0, 0, 0}}, input->getDimensions(), nvinfer1::Dims{3, {1, 1, 1}});
     assert(slice != nullptr);
     std::string sliceLayerName = "slice_" + std::to_string(layerIdx);
     slice->setName(sliceLayerName.c_str());
     output = slice->getOutput(0);
   }
   else
-    {
     output = shortcutInput;
-    }
 
   nvinfer1::IElementWiseLayer* shortcut = network->addElementWise(*input, *output, operation);
   assert(shortcut != nullptr);

nvdsinfer_custom_impl_Yolo/layers/shortcut_layer.h

@@ -12,15 +12,8 @@
 
 #include "activation_layer.h"
 
-nvinfer1::ITensor* shortcutLayer(
+nvinfer1::ITensor* shortcutLayer(int layerIdx, std::string mode, std::string activation, std::string inputVol,
-    int layerIdx,
+    std::string shortcutVol, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    std::string mode,
+    nvinfer1::ITensor* shortcut, nvinfer1::INetworkDefinition* network);
-    std::string activation,
-    std::string inputVol,
-    std::string shortcutVol,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
-    nvinfer1::ITensor* shortcut,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/shuffle_layer.cpp

@@ -5,13 +5,9 @@
 
 #include "shuffle_layer.h"
 
-nvinfer1::ITensor* shuffleLayer(
+nvinfer1::ITensor*
-    int layerIdx,
+shuffleLayer(int layerIdx, std::string& layer, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    std::string& layer,
+    std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network)
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
-    std::vector<nvinfer1::ITensor*> tensorOutputs,
-    nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;
 
@@ -22,25 +18,7 @@ nvinfer1::ITensor* shuffleLayer(
   std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
   shuffle->setName(shuffleLayerName.c_str());
 
-    if (block.find("reshape") != block.end())
+  if (block.find("reshape") != block.end()) {
-    {
-        std::string strReshape = block.at("reshape");
-        std::vector<int32_t> reshape;
-        size_t lastPos = 0, pos = 0;
-        while ((pos = strReshape.find(',', lastPos)) != std::string::npos)
-        {
-            int vL = std::stoi(trim(strReshape.substr(lastPos, pos - lastPos)));
-            reshape.push_back(vL);
-            lastPos = pos + 1;
-        }
-        if (lastPos < strReshape.length())
-        {
-            std::string lastV = trim(strReshape.substr(lastPos));
-            if (!lastV.empty())
-                reshape.push_back(std::stoi(lastV));
-        }
-        assert(!reshape.empty());
-
     int from = -1;
     if (block.find("from") != block.end())
         from = std::stoi(block.at("from"));
@@ -51,33 +29,72 @@ nvinfer1::ITensor* shuffleLayer(
     layer = std::to_string(from);
 
     nvinfer1::Dims inputTensorDims = tensorOutputs[from]->getDimensions();
-        int32_t l = inputTensorDims.d[1] * inputTensorDims.d[2];
+
+    std::string strReshape = block.at("reshape");
+    std::vector<int32_t> reshape;
+    size_t lastPos = 0, pos = 0;
+    while ((pos = strReshape.find(',', lastPos)) != std::string::npos) {
+      std::string V = trim(strReshape.substr(lastPos, pos - lastPos));
+      if (V == "c")
+        reshape.push_back(inputTensorDims.d[0]);
+      else if (V == "ch")
+        reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1]);
+      else if (V == "cw")
+        reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[2]);
+      else if (V == "h")
+        reshape.push_back(inputTensorDims.d[1]);
+      else if (V == "hw")
+        reshape.push_back(inputTensorDims.d[1] * inputTensorDims.d[2]);
+      else if (V == "w")
+        reshape.push_back(inputTensorDims.d[2]);
+      else if (V == "chw")
+        reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1] * inputTensorDims.d[2]);
+      else
+        reshape.push_back(std::stoi(V));
+      lastPos = pos + 1;
+    }
+    if (lastPos < strReshape.length()) {
+      std::string lastV = trim(strReshape.substr(lastPos));
+      if (!lastV.empty()) {
+        if (lastV == "c")
+          reshape.push_back(inputTensorDims.d[0]);
+        else if (lastV == "ch")
+          reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1]);
+        else if (lastV == "cw")
+          reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[2]);
+        else if (lastV == "h")
+          reshape.push_back(inputTensorDims.d[1]);
+        else if (lastV == "hw")
+          reshape.push_back(inputTensorDims.d[1] * inputTensorDims.d[2]);
+        else if (lastV == "w")
+          reshape.push_back(inputTensorDims.d[2]);
+        else if (lastV == "chw")
+          reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1] * inputTensorDims.d[2]);
+        else
+          reshape.push_back(std::stoi(lastV));
+      }
+    }
+    assert(!reshape.empty());
 
     nvinfer1::Dims reshapeDims;
     reshapeDims.nbDims = reshape.size();
 
     for (uint i = 0; i < reshape.size(); ++i)
-            if (reshape[i] == 0)
-                reshapeDims.d[i] = l;
-            else
       reshapeDims.d[i] = reshape[i];
 
     shuffle->setReshapeDimensions(reshapeDims);
   }
 
-    if (block.find("transpose1") != block.end())
+  if (block.find("transpose1") != block.end()) {
-    {
     std::string strTranspose1 = block.at("transpose1");
     std::vector<int32_t> transpose1;
     size_t lastPos = 0, pos = 0;
-        while ((pos = strTranspose1.find(',', lastPos)) != std::string::npos)
+    while ((pos = strTranspose1.find(',', lastPos)) != std::string::npos) {
-        {
       int vL = std::stoi(trim(strTranspose1.substr(lastPos, pos - lastPos)));
       transpose1.push_back(vL);
       lastPos = pos + 1;
     }
-        if (lastPos < strTranspose1.length())
+    if (lastPos < strTranspose1.length()) {
-        {
       std::string lastV = trim(strTranspose1.substr(lastPos));
       if (!lastV.empty())
         transpose1.push_back(std::stoi(lastV));
@@ -91,19 +108,16 @@ nvinfer1::ITensor* shuffleLayer(
     shuffle->setFirstTranspose(permutation1);
   }
 
-    if (block.find("transpose2") != block.end())
+  if (block.find("transpose2") != block.end()) {
-    {
     std::string strTranspose2 = block.at("transpose2");
     std::vector<int32_t> transpose2;
     size_t lastPos = 0, pos = 0;
-        while ((pos = strTranspose2.find(',', lastPos)) != std::string::npos)
+    while ((pos = strTranspose2.find(',', lastPos)) != std::string::npos) {
-        {
       int vL = std::stoi(trim(strTranspose2.substr(lastPos, pos - lastPos)));
       transpose2.push_back(vL);
       lastPos = pos + 1;
     }
-        if (lastPos < strTranspose2.length())
+    if (lastPos < strTranspose2.length()) {
-        {
       std::string lastV = trim(strTranspose2.substr(lastPos));
       if (!lastV.empty())
         transpose2.push_back(std::stoi(lastV));

nvdsinfer_custom_impl_Yolo/layers/shuffle_layer.h

@@ -6,15 +6,9 @@
 #ifndef __SHUFFLE_LAYER_H__
 #define __SHUFFLE_LAYER_H__
 
-#include "NvInfer.h"
 #include "../utils.h"
 
-nvinfer1::ITensor* shuffleLayer(
+nvinfer1::ITensor* shuffleLayer(int layerIdx, std::string& layer, std::map<std::string, std::string>& block,
-    int layerIdx,
+    nvinfer1::ITensor* input, std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network);
-    std::string& layer,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
-    std::vector<nvinfer1::ITensor*> tensorOutputs,
-    nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/softmax_layer.cpp

@@ -5,10 +5,10 @@
 
 #include "softmax_layer.h"
 
-nvinfer1::ITensor* softmaxLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    nvinfer1::ITensor* input,
+softmaxLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;

nvdsinfer_custom_impl_Yolo/layers/softmax_layer.h

@@ -7,14 +7,10 @@
 #define __SOFTMAX_LAYER_H__
 
 #include <map>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* softmaxLayer(
+nvinfer1::ITensor* softmaxLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/layers/upsample_layer.cpp

@@ -5,10 +5,10 @@
 
 #include "upsample_layer.h"
 
-nvinfer1::ITensor* upsampleLayer(
+#include <cassert>
-    int layerIdx,
+
-    std::map<std::string, std::string>& block,
+nvinfer1::ITensor*
-    nvinfer1::ITensor* input,
+upsampleLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network)
 {
   nvinfer1::ITensor* output;

nvdsinfer_custom_impl_Yolo/layers/upsample_layer.h

@@ -7,14 +7,10 @@
 #define __UPSAMPLE_LAYER_H__
 
 #include <map>
-#include <cassert>
 
 #include "NvInfer.h"
 
-nvinfer1::ITensor* upsampleLayer(
+nvinfer1::ITensor* upsampleLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
-    int layerIdx,
-    std::map<std::string, std::string>& block,
-    nvinfer1::ITensor* input,
     nvinfer1::INetworkDefinition* network);
 
 #endif

nvdsinfer_custom_impl_Yolo/nvdsinfer_yolo_engine.cpp

@@ -23,16 +23,17 @@
  * https://www.github.com/marcoslucianops
  */
 
+#include <algorithm>
+
 #include "nvdsinfer_custom_impl.h"
 #include "nvdsinfer_context.h"
-#include "yoloPlugins.h"
-#include "yolo.h"
 
-#include <algorithm>
+#include "yolo.h"
 
 #define USE_CUDA_ENGINE_GET_API 1
 
-static bool getYoloNetworkInfo(NetworkInfo &networkInfo, const NvDsInferContextInitParams* initParams)
+static bool
+getYoloNetworkInfo(NetworkInfo& networkInfo, const NvDsInferContextInitParams* initParams)
 {
   std::string yoloCfg = initParams->customNetworkConfigFilePath;
   std::string yoloType;
@@ -60,14 +61,12 @@ static bool getYoloNetworkInfo(NetworkInfo &networkInfo, const NvDsInferContextI
   else if (initParams->networkMode == 2)
     networkInfo.networkMode = "FP16";
 
-    if (networkInfo.configFilePath.empty() ||  networkInfo.wtsFilePath.empty())
+  if (networkInfo.configFilePath.empty() ||  networkInfo.wtsFilePath.empty()) {
-    {
     std::cerr << "YOLO config file or weights file is not specified\n" << std::endl;
     return false;
   }
 
-    if (!fileExists(networkInfo.configFilePath) || !fileExists(networkInfo.wtsFilePath))
+  if (!fileExists(networkInfo.configFilePath) || !fileExists(networkInfo.wtsFilePath)) {
-    {
     std::cerr << "YOLO config file or weights file is not exist\n" << std::endl;
     return false;
   }
@@ -76,8 +75,9 @@ static bool getYoloNetworkInfo(NetworkInfo &networkInfo, const NvDsInferContextI
 }
 
 #if !USE_CUDA_ENGINE_GET_API
-IModelParser* NvDsInferCreateModelParser(
+IModelParser*
-    const NvDsInferContextInitParams* initParams) {
+NvDsInferCreateModelParser(const NvDsInferContextInitParams* initParams)
+{
   NetworkInfo networkInfo;
   if (!getYoloNetworkInfo(networkInfo, initParams))
     return nullptr;
@@ -85,19 +85,13 @@ IModelParser* NvDsInferCreateModelParser(
   return new Yolo(networkInfo);
 }
 #else
-extern "C"
+extern "C" bool
-bool NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder * const builder,
+NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder* const builder, nvinfer1::IBuilderConfig* const builderConfig,
-        nvinfer1::IBuilderConfig * const builderConfig,
+    const NvDsInferContextInitParams* const initParams, nvinfer1::DataType dataType, nvinfer1::ICudaEngine*& cudaEngine);
-        const NvDsInferContextInitParams * const initParams,
-        nvinfer1::DataType dataType,
-        nvinfer1::ICudaEngine *& cudaEngine);
 
-extern "C"
+extern "C" bool
-bool NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder * const builder,
+NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder* const builder, nvinfer1::IBuilderConfig* const builderConfig,
-        nvinfer1::IBuilderConfig * const builderConfig,
+    const NvDsInferContextInitParams* const initParams, nvinfer1::DataType dataType, nvinfer1::ICudaEngine*& cudaEngine)
-        const NvDsInferContextInitParams * const initParams,
-        nvinfer1::DataType dataType,
-        nvinfer1::ICudaEngine *& cudaEngine)
 {
   NetworkInfo networkInfo;
   if (!getYoloNetworkInfo(networkInfo, initParams))
@@ -105,8 +99,7 @@ bool NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder * const builder,
 
   Yolo yolo(networkInfo);
   cudaEngine = yolo.createEngine(builder, builderConfig);
-    if (cudaEngine == nullptr)
+  if (cudaEngine == nullptr) {
-    {
     std::cerr << "Failed to build CUDA engine on " << networkInfo.configFilePath << std::endl;
     return false;
   }

nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp

@@ -23,20 +23,17 @@
  * https://www.github.com/marcoslucianops
  */
 
-#include <algorithm>
-#include <cmath>
-#include <sstream>
 #include "nvdsinfer_custom_impl.h"
-#include "utils.h"
 
