GPU Coder Error with OpenCV
3 次查看(过去 30 天)
显示 更早的评论
I'm trying to use the example "Semantic Segmentation on NVIDIA DRIVE" just modifying it to be executing on a Jetson Nano board. I train my own network, then Ijust load my network instead of the one defined on the example, nevertheless when I try to deploy it to the Nano I have the following error:
/main.cu:10:10: fatal error: opencv2/opencv.hpp: No such file or directory
#include "opencv2/opencv.hpp"
I check on the nano to see if the installation of OpenCV is correct:
alvaro@alvaro-nano:~$ pkg-config --modversion opencv
2.4.9
The installation is correct, also when I call the code generation I pass the path as an -I command:
codegen -config cfg autoSemanticSegmentation -args {img} -I /usr/include/opencv4 -report -v
And I saw that this is inluded on the generated make file.
How can I fix this issue to be able to compile and execute the example on the Nano?
4 个评论
Darshan Ramakant Bhat
2020-3-6
This looks like the path issue. Are you able to locate "opencv2/opencv.hpp" file in the target installation ?
Once you locate the correct folder containing "opencv.hpp" file, you can include the folder using "CustomInclude" command in "cfg" :
Hope this will be helpful for you.
Alvaro Izaguirre Serrano
2020-3-6
Hello Darshan,
Thanks for your answer, I can locate the opencv.hpp file on the target on the path "/usr/include/opencv4/opencv2" which is the standard location. I already thry this, I try the following confguration for the GPU Coder:
% Create the configuration for the code generation
cfg = coder.gpuConfig('exe');
cfg.Hardware = coder.hardware('NVIDIA Jetson');
cfg.Hardware.BuildDir = '~/remoteBuildDir';
cfg.CustomInclude = fullfile('/usr/include/opencv4');
cfg.CustomSource = fullfile('segnet_deploy_Jetson', 'main.cu');
When I generate the code with GPU coder the path is included on the _rtw.mk file. I also try to call the code generation with the option:
-I '/usr/include/opencv4'
Also the make file incudes the defined, but I have the same error at compilation, for example on the make file I can see the following:
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/include/opencv4
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/include/opencv4/opencv2
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert
As you can see the path '/usr/include/opencv4' and '/usr/include/opencv4/opencv2' are included to call the compiler.
Even on the target if I check the version of opencv with " pkg-config --modversion opencv" I have the answer of the version, and if I try to use it in Phyton with import cv2 I can.
alvaro@alvaro-nano:~$ pkg-config --modversion opencv
2.4.9
Then I don't know what else to do.
Walter Roberson
2020-3-11
alvaro@alvaro-nano:~$ pkg-config --modversion opencv
opencv pkg is not the same as openvc2 pkg. opencv still exists as a package
采纳的回答
Hariprasad Ravishankar
2020-3-9
Hi Alvaro,
I believe the issue is due to the line below,
cfg.CustomInclude = fullfile('/usr/include/opencv4');
For hardware deployment workflow, cfg.CustomInclude prepends the host working directory structure to create the following structure
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/include/opencv4
-I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/include/opencv4/opencv2
Rather than,
-I/usr/include/opencv4
-I/usr/include/opencv4/opencv2
To resolve the issue you can try one of the two approaches
1) Rename /usr/include/opencv4/opencv2 to /usr/include/opencv2
mv /usr/include/opencv4/opencv2 /usr/include/opencv2
This should ensure that the compiler is able to find opencv2/opencv.hpp under /usr/include/
2) Use coder.updateBuildInfo('addIncludePaths', '/usr/include/opencv4/');
function out = segnet_predict(in)
%#codegen
% A persistent object mynet is used to load the DAG network object.
% At the first call to this function, the persistent object is constructed and
% setup. When the function is called subsequent times, the same object is reused
% to call predict on inputs, thus avoiding reconstructing and reloading the
% network object.
