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Li-HongdaLi-Hongda
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update readme
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‎.gitignore‎

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bin
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.vscode
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weights
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yaml-cpp-yaml-cpp-0.6.3
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yaml-cpp
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build
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demo
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results
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demo

‎README.md‎

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- [x] [YOLOv8](https://github.com/ultralytics/ultralytics)<br>
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- [x] [YOLOv8-seg](https://github.com/ultralytics/ultralytics)<br>
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- [x] [RT-DETR](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rtdetr)<br>
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- [] [YOLOv6](https://github.com/meituan/YOLOv6) (to be continued)<br>
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- [] [YOLO-NAS](https://github.com/Deci-AI/super-gradients) (to be continued)<br>
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- [ ] [YOLOv6](https://github.com/meituan/YOLOv6) (to be continued)<br>
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- [ ] [YOLO-NAS](https://github.com/Deci-AI/super-gradients) (to be continued)<br>
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</details>
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All speed tests were performed on RTX 3090 with COCO Val set.The time calculated here is the sum of the time of image loading, preprocess, inference and postprocess, so it's going to be slower than what's reported in the paper.
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<div align='center'>
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| Models | BatchSize | Mode | Input Shape(HxW) | FPS |
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| Models | BatchSize | Mode | Resolution | FPS |
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|-|-|:-:|:-:|:-:|
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| YOLOv5-s v7.0 | 1 | FP32 | 640x640 | 468 |
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| YOLOv5-s v7.0 | 32 | FP32 | 640x640 | - |
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## 4.Usage
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1. Clone the repo.
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```
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git clone https://github.com/Li-Hongda/TensorRT_Inference_Demo.git
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cd TensorRT_Inference_Demo/object_detection
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```
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2. Change the path [here](https://github.com/Li-Hongda/TensorRT_Inference_Demo/blob/main/object_detection/CMakeLists.txt#L19) to your TensorRT path, and [here](https://github.com/Li-Hongda/TensorRT_Inference_Demo/blob/main/object_detection/CMakeLists.txt#L11) to your CUDA path. Then,
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2. Install the dependencies.
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### TensorRT
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Following [NVIDIA offical docs](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing) to install TensorRT.
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### yaml-cpp
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```
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git clone https://github.com/jbeder/yaml-cpp
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mkdir build && cd build
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cmake ..
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make -j20
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cmake -DYAML_BUILD_SHARED_LIBS=on ..
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make -j20
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cd ..
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```
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3. Change the path [here](https://github.com/Li-Hongda/TensorRT_Inference_Demo/blob/main/object_detection/CMakeLists.txt#L19) to your TensorRT path, and [here](https://github.com/Li-Hongda/TensorRT_Inference_Demo/blob/main/object_detection/CMakeLists.txt#L11) to your CUDA path. Then,
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```
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cd TensorRT_Inference_Demo/object_detection
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mkdir build && cd build
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cmake ..
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make -j$(nproc)
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```
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3. The executable file will be generated in `bin` in the repo directory if compile successfully.Then enjoy yourself with command like this:
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4. The executable file will be generated in `bin` in the repo directory if compile successfully.Then enjoy yourself with command like this:
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```
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cd bin
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./object_detection yolov5 /path/to/input/dir

‎configs/yolov6.yaml‎

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yolov7:
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onnx_file: "../weights/yolov6/yolov6.onnx"
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engine_file: "../weights/yolov6/yolov6.trt"
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type: "coco"
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mode: "fp32"
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batchSize: 1
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imageWidth: 640
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imageHeight: 640
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conf_thr: 0.25
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nms_thr: 0.45
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strides: [8, 16, 32]
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imgMean: [ 0, 0, 0 ]
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imgStd: [ 1, 1, 1 ]

‎include/yolov6.h‎

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#ifndef YOLOV6_H
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#define YOLOV6_H
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#include "yolo.h"
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class YOLOv6 : public YOLO {
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public:
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explicit YOLOv6(const YAML::Node &config);
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};
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#endif