+#include "utils.h"
 #include "yoloPlugins.h"
 
-extern "C" bool NvDsInferParseYolo(
+extern "C" bool
-    std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
+NvDsInferParseYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
     NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList);
 
-static NvDsInferParseObjectInfo convertBBox(
+static NvDsInferParseObjectInfo
-    const float& bx1, const float& by1, const float& bx2, const float& by2, const uint& netW, const uint& netH)
+convertBBox(const float& bx1, const float& by1, const float& bx2, const float& by2, const uint& netW, const uint& netH)
 {
   NvDsInferParseObjectInfo b;
 
@@ -58,8 +55,8 @@ static NvDsInferParseObjectInfo convertBBox(
   return b;
 }
 
-static void addBBoxProposal(
+static void
-    const float bx1, const float by1, const float bx2, const float by2, const uint& netW, const uint& netH,
+addBBoxProposal(const float bx1, const float by1, const float bx2, const float by2, const uint& netW, const uint& netH,
     const int maxIndex, const float maxProb, std::vector<NvDsInferParseObjectInfo>& binfo)
 {
   NvDsInferParseObjectInfo bbi = convertBBox(bx1, by1, bx2, by2, netW, netH);
@@ -70,14 +67,14 @@ static void addBBoxProposal(
   binfo.push_back(bbi);
 }
 
-static std::vector<NvDsInferParseObjectInfo> decodeYoloTensor(
+static std::vector<NvDsInferParseObjectInfo>
-    const int* counts, const float* boxes, const float* scores, const int* classes, const uint& netW, const uint& netH)
+decodeYoloTensor(const int* counts, const float* boxes, const float* scores, const int* classes, const uint& netW,
+    const uint& netH)
 {
   std::vector<NvDsInferParseObjectInfo> binfo;
 
   uint numBoxes = counts[0];
-    for (uint b = 0; b < numBoxes; ++b)
+  for (uint b = 0; b < numBoxes; ++b) {
-    {
     float bx1 = boxes[b * 4 + 0];
     float by1 = boxes[b * 4 + 1];
     float bx2 = boxes[b * 4 + 2];
@@ -91,23 +88,15 @@ static std::vector<NvDsInferParseObjectInfo> decodeYoloTensor(
   return binfo;
 }
 
-static bool NvDsInferParseCustomYolo(
+static bool
-    std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
+NvDsInferParseCustomYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
-    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList,
+    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
-    const uint &numClasses)
-{
-    if (outputLayersInfo.empty())
 {
+  if (outputLayersInfo.empty()) {
     std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
     return false;
   }
 
-    if (numClasses != detectionParams.numClassesConfigured)
-    {
-        std::cerr << "WARNING: Num classes mismatch. Configured: " << detectionParams.numClassesConfigured
-                  << ", detected by network: " << numClasses << std::endl;
-    }
-
   std::vector<NvDsInferParseObjectInfo> objects;
 
   const NvDsInferLayerInfo& counts = outputLayersInfo[0];
@@ -115,10 +104,9 @@ static bool NvDsInferParseCustomYolo(
   const NvDsInferLayerInfo& scores = outputLayersInfo[2];
   const NvDsInferLayerInfo& classes = outputLayersInfo[3];
 
-    std::vector<NvDsInferParseObjectInfo> outObjs =
+  std::vector<NvDsInferParseObjectInfo> outObjs = decodeYoloTensor((const int*) (counts.buffer),
-        decodeYoloTensor(
+      (const float*) (boxes.buffer), (const float*) (scores.buffer), (const int*) (classes.buffer), networkInfo.width,
-            (const int*)(counts.buffer), (const float*)(boxes.buffer), (const float*)(scores.buffer),
+      networkInfo.height);
-            (const int*)(classes.buffer), networkInfo.width, networkInfo.height);
 
   objects.insert(objects.end(), outObjs.begin(), outObjs.end());
 
@@ -127,14 +115,11 @@ static bool NvDsInferParseCustomYolo(
   return true;
 }
 
-extern "C" bool NvDsInferParseYolo(
+extern "C" bool
-    std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
+NvDsInferParseYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
     NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
 {
-    int num_classes = kNUM_CLASSES;
+  return NvDsInferParseCustomYolo(outputLayersInfo, networkInfo, detectionParams, objectList);
-
-    return NvDsInferParseCustomYolo (
-        outputLayersInfo, networkInfo, detectionParams, objectList, num_classes);
 }
 
 CHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(NvDsInferParseYolo);

nvdsinfer_custom_impl_Yolo/utils.cpp

@@ -25,45 +25,50 @@
 
 #include "utils.h"
 
-#include <experimental/filesystem>
 #include <iomanip>
 #include <algorithm>
-#include <math.h>
+#include <experimental/filesystem>
 
-static void leftTrim(std::string& s)
+static void
+leftTrim(std::string& s)
 {
   s.erase(s.begin(), std::find_if(s.begin(), s.end(), [](int ch) { return !isspace(ch); }));
 }
 
-static void rightTrim(std::string& s)
+static void
+rightTrim(std::string& s)
 {
   s.erase(std::find_if(s.rbegin(), s.rend(), [](int ch) { return !isspace(ch); }).base(), s.end());
 }
 
-std::string trim(std::string s)
+std::string
+trim(std::string s)
 {
   leftTrim(s);
   rightTrim(s);
   return s;
 }
 
-float clamp(const float val, const float minVal, const float maxVal)
+float
+clamp(const float val, const float minVal, const float maxVal)
 {
   assert(minVal <= maxVal);
   return std::min(maxVal, std::max(minVal, val));
 }
 
-bool fileExists(const std::string fileName, bool verbose)
+bool
+fileExists(const std::string fileName, bool verbose)
 {
-    if (!std::experimental::filesystem::exists(std::experimental::filesystem::path(fileName)))
+  if (!std::experimental::filesystem::exists(std::experimental::filesystem::path(fileName))) {
-    {
+    if (verbose)
-        if (verbose) std::cout << "\nFile does not exist: " << fileName << std::endl;
+      std::cout << "\nFile does not exist: " << fileName << std::endl;
     return false;
   }
   return true;
 }
 
-std::vector<float> loadWeights(const std::string weightsFilePath, const std::string& networkType)
+std::vector<float>
+loadWeights(const std::string weightsFilePath, const std::string& networkType)
 {
   assert(fileExists(weightsFilePath));
   std::cout << "\nLoading pre-trained weights" << std::endl;
@@ -75,27 +80,24 @@ std::vector<float> loadWeights(const std::string weightsFilePath, const std::str
     assert(file.good());
     std::string line;
 
-        if (networkType.find("yolov2") != std::string::npos && networkType.find("yolov2-tiny") == std::string::npos)
+    if (networkType.find("yolov2") != std::string::npos && networkType.find("yolov2-tiny") == std::string::npos) {
-        {
         // Remove 4 int32 bytes of data from the stream belonging to the header
         file.ignore(4 * 4);
     }
-        else
+    else {
-        {
         // Remove 5 int32 bytes of data from the stream belonging to the header
         file.ignore(4 * 5);
     }
 
     char floatWeight[4];
-        while (!file.eof())
+    while (!file.eof()) {
-        {
       file.read(floatWeight, 4);
       assert(file.gcount() == 4);
       weights.push_back(*reinterpret_cast<float*>(floatWeight));
-            if (file.peek() == std::istream::traits_type::eof()) break;
+      if (file.peek() == std::istream::traits_type::eof())
+        break;
     }
   }
-
   else if (weightsFilePath.find(".wts") != std::string::npos) {
     std::ifstream file(weightsFilePath);
     assert(file.good());
@@ -109,29 +111,29 @@ std::vector<float> loadWeights(const std::string weightsFilePath, const std::str
 
     while (count--) {
       file >> name >> std::dec >> size;
-            for (uint32_t x = 0, y = size; x < y; ++x)
+      for (uint32_t x = 0, y = size; x < y; ++x) {
-            {
         file >> std::hex >> floatWeight;
         weights.push_back(*reinterpret_cast<float*>(&floatWeight));
       };
     }
   }
-
   else {
     std::cerr << "\nFile " << weightsFilePath << " is not supported" << std::endl;
-        std::abort();
+    assert(0);
   }
 
-    std::cout << "Loading weights of " << networkType << " complete"
+  std::cout << "Loading weights of " << networkType << " complete" << std::endl;
-            << std::endl;
   std::cout << "Total weights read: " << weights.size() << std::endl;
+
   return weights;
 }
 
-std::string dimsToString(const nvinfer1::Dims d)
+std::string
+dimsToString(const nvinfer1::Dims d)
 {
-    std::stringstream s;
   assert(d.nbDims >= 1);
+
+  std::stringstream s;
   s << "[";
   for (int i = 0; i < d.nbDims - 1; ++i)
     s << d.d[i] << ", ";
@@ -140,7 +142,8 @@ std::string dimsToString(const nvinfer1::Dims d)
   return s.str();
 }
 
-int getNumChannels(nvinfer1::ITensor* t)
+int
+getNumChannels(nvinfer1::ITensor* t)
 {
   nvinfer1::Dims d = t->getDimensions();
   assert(d.nbDims == 3);
@@ -148,8 +151,9 @@ int getNumChannels(nvinfer1::ITensor* t)
   return d.d[0];
 }
 
-void printLayerInfo(
+void
-    std::string layerIndex, std::string layerName, std::string layerInput, std::string layerOutput, std::string weightPtr)
+printLayerInfo(std::string layerIndex, std::string layerName, std::string layerInput, std::string layerOutput,
+    std::string weightPtr)
 {
   std::cout << std::setw(8) << std::left << layerIndex << std::setw(30) << std::left << layerName;
   std::cout << std::setw(20) << std::left << layerInput << std::setw(20) << std::left << layerOutput;

nvdsinfer_custom_impl_Yolo/utils.h

@@ -23,7 +23,6 @@
  * https://www.github.com/marcoslucianops
  */
 
-
 #ifndef __UTILS_H__
 #define __UTILS_H__
 
@@ -36,11 +35,17 @@
 #include "NvInfer.h"
 
 std::string trim(std::string s);
+
 float clamp(const float val, const float minVal, const float maxVal);
+
 bool fileExists(const std::string fileName, bool verbose = true);
+
 std::vector<float> loadWeights(const std::string weightsFilePath, const std::string& networkType);
+
 std::string dimsToString(const nvinfer1::Dims d);
+
 int getNumChannels(nvinfer1::ITensor* t);
+
 void printLayerInfo(
     std::string layerIndex, std::string layerName, std::string layerInput,  std::string layerOutput, std::string weightPtr);
 

nvdsinfer_custom_impl_Yolo/yolo.cpp

@@ -25,39 +25,27 @@
 
 #include "yolo.h"
 #include "yoloPlugins.h"
-#include <stdlib.h>
 
 #ifdef OPENCV
 #include "calibrator.h"
 #endif
 
-Yolo::Yolo(const NetworkInfo& networkInfo)
+Yolo::Yolo(const NetworkInfo& networkInfo) : m_InputBlobName(networkInfo.inputBlobName),
-    : m_InputBlobName(networkInfo.inputBlobName),
+    m_NetworkType(networkInfo.networkType), m_ConfigFilePath(networkInfo.configFilePath),
-      m_NetworkType(networkInfo.networkType),
+    m_WtsFilePath(networkInfo.wtsFilePath), m_Int8CalibPath(networkInfo.int8CalibPath), m_DeviceType(networkInfo.deviceType),
-      m_ConfigFilePath(networkInfo.configFilePath),
+    m_NumDetectedClasses(networkInfo.numDetectedClasses), m_ClusterMode(networkInfo.clusterMode),
-      m_WtsFilePath(networkInfo.wtsFilePath),
+    m_NetworkMode(networkInfo.networkMode), m_ScoreThreshold(networkInfo.scoreThreshold), m_InputH(0), m_InputW(0),
-      m_Int8CalibPath(networkInfo.int8CalibPath),
+    m_InputC(0), m_InputSize(0), m_NumClasses(0), m_LetterBox(0), m_NewCoords(0), m_YoloCount(0)
-      m_DeviceType(networkInfo.deviceType),
+{
-      m_NumDetectedClasses(networkInfo.numDetectedClasses),
+}
-      m_ClusterMode(networkInfo.clusterMode),
-      m_NetworkMode(networkInfo.networkMode),
-      m_ScoreThreshold(networkInfo.scoreThreshold),
-      m_InputH(0),
-      m_InputW(0),
-      m_InputC(0),
-      m_InputSize(0),
-      m_NumClasses(0),
-      m_LetterBox(0),
-      m_NewCoords(0),
-      m_YoloCount(0)
-{}
 
 Yolo::~Yolo()
 {
   destroyNetworkUtils();
 }
 
-nvinfer1::ICudaEngine *Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config)
+nvinfer1::ICudaEngine* 
+Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config)
 {
   assert (builder);
 
