DeepStream 使用 ONNX 模型

參考 How to deploy ONNX models on NVIDIA Jetson Nano using DeepStream

到 ONNX model zoo 找尋 Tiny YOLOv2 model 下載 tinyyolov2-8.onnx (Opset version 8)

Tiny YOLOv2 解說

  Input Shape (1x3x416x416)

  Output Shape (1x125x13x13)

    125 分成 5 個 BBox, 每個有 25 cells(32 bits)

    前 5 個是 bounding box, 後 20 是 class probabilities

$ git clone https://github.com/thatbrguy/Deep-Stream-ONNX.git

$ cd Deep-Stream-ONNX

$ mv ~/Downloads/tinyyolov2-8.onnx .

下載 sample.tar.gz 並解壓縮

$ vi config/config_infer_custom_yolo.txt

onnx-file=../tinyyolov2-8.onnx

parse-bbox-func-name=NvDsInferParseCustomYoloV2Tiny

custom-lib-path=../custom_bbox_parser/libnvdsinfer_custom_bbox_tiny_yolo.so

$ vi custom_bbox_parser/Makefile

SRCFILES:=nvdsparsebbox_tiny_yolo.cpp

TARGET_LIB:=libnvdsinfer_custom_bbox_tiny_yolo.so

DEEPSTREAM_PATH:=/opt/nvidia/deepstream/deepstream-5.0

$ vi custom_bbox_parser/nvdsparsebbox_tiny_yolo.cpp

    //assert (layer.dims.numDims == 3); 修改

    assert (layer.inferDims.numDims == 3);

extern "C" bool NvDsInferParseCustomYoloV2Tiny(

    std::vector<NvDsInferLayerInfo> const& outputLayersInfo,

    NvDsInferNetworkInfo const& networkInfo,

    NvDsInferParseDetectionParams const& detectionParams,

    std::vector<NvDsInferParseObjectInfo>& objectList);

outputLayersInfo: 為 ONNX 的輸出,  outputLayersInfo[0].buffer

networkInfo: 為 ONNX 模型的資訊

detectionParams: 有 config params 的資料，如 numClassesConfigured

objectList: 是我們要產生的輸出

測試

$ deepstream-app -c config/deepstream_app_custom_yolo.txt