% Update buildinfo with the OpenCV library flags.
opencv_link_flags = '`pkg-config --cflags --libs opencv`';
coder.updateBuildInfo('addLinkFlags',opencv_link_flags);
coder.updateBuildInfo('addIncludePaths', '/usr/include/opencv4/');
persistent mynet;
if isempty(mynet)
mynet = coder.loadDeepLearningNetwork('SegNet.mat');
end
% pass in input
out = predict(mynet,in);
Hari
13 个评论
Alvaro Izaguirre Serrano
2020-3-10
Hi Hariprashad,
I modify using both option, option 2 didn't work but option 1 did work. Nevertheless now I have the following error:
STDERR: /usr/bin/ld: cannot find -lopencv_contrib
/usr/bin/ld: cannot find -lopencv_gpu
/usr/bin/ld: cannot find -lopencv_legacy
/usr/bin/ld: cannot find -lopencv_superres
/usr/bin/ld: cannot find -lopencv_ts
/usr/bin/ld: cannot find -lopencv_videostab
/usr/bin/ld: cannot find -lopencv_facedetect
/usr/bin/ld: cannot find -lopencv_imuvstab
/usr/bin/ld: cannot find -lopencv_tegra
/usr/bin/ld: cannot find -lopencv_vstab
/usr/bin/ld: cannot find -lnppi
collect2: error: ld returned 1 exit status
I check the opencv.pc file, which is located on the path:
/usr/share/visionworks/sources
The contents of that file has the following:
# Package Information for pkg-config
prefix=/usr
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir_old=${prefix}/include/opencv
includedir_new=${prefix}/include
Name: OpenCV
Description: Open Source Computer Vision Library
Version: 2.4.9
Libs: -L${libdir} -lopencv_calib3d -lopencv_contrib -lopencv_core -lopencv_features2d -lopencv_flann -lopencv_gpu -lopencv_highgui -lopencv_imgproc -lopencv_legacy -lopencv_ml -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_superres -lopencv_ts -lopencv_video -lopencv_videostab -lopencv_facedetect -lopencv_imuvstab -lopencv_tegra -lopencv_vstab -lcufft -lnpps -lnppi -lnppc -lcudart -lstdc++ -lrt -lpthread -lm -ldl
Cflags: -I${prefix}/include -I${prefix}/include/opencv
I think the issues come form here, if I modify the Cflags to be -I${prefix}/include/opencv4 instead of -I${prefix}/include/opencv shall fix the issue to include the file opencv.hpp.
But I tried to modify this file to change the Cflags and the Libs, because on my Nano system under the path /usr/lib/aarch64-linux-gnu the lib files are located but with different names, instead of being lopencv_XXX they are libopencv_XXX, nevertheless after this change I had different issues.
Do you have any idea of why my current implementation does not find the library files?
Regards
Alvaro Izaguirre
David Brisson Andersen
2020-3-10
I have the exact same problem. I tried the second approach (Use coder.updateBuildInfo('addIncludePaths', '/usr/include/opencv4/');) without any luck.
The first approach (Rename /usr/include/opencv4/opencv2 to /usr/include/opencv2) seems wrong somehow as it requires modifications to the usr folder. Is there any other way around this Hariprasad?
My target is the Jetson AGX Xavier and opencv 4.1.1. I used Nvidia SDK Manager to setup the board.
Hariprasad Ravishankar
2020-3-10
Hi Alvaro and David,
Can you please share the generated makefile (it should be generated in the codegen folder and named xxxx_rtw.mk) and the path to your opencv on the target board?
Here is what I get on my Jetson AGX Xavier setup
pkg-config --libs opencv
-lopencv_dnn -lopencv_ml -lopencv_objdetect -lopencv_shape -lopencv_stitching -lopencv_superres -lopencv_videostab -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_video -lopencv_photo -lopencv_imgproc -lopencv_flann -lopencv_core
pkg-config --cflags opencv
-I/usr/include/opencv
David Brisson Andersen
2020-3-10
As I understand it from https://github.com/opencv/opencv/issues/13154 pkg-config support has been dropped or something. The correct 'new' mehtod is pkg-config --cflags --libs opencv4 according to tutorial.
pkg-config --cflags opencv4
-I/usr/include/opencv4/opencv -I/usr/include/opencv4
pkg-config --libs opencv4
-lopencv_dnn -lopencv_gapi -lopencv_highgui -lopencv_ml -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_video -lopencv_calib3d -lopencv_features2d -lopencv_flann -lopencv_videoio -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
Maybe that is bad somehow?
