⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
报错内容如下
ddledetection/cpp/build$ ./infer_yolov5_demo ./yolov5_s_300e_coco 000000014439.jpg 2
[INFO] fastdeploy/vision/common/processors/transform.cc(45)::FuseNormalizeCast Normalize and Cast are fused to Normalize in preprocessing pipeline.
[INFO] fastdeploy/vision/common/processors/transform.cc(93)::FuseNormalizeHWC2CHW Normalize and HWC2CHW are fused to NormalizeAndPermute in preprocessing pipeline.
[INFO] fastdeploy/vision/common/processors/transform.cc(159)::FuseNormalizeColorConvert BGR2RGB and NormalizeAndPermute are fused to NormalizeAndPermute with swap_rb=1
[ERROR] fastdeploy/runtime/backends/tensorrt/trt_backend.cc(696)::CreateTrtEngineFromOnnx Failed to parse ONNX model by TensorRT.
[ERROR] fastdeploy/runtime/backends/tensorrt/trt_backend.cc(298)::InitFromOnnx Failed to create tensorrt engine.
[ERROR] fastdeploy/runtime/runtime.cc(307)::CreateTrtBackend Failed to initialize TensorRT backend.
Aborted (core dumped)
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环境
【FastDeploy版本】: FastDeploy-release-1.0.7
【编译命令】 cd FastDeploy-release-1.0.7 mkdir build && cd build cmake .. -DBUILD_ON_JETSON=ON \ DENABLE_VISION=ON \ DENABLE_PADDLE_BACKEND=ON \ DPADDLEINFERENCE_DIRECTORY=/home/racobit/Downloads/paddle_inference_install_dir \ DCMAKE_INSTALL_PREFIX=${PWD}/installed_fastdeploy make install
【系统平台】: NX Linux aarch64(Ubuntu 18.04)
【硬件】: jetpack4.6.3, CUDA 10.2.300, CUDNN 8.2.1.32, tensorrt 8.2, opencv4.5.3(cuda yes), python 3.6.9
【编译语言】: C++
问题日志及出现问题的操作流程
附上详细的问题日志有助于快速定位分析
【模型开启GPU-TRT后端跑不通】
FastDeploy-release-1.0.7/examples/vision/detection/paddledetection/cpp/
下的yolov5部署示例,采用官方模型与代码, (./examples/vision/detection/paddledetection/cpp/infer_yolov5.cc), 其他后端均可以正确执行, 只有gpu-trt后端报错,