Darknet-cpp-opencl

Darknet-cpp-opencl project is a fork of the bug-fixed and C++ compilable version of darknet, an open source neural network framework written in C, CUDA and now with OpenCL support. Darknet-cpp-opencl builds and is tested on Linux. It should also work on Windows and Mac, but is still untested.

Features

Uses same code-base as original darknet (ie same .c files are used). Modification is done only for runtime bug-fixes, compile time fixes for c++, and the build system itself. The OpenCL support is realised by adding new .c and .cl files which complement the cuda and cpu implementation.

The Linux build system supports 3 targets

• original darknet (with gcc compiler)
• darknet-cpp (with g++ compiler and Visual Studio compiler)
• Shared library (libdarknet-cpp-shared.so)

The Linux build system also supports 3 computation backends

• The original cpu implementation
• The original cuda implementation
• The new OpenCL implementation

For questions about this port, please use the Google group

Requirements

The new OpenCL backend needs the AMD clBLAS libraries as a replacement for the cuda BLAS libraries. You can find these libraries here. Also you should install your OpenCL drivers.

Also catch is needed for the unit testing and can be found here - catch

On Ubuntu you can run the following apt-get command

• sudo apt-get install libclblas2 clblas-client libclblas-dev catch

For the OpenCL drivers please follow the instructions given by your OpenCL vendor

• AMD
• INTEL
• NVIDIA

Usage

Using the Makefile in the root directory of the darknet source repository,

• make darknet - darknet, without any GPU support.
• make darknet GPU=1 CUDA=1 - darknet, with cuda support.
• make darknet GPU=1 OPENCL=1 - darknet, with OpenCL support.
• make darknet-unit GPU=1 GPU_UNIT=1 OPENCL=1 - Builds the cpu/gpu comparison unit tests.
• make darknet-unit GPU=1 GPU_UNIT=1 CUDA=1 - Same as above but with cuda support.
• make darknet-cpp - The cpp fixes from the darknet-cpp port are also available.

Please begin by runnning darknet-unit and verifying that your opencl platform is available.

CMake

** Caution CMake support is experimental **

You can also use cmake to generate a Makefile. Be warned just running the cmake command will replace the hand crafted makefile. Instead use the following commands to generate a makefile:

• mkdir build - Create a build folder
• cmake -Bbuild -H. -DDARKNET_OPENCL:BOOL=ON - Generate the makefiles
• cmake --build build - Compile the darknet sources
• cp build/darknet . - Place the binary where it should be
• ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg - Try it out

Training the darknet with opencl support

**Training a network should be identical to the process described here. But this is still untested. **

How to file issues

For issues with the darknet-cpp port, use the link - darknet-cpp

For issues with the darknet-cpp-opencl port, use the link - darknet-cpp-opencl

For general issues with the darknet use the original mailing list - darknet

Information required for filing an issue:

• Options enabled in Makefile
• Did the unit test run?
• Platform being used (OS version, GPU type, OpenCL version, and OpenCV version)

Darknet-cpp for Windows

I have not tested this on windows yet, but it should still work.

The solution file requires the below repository.

https://github.com/prabindh/darknet-cpp-windows

The Windows port does not require any additional downloads (like pthreads), and builds the same darknet code-base for Windows, to generate the darknet.dll. Building the Arapaho C++ API and test wrapper, creates arapaho.exe, that works exactly the same way as arapaho on Linux.

Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

For more information see the Darknet project website.

For questions or issues please use the Google Group.