I would love to share the mk file but I cannot do it here, due to company rules. Can I mail it to you?
Alvaro Izaguirre Serrano
2020-3-11
Hi Hariprasad:
Here are the outputs of the command:
pkg-config --libs opencv
-lopencv_calib3d -lopencv_contrib -lopencv_core -lopencv_features2d -lopencv_flann -lopencv_gpu -lopencv_highgui -lopencv_imgproc -lopencv_legacy -lopencv_ml -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_superres -lopencv_ts -lopencv_video -lopencv_videostab -lopencv_facedetect -lopencv_imuvstab -lopencv_tegra -lopencv_vstab -lcufft -lnpps -lnppi -lnppc -lcudart -lstdc++ -lrt -lpthread -lm -ldl
pkg-config --cflags opencv
-I/usr/include/opencv
The path for the opencv.pc on the Nano is: /usr/shared/visionworks/sources
I attached the make file generated, I just modify the extension to be .txt instead of .mk, due to rules on the page.
Regards
Alvaro Izaguirre Serrano
2020-3-11
编辑:Alvaro Izaguirre Serrano
2020-3-11
Also using the commands suggested by David:
pkg-config --libs opencv4
-lopencv_dnn -lopencv_gapi -lopencv_highgui -lopencv_ml -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_video -lopencv_calib3d -lopencv_features2d -lopencv_flann -lopencv_videoio -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
pkg-config --cflags opencv4
-I/usr/include/opencv4/opencv -I/usr/include/opencv4
If I still move the opencv2 folder to be on the /usr/include folder and I do the following modification:
function out = segnet_predict(in)
%#codegen
% A persistent object mynet is used to load the DAG network object.
% At the first call to this function, the persistent object is constructed and
% setup. When the function is called subsequent times, the same object is reused
% to call predict on inputs, thus avoiding reconstructing and reloading the
% network object.
% Update buildinfo with the OpenCV library flags.
opencv_link_flags = '`pkg-config --cflags --libs opencv4`';
coder.updateBuildInfo('addLinkFlags',opencv_link_flags);
persistent mynet;
if isempty(mynet)
mynet = coder.loadDeepLearningNetwork('SegNet.mat');
end
% pass in input
out = predict(mynet,in);
Then I can compile succesfully, nevertheless when I execute the code with:
hwobj.runApplication('autoSemanticSegmentation','CamVid.avi');
Or directly in the nano with:
./autoSemanticSegmentation.elf CamVid.avi
The code is called autoSemanticSegmentation because I have modified the deep network and to adapt it to the Nano
Do you know why this is not working?, when I try to execute from Matlab I have the following:
>> hwobj.runApplication('autoSemanticSegmentation','CamVid.avi')
### Launching the executable on the target...
Executable launched successfully with process ID 9092.
Displaying the simple runtime log for the executable...
Note: For the complete log, run the following command in the MATLAB command window:
system(hwobj,'cat /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.log');
ans =
9092
And if I execute directly on the Nano I have the following:
Segmentation fault (core dumped)
What can be the issue?
This is the result from the compilation from Matlab:
Checking for CUDA availability on the Target...
Checking for 'nvcc' in the target system path...
Checking for cuDNN library availability on the Target...
Checking for TensorRT library availability on the Target...
Checking for prerequisite libraries is complete.
Gathering hardware details...
Checking for third-party library availability on the Target...
Gathering hardware details is complete.