@@ -65,52 +53,44 @@ nvinfer1::ICudaEngine *Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1:
   parseConfigBlocks();
 
   nvinfer1::INetworkDefinition *network = builder->createNetworkV2(0);
-    if (parseModel(*network) != NVDSINFER_SUCCESS)
+  if (parseModel(*network) != NVDSINFER_SUCCESS) {
-    {
     delete network;
     return nullptr;
   }
 
   std::cout << "Building the TensorRT Engine\n" << std::endl;
 
-    if (m_NumClasses != m_NumDetectedClasses)
+  if (m_NumClasses != m_NumDetectedClasses) {
-    {
     std::cout << "NOTE: Number of classes mismatch, make sure to set num-detected-classes=" << m_NumClasses
         << " in config_infer file\n" << std::endl;
   }
-    if (m_LetterBox == 1)
+  if (m_LetterBox == 1) {
-    {
       std::cout << "NOTE: letter_box is set in cfg file, make sure to set maintain-aspect-ratio=1 in config_infer file"
           << " to get better accuracy\n" << std::endl;
   }
-    if (m_ClusterMode != 2)
+  if (m_ClusterMode != 2) {
-    {
+      std::cout << "NOTE: Wrong cluster-mode is set, make sure to set cluster-mode=2 in config_infer file\n" << std::endl;
-        std::cout << "NOTE: Wrong cluster-mode is set, make sure to set cluster-mode=2 in config_infer file\n"
-                  << std::endl;
   }
 
-    if (m_NetworkMode == "INT8" && !fileExists(m_Int8CalibPath))
+  if (m_NetworkMode == "INT8" && !fileExists(m_Int8CalibPath)) {
-    {
     assert(builder->platformHasFastInt8());
 #ifdef OPENCV
     std::string calib_image_list;
     int calib_batch_size;
     if (getenv("INT8_CALIB_IMG_PATH"))
       calib_image_list = getenv("INT8_CALIB_IMG_PATH");
-        else
+    else {
-        {
       std::cerr << "INT8_CALIB_IMG_PATH not set" << std::endl;
-            std::abort();
+      assert(0);
     }
     if (getenv("INT8_CALIB_BATCH_SIZE"))
       calib_batch_size = std::stoi(getenv("INT8_CALIB_BATCH_SIZE"));
-        else
+    else {
-        {
       std::cerr << "INT8_CALIB_BATCH_SIZE not set" << std::endl;
-            std::abort();
+      assert(0);
     }
-        nvinfer1::Int8EntropyCalibrator2 *calibrator = new nvinfer1::Int8EntropyCalibrator2(
+    nvinfer1::IInt8EntropyCalibrator2 *calibrator = new Int8EntropyCalibrator2(calib_batch_size, m_InputC, m_InputH,
-            calib_batch_size, m_InputC, m_InputH, m_InputW, m_LetterBox, calib_image_list, m_Int8CalibPath);
+        m_InputW, m_LetterBox, calib_image_list, m_Int8CalibPath);
     config->setFlag(nvinfer1::BuilderFlag::kINT8);
     config->setInt8Calibrator(calibrator);
 #else
@@ -129,7 +109,8 @@ nvinfer1::ICudaEngine *Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1:
   return engine;
 }
 
-NvDsInferStatus Yolo::parseModel(nvinfer1::INetworkDefinition& network) {
+NvDsInferStatus
+Yolo::parseModel(nvinfer1::INetworkDefinition& network) {
   destroyNetworkUtils();
 
   std::vector<float> weights = loadWeights(m_WtsFilePath, m_NetworkType);
@@ -144,24 +125,23 @@ NvDsInferStatus Yolo::parseModel(nvinfer1::INetworkDefinition& network) {
   return status;
 }
 
-NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::INetworkDefinition& network)
+NvDsInferStatus
+Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::INetworkDefinition& network)
 {
   int weightPtr = 0;
 
-    std::string weightsType;
+  std::string weightsType = "wts";
   if (m_WtsFilePath.find(".weights") != std::string::npos)
       weightsType = "weights";
-    else
-        weightsType = "wts";
 
   float eps = 1.0e-5;
-    if (m_NetworkType.find("yolov5") != std::string::npos || m_NetworkType.find("yolov7") != std::string::npos)
+  if (m_NetworkType.find("yolov5") != std::string::npos || m_NetworkType.find("yolov7") != std::string::npos ||
+      m_NetworkType.find("yolov8") != std::string::npos)
     eps = 1.0e-3;
   else if (m_NetworkType.find("yolor") != std::string::npos)
     eps = 1.0e-4;
 
-    nvinfer1::ITensor* data = network.addInput(
+  nvinfer1::ITensor* data = network.addInput(m_InputBlobName.c_str(), nvinfer1::DataType::kFLOAT,
-        m_InputBlobName.c_str(), nvinfer1::DataType::kFLOAT,
       nvinfer1::Dims{3, {static_cast<int>(m_InputC), static_cast<int>(m_InputH), static_cast<int>(m_InputW)}});
   assert(data != nullptr && data->getDimensions().nbDims > 0);
 
@@ -173,40 +153,42 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
 
   int modelType = -1;
 
-    for (uint i = 0; i < m_ConfigBlocks.size(); ++i)
+  for (uint i = 0; i < m_ConfigBlocks.size(); ++i) {
-    {
     std::string layerIndex = "(" + std::to_string(tensorOutputs.size()) + ")";
 
     if (m_ConfigBlocks.at(i).at("type") == "net")
         printLayerInfo("", "Layer", "Input Shape", "Output Shape", "WeightPtr");
-        
+    else if (m_ConfigBlocks.at(i).at("type") == "convolutional") {
-        else if (m_ConfigBlocks.at(i).at("type") == "convolutional")
-        {
       int channels = getNumChannels(previous);
       std::string inputVol = dimsToString(previous->getDimensions());
-            previous = convolutionalLayer(
+      previous = convolutionalLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, channels, eps,
-                i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, channels, eps, previous, &network);
+          previous, &network);
       assert(previous != nullptr);
       std::string outputVol = dimsToString(previous->getDimensions());
       tensorOutputs.push_back(previous);
       std::string layerName = "conv_" + m_ConfigBlocks.at(i).at("activation");
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, std::to_string(weightPtr));
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "c2f") {
-        else if (m_ConfigBlocks.at(i).at("type") == "batchnorm")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
-            previous = batchnormLayer(
+      previous = c2fLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, eps, previous, &network);
-                i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, eps, previous, &network);
+      assert(previous != nullptr);
+      std::string outputVol = dimsToString(previous->getDimensions());
+      tensorOutputs.push_back(previous);
+      std::string layerName = "c2f_" + m_ConfigBlocks.at(i).at("activation");
+      printLayerInfo(layerIndex, layerName, inputVol, outputVol, std::to_string(weightPtr));
+    }
+    else if (m_ConfigBlocks.at(i).at("type") == "batchnorm") {
+      std::string inputVol = dimsToString(previous->getDimensions());
+      previous = batchnormLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, eps, previous,
+          &network);
       assert(previous != nullptr);
       std::string outputVol = dimsToString(previous->getDimensions());
       tensorOutputs.push_back(previous);
       std::string layerName = "batchnorm_" + m_ConfigBlocks.at(i).at("activation");
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, std::to_string(weightPtr));
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "implicit_add" || m_ConfigBlocks.at(i).at("type") == "implicit_mul") {
-        else if (m_ConfigBlocks.at(i).at("type") == "implicit_add" || m_ConfigBlocks.at(i).at("type") == "implicit_mul")
-        {
       previous = implicitLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, &network);
       assert(previous != nullptr);
       std::string outputVol = dimsToString(previous->getDimensions());
@@ -214,10 +196,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName =  m_ConfigBlocks.at(i).at("type");
       printLayerInfo(layerIndex, layerName, "-", outputVol, std::to_string(weightPtr));
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "shift_channels" || m_ConfigBlocks.at(i).at("type") == "control_channels") {
-        else if (m_ConfigBlocks.at(i).at("type") == "shift_channels" ||
-            m_ConfigBlocks.at(i).at("type") == "control_channels")
-        {
       assert(m_ConfigBlocks.at(i).find("from") != m_ConfigBlocks.at(i).end());
       int from = stoi(m_ConfigBlocks.at(i).at("from"));
       if (from > 0)
@@ -234,9 +213,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = m_ConfigBlocks.at(i).at("type") + ": " + std::to_string(i + from - 1);
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "shortcut") {
-        else if (m_ConfigBlocks.at(i).at("type") == "shortcut")
-        {
       assert(m_ConfigBlocks.at(i).find("from") != m_ConfigBlocks.at(i).end());
       int from = stoi(m_ConfigBlocks.at(i).at("from"));
       if (from > 0)
@@ -255,9 +232,8 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
 
       std::string inputVol = dimsToString(previous->getDimensions());
       std::string shortcutVol = dimsToString(tensorOutputs[i + from - 1]->getDimensions());
-            previous = shortcutLayer(
+      previous = shortcutLayer(i, mode, activation, inputVol, shortcutVol, m_ConfigBlocks.at(i), previous,
-                i, mode, activation, inputVol, shortcutVol, m_ConfigBlocks.at(i), previous, tensorOutputs[i + from - 1],
+          tensorOutputs[i + from - 1], &network);
-                &network);
       assert(previous != nullptr);
       std::string outputVol = dimsToString(previous->getDimensions());
       tensorOutputs.push_back(previous);
@@ -267,9 +243,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       if (mode == "add" && inputVol != shortcutVol)
         std::cout << inputVol << " +" << shortcutVol << std::endl;
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "route") {
-        else if (m_ConfigBlocks.at(i).at("type") == "route")
-        {
       std::string layers;
       previous = routeLayer(i, layers, m_ConfigBlocks.at(i), tensorOutputs, &network);
       assert(previous != nullptr);
@@ -278,9 +252,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "route: " + layers;
       printLayerInfo(layerIndex, layerName, "-", outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "upsample") {
-        else if (m_ConfigBlocks.at(i).at("type") == "upsample")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
       previous = upsampleLayer(i, m_ConfigBlocks[i], previous, &network);
       assert(previous != nullptr);
@@ -289,9 +261,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "upsample";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "maxpool" || m_ConfigBlocks.at(i).at("type") == "avgpool") {
-        else if (m_ConfigBlocks.at(i).at("type") == "maxpool" || m_ConfigBlocks.at(i).at("type") == "avgpool")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
       previous = poolingLayer(i, m_ConfigBlocks.at(i), previous, &network);
       assert(previous != nullptr);
@@ -300,9 +270,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = m_ConfigBlocks.at(i).at("type");
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "reorg") {
-        else if (m_ConfigBlocks.at(i).at("type") == "reorg")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
       if (m_NetworkType.find("yolov2") != std::string::npos) {
         nvinfer1::IPluginV2* reorgPlugin = createReorgPlugin(2);
@@ -321,9 +289,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "reorg";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "reduce") {
-        else if (m_ConfigBlocks.at(i).at("type") == "reduce")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
       previous = reduceLayer(i, m_ConfigBlocks.at(i), previous, &network);
       assert(previous != nullptr);
@@ -332,9 +298,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "reduce";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "shuffle") {
-        else if (m_ConfigBlocks.at(i).at("type") == "shuffle")
-        {
       std::string layer;
       std::string inputVol = dimsToString(previous->getDimensions());
       previous = shuffleLayer(i, layer, m_ConfigBlocks.at(i), previous, tensorOutputs, &network);
@@ -344,9 +308,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "shuffle: " + layer;
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "softmax") {
-        else if (m_ConfigBlocks.at(i).at("type") == "softmax")
-        {
       std::string inputVol = dimsToString(previous->getDimensions());
       previous = softmaxLayer(i, m_ConfigBlocks.at(i), previous, &network);
       assert(previous != nullptr);
@@ -355,9 +317,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "softmax";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "yolo" || m_ConfigBlocks.at(i).at("type") == "region") {
-        else if (m_ConfigBlocks.at(i).at("type") == "yolo" || m_ConfigBlocks.at(i).at("type") == "region")
-        {
       if (m_ConfigBlocks.at(i).at("type") == "yolo")
         if (m_NetworkType.find("yolor") != std::string::npos)
           modelType = 2;
@@ -380,9 +340,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = modelType != 0 ? "yolo" : "region";
       printLayerInfo(layerIndex, layerName, inputVol, "-", "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "cls") {
-        else if (m_ConfigBlocks.at(i).at("type") == "cls")
-        {
       modelType = 3;
 
       std::string blobName = "cls_" + std::to_string(i);
@@ -402,9 +360,7 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "cls";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, "-");
     }
-
+    else if (m_ConfigBlocks.at(i).at("type") == "reg") {
-        else if (m_ConfigBlocks.at(i).at("type") == "reg")
-        {
       modelType = 3;
 
       std::string blobName = "reg_" + std::to_string(i);
@@ -423,36 +379,50 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
       std::string layerName = "reg";
       printLayerInfo(layerIndex, layerName, inputVol, outputVol, std::to_string(weightPtr));
     }
+    else if (m_ConfigBlocks.at(i).at("type") == "detect_v8") {
+      modelType = 4;
 