‎object_detection/CMakeLists.txt‎

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list(APPEND ALL_INCLUDE ${OpenCV_INCLUDE_DIRS})
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# YAML
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set(YAML_LIBRARY ../yaml-cpp-yaml-cpp-0.6.3/build)
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set(YAML_INCLUDE ../yaml-cpp-yaml-cpp-0.6.3/include)
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set(YAML_LIBRARY ../yaml-cpp/build)
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set(YAML_INCLUDE ../yaml-cpp/include)
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link_directories(${YAML_LIBRARY})
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list(APPEND ALL_INCLUDE ${YAML_INCLUDE})
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‎object_detection/rtdetr/CMakeLists.txt‎

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set(CMAKE_CXX_STANDARD 14)
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# YAML
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set(YAML_LIBRARY ../../yaml-cpp-yaml-cpp-0.6.3/build)
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set(YAML_INCLUDE ../../yaml-cpp-yaml-cpp-0.6.3/include)
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set(YAML_LIBRARY ../../yaml-cpp/build)
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set(YAML_INCLUDE ../../yaml-cpp/include)
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link_directories(${YAML_LIBRARY})
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list(APPEND ALL_INCLUDE ${YAML_INCLUDE})
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‎object_detection/yolov5/CMakeLists.txt‎

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set(CMAKE_CXX_STANDARD 14)
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# YAML
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set(YAML_LIBRARY ../../yaml-cpp-yaml-cpp-0.6.3/build)
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set(YAML_INCLUDE ../../yaml-cpp-yaml-cpp-0.6.3/include)
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set(YAML_LIBRARY ../../yaml-cpp/build)
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set(YAML_INCLUDE ../../yaml-cpp/include)
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link_directories(${YAML_LIBRARY})
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list(APPEND ALL_INCLUDE ${YAML_INCLUDE})
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‎object_detection/yolov7/CMakeLists.txt‎

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set(CMAKE_CXX_STANDARD 14)
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# YAML
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set(YAML_LIBRARY ../../yaml-cpp-yaml-cpp-0.6.3/build)
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set(YAML_INCLUDE ../../yaml-cpp-yaml-cpp-0.6.3/include)
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set(YAML_LIBRARY ../../yaml-cpp/build)
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set(YAML_INCLUDE ../../yaml-cpp/include)
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link_directories(${YAML_LIBRARY})
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list(APPEND ALL_INCLUDE ${YAML_INCLUDE})
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‎object_detection/yolov8/CMakeLists.txt‎

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set(CMAKE_CXX_STANDARD 14)
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# YAML
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set(YAML_LIBRARY ../../yaml-cpp-yaml-cpp-0.6.3/build)
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set(YAML_INCLUDE ../../yaml-cpp-yaml-cpp-0.6.3/include)
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set(YAML_LIBRARY ../../yaml-cpp/build)
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set(YAML_INCLUDE ../../yaml-cpp/include)
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link_directories(${YAML_LIBRARY})
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list(APPEND ALL_INCLUDE ${YAML_INCLUDE})
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‎src/rtdetr.cpp‎

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auto boxes = PostProcess(imgBatch, gpu_buffers[1], gpu_buffers[2]);
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auto t_end_post = std::chrono::high_resolution_clock::now();
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float total_post = std::chrono::duration<float, std::milli>(t_end_post - t_start_post).count();
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// std::cout << "preprocess time: "<< total_pre << "ms " <<
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// "detection inference time: " << total_inf << "ms "
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// "postprocess time: " << total_post << "ms " << std::endl;
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std::cout << "preprocess time: "<< total_pre << "ms " <<
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"detection inference time: " << total_inf << "ms "
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"postprocess time: " << total_post << "ms " << std::endl;
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return boxes;
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}
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float* box_per_img = output1 + index * predboxSize;
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float* score_per_img = output2 + index * predscoreSize;
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cuda_postprocess_init(6, imageWidth, imageHeight);
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auto t_start_post = std::chrono::high_resolution_clock::now();
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rtdetr_postprocess_box(box_per_img, score_per_img, num_bboxes, num_classes, 6,
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conf_thr, imageWidth, imageHeight, dst2src, stream, cpu_buffers[2]);
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auto t_end_post = std::chrono::high_resolution_clock::now();
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float total_post = std::chrono::duration<float, std::milli>(t_end_post - t_start_post).count();
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std::cout << "postprocess time: " << total_post << "ms " << std::endl;
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int num_boxes = std::min((int)cpu_buffers[2][0], 300);
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for (int i = 0; i < num_boxes; i++) {
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Box box;

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