Board name : NVIDIA Jetson TX1
CUDA Version : 10.0
cuDNN Version : 7.6
TensorRT Version : 6.0
GStreamer Version : 1.14.5
V4L2 Version : 1.14.2-1
SDL Version : 1.2
Available Webcams : FULL HD 1080P Webcam
Available GPUs : NVIDIA Tegra X1
### Using toolchain: NVCC for NVIDIA Embedded Processors
### '/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation_rtw.mk' is up to date
### Building 'autoSemanticSegmentation': make -f autoSemanticSegmentation_rtw.mk all
make: Entering directory '/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation'
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWElementwiseAffineLayer.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWElementwiseAffineLayer.cpp
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWFusedConvReLULayer.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWFusedConvReLULayer.cpp
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWMaxUnpoolingLayer.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWMaxUnpoolingLayer.cpp
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o cnn_api.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/cnn_api.cpp
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWCNNLayerImpl.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWCNNLayerImpl.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWElementwiseAffineLayerImpl.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWElementwiseAffineLayerImpl.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWElementwiseAffineLayerImplKernel.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWElementwiseAffineLayerImplKernel.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWFusedConvReLULayerImpl.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWFusedConvReLULayerImpl.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWMaxUnpoolingLayerImpl.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWMaxUnpoolingLayerImpl.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWMaxUnpoolingLayerImplKernel.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWMaxUnpoolingLayerImplKernel.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWTargetNetworkImpl.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWTargetNetworkImpl.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o main.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson/main.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o autoSemanticSegmentation_rtwutil.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation_rtwutil.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o autoSemanticSegmentation_data.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation_data.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o autoSemanticSegmentation_initialize.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation_initialize.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o autoSemanticSegmentation_terminate.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation_terminate.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o autoSemanticSegmentation.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/autoSemanticSegmentation.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o DeepLearningNetwork.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/DeepLearningNetwork.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o predict.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/predict.cu
nvcc -rdc=true -Xcudafe "--diag_suppress=unsigned_compare_with_zero" -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -c -Xcompiler -MMD,-MP -DMW_DL_DATA_PATH="/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation" -O2 -arch sm_35 -DMW_CUDA_ARCH=350 -D__MW_TARGET_USE_HARDWARE_RESOURCES_H__ -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -DARM_PROJECT -D_USE_TARGET_UDP_ -D_RUNONTARGETHARDWARE_BUILD_ -DSTACK_SIZE=200000 -DMODEL=autoSemanticSegmentation -DHAVESTDIO -DUSE_RTMODEL -DUNIX -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/segnet_deploy_Jetson -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/Documents/MATLAB/SupportPackages/R2019b/toolbox/target/supportpackages/nvidia/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/extern/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/simulink/include -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/src/ext_mode/common -I/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/usr/local/MATLAB/R2019b/rtw/c/ert -o MWCudaDimUtility.o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation/MWCudaDimUtility.cu
echo "### Creating standalone executable "/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.elf" ..."
### Creating standalone executable /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.elf ...
nvcc -lm -lrt -ldl -Xlinker -rpath,/usr/lib32 -Xnvlink -w -lcudart -lcuda -Wno-deprecated-gpu-targets `pkg-config --cflags --libs opencv4` -lcudnn -lcublas -arch sm_35 -lcufft -lcublas -o /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.elf MWElementwiseAffineLayer.o MWFusedConvReLULayer.o MWMaxUnpoolingLayer.o cnn_api.o MWCNNLayerImpl.o MWElementwiseAffineLayerImpl.o MWElementwiseAffineLayerImplKernel.o MWFusedConvReLULayerImpl.o MWMaxUnpoolingLayerImpl.o MWMaxUnpoolingLayerImplKernel.o MWTargetNetworkImpl.o main.o autoSemanticSegmentation_rtwutil.o autoSemanticSegmentation_data.o autoSemanticSegmentation_initialize.o autoSemanticSegmentation_terminate.o autoSemanticSegmentation.o DeepLearningNetwork.o predict.o MWCudaDimUtility.o -lm -lm
echo "### Created: /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.elf"
### Created: /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.elf
echo "### Successfully generated all binary outputs."
### Successfully generated all binary outputs.
make: Leaving directory '/home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/codegen/exe/autoSemanticSegmentation'
### Successfully generated all binary outputs.
Code generation successful: View report
### Launching the executable on the target...
Executable launched successfully with process ID 8711.
Displaying the simple runtime log for the executable...