-        else
+      std::string blobName = "detect_v8_" + std::to_string(i);
-        {
+      nvinfer1::Dims prevTensorDims = previous->getDimensions();
-            std::cout << "\nUnsupported layer type --> \"" << m_ConfigBlocks.at(i).at("type") << "\"" << std::endl;
+      TensorInfo& curYoloTensor = m_YoloTensors.at(yoloCountInputs);
+      curYoloTensor.blobName = blobName;
+      curYoloTensor.numBBoxes = prevTensorDims.d[1];
+
+      std::string inputVol = dimsToString(previous->getDimensions());
+      previous = detectV8Layer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, previous, &network);
+      assert(previous != nullptr);
+      std::string outputVol = dimsToString(previous->getDimensions());
+      tensorOutputs.push_back(previous);
+      yoloTensorInputs[yoloCountInputs] = previous;
+      ++yoloCountInputs;
+      std::string layerName = "detect_v8";
+      printLayerInfo(layerIndex, layerName, inputVol, outputVol, std::to_string(weightPtr));
+    }
+    else {
+      std::cerr << "\nUnsupported layer type --> \"" << m_ConfigBlocks.at(i).at("type") << "\"" << std::endl;
       assert(0);
     }
   }
 
-    if ((int)weights.size() != weightPtr)
+  if ((int) weights.size() != weightPtr) {
-    {
+    std::cerr << "\nNumber of unused weights left: " << weights.size() - weightPtr << std::endl;
-        std::cout << "\nNumber of unused weights left: " << weights.size() - weightPtr << std::endl;
     assert(0);
   }
 
-    if (m_YoloCount == yoloCountInputs)
+  if (m_YoloCount == yoloCountInputs) {
-    {
     assert((modelType != -1) && "\nCould not determine model type"); 
 
     uint64_t outputSize = 0;
-        for (uint j = 0; j < yoloCountInputs; ++j)
+    for (uint j = 0; j < yoloCountInputs; ++j) {
-        {
       TensorInfo& curYoloTensor = m_YoloTensors.at(j);
-            if (modelType == 3)
+      if (modelType == 3 || modelType == 4)
         outputSize = curYoloTensor.numBBoxes;
       else
         outputSize += curYoloTensor.gridSizeX * curYoloTensor.gridSizeY * curYoloTensor.numBBoxes;
     }
 
-        nvinfer1::IPluginV2* yoloPlugin = new YoloLayer(
+    nvinfer1::IPluginV2* yoloPlugin = new YoloLayer(m_InputW, m_InputH, m_NumClasses, m_NewCoords, m_YoloTensors, outputSize,
-            m_InputW, m_InputH, m_NumClasses, m_NewCoords, m_YoloTensors, outputSize, modelType, m_ScoreThreshold);
+        modelType, m_ScoreThreshold);
     assert(yoloPlugin != nullptr);
     nvinfer1::IPluginV2Layer* yolo = network.addPluginV2(yoloTensorInputs, m_YoloCount, *yoloPlugin);
     assert(yolo != nullptr);
@@ -478,15 +448,13 @@ NvDsInferStatus Yolo::buildYoloNetwork(std::vector<float>& weights, nvinfer1::IN
     network.markOutput(*detection_classes);
   }
   else {
-        std::cout << "\nError in yolo cfg file" << std::endl;
+    std::cerr << "\nError in yolo cfg file" << std::endl;
     assert(0);
   }
 
   std::cout << "\nOutput YOLO blob names: " << std::endl;
   for (auto& tensor : m_YoloTensors)
-    {
     std::cout << tensor.blobName << std::endl;
-    }
 
   int nbLayers = network.getNbLayers();
   std::cout << "\nTotal number of YOLO layers: " << nbLayers << "\n" << std::endl;
@@ -504,16 +472,13 @@ Yolo::parseConfigFile (const std::string cfgFilePath)
   std::vector<std::map<std::string, std::string>> blocks;
   std::map<std::string, std::string> block;
 
-    while (getline(file, line))
+  while (getline(file, line)) {
-    {
+    if (line.size() == 0 || line.front() == ' ' || line.front() == '#')
-        if (line.size() == 0) continue;
+      continue;
-        if (line.front() == ' ') continue;
+
-        if (line.front() == '#') continue;
     line = trim(line);
-        if (line.front() == '[')
+    if (line.front() == '[') {
-        {
+      if (block.size() > 0) {
-            if (block.size() > 0)
-            {
         blocks.push_back(block);
         block.clear();
       }
@@ -521,24 +486,23 @@ Yolo::parseConfigFile (const std::string cfgFilePath)
       std::string value = trim(line.substr(1, line.size() - 2));
       block.insert(std::pair<std::string, std::string>(key, value));
     }
-        else
+    else {
-        {
       int cpos = line.find('=');
       std::string key = trim(line.substr(0, cpos));
       std::string value = trim(line.substr(cpos + 1));
       block.insert(std::pair<std::string, std::string>(key, value));
     }
   }
+
   blocks.push_back(block);
   return blocks;
 }
 
-void Yolo::parseConfigBlocks()
+void
-{
+Yolo::parseConfigBlocks()
-    for (auto block : m_ConfigBlocks)
-    {
-        if (block.at("type") == "net")
 {
+  for (auto block : m_ConfigBlocks) {
+    if (block.at("type") == "net") {
       assert((block.find("height") != block.end()) && "Missing 'height' param in network cfg");
       assert((block.find("width") != block.end()) && "Missing 'width' param in network cfg");
       assert((block.find("channels") != block.end()) && "Missing 'channels' param in network cfg");
@@ -549,62 +513,51 @@ void Yolo::parseConfigBlocks()
       m_InputSize = m_InputC * m_InputH * m_InputW;
 
       if (block.find("letter_box") != block.end())
-            {
         m_LetterBox = std::stoul(block.at("letter_box"));
     }
-        }
     else if ((block.at("type") == "region") || (block.at("type") == "yolo"))
     {
-            assert((block.find("num") != block.end())
+      assert((block.find("num") != block.end()) &&
-                   && std::string("Missing 'num' param in " + block.at("type") + " layer").c_str());
+          std::string("Missing 'num' param in " + block.at("type") + " layer").c_str());
-            assert((block.find("classes") != block.end())
+      assert((block.find("classes") != block.end()) &&
-                   && std::string("Missing 'classes' param in " + block.at("type") + " layer").c_str());
+          std::string("Missing 'classes' param in " + block.at("type") + " layer").c_str());
-            assert((block.find("anchors") != block.end())
+      assert((block.find("anchors") != block.end()) &&
-                   && std::string("Missing 'anchors' param in " + block.at("type") + " layer").c_str());
+          std::string("Missing 'anchors' param in " + block.at("type") + " layer").c_str());
 
       ++m_YoloCount;
 
       m_NumClasses = std::stoul(block.at("classes"));
 
       if (block.find("new_coords") != block.end())
-            {
         m_NewCoords = std::stoul(block.at("new_coords"));
-            }
 
       TensorInfo outputTensor;
 
       std::string anchorString = block.at("anchors");
-            while (!anchorString.empty())
+      while (!anchorString.empty()) {
-            {
         int npos = anchorString.find_first_of(',');
-                if (npos != -1)
+        if (npos != -1) {
-                {
           float anchor = std::stof(trim(anchorString.substr(0, npos)));
           outputTensor.anchors.push_back(anchor);
           anchorString.erase(0, npos + 1);
         }
-                else
+        else {
-                {
           float anchor = std::stof(trim(anchorString));
           outputTensor.anchors.push_back(anchor);
           break;
         }
       }
 
-            if (block.find("mask") != block.end())
+      if (block.find("mask") != block.end()) {
-            {
         std::string maskString = block.at("mask");
-                while (!maskString.empty())
+        while (!maskString.empty()) {
-                {
           int npos = maskString.find_first_of(',');
-                    if (npos != -1)
+          if (npos != -1) {
-                    {
             int mask = std::stoul(trim(maskString.substr(0, npos)));
             outputTensor.mask.push_back(mask);
             maskString.erase(0, npos + 1);
           }
-                    else
+          else {
-                    {
             int mask = std::stoul(trim(maskString));
             outputTensor.mask.push_back(mask);
             break;
@@ -613,29 +566,32 @@ void Yolo::parseConfigBlocks()
       }
 
       if (block.find("scale_x_y") != block.end())
-            {
         outputTensor.scaleXY = std::stof(block.at("scale_x_y"));
-            }
       else
-            {
         outputTensor.scaleXY = 1.0;
-            }
 
-            outputTensor.numBBoxes
+      outputTensor.numBBoxes = outputTensor.mask.size() > 0 ? outputTensor.mask.size() : std::stoul(trim(block.at("num")));
-                = outputTensor.mask.size() > 0 ? outputTensor.mask.size() : std::stoul(trim(block.at("num")));
       
       m_YoloTensors.push_back(outputTensor);
     }
-        else if ((block.at("type") == "cls") || (block.at("type") == "reg"))
+    else if ((block.at("type") == "cls") || (block.at("type") == "reg")) {
-        {
       ++m_YoloCount;
       TensorInfo outputTensor;
       m_YoloTensors.push_back(outputTensor);
     }
+    else if (block.at("type") == "detect_v8") {
+      ++m_YoloCount;
+
+      m_NumClasses = std::stoul(block.at("classes"));
+      
+      TensorInfo outputTensor;
+      m_YoloTensors.push_back(outputTensor);
+    }
   }
 }
 
-void Yolo::destroyNetworkUtils()
+void
+Yolo::destroyNetworkUtils()
 {
   for (uint i = 0; i < m_TrtWeights.size(); ++i)
     if (m_TrtWeights[i].count > 0)

nvdsinfer_custom_impl_Yolo/yolo.h

@@ -26,7 +26,11 @@
 #ifndef _YOLO_H_
 #define _YOLO_H_
 
+#include "NvInferPlugin.h"
+#include "nvdsinfer_custom_impl.h"
+
 #include "layers/convolutional_layer.h"
+#include "layers/c2f_layer.h"
 #include "layers/batchnorm_layer.h"
 #include "layers/implicit_layer.h"
 #include "layers/channels_layer.h"
@@ -40,8 +44,7 @@
 #include "layers/softmax_layer.h"
 #include "layers/cls_layer.h"
 #include "layers/reg_layer.h"
-
+#include "layers/detect_v8_layer.h"
-#include "nvdsinfer_custom_impl.h"
 
 struct NetworkInfo
 {

nvdsinfer_custom_impl_Yolo/yoloForward.cu

@@ -7,10 +7,10 @@
 
 inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
 
-__global__ void gpuYoloLayer(
+__global__ void gpuYoloLayer(const float* input, int* num_detections, float* detection_boxes, float* detection_scores,
-    const float* input, int* num_detections, float* detection_boxes, float* detection_scores, int* detection_classes,
+    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX,
-    const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY,
+    const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors,
-    const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
+    const int* mask)
 {
   uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
   uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -22,8 +22,7 @@ __global__ void gpuYoloLayer(
   const int numGridCells = gridSizeX * gridSizeY;
   const int bbindex = y_id * gridSizeX + x_id;
 
-    const float objectness
+  const float objectness = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
-        = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
 
   if (objectness < scoreThreshold)
     return;
@@ -33,32 +32,22 @@ __global__ void gpuYoloLayer(
   const float alpha = scaleXY;
   const float beta = -0.5 * (scaleXY - 1);
 
-    float x
+  float x = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id)
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)])
+      * netWidth / gridSizeX;
-          * alpha + beta + x_id) * netWidth / gridSizeX;
 
-    float y
+  float y = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id)
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)])
+      * netHeight / gridSizeY;
-          * alpha + beta + y_id) * netHeight / gridSizeY;
 
-    float w
+  float w = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[mask[z_id] * 2];
-        = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)])
-          * anchors[mask[z_id] * 2];
 
-    float h
+  float h = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[mask[z_id] * 2 + 1];
-        = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)])
-          * anchors[mask[z_id] * 2 + 1];
 
   float maxProb = 0.0f;
   int maxIndex = -1;
 
-    for (uint i = 0; i < numOutputClasses; ++i)
+  for (uint i = 0; i < numOutputClasses; ++i) {
-    {
+    float prob = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
-        float prob
+    if (prob > maxProb) {
-            = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
-
-        if (prob > maxProb)
-        {
       maxProb = prob;
       maxIndex = i;
     }
@@ -72,33 +61,28 @@ __global__ void gpuYoloLayer(
   detection_classes[count] = maxIndex;
 }
 
-cudaError_t cudaYoloLayer(
+cudaError_t cudaYoloLayer(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
 
-cudaError_t cudaYoloLayer(
+cudaError_t cudaYoloLayer(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
 {
   dim3 threads_per_block(16, 16, 4);
-    dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
+  dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1, (gridSizeY / threads_per_block.y) + 1,
-                          (gridSizeY / threads_per_block.y) + 1,
       (numBBoxes / threads_per_block.z) + 1);
 
