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SeokjuLee / VPGNet

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (ICCV 2017)
MIT License
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Make runtest failed on Ubuntu 16.04 and CUDA 10.0 #47

Open earlysleepearlyup opened 5 years ago

earlysleepearlyup commented 5 years ago

Issue summary

Make runtest failed on Ubuntu 16.04 and CUDA 10.0

Aborted at 1573612683 (unix time) try "date -d @1573612683" if you are using GNU date PC: @ 0x7fad4225218e caffe::CuDNNConvolutionLayer<>::LayerSetUp() SIGFPE (@0x7fad4225218e) received by PID 27175 (TID 0x7fad4d5b9740) from PID 1109729678; stack trace: @ 0x7fad41776390 (unknown) @ 0x7fad4225218e caffe::CuDNNConvolutionLayer<>::LayerSetUp() @ 0x4a10b4 caffe::Layer<>::SetUp() @ 0x7b36d8 caffe::CuDNNConvolutionLayerTest_TestSimpleConvolutionCuDNN_Test<>::TestBody() @ 0x88cc8c testing::internal::HandleSehExceptionsInMethodIfSupported<>() @ 0x887d99 testing::internal::HandleExceptionsInMethodIfSupported<>() @ 0x8736d4 testing::Test::Run() @ 0x873ec6 testing::TestInfo::Run() @ 0x874511 testing::TestCase::Run() @ 0x8799bf testing::internal::UnitTestImpl::RunAllTests() @ 0x88de69 testing::internal::HandleSehExceptionsInMethodIfSupported<>() @ 0x888a4a testing::internal::HandleExceptionsInMethodIfSupported<>() @ 0x8785ce testing::UnitTest::Run() @ 0x496d9b main @ 0x7fad413bb830 __libc_start_main @ 0x496429 _start @ 0x0 (unknown) make: *** [Makefile:478: runtest] Floating point exception (core dumped)

Steps to reproduce

make clean make all -j16 make test -j16 make runtest

Tried solutions

modify the makefile.config

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda-10.0
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
        -gencode arch=compute_35,code=sm_35 \
        -gencode arch=compute_50,code=sm_50 \
        -gencode arch=compute_52,code=sm_52 \
            -gencode arch=compute_60,code=sm_60 \
            -gencode arch=compute_61,code=sm_61 \
            -gencode arch=compute_61,code=compute_61 

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
MATLAB_DIR := /usr/local/MATLAB/R2018b
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python3.5 \
        /home/liying/.local/lib/python3.5/site-packages/numpy/core/include/numpy    
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/local/include/opencv /usr/local/include/opencv2
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
USE_PKG_CONFIG := 1

BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

System configuration