Closed philleer closed 5 years ago
Got it, finally! And here is my solution: i google my problem and find these issues cuda runtime error (48): no kernel image is available for execution on the device and GPU 0: no kernel image is available for execution on the device according to the reply, i find line 10 in CMakeLists.txt, here is obviously commented for different gpu architecture, so i uncomment line 11 and comment line 10.
#set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v -std=c++11 --compiler-options -Wall -gencode arch=compute_30,code=sm_30 -gencode arch=compute_52,code=sm_52)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 --use_fast_math --ptxas-options=-v -std=c++11 --compiler-options -Wall -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61)
arch=60 and arch=61 is added just for compatibility according to nvcc fatal Then i recompile the project and now it works. BTW, i think a practical pipeline is needed for those who want to run the project quickly for experiments.
git clone https://github.com/kysucix/fusibile.git
cd fusibile
mkdir build
cd build
cmake ..
make
remember to config the CMakeLists.txt to make it works well on your machine, especially with graphical card. And after compilation you can run the executable as
./fusibile -input_folder /path/to/point_folder/ \
-p_folder /path/to/cam_folder/ \
-images_folder=/path/to/image_folder/ \
--depth_min=depth_min_value \
--depth_max=depth_max_value \
--normal_thresh=normal_thresh_value \
--disp_thresh=disp_thresh_value \
--num_consistent=num_consistent_value
The Compute Capability of NVIDIA GPUs can be found here. https://developer.nvidia.com/cuda-gpus#collapse4
Hi, @kysucix I come across such problem when i want to do post processing of the output from mvenet there is a py script to utilize the executable binary fusibile here is the output
i have 49 images in my images folder and i have got no result folder or files after the executation btw,i run it on my ubuntu 16.04 system with GTX1080 graphic card tensorflow-gpu 1.5.0+cuda9.0+cudnn7.0 opencv 4.0.0 gcc 5.4.0 what wrong with it, and any idea how to fix it? much thx for your help