jiadilee/ncnn-with-cuda

Tencent NCNN with added CUDA support

This project implements GPU NVIDIA CUDA inference support for well known Tencent NCNN inference engine. Many of the Edge AI projects on NVIDIA Jetson family of devices could benefit from this support.

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Development Status

Following layers have been currently implemented in CUDA: AbsVal, BatchNorm, Bias, BinaryOp, BNLL, Concat, Convolution, ConvolutionDepthWise, Crop, Flatten, InnerProduct, Input, Packing, Padding, Pooling, Quantize, ReLU, Reshape, Softmax, Split

Development plan for the near future:

• Cuda implementation of layers Eltwise, HardSigmoid, HardSwish, Interp, Scale, Yolov3DetectionOutput
• Further optimization of existing CUDA layers (with the goal to beat Vulkan performance ;) )

For usecases where some CUDA layer implementation is missing, CPU/GPU data ping-pong will slow the execution significantly.

Develop branch is used for active development. Development of new layers is performed on develop_<layer_name> branch which is squashed before merging to develop branch. Occasionaly upstream updates and fixes would be added to the project.

Build and test

Build project:

git clone https://github.com/atanmarko/ncnn-with-cuda
cd ncnn-with-cuda
mkdir build
cd build
cmake -DNCNN_VULKAN=OFF -DNCNN_CUDA=ON -DLOG_LAYERS=OFF ..
make

Test particular layer

To run test for particular layer, where CPU vs CUDA implementation and execution speed is tested (<layer_name> is name of the CUDA layer in small case, e.g. test_convolution):

cd build/tests
./test_<layer_name>

Check which layers are not executed on CUDA

Build project with turned on LOG_LAYERS config parameter:

cmake -DNCNN_VULKAN=OFF -DNCNN_CUDA=ON -DLOG_LAYERS=ON ..

Run the particular example or network benchmark and grep for non cuda layers:

./retinaface <path to image file> | grep forward | grep -v cuda

Run retinaface test program:

Copy mnet.25-opt.bin and mnet.25-opt.param files to the build/examples directory available here:

cd build/examples
./retinaface <path_to_picture_file>

Benchmark Retinaface:

Copy mnet.25-opt.bin and mnet.25-opt.param files to the build/benchmark directory.

cd build/benchmark
./retinaface-benchmark <path_to_picture_file>

It will run 10 loops of Retinaface face detection and print inference timing results. Retinaface stride 32 has all the layers implemented in CUDA.

	Image Size	Stride	CPU av. time (us)	Vulkan av. time (us)	CUDA av. time (us)
i7-4790, GTX 1060	640x480	32	28.90	33.20	31.10
i7-4790, GTX 1060	1280x720	32	92.70	96.90	54.00
i7-4790, GTX 1060	1920x1080	32	167.50	204.50	91.70
Jetson AGX Xavier	640x480	32	373.20	402.10	343.60
Jetson AGX Xavier	1280x720	32	508.30	738.40	327.60
Jetson AGX Xavier	1920x1080	32	812.00	934.70	436.70

License

NCNN CUDA implementation: BSD 3 Clause

Original NCNN Licence: BSD 3 Clause