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TROUBLESHOOTING.md

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Troubleshooting

Here is a compilation if common issues that you might face while compiling / running this code:

Compilation errors when compiling the library

If you encounter build errors like the following:

/usr/include/c++/6/type_traits:1558:8: note: provided for ‘template<class _From, class _To> struct std::is_convertible’
     struct is_convertible
        ^~~~~~~~~~~~~~
/usr/include/c++/6/tuple:502:1: error: body of constexpr function ‘static constexpr bool std::_TC<<anonymous>, _Elements>::_NonNestedTuple() [with _SrcTuple = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor>&&; bool <anonymous> = true; _Elements = {at::Tensor, at::Tensor, at::Tensor, at::Tensor}]’ not a return-statement
     }
 ^
error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1

check your CUDA version and your gcc version.

nvcc --version
gcc --version

If you are using CUDA 9.0 and gcc 6.4.0, then refer to facebookresearch/maskrcnn-benchmark#25, which has a summary of the solution. Basically, CUDA 9.0 is not compatible with gcc 6.4.0.

ImportError: No module named maskrcnn_benchmark.config when running webcam.py

This means that maskrcnn-benchmark has not been properly installed. Refer to facebookresearch/maskrcnn-benchmark#22 for a few possible issues. Note that we now support Python 2 as well.

ImportError: Undefined symbol: __cudaPopCallConfiguration error when import _C

This probably means that the inconsistent version of NVCC compile and your conda CUDAToolKit package. This is firstly mentioned in facebookresearch/maskrcnn-benchmark#45 . All you need to do is:

# Check the NVCC compile version(e.g.)
/usr/cuda-9.2/bin/nvcc --version
# Check the CUDAToolKit version(e.g.)
~/anaconda3/bin/conda list | grep cuda

# If you need to update your CUDAToolKit
~/anaconda3/bin/conda install -c anaconda cudatoolkit==9.2

Both of them should have the same version. For example, if NVCC==9.2 and CUDAToolKit==9.2, this will be fine while when NVCC==9.2 but CUDAToolKit==9, it fails.

Segmentation fault (core dumped) when running the library

This probably means that you have compiled the library using GCC < 4.9, which is ABI incompatible with PyTorch. Indeed, during installation, you probably saw a message like

Your compiler (g++ 4.8) may be ABI-incompatible with PyTorch!
Please use a compiler that is ABI-compatible with GCC 4.9 and above.
See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.

See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
for instructions on how to install GCC 4.9 or higher.

Follow the instructions on https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6 to install GCC 4.9 or higher, and try recompiling maskrcnn-benchmark again, after cleaning the build folder with

rm -rf build