-    for (unsigned int batch = 0; batch < batchSize; ++batch)
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
-    {
     gpuYoloLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
-            reinterpret_cast<const float*>(input) + (batch * inputSize),
+        reinterpret_cast<const float*>(input) + (batch * inputSize), reinterpret_cast<int*>(num_detections) + (batch),
-            reinterpret_cast<int*>(num_detections) + (batch),
         reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
-            reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight, gridSizeX,
-            scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, scaleXY,
+        gridSizeY, numOutputClasses, numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors),
-            reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
+        reinterpret_cast<const int*>(mask));
   }
   return cudaGetLastError();
 }

nvdsinfer_custom_impl_Yolo/yoloForward_e.cu

@@ -4,11 +4,9 @@
  */
 
 #include <stdint.h>
-#include <stdio.h>
 
-__global__ void gpuYoloLayer_e(
+__global__ void gpuYoloLayer_e(const float* cls, const float* reg, int* num_detections, float* detection_boxes,
-    const float* cls, const float* reg, int* num_detections, float* detection_boxes, float* detection_scores,
+    float* detection_scores, int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
-    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
     const uint numOutputClasses, const uint64_t outputSize)
 {
   uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -19,13 +17,9 @@ __global__ void gpuYoloLayer_e(
   float maxProb = 0.0f;
   int maxIndex = -1;
 
-    for (uint i = 0; i < numOutputClasses; ++i)
+  for (uint i = 0; i < numOutputClasses; ++i) {
-    {
+    float prob = cls[x_id * numOutputClasses + i];
-        float prob
+    if (prob > maxProb) {
-            = cls[x_id * numOutputClasses + i];
-
-        if (prob > maxProb)
-        {
       maxProb = prob;
       maxIndex = i;
     }
@@ -44,29 +38,27 @@ __global__ void gpuYoloLayer_e(
   detection_classes[count] = maxIndex;
 }
 
-cudaError_t cudaYoloLayer_e(
+cudaError_t cudaYoloLayer_e(const void* cls, const void* reg, void* num_detections, void* detection_boxes,
-    const void* cls, const void* reg, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& outputSize,
-    void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& numOutputClasses,
-    const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream);
+    cudaStream_t stream);
 
-cudaError_t cudaYoloLayer_e(
+cudaError_t cudaYoloLayer_e(const void* cls, const void* reg, void* num_detections, void* detection_boxes,
-    const void* cls, const void* reg, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& outputSize,
-    void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& numOutputClasses,
-    const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream)
+    cudaStream_t stream)
 {
   int threads_per_block = 16;
   int number_of_blocks = (outputSize / threads_per_block) + 1;
 
-    for (unsigned int batch = 0; batch < batchSize; ++batch)
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
-    {
     gpuYoloLayer_e<<<number_of_blocks, threads_per_block, 0, stream>>>(
         reinterpret_cast<const float*>(cls) + (batch * numOutputClasses * outputSize),
-            reinterpret_cast<const float*>(reg) + (batch * 4 * outputSize),
+        reinterpret_cast<const float*>(reg) + (batch * 4 * outputSize), reinterpret_cast<int*>(num_detections) + (batch),
-            reinterpret_cast<int*>(num_detections) + (batch),
         reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
-            reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight,
-            scoreThreshold, netWidth, netHeight, numOutputClasses, outputSize);
+        numOutputClasses, outputSize);
   }
   return cudaGetLastError();
 }

nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu

@@ -5,10 +5,10 @@
 
 #include <stdint.h>
 
-__global__ void gpuYoloLayer_nc(
+__global__ void gpuYoloLayer_nc(const float* input, int* num_detections, float* detection_boxes, float* detection_scores,
-    const float* input, int* num_detections, float* detection_boxes, float* detection_scores, int* detection_classes,
+    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX,
-    const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY,
+    const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors,
-    const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
+    const int* mask)
 {
   uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
   uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -20,8 +20,7 @@ __global__ void gpuYoloLayer_nc(
   const int numGridCells = gridSizeX * gridSizeY;
   const int bbindex = y_id * gridSizeX + x_id;
 
-    const float objectness
+  const float objectness = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
-        = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)];
 
   if (objectness < scoreThreshold)
     return;
@@ -31,32 +30,22 @@ __global__ void gpuYoloLayer_nc(
   const float alpha = scaleXY;
   const float beta = -0.5 * (scaleXY - 1);
 
-    float x
+  float x = (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)] * alpha + beta + x_id) * netWidth /
-        = (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]
+      gridSizeX;
-          * alpha + beta + x_id) * netWidth / gridSizeX;
 
-    float y
+  float y = (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)] * alpha + beta + y_id) * netHeight /
-        = (input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]
+      gridSizeY;
-          * alpha + beta + y_id) * netHeight / gridSizeY;
 
-    float w
+  float w = __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2) * anchors[mask[z_id] * 2];
-        = __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)] * 2, 2)
-          * anchors[mask[z_id] * 2];
 
-    float h
+  float h = __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2) * anchors[mask[z_id] * 2 + 1];
-        = __powf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)] * 2, 2)
-          * anchors[mask[z_id] * 2 + 1];
 
   float maxProb = 0.0f;
   int maxIndex = -1;
 
-    for (uint i = 0; i < numOutputClasses; ++i)
+  for (uint i = 0; i < numOutputClasses; ++i) {
-    {
+    float prob = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
-        float prob
+    if (prob > maxProb) {
-            = input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
-
-        if (prob > maxProb)
-        {
       maxProb = prob;
       maxIndex = i;
     }
@@ -70,33 +59,28 @@ __global__ void gpuYoloLayer_nc(
   detection_classes[count] = maxIndex;
 }
 
-cudaError_t cudaYoloLayer_nc(
+cudaError_t cudaYoloLayer_nc(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
 
-cudaError_t cudaYoloLayer_nc(
+cudaError_t cudaYoloLayer_nc(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
 {
   dim3 threads_per_block(16, 16, 4);
-    dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
+  dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1, (gridSizeY / threads_per_block.y) + 1,
-                          (gridSizeY / threads_per_block.y) + 1,
       (numBBoxes / threads_per_block.z) + 1);
 
-    for (unsigned int batch = 0; batch < batchSize; ++batch)
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
-    {
     gpuYoloLayer_nc<<<number_of_blocks, threads_per_block, 0, stream>>>(
-            reinterpret_cast<const float*>(input) + (batch * inputSize),
+        reinterpret_cast<const float*>(input) + (batch * inputSize), reinterpret_cast<int*>(num_detections) + (batch),
-            reinterpret_cast<int*>(num_detections) + (batch),
         reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
-            reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight, gridSizeX,
-            scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, scaleXY,
+        gridSizeY, numOutputClasses, numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors),
-            reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
+        reinterpret_cast<const int*>(mask));
   }
   return cudaGetLastError();
 }

nvdsinfer_custom_impl_Yolo/yoloForward_r.cu

@@ -7,10 +7,10 @@
 
 inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
 
-__global__ void gpuYoloLayer_r(
+__global__ void gpuYoloLayer_r(const float* input, int* num_detections, float* detection_boxes, float* detection_scores,
-    const float* input, int* num_detections, float* detection_boxes, float* detection_scores, int* detection_classes,
+    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX,
-    const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY,
+    const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors,
-    const uint numOutputClasses, const uint numBBoxes, const float scaleXY, const float* anchors, const int* mask)
+    const int* mask)
 {
   uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
   uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -22,8 +22,7 @@ __global__ void gpuYoloLayer_r(
   const int numGridCells = gridSizeX * gridSizeY;
   const int bbindex = y_id * gridSizeX + x_id;
 
-    const float objectness
+  const float objectness = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
-        = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
 
   if (objectness < scoreThreshold)
     return;
@@ -33,32 +32,24 @@ __global__ void gpuYoloLayer_r(
   const float alpha = scaleXY;
   const float beta = -0.5 * (scaleXY - 1);
 
-    float x
+  float x = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) * alpha + beta + x_id)
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)])
+      * netWidth / gridSizeX;
-          * alpha + beta + x_id) * netWidth / gridSizeX;
 
-    float y
+  float y = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) * alpha + beta + y_id)
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)])
+      * netHeight / gridSizeY;
-          * alpha + beta + y_id) * netHeight / gridSizeY;
 
-    float w
+  float w = __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2)
-        = __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * 2, 2)
       * anchors[mask[z_id] * 2];
 
-    float h
+  float h = __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2)
-        = __powf(sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * 2, 2)
       * anchors[mask[z_id] * 2 + 1];
 
   float maxProb = 0.0f;
   int maxIndex = -1;
 
-    for (uint i = 0; i < numOutputClasses; ++i)
+  for (uint i = 0; i < numOutputClasses; ++i) {
-    {
+    float prob = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
-        float prob
+    if (prob > maxProb) {
-            = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))]);
-
-        if (prob > maxProb)
-        {
       maxProb = prob;
       maxIndex = i;
     }
@@ -72,33 +63,28 @@ __global__ void gpuYoloLayer_r(
   detection_classes[count] = maxIndex;
 }
 
-cudaError_t cudaYoloLayer_r(
+cudaError_t cudaYoloLayer_r(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
 
-cudaError_t cudaYoloLayer_r(
+cudaError_t cudaYoloLayer_r(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream)
 {
   dim3 threads_per_block(16, 16, 4);
-    dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
+  dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1, (gridSizeY / threads_per_block.y) + 1,
-                          (gridSizeY / threads_per_block.y) + 1,
       (numBBoxes / threads_per_block.z) + 1);
 
-    for (unsigned int batch = 0; batch < batchSize; ++batch)
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
-    {
     gpuYoloLayer_r<<<number_of_blocks, threads_per_block, 0, stream>>>(
-            reinterpret_cast<const float*>(input) + (batch * inputSize),
+        reinterpret_cast<const float*>(input) + (batch * inputSize), reinterpret_cast<int*>(num_detections) + (batch),
-            reinterpret_cast<int*>(num_detections) + (batch),
         reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
-            reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight, gridSizeX,
-            scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, scaleXY,
+        gridSizeY, numOutputClasses, numBBoxes, scaleXY, reinterpret_cast<const float*>(anchors),
-            reinterpret_cast<const float*>(anchors), reinterpret_cast<const int*>(mask));
+        reinterpret_cast<const int*>(mask));
   }
   return cudaGetLastError();
 }

nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu

@@ -7,9 +7,8 @@
 
 inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
 
-__device__ void softmaxGPU(
+__device__ void softmaxGPU(const float* input, const int bbindex, const int numGridCells, uint z_id,
-    const float* input, const int bbindex, const int numGridCells, uint z_id, const uint numOutputClasses, float temp,
+    const uint numOutputClasses, float temp, float* output)
-    float* output)
 {
   int i;
   float sum = 0;
@@ -28,10 +27,9 @@ __device__ void softmaxGPU(
   }
 }
 
-__global__ void gpuRegionLayer(
+__global__ void gpuRegionLayer(const float* input, float* softmax, int* num_detections, float* detection_boxes,
-    const float* input, float* softmax, int* num_detections, float* detection_boxes, float* detection_scores,
+    float* detection_scores, int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
-    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight, const uint gridSizeX,
+    const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float* anchors)
-    const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes, const float* anchors)
 {
   uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
   uint y_id = blockIdx.y * blockDim.y + threadIdx.y;
@@ -43,42 +41,31 @@ __global__ void gpuRegionLayer(
   const int numGridCells = gridSizeX * gridSizeY;
   const int bbindex = y_id * gridSizeX + x_id;
 
-    const float objectness
+  const float objectness = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
-        = sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 4)]);
 
   if (objectness < scoreThreshold)
     return;
 
   int count = (int)atomicAdd(num_detections, 1);
 
-    float x
+  float x = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)]) + x_id) * netWidth / gridSizeX;
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 0)])
-          + x_id) * netWidth / gridSizeX;
 
-    float y
+  float y = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)]) + y_id) * netHeight / gridSizeY;
-        = (sigmoidGPU(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 1)])
-          + y_id) * netHeight / gridSizeY;
 
-    float w
+  float w = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)]) * anchors[z_id * 2] * netWidth /
-        = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 2)])
+      gridSizeX;
-          * anchors[z_id * 2] * netWidth / gridSizeX;
 
-    float h
+  float h = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)]) * anchors[z_id * 2 + 1] * netHeight /
-        = __expf(input[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + 3)])
+      gridSizeY;
-          * anchors[z_id * 2 + 1] * netHeight / gridSizeY;
 
   softmaxGPU(input, bbindex, numGridCells, z_id, numOutputClasses, 1.0, softmax);
 
   float maxProb = 0.0f;
   int maxIndex = -1;
 
-    for (uint i = 0; i < numOutputClasses; ++i)
+  for (uint i = 0; i < numOutputClasses; ++i) {
-    {
+    float prob = softmax[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
-        float prob
+    if (prob > maxProb) {
-            = softmax[bbindex + numGridCells * (z_id * (5 + numOutputClasses) + (5 + i))];
-
-        if (prob > maxProb)
-        {
       maxProb = prob;
       maxIndex = i;
     }
@@ -92,34 +79,28 @@ __global__ void gpuRegionLayer(
   detection_classes[count] = maxIndex;
 }
 
-cudaError_t cudaRegionLayer(
+cudaError_t cudaRegionLayer(const void* input, void* softmax, void* num_detections, void* detection_boxes,
-    const void* input, void* softmax, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize,
-    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY,
-    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
+    const uint& numOutputClasses, const uint& numBBoxes, const void* anchors, cudaStream_t stream);
-    const uint& numBBoxes, const void* anchors, cudaStream_t stream);
 
