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Frequently Asked Questions (FAQ)

1. fatal error: NvInfer.h: No such file or directory

NvInfer.h is one of the headers of TensorRT. If you install the tensorrt DEB package, the headers should in /usr/include/x86_64-linux-gnu/. If you install tensorrt TAR or ZIP file, the include_directories and link_directories of tensorrt should be added in CMakeLists.txt.

dpkg -L can print out the contents of a DEB package.

$ dpkg -L libnvinfer-dev 
/.
/usr
/usr/lib
/usr/lib/x86_64-linux-gnu
/usr/lib/x86_64-linux-gnu/libnvinfer_static.a
/usr/lib/x86_64-linux-gnu/libmyelin_compiler_static.a
/usr/lib/x86_64-linux-gnu/libmyelin_executor_static.a
/usr/lib/x86_64-linux-gnu/libmyelin_pattern_library_static.a
/usr/lib/x86_64-linux-gnu/libmyelin_pattern_runtime_static.a
/usr/include
/usr/include/x86_64-linux-gnu
/usr/include/x86_64-linux-gnu/NvInfer.h
/usr/include/x86_64-linux-gnu/NvInferRuntime.h
/usr/include/x86_64-linux-gnu/NvInferRuntimeCommon.h
/usr/include/x86_64-linux-gnu/NvInferVersion.h
/usr/include/x86_64-linux-gnu/NvUtils.h
/usr/share
/usr/share/doc
/usr/share/doc/libnvinfer-dev
/usr/share/doc/libnvinfer-dev/copyright
/usr/share/doc/libnvinfer-dev/changelog.Debian
/usr/lib/x86_64-linux-gnu/libmyelin.so
/usr/lib/x86_64-linux-gnu/libnvinfer.so

2. fatal error: cuda_runtime_api.h: No such file or directory

cuda_runtime_api.h is from cuda-cudart. If you met this error, you need find where it is and adapt the include_directories and link_directories of cuda in CMakeLists.txt.

$ dpkg -L cuda-cudart-dev-10-0 
/.
/usr
/usr/local
/usr/local/cuda-10.0
/usr/local/cuda-10.0/targets
/usr/local/cuda-10.0/targets/x86_64-linux
/usr/local/cuda-10.0/targets/x86_64-linux/lib
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudadevrt.a
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libOpenCL.so.1.1
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libculibos.a
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudart_static.a
/usr/local/cuda-10.0/targets/x86_64-linux/include
/usr/local/cuda-10.0/targets/x86_64-linux/include/cuda_runtime_api.h
/usr/local/cuda-10.0/targets/x86_64-linux/include/cudart_platform.h
/usr/local/cuda-10.0/targets/x86_64-linux/include/cuda_device_runtime_api.h
/usr/local/cuda-10.0/targets/x86_64-linux/include/cuda_runtime.h
/usr/lib
/usr/lib/pkgconfig
/usr/lib/pkgconfig/cudart-10.0.pc
/usr/share
/usr/share/doc
/usr/share/doc/cuda-cudart-dev-10-0
/usr/share/doc/cuda-cudart-dev-10-0/changelog.Debian.gz
/usr/share/doc/cuda-cudart-dev-10-0/copyright
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libOpenCL.so
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libOpenCL.so.1
/usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudart.so

3. .wts not prepared or not in the right directory

If .wts file not in the right directory. The loadWeights() function will report error. Error logs like following.

By default, the .wts file usually should be put in the same dir as build. For example, tensorrtx/yolov5/yolov5s.wts. And the .wts path defined in yolov5.cpp.

std::map<std::__cxx11::basic_string, nvinfer1::Weights> loadWeights(std::__cxx11::string): Assertion `input.is_open() && "Unable to load weight file."' failed.
Aborted (core dumped)

4. yolo -s failed, class_num not adapted

If you train your own yolo model, you need set the CLASS_NUM in yololayer.h. Which is 80 by default. Otherwise, you will get errors like following.

[Convolution]: kernel weights has count xxx but xxx was expected
void APIToModel(unsigned int, nvinfer1::IHostMemory**): Assertion `engine != nullptr' failed.
Aborted (core dumped)