haotian/duan8
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add faq

Browse Source
This commit is contained in:
wang-xinyu 2020-08-04 22:04:21 +08:00
parent 4897bb32ee
commit a564557bb6
2 changed files with 90 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -23,6 +23,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
- [A guide for quickly getting started, taking lenet5 as a demo.](./tutorials/getting_started.md)
- [Migrating from TensorRT 4 to 7](./tutorials/migrating_from_tensorrt_4_to_7.md)
- [How to implement multi-GPU processing, taking YOLOv4 as example](./tutorials/multi_GPU_processing.md)
- [Frequently Asked Questions (FAQ)](./tutorials/faq.md)
## Test Environment

89
tutorials/faq.md Normal file
View File

@ -0,0 +1,89 @@
# 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 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)
```