-cudaError_t cudaRegionLayer(
+cudaError_t cudaRegionLayer(const void* input, void* softmax, void* num_detections, void* detection_boxes,
-    const void* input, void* softmax, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize,
-    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY,
-    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
+    const uint& numOutputClasses, const uint& numBBoxes, const void* anchors, cudaStream_t stream)
-    const uint& numBBoxes, const void* anchors, cudaStream_t stream)
 {
   dim3 threads_per_block(16, 16, 4);
-    dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1,
+  dim3 number_of_blocks((gridSizeX / threads_per_block.x) + 1, (gridSizeY / threads_per_block.y) + 1,
-                          (gridSizeY / threads_per_block.y) + 1,
       (numBBoxes / threads_per_block.z) + 1);
 
-    for (unsigned int batch = 0; batch < batchSize; ++batch)
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
-    {
     gpuRegionLayer<<<number_of_blocks, threads_per_block, 0, stream>>>(
-            reinterpret_cast<const float*>(input) + (batch * inputSize),
+        reinterpret_cast<const float*>(input) + (batch * inputSize), reinterpret_cast<float*>(softmax) + (batch * inputSize),
-            reinterpret_cast<float*>(softmax) + (batch * inputSize),
         reinterpret_cast<int*>(num_detections) + (batch),
         reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
-            reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize), scoreThreshold, netWidth, netHeight, gridSizeX,
-            scoreThreshold, netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes,
+        gridSizeY, numOutputClasses, numBBoxes, reinterpret_cast<const float*>(anchors));
-            reinterpret_cast<const float*>(anchors));
   }
   return cudaGetLastError();
 }

nvdsinfer_custom_impl_Yolo/yoloForward_v8.cu

@@ -0,0 +1,62 @@
+/*
+ * Created by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#include <stdint.h>
+
+__global__ void gpuYoloLayer_v8(const float* input, int* num_detections, float* detection_boxes, float* detection_scores,
+    int* detection_classes, const float scoreThreshold, const uint netWidth, const uint netHeight,
+    const uint numOutputClasses, const uint64_t outputSize)
+{
+  uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (x_id >= outputSize)
+    return;
+
+  float maxProb = 0.0f;
+  int maxIndex = -1;
+
+  for (uint i = 0; i < numOutputClasses; ++i) {
+    float prob = input[x_id * (4 + numOutputClasses) + i + 4];
+    if (prob > maxProb) {
+      maxProb = prob;
+      maxIndex = i;
+    }
+  }
+
+  if (maxProb < scoreThreshold)
+    return;
+
+  int count = (int)atomicAdd(num_detections, 1);
+
+  detection_boxes[count * 4 + 0] = input[x_id * (4 + numOutputClasses) + 0];
+  detection_boxes[count * 4 + 1] = input[x_id * (4 + numOutputClasses) + 1];
+  detection_boxes[count * 4 + 2] = input[x_id * (4 + numOutputClasses) + 2];
+  detection_boxes[count * 4 + 3] = input[x_id * (4 + numOutputClasses) + 3];
+  detection_scores[count] = maxProb;
+  detection_classes[count] = maxIndex;
+}
+
+cudaError_t cudaYoloLayer_v8(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream);
+
+cudaError_t cudaYoloLayer_v8(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream)
+{
+  int threads_per_block = 16;
+  int number_of_blocks = (outputSize / threads_per_block) + 1;
+
+  for (unsigned int batch = 0; batch < batchSize; ++batch) {
+    gpuYoloLayer_v8<<<number_of_blocks, threads_per_block, 0, stream>>>(
+        reinterpret_cast<const float*>(input) + (batch * (4 + numOutputClasses) * outputSize),
+        reinterpret_cast<int*>(num_detections) + (batch),
+        reinterpret_cast<float*>(detection_boxes) + (batch * 4 * outputSize),
+        reinterpret_cast<float*>(detection_scores) + (batch * outputSize),
+        reinterpret_cast<int*>(detection_classes) + (batch * outputSize),
+        scoreThreshold, netWidth, netHeight, numOutputClasses, outputSize);
+  }
+  return cudaGetLastError();
+}

nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp

@@ -24,60 +24,50 @@
  */
 
 #include "yoloPlugins.h"
-#include "NvInferPlugin.h"
-#include <cassert>
-#include <iostream>
-#include <memory>
-
-uint kNUM_CLASSES;
 
 namespace {
   template <typename T>
-    void write(char*& buffer, const T& val)
+  void write(char*& buffer, const T& val) {
-    {
     *reinterpret_cast<T*>(buffer) = val;
     buffer += sizeof(T);
   }
-
   template <typename T>
-    void read(const char*& buffer, T& val)
+  void read(const char*& buffer, T& val) {
-    {
     val = *reinterpret_cast<const T*>(buffer);
     buffer += sizeof(T);
   }
 }
 
-cudaError_t cudaYoloLayer_e(
+cudaError_t cudaYoloLayer_v8(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* cls, const void* reg, void* num_detections, void* detection_boxes, void* detection_scores,
     void* detection_classes, const uint& batchSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
     const uint& netHeight, const uint& numOutputClasses, cudaStream_t stream);
 
-cudaError_t cudaYoloLayer_r(
+cudaError_t cudaYoloLayer_e(const void* cls, const void* reg, void* num_detections, void* detection_boxes,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& outputSize,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& numOutputClasses,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
+    cudaStream_t stream);
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
 
-cudaError_t cudaYoloLayer_nc(
+cudaError_t cudaYoloLayer_r(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
-
-cudaError_t cudaYoloLayer(
-    const void* input, void* num_detections, void* detection_boxes, void* detection_scores, void* detection_classes,
-    const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
-    const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
-    const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
-
-cudaError_t cudaRegionLayer(
-    const void* input, void* softmax, void* num_detections, void* detection_boxes, void* detection_scores,
     void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
     const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
-    const uint& numBBoxes, const void* anchors, cudaStream_t stream);
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
 
-YoloLayer::YoloLayer (const void* data, size_t length)
+cudaError_t cudaYoloLayer_nc(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
-{
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
+
+cudaError_t cudaYoloLayer(const void* input, void* num_detections, void* detection_boxes, void* detection_scores,
+    void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold,
+    const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
+    const uint& numBBoxes, const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
+
+cudaError_t cudaRegionLayer(const void* input, void* softmax, void* num_detections, void* detection_boxes,
+    void* detection_scores, void* detection_classes, const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize,
+    const float& scoreThreshold, const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY,
+    const uint& numOutputClasses, const uint& numBBoxes, const void* anchors, cudaStream_t stream);
+
+YoloLayer::YoloLayer(const void* data, size_t length) {
   const char* d = static_cast<const char*>(data);
 
   read(d, m_NetWidth);
@@ -88,11 +78,10 @@ YoloLayer::YoloLayer (const void* data, size_t length)
   read(d, m_Type);
   read(d, m_ScoreThreshold);
 
-    if (m_Type != 3) {
+  if (m_Type != 3 && m_Type != 4) {
     uint yoloTensorsSize;
     read(d, yoloTensorsSize);
-        for (uint i = 0; i < yoloTensorsSize; ++i)
+    for (uint i = 0; i < yoloTensorsSize; ++i) {
-        {
       TensorInfo curYoloTensor;
       read(d, curYoloTensor.gridSizeX);
       read(d, curYoloTensor.gridSizeY);
@@ -101,8 +90,7 @@ YoloLayer::YoloLayer (const void* data, size_t length)
 
       uint anchorsSize;
       read(d, anchorsSize);
-            for (uint j = 0; j < anchorsSize; j++)
+      for (uint j = 0; j < anchorsSize; ++j) {
-            {
         float result;
         read(d, result);
         curYoloTensor.anchors.push_back(result);
@@ -110,72 +98,55 @@ YoloLayer::YoloLayer (const void* data, size_t length)
 
       uint maskSize;
       read(d, maskSize);
-            for (uint j = 0; j < maskSize; j++)
+      for (uint j = 0; j < maskSize; ++j) {
-            {
         int result;
         read(d, result);
         curYoloTensor.mask.push_back(result);
       }
+
       m_YoloTensors.push_back(curYoloTensor);
     }
   }
-
-    kNUM_CLASSES = m_NumClasses;
 };
 
-YoloLayer::YoloLayer(
+YoloLayer::YoloLayer(const uint& netWidth, const uint& netHeight, const uint& numClasses, const uint& newCoords,
-    const uint& netWidth, const uint& netHeight, const uint& numClasses, const uint& newCoords,
     const std::vector<TensorInfo>& yoloTensors, const uint64_t& outputSize, const uint& modelType,
-    const float& scoreThreshold) :
+    const float& scoreThreshold) : m_NetWidth(netWidth), m_NetHeight(netHeight), m_NumClasses(numClasses),
-    m_NetWidth(netWidth),
+    m_NewCoords(newCoords), m_YoloTensors(yoloTensors), m_OutputSize(outputSize), m_Type(modelType),
-    m_NetHeight(netHeight),
-    m_NumClasses(numClasses),
-    m_NewCoords(newCoords),
-    m_YoloTensors(yoloTensors),
-    m_OutputSize(outputSize),
-    m_Type(modelType),
     m_ScoreThreshold(scoreThreshold)
 {
   assert(m_NetWidth > 0);
   assert(m_NetHeight > 0);
-
-    kNUM_CLASSES = m_NumClasses;
 };
 
 nvinfer1::Dims
-YoloLayer::getOutputDimensions(
+YoloLayer::getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) noexcept
-    int index, const nvinfer1::Dims* inputs, int nbInputDims) noexcept
 {
   assert(index <= 4);
-    if (index == 0) {
+  if (index == 0)
     return nvinfer1::Dims{1, {1}};
-    }
+  else if (index == 1)
-    else if (index == 1) {
     return nvinfer1::Dims{2, {static_cast<int>(m_OutputSize), 4}};
-    }
   return nvinfer1::Dims{1, {static_cast<int>(m_OutputSize)}};
 }
 
-bool YoloLayer::supportsFormat (
+bool
-    nvinfer1::DataType type, nvinfer1::PluginFormat format) const noexcept {
+YoloLayer::supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const noexcept {
-    return (type == nvinfer1::DataType::kFLOAT &&
+  return (type == nvinfer1::DataType::kFLOAT && format == nvinfer1::PluginFormat::kLINEAR);
-            format == nvinfer1::PluginFormat::kLINEAR);
 }
 
 void
-YoloLayer::configureWithFormat (
+YoloLayer::configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims,
-    const nvinfer1::Dims* inputDims, int nbInputs,
+    int nbOutputs, nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize) noexcept
-    const nvinfer1::Dims* outputDims, int nbOutputs,
-    nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize) noexcept
 {
   assert(nbInputs > 0);
   assert(format == nvinfer1::PluginFormat::kLINEAR);
   assert(inputDims != nullptr);
 }
 
-int32_t YoloLayer::enqueue (
+int32_t
-    int batchSize, void const* const* inputs, void* const* outputs, void* workspace,	
+YoloLayer::enqueue(int batchSize, void const* const* inputs, void* const* outputs, void* workspace,	cudaStream_t stream)
-    cudaStream_t stream) noexcept
+    noexcept
 {
   void* num_detections = outputs[0];
   void* detection_boxes = outputs[1];
@@ -187,17 +158,17 @@ int32_t YoloLayer::enqueue (
   CUDA_CHECK(cudaMemsetAsync((float*)detection_scores, 0, sizeof(float) * m_OutputSize * batchSize, stream));
   CUDA_CHECK(cudaMemsetAsync((int*)detection_classes, 0, sizeof(int) * m_OutputSize * batchSize, stream));
 
