yolov6/deploy/TensorRT

• YOLOv6-TensorRT in C++
• • Dependencies
  • Step 1: Get onnx model
  • Step 2: Prepare serialized engine file
  • Step 3: build the demo
• Testing on image

YOLOv6-TensorRT in C++

Dependencies

• TensorRT-8.2.3.0
• OpenCV-4.1.0

Step 1: Get onnx model

Follow the file ONNX README to convert the pt mdoel to onnx yolov6n.onnx.
Now do't support end2end onnx model which include the nms plugin

python ./deploy/ONNX/export_onnx.py \
    --weights yolov6n.pt \
    --img 640 \
    --batch 1

Step 2: Prepare serialized engine file

Follow the file post training README to convert and save the serialized engine file yolov6.engine.

python3 onnx_to_tensorrt.py --fp16 --int8 -v \
        --max_calibration_size=${MAX_CALIBRATION_SIZE} \
        --calibration-data=${CALIBRATION_DATA} \
        --calibration-cache=${CACHE_FILENAME} \
        --preprocess_func=${PREPROCESS_FUNC} \
        --explicit-batch \
        --onnx ${ONNX_MODEL} -o ${OUTPUT}

Step 3: build the demo

Please follow the TensorRT Installation Guide to install TensorRT.

And you should set the TensorRT path and CUDA path in CMakeLists.txt.

If you train your custom dataset, you may need to modify the value of num_class, image width height, and class name.

const int num_class = 80;
static const int INPUT_W = 640;
static const int INPUT_H = 640;
static const char* class_names[] = {
        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"
    };

build the demo:

mkdir build
cd build
cmake ..
make

Then run the demo:

./yolov6 ../you.engine -i image_path

Testing on image

You can do testing on images using .trt weights, just give path of image directory & its annotation path

python3 deploy/TensorRT/eval_yolo_trt.py -v -m model.trt \
--imgs-dir /workdir/datasets/coco/images/val2017 \
--annotations /workdir/datasets/coco/annotations/instances_val2017.json \
--conf-thres 0.40 --iou-thres 0.45