Note: For the complete log, run the following command in the MATLAB command window:
system(hwobj,'cat /home/alvaro/remoteBuildDir/MATLAB_ws/R2019b/home/alvaro/SemanticSegmentation/Nano/autoSemanticSegmentation.log');
Hariprasad Ravishankar
2020-3-11
Hi Alvaro,
It would be good to test the generated code without any opencv dependencies to isolate the cause of the 'Segmentation fault'. You can generate an executable using the auto-generated main file and run the executable as follows.
cfg = coder.gpuConfig('exe');
cfg.GenerateExampleMain = 'GenerateCodeAndCompile';
You can comment the call to coder.updateBuildInfo('addLinkFlags',opencv_link_flags); in the design file when using the auto-generated main file.
Note that the auto-generated main file would pass in all zeros as inputs.
If you have a host gpu setup, you may test the executable on the host before deployment.
Alternatively, you may also generate code with debugging symbols by passing the '-g' flag to the codegen command. This can help in debugging issues using gdb or cuda-gdb.
@David, I do not see anything incorrect with the setup. Unfortunately my setup is using opencv 3.3.1 and from the example's prerequisites, it is perhaps also written for opencv 3.3.
You may try installing opencv 3.3 to see if it resolves the issue.
Hari
Alvaro Izaguirre Serrano
2020-3-11
Hi Hariprasad,
Doing this the .elf executes but does do anything, nevertheless it does not return any error.
Alvaro Izaguirre Serrano
2020-3-11
Hi Hariprasad,
Finally I was able to compile and execute, the issue of the access was that I have modified the trained network, nevertheless I updated the main.cu but one part still needed and update.
Now I only have one issue, the logi is running at 1.09 FPS, is there a way to speed up this?
Hariprasad Ravishankar
2020-3-11
Hi Alvaro,
Glad to know you got the example working. As for the performance, it may be limited by the network architecture and hardware used.
You can try training a new semantic segmentation network based on DeepLabV3+ and using a base network like resnet18 or mobilenetv2.
Side note: There are ways to overclock the CPU and GPU on Jetson boards using the jetson_clocks.sh script, which can help improve performance, but it would be good to proceed with caution when taking this route.
Hari
David Brisson Andersen
2020-3-16
Hi you two,
I have refalshed my jetson to jetpack 4.2.3 which includes the old opencv 3.something.something and now everything works.
\david
Hariprasad Ravishankar
2020-3-16
Hi David,
Thats good to know. We will look into updating the example to work with opencv4 in a future release.
Hari
Rodrigo Botelho
2022-7-31
编辑:Rodrigo Botelho
2024-7-8
Hi, Hariprasad,
I'm trying the Segnet demo from the Support Package, but I'm running into the same problem with opencv4.
I've tried the suggestion no.2, but I'm getting the same error.
Any udpates on how to use this demo with Opencv4?
rcbb
更多回答(2 个)
Liwei
2024-7-8
I am using the Matlab 2023b, I have no issue to compile the InceptionV3 classification to the Nvidia Jetson orin 32 GB. Now I got NVIDIA JETSON ORIN 64 BIT, I compiled the executable using GPU coder. I got issue. I upgraded matlab to 2024b, I have the same issue.
i am using the following codes
opencv_linkflags = '`pkg-config --cflags --libs opencv4`';
coder.updateBuildInfo('addLinkFlags',opencv_linkflags);
coder.updateBuildInfo('addIncludePaths', '/usr/include/opencv4/'); %both remove or add this line, the GPU coder compilation process does not work.
fatal error: opencv2/imgproc/imgproc.hpp: No such file or directory
57 | #include <opencv2/imgproc/imgproc.hpp>
Any idea to help solve the issue?
Ramakrishna Mandalapu
2024-9-12
Hi Lewei,
Can you check if you have OpenCV installed?
please run the below command on the terminal of the NVIDIA DRIVE hardware
pkg-config --cflags --libs opencv4
If not installed then please install.
Thanks
另请参阅
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!发生错误
由于页面发生更改,无法完成操作。请重新加载页面以查看其更新后的状态。
您也可以从以下列表中选择网站:
如何获得最佳网站性能
选择中国网站(中文或英文)以获得最佳网站性能。其他 MathWorks 国家/地区网站并未针对您所在位置的访问进行优化。
美洲
- América Latina (Español)
- Canada (English)
- United States (English)
欧洲
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom(English)
亚太
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)