-    if (m_Type == 3)
+  if (m_Type == 4) {
-    {
+    CUDA_CHECK(cudaYoloLayer_v8(inputs[0], num_detections, detection_boxes, detection_scores, detection_classes, batchSize,
-        CUDA_CHECK(cudaYoloLayer_e(
-            inputs[0], inputs[1], num_detections, detection_boxes, detection_scores, detection_classes, batchSize,
         m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, m_NumClasses, stream));
   }
-    else
+  else if (m_Type == 3) {
-    {
+    CUDA_CHECK(cudaYoloLayer_e(inputs[0], inputs[1], num_detections, detection_boxes, detection_scores, detection_classes,
+        batchSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, m_NumClasses, stream));
+  }
+  else {
     uint yoloTensorsSize = m_YoloTensors.size();
-        for (uint i = 0; i < yoloTensorsSize; ++i)
+    for (uint i = 0; i < yoloTensorsSize; ++i) {
-        {
       TensorInfo& curYoloTensor = m_YoloTensors.at(i);
 
       uint numBBoxes = curYoloTensor.numBBoxes;
@@ -212,8 +183,7 @@ int32_t YoloLayer::enqueue (
       if (anchors.size() > 0) {
         float* f_anchors = anchors.data();
         CUDA_CHECK(cudaMalloc(&v_anchors, sizeof(float) * anchors.size()));
-                CUDA_CHECK(cudaMemcpyAsync(v_anchors, f_anchors, sizeof(float) * anchors.size(), cudaMemcpyHostToDevice,
+        CUDA_CHECK(cudaMemcpyAsync(v_anchors, f_anchors, sizeof(float) * anchors.size(), cudaMemcpyHostToDevice, stream));
-                    stream));
       }
       if (mask.size() > 0) {
         int* f_mask = mask.data();
@@ -224,22 +194,19 @@ int32_t YoloLayer::enqueue (
       uint64_t inputSize = gridSizeX * gridSizeY * (numBBoxes * (4 + 1 + m_NumClasses));
 
       if (m_Type == 2) {  // YOLOR incorrect param: scale_x_y = 2.0
-                CUDA_CHECK(cudaYoloLayer_r(
+        CUDA_CHECK(cudaYoloLayer_r(inputs[i], num_detections, detection_boxes, detection_scores, detection_classes,
-                    inputs[i], num_detections, detection_boxes, detection_scores, detection_classes, batchSize, inputSize,
+            batchSize, inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
-                    m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes,
+            m_NumClasses, numBBoxes, 2.0, v_anchors, v_mask, stream));
-                    2.0, v_anchors, v_mask, stream));
       }
       else if (m_Type == 1) {
         if (m_NewCoords) {
-                    CUDA_CHECK(cudaYoloLayer_nc(
+          CUDA_CHECK(cudaYoloLayer_nc( inputs[i], num_detections, detection_boxes, detection_scores, detection_classes,
-                        inputs[i], num_detections, detection_boxes, detection_scores, detection_classes, batchSize,
+              batchSize, inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
-                        inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
               m_NumClasses, numBBoxes, scaleXY, v_anchors, v_mask, stream));
         }
         else {
-                    CUDA_CHECK(cudaYoloLayer(
+          CUDA_CHECK(cudaYoloLayer(inputs[i], num_detections, detection_boxes, detection_scores, detection_classes,
-                        inputs[i], num_detections, detection_boxes, detection_scores, detection_classes, batchSize,
+              batchSize, inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
-                        inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
               m_NumClasses, numBBoxes, scaleXY, v_anchors, v_mask, stream));
         }
       }
@@ -248,10 +215,9 @@ int32_t YoloLayer::enqueue (
         CUDA_CHECK(cudaMalloc(&softmax, sizeof(float) * inputSize * batchSize));
         CUDA_CHECK(cudaMemsetAsync((float*)softmax, 0, sizeof(float) * inputSize * batchSize, stream));
 
-                CUDA_CHECK(cudaRegionLayer(
+        CUDA_CHECK(cudaRegionLayer(inputs[i], softmax, num_detections, detection_boxes, detection_scores, detection_classes,
-                    inputs[i], softmax, num_detections, detection_boxes, detection_scores, detection_classes, batchSize,
+            batchSize, inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY,
-                    inputSize, m_OutputSize, m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses,
+            m_NumClasses, numBBoxes, v_anchors, stream));
-                    numBBoxes, v_anchors, stream));
 
         CUDA_CHECK(cudaFree(softmax));
       }
@@ -268,7 +234,8 @@ int32_t YoloLayer::enqueue (
   return 0;
 }
 
-size_t YoloLayer::getSerializationSize() const noexcept
+size_t
+YoloLayer::getSerializationSize() const noexcept
 {
   size_t totalSize = 0;
 
@@ -280,12 +247,11 @@ size_t YoloLayer::getSerializationSize() const noexcept
   totalSize += sizeof(m_Type);
   totalSize += sizeof(m_ScoreThreshold);
 
-    if (m_Type != 3) {
+  if (m_Type != 3 && m_Type != 4) {
     uint yoloTensorsSize = m_YoloTensors.size();
     totalSize += sizeof(yoloTensorsSize);
 
-        for (uint i = 0; i < yoloTensorsSize; ++i)
+    for (uint i = 0; i < yoloTensorsSize; ++i) {
-        {
       const TensorInfo& curYoloTensor = m_YoloTensors.at(i);
       totalSize += sizeof(curYoloTensor.gridSizeX);
       totalSize += sizeof(curYoloTensor.gridSizeY);
@@ -299,7 +265,8 @@ size_t YoloLayer::getSerializationSize() const noexcept
   return totalSize;
 }
 
-void YoloLayer::serialize(void* buffer) const noexcept
+void
+YoloLayer::serialize(void* buffer) const noexcept
 {
   char* d = static_cast<char*>(buffer);
 
@@ -311,11 +278,10 @@ void YoloLayer::serialize(void* buffer) const noexcept
   write(d, m_Type);
   write(d, m_ScoreThreshold);
 
-    if (m_Type != 3) {
+  if (m_Type != 3 && m_Type != 4) {
     uint yoloTensorsSize = m_YoloTensors.size();
     write(d, yoloTensorsSize);
-        for (uint i = 0; i < yoloTensorsSize; ++i)
+    for (uint i = 0; i < yoloTensorsSize; ++i) {
-        {
       const TensorInfo& curYoloTensor = m_YoloTensors.at(i);
       write(d, curYoloTensor.gridSizeX);
       write(d, curYoloTensor.gridSizeY);
@@ -325,24 +291,21 @@ void YoloLayer::serialize(void* buffer) const noexcept
       uint anchorsSize = curYoloTensor.anchors.size();
       write(d, anchorsSize);
       for (uint j = 0; j < anchorsSize; ++j)
-            {
         write(d, curYoloTensor.anchors[j]);
-            }
 
       uint maskSize = curYoloTensor.mask.size();
       write(d, maskSize);
       for (uint j = 0; j < maskSize; ++j)
-            {
         write(d, curYoloTensor.mask[j]);
     }
   }
 }
-}
 
-nvinfer1::IPluginV2* YoloLayer::clone() const noexcept
+nvinfer1::IPluginV2*
+YoloLayer::clone() const noexcept
 {
-    return new YoloLayer (
+  return new YoloLayer(m_NetWidth, m_NetHeight, m_NumClasses, m_NewCoords, m_YoloTensors, m_OutputSize, m_Type,
-        m_NetWidth, m_NetHeight, m_NumClasses, m_NewCoords, m_YoloTensors, m_OutputSize, m_Type, m_ScoreThreshold);
+      m_ScoreThreshold);
 }
 
 REGISTER_TENSORRT_PLUGIN(YoloLayerPluginCreator);

nvdsinfer_custom_impl_Yolo/yoloPlugins.h

@@ -26,41 +26,26 @@
 #ifndef __YOLO_PLUGINS__
 #define __YOLO_PLUGINS__
 
-#include <cassert>
-#include <cstring>
-#include <cuda_runtime_api.h>
-#include <iostream>
-#include <memory>
-
-#include <vector>
-
-#include "NvInferPlugin.h"
-
 #include "yolo.h"
 
-#define CUDA_CHECK(status)                                                                                         \
+#define CUDA_CHECK(status) {                                                                                               \
-    {                                                                                                              \
+  if (status != 0) {                                                                                                       \
-        if (status != 0)                                                                                           \
+    std::cout << "CUDA failure: " << cudaGetErrorString(status) << " in file " << __FILE__  << " at line "  << __LINE__ << \
-        {                                                                                                          \
+        std::endl;                                                                                                         \
-            std::cout << "CUDA failure: " << cudaGetErrorString(status) << " in file " << __FILE__  << " at line " \
-                      << __LINE__ << std::endl;                                                                    \
     abort();                                                                                                               \
   }                                                                                                                        \
 }
 
-namespace
+namespace {
-{
   const char* YOLOLAYER_PLUGIN_VERSION {"1"};
   const char* YOLOLAYER_PLUGIN_NAME {"YoloLayer_TRT"};
 } // namespace
 
-class YoloLayer : public nvinfer1::IPluginV2
+class YoloLayer : public nvinfer1::IPluginV2 {
-{
   public:
     YoloLayer(const void* data, size_t length);
 
-    YoloLayer (
+    YoloLayer(const uint& netWidth, const uint& netHeight, const uint& numClasses, const uint& newCoords,
-        const uint& netWidth, const uint& netHeight, const uint& numClasses, const uint& newCoords,
         const std::vector<TensorInfo>& yoloTensors, const uint64_t& outputSize, const uint& modelType,
         const float& scoreThreshold);
 
@@ -70,15 +55,11 @@ public:
 
     int getNbOutputs() const noexcept override { return 4; }
 
-    nvinfer1::Dims getOutputDimensions (
+    nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs, int nbInputDims) noexcept override;
-        int index, const nvinfer1::Dims* inputs,
-        int nbInputDims) noexcept override;
 
-    bool supportsFormat (
+    bool supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const noexcept override;
-        nvinfer1::DataType type, nvinfer1::PluginFormat format) const noexcept override;
 
-    void configureWithFormat (
+    void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims, int nbOutputs,
-        const nvinfer1::Dims* inputDims, int nbInputs, const nvinfer1::Dims* outputDims, int nbOutputs,
         nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize) noexcept override;
 
     int initialize() noexcept override { return 0; }
@@ -87,8 +68,7 @@ public:
 
     size_t getWorkspaceSize(int maxBatchSize) const noexcept override { return 0; }
 
-    int32_t enqueue (
+    int32_t enqueue(int batchSize, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
-        int batchSize, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
         noexcept override;
 
     size_t getSerializationSize() const noexcept override;
@@ -99,13 +79,9 @@ public:
 
     nvinfer1::IPluginV2* clone() const noexcept override;
 
-    void setPluginNamespace (const char* pluginNamespace) noexcept override {
+    void setPluginNamespace(const char* pluginNamespace) noexcept override { m_Namespace = pluginNamespace; }
-        m_Namespace = pluginNamespace;
-    }
 
-    virtual const char* getPluginNamespace () const noexcept override {
+    virtual const char* getPluginNamespace() const noexcept override { return m_Namespace.c_str(); }
-        return m_Namespace.c_str();
-    }
 
   private:
     std::string m_Namespace {""};
@@ -119,8 +95,7 @@ private:
     float m_ScoreThreshold {0};
 };
 
-class YoloLayerPluginCreator : public nvinfer1::IPluginCreator
+class YoloLayerPluginCreator : public nvinfer1::IPluginCreator {
-{
   public:
     YoloLayerPluginCreator() {}
 
@@ -135,31 +110,22 @@ public:
       return nullptr;
     }
 
-    nvinfer1::IPluginV2* createPlugin (
+    nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) noexcept override {
-        const char* name, const nvinfer1::PluginFieldCollection* fc) noexcept override
-    {
       std::cerr<< "YoloLayerPluginCreator::getFieldNames is not implemented";
       return nullptr;
     }
 
-    nvinfer1::IPluginV2* deserializePlugin (
+    nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) noexcept override {
-        const char* name, const void* serialData, size_t serialLength) noexcept override
-    {
       std::cout << "Deserialize yoloLayer plugin: " << name << std::endl;
       return new YoloLayer(serialData, serialLength);
     }
 
-    void setPluginNamespace(const char* libNamespace) noexcept override {
+    void setPluginNamespace(const char* libNamespace) noexcept override { m_Namespace = libNamespace; }
-        m_Namespace = libNamespace;
+
-    }
+    const char* getPluginNamespace() const noexcept override { return m_Namespace.c_str(); }
-    const char* getPluginNamespace() const noexcept override {
-        return m_Namespace.c_str();
-    }
 
   private:
     std::string m_Namespace {""};
 };
 
-extern uint kNUM_CLASSES;
-
 #endif // __YOLO_PLUGINS__

utils/gen_wts_ppyoloe.py

@@ -8,6 +8,7 @@ from ppdet.utils.cli import ArgsParser
 from ppdet.engine import Trainer
 from ppdet.slim import build_slim_model
 
+
 class Layers(object):
     def __init__(self, size, fw, fc, letter_box):
         self.blocks = [0 for _ in range(300)]
@@ -123,7 +124,7 @@ class Layers(object):
     def Shuffle(self, reshape=None, transpose1=None, transpose2=None, route=None, output=''):
         self.current += 1
 
-        r = 0
+        r = None
         if route is not None:
             r = self.get_route(route)
         self.shuffle(reshape=reshape, transpose1=transpose1, transpose2=transpose2, route=r)
@@ -156,7 +157,7 @@ class Layers(object):
                       'channels=3\n' +
                       lb)
 
-    def convolutional(self, cv, act='linear', detect=False):
+    def convolutional(self, cv, act='linear'):
         self.blocks[self.current] += 1
 
         self.get_state_dict(cv.state_dict())
@@ -178,9 +179,6 @@ class Layers(object):
             bias = cv.conv.bias
             bn = True if hasattr(cv, 'bn') else False
 
-        if detect:
-            act = 'logistic'
-
         b = 'batch_normalize=1\n' if bn is True else ''
         g = 'groups=%d\n' % groups if groups > 1 else ''
         w = 'bias=0\n' if bias is None and bn is False else ''
@@ -251,9 +249,9 @@ class Layers(object):
     def shuffle(self, reshape=None, transpose1=None, transpose2=None, route=None):
         self.blocks[self.current] += 1
 
-        r = 'reshape=%s\n' % str(reshape)[1:-1] if reshape is not None else ''
+        r = 'reshape=%s\n' % ', '.join(str(x) for x in reshape) if reshape is not None else ''
-        t1 = 'transpose1=%s\n' % str(transpose1)[1:-1] if transpose1 is not None else ''
+        t1 = 'transpose1=%s\n' % ', '.join(str(x) for x in transpose1) if transpose1 is not None else ''
-        t2 = 'transpose2=%s\n' % str(transpose2)[1:-1] if transpose2 is not None else ''
+        t2 = 'transpose2=%s\n' % ', '.join(str(x) for x in transpose2) if transpose2 is not None else ''
         f = 'from=%d\n' % route if route is not None else ''
 
         self.fc.write('\n[shuffle]\n' +
@@ -419,13 +417,13 @@ with open(wts_file, 'w') as fw, open(cfg_file, 'w') as fc:
                 layers.AvgPool2d()
             layers.ESEAttn(model.yolo_head.stem_cls[i])
             layers.Conv2D(model.yolo_head.pred_cls[i], act='sigmoid')
-            layers.Shuffle(reshape=[model.yolo_head.num_classes, 0], route=feat, output='cls')
+            layers.Shuffle(reshape=[model.yolo_head.num_classes, 'hw'], route=feat, output='cls')
             layers.ESEAttn(model.yolo_head.stem_reg[i], route=-7)
             layers.Conv2D(model.yolo_head.pred_reg[i])
-            layers.Shuffle(reshape=[4, model.yolo_head.reg_max + 1, 0], transpose2=[1, 0, 2], route=feat)
+            layers.Shuffle(reshape=[4, model.yolo_head.reg_max + 1, 'hw'], transpose2=[1, 0, 2], route=feat)
             layers.SoftMax(0)
             layers.Conv2D(model.yolo_head.proj_conv)
-            layers.Shuffle(reshape=[4, 0], route=feat, output='reg')
+            layers.Shuffle(reshape=[4, 'hw'], route=feat, output='reg')
         layers.Detect('cls')
         layers.Detect('reg')
         layers.get_anchors(model.yolo_head.anchor_points.reshape([-1]), model.yolo_head.stride_tensor)

utils/gen_wts_yoloV8.py

@@ -0,0 +1,315 @@
+import argparse
+import os
+import struct
+import torch
+from ultralytics.yolo.utils.torch_utils import select_device
+
+
+class Layers(object):
+    def __init__(self, n, size, fw, fc):
+        self.blocks = [0 for _ in range(n)]
+        self.current = -1
+
+        self.width = size[0] if len(size) == 1 else size[1]
+        self.height = size[0]
+
+        self.fw = fw
+        self.fc = fc
+        self.wc = 0
+
+        self.net()
+
+    def Conv(self, child):
+        self.current = child.i
+        self.fc.write('\n# Conv\n')
+
+        self.convolutional(child)
+
+    def C2f(self, child):
+        self.current = child.i
+        self.fc.write('\n# C2f\n')
+
+        self.convolutional(child.cv1)
+        self.c2f(child.m)
+        self.convolutional(child.cv2)
+
+    def SPPF(self, child):
+        self.current = child.i
+        self.fc.write('\n# SPPF\n')
+
+        self.convolutional(child.cv1)
+        self.maxpool(child.m)
+        self.maxpool(child.m)
+        self.maxpool(child.m)
+        self.route('-4, -3, -2, -1')
+        self.convolutional(child.cv2)
+
+    def Upsample(self, child):
+        self.current = child.i
+        self.fc.write('\n# Upsample\n')
+
+        self.upsample(child)
+
+    def Concat(self, child):
+        self.current = child.i
+        self.fc.write('\n# Concat\n')
+
+        r = []
+        for i in range(1, len(child.f)):
+            r.append(self.get_route(child.f[i]))
+        self.route('-1, %s' % str(r)[1:-1])
+
+    def Detect(self, child):
+        self.current = child.i
+        self.fc.write('\n# Detect\n')
+
+        output_idxs = [0 for _ in range(child.nl)]
+        for i in range(child.nl):
+            r = self.get_route(child.f[i])
+            self.route('%d' % r)
+            for j in range(len(child.cv3[i])):
+                self.convolutional(child.cv3[i][j])
+            self.route('%d' % (-1 - len(child.cv3[i])))
+            for j in range(len(child.cv2[i])):
+                self.convolutional(child.cv2[i][j])
+            self.route('-1, %d' % (-2 - len(child.cv2[i])))
+            self.shuffle(reshape=[child.no, -1])
+            output_idxs[i] = (-1 + i * (-4 - len(child.cv3[i]) - len(child.cv2[i])))
+        self.route('%s' % str(output_idxs[::-1])[1:-1], axis=1)
+        self.yolo(child)
+
+    def net(self):
+        self.fc.write('[net]\n' +
+                      'width=%d\n' % self.width +
+                      'height=%d\n' % self.height +
+                      'channels=3\n' +
+                      'letter_box=1\n')
+
+    def convolutional(self, cv, act=None, detect=False):
+        self.blocks[self.current] += 1
+
+        self.get_state_dict(cv.state_dict())
+
+        if cv._get_name() == 'Conv2d':
+            filters = cv.out_channels
+            size = cv.kernel_size
+            stride = cv.stride
+            pad = cv.padding
+            groups = cv.groups
+            bias = cv.bias
+            bn = False
+            act = 'linear' if not detect else 'logistic'
+        else:
+            filters = cv.conv.out_channels
+            size = cv.conv.kernel_size
+            stride = cv.conv.stride
+            pad = cv.conv.padding
+            groups = cv.conv.groups
+            bias = cv.conv.bias
+            bn = True if hasattr(cv, 'bn') else False
+            if act is None:
+                act = self.get_activation(cv.act._get_name()) if hasattr(cv, 'act') else 'linear'
+
+        b = 'batch_normalize=1\n' if bn is True else ''
+        g = 'groups=%d\n' % groups if groups > 1 else ''
+        w = 'bias=0\n' if bias is None and bn is False else ''
+
+        self.fc.write('\n[convolutional]\n' +
+                      b +
+                      'filters=%d\n' % filters +
+                      'size=%s\n' % self.get_value(size) +
+                      'stride=%s\n' % self.get_value(stride) +
+                      'pad=%s\n' % self.get_value(pad) +
+                      g +
+                      w +
+                      'activation=%s\n' % act)
+
+    def c2f(self, m):
+        self.blocks[self.current] += 1
+
+        for x in m:
+            self.get_state_dict(x.state_dict())
+
+        n = len(m)
+        shortcut = 1 if m[0].add else 0
+        filters = m[0].cv1.conv.out_channels
+        size = m[0].cv1.conv.kernel_size
+        stride = m[0].cv1.conv.stride
+        pad = m[0].cv1.conv.padding
+        groups = m[0].cv1.conv.groups
+        bias = m[0].cv1.conv.bias
+        bn = True if hasattr(m[0].cv1, 'bn') else False
+        act = 'linear'
+        if hasattr(m[0].cv1, 'act'):
+            act = self.get_activation(m[0].cv1.act._get_name()) 
+
+        b = 'batch_normalize=1\n' if bn is True else ''
+        g = 'groups=%d\n' % groups if groups > 1 else ''
+        w = 'bias=0\n' if bias is None and bn is False else ''
+
+        self.fc.write('\n[c2f]\n' +
+                      'n=%d\n' % n +
+                      'shortcut=%d\n' % shortcut +
+                      b +
+                      'filters=%d\n' % filters +
+                      'size=%s\n' % self.get_value(size) +
+                      'stride=%s\n' % self.get_value(stride) +
+                      'pad=%s\n' % self.get_value(pad) +
+                      g +
+                      w +
+                      'activation=%s\n' % act)
+
+    def route(self, layers, axis=0):
+        self.blocks[self.current] += 1
+
+        a = 'axis=%d\n' % axis if axis != 0 else ''
+
+        self.fc.write('\n[route]\n' +
+                      'layers=%s\n' % layers +
+                      a)
+
+    def shortcut(self, r, ew='add', act='linear'):
+        self.blocks[self.current] += 1
+
+        m = 'mode=mul\n' if ew == 'mul' else ''
+
+        self.fc.write('\n[shortcut]\n' +
+                      'from=%d\n' % r +
+                      m +
+                      'activation=%s\n' % act)
+
+    def maxpool(self, m):
+        self.blocks[self.current] += 1
+
+        stride = m.stride
+        size = m.kernel_size
+        mode = m.ceil_mode
+
+        m = 'maxpool_up' if mode else 'maxpool'
+
+        self.fc.write('\n[%s]\n' % m +
+                      'stride=%d\n' % stride +
+                      'size=%d\n' % size)
+
+    def upsample(self, child):
+        self.blocks[self.current] += 1
+
+        stride = child.scale_factor
+
+        self.fc.write('\n[upsample]\n' +
+                      'stride=%d\n' % stride)
+
+    def shuffle(self, reshape=None, transpose1=None, transpose2=None, route=None):
+        self.blocks[self.current] += 1
+
+        r = 'reshape=%s\n' % ', '.join(str(x) for x in reshape) if reshape is not None else ''
+        t1 = 'transpose1=%s\n' % ', '.join(str(x) for x in transpose1) if transpose1 is not None else ''
+        t2 = 'transpose2=%s\n' % ', '.join(str(x) for x in transpose2) if transpose2 is not None else ''
+        f = 'from=%d\n' % route if route is not None else ''
+
+        self.fc.write('\n[shuffle]\n' +
+                      r +
+                      t1 +
+                      t2 +
+                      f)
+
+    def yolo(self, child):
+        self.blocks[self.current] += 1
+
+        self.fc.write('\n[detect_v8]\n' +
+                      'num=%d\n' % (child.reg_max * 4) +
+                      'classes=%d\n' % child.nc)
+
+    def get_state_dict(self, state_dict):
+        for k, v in state_dict.items():
+            if 'num_batches_tracked' not in k:
+                vr = v.reshape(-1).numpy()
+                self.fw.write('{} {} '.format(k, len(vr)))
+                for vv in vr:
+                    self.fw.write(' ')
+                    self.fw.write(struct.pack('>f', float(vv)).hex())
+                self.fw.write('\n')
+                self.wc += 1
+
+    def get_anchors(self, anchor_points, stride_tensor):
+        vr = anchor_points.numpy()
+        self.fw.write('{} {} '.format('anchor_points', len(vr)))
+        for vv in vr:
+            self.fw.write(' ')
+            self.fw.write(struct.pack('>f', float(vv)).hex())
+        self.fw.write('\n')
+        self.wc += 1
+        vr = stride_tensor.numpy()
+        self.fw.write('{} {} '.format('stride_tensor', len(vr)))
+        for vv in vr:
+            self.fw.write(' ')
+            self.fw.write(struct.pack('>f', float(vv)).hex())
+        self.fw.write('\n')
+        self.wc += 1
+
+    def get_value(self, key):
+        if type(key) == int:
+            return key
+        return key[0] if key[0] == key[1] else str(key)[1:-1]
+
+    def get_route(self, n):
+        r = 0
+        for i, b in enumerate(self.blocks):
+            if i <= n:
+                r += b
+            else:
+                break
+        return r - 1
+
+    def get_activation(self, act):
+        if act == 'Hardswish':
+            return 'hardswish'
+        elif act == 'LeakyReLU':
+            return 'leaky'
+        elif act == 'SiLU':
+            return 'silu'
+        return 'linear'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='PyTorch YOLOv8 conversion')
+    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
+    parser.add_argument(
+        '-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
+    args = parser.parse_args()
+    if not os.path.isfile(args.weights):
+        raise SystemExit('Invalid weights file')
+    return args.weights, args.size
+
+
+pt_file, inference_size = parse_args()
+
+model_name = os.path.basename(pt_file).split('.pt')[0]
+wts_file = model_name + '.wts' if 'yolov8' in model_name else 'yolov8_' + model_name + '.wts'
+cfg_file = model_name + '.cfg' if 'yolov8' in model_name else 'yolov8_' + model_name + '.cfg'
+
+device = select_device('cpu')
+model = torch.load(pt_file, map_location=device)['model'].float()
+model.to(device).eval()
+
+with open(wts_file, 'w') as fw, open(cfg_file, 'w') as fc:
+    layers = Layers(len(model.model), inference_size, fw, fc)
+
+    for child in model.model.children():
+        if child._get_name() == 'Conv':
+            layers.Conv(child)
+        elif child._get_name() == 'C2f':
+            layers.C2f(child)
+        elif child._get_name() == 'SPPF':
+            layers.SPPF(child)
+        elif child._get_name() == 'Upsample':
+            layers.Upsample(child)
+        elif child._get_name() == 'Concat':
+            layers.Concat(child)
+        elif child._get_name() == 'Detect':
+            layers.Detect(child)
+            layers.get_anchors(child.anchors.reshape([-1]), child.strides.reshape([-1]))
+        else:
+            raise SystemExit('Model not supported')
+
+os.system('echo "%d" | cat - %s > temp && mv temp %s' % (layers.wc, wts_file, wts_file))