ncnn-master.zip

benchncnn can be used to test neural network inference performance Only the network definition files (ncnn param) are required. The large model binary files (ncnn bin) are not loaded but generated randomly for speed test. More model networks may be added later. --- Build ```shell # assume you have already build ncnn library successfully # uncomment the following line in <ncnn-root-dir>/CMakeLists.txt with your favorite editor # add_subdirectory(benchmark) cd <ncnn-root-dir>/<your-build-dir> make -j4 # you can find benchncnn binary in <ncnn-root-dir>/<your-build-dir>/benchmark ``` Usage ```shell # copy all param files to the current directory ./benchncnn [loop count] [num threads] [powersave] [gpu device] [cooling down] ``` run benchncnn on android device ```shell # for running on android device, upload to /data/local/tmp/ folder adb push benchncnn /data/local/tmp/ adb push <ncnn-root-dir>/benchmark/*.param /data/local/tmp/ adb shell # executed in android adb shell cd /data/local/tmp/ ./benchncnn [loop count] [num threads] [powersave] [gpu device] [cooling down] ``` Parameter |param|options|default| |---|---|---| |loop count|1~N|4| |num threads|1~N|max_cpu_count| |powersave|0=all cores, 1=little cores only, 2=big cores only|0| |gpu device|-1=cpu-only, 0=gpu0, 1=gpu1 ...|-1| |cooling down|0=disable, 1=enable|1| Tips: Disable android UI server and set CPU and GPU to max frequency ```shell # stopping android ui server, can be retarted later via adb shell start adb root adb shell stop # executed in android adb shell # set cpu performance mode echo "performance" > /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor echo "performance" > /sys/devices/system/cpu/cpu1/cpufreq/scaling_governor echo "performance" > /sys/devices/system/cpu/cpu2/cpufreq/scaling_governor echo "performance" > /sys/devices/system/cpu/cpu3/cpufreq/scaling_governor echo "performance" > /sys/devices/system/cpu/cpu4/cpufreq/scaling_governor echo "performance" > /sys/devices/system/cpu/cpu5/cpufreq/scaling_governor # set gpu performance mode (eg. RK3399) echo "performance" > /sys/class/misc/mali0/device/devfreq/ff9a0000.gpu/governor # set gpu performance mode (eg. Android Adreno) echo 1 > /sys/class/kgsl/kgsl-3d0/force_clk_on echo 10000000 > /sys/class/kgsl/kgsl-3d0/idle_timer echo "performance" > /sys/class/kgsl/kgsl-3d0/devfreq/governor echo <max freq> > /sys/class/kgsl/kgsl-3d0/gpuclk ``` --- Typical output (executed in android adb shell) ### NVIDIA Jetson AGX Orin (Cortex-A78AE 2.2 GHz x 12 + [email protected] GHz Tensor Cores 64) ``` i@orin:~/projects/ncnn/benchmark$ ./benchncnn 64 1 0 -1 0 loop_count = 64 num_threads = 1 powersave = 0 gpu_device = -1 cooling_down = 0 squeezenet min = 11.66 max = 11.80 avg = 11.74 squeezenet_int8 min = 12.24 max = 12.39 avg = 12.31 mobilenet min = 19.56 max = 19.73 avg = 19.65 mobilenet_int8 min = 16.06 max = 16.25 avg = 16.14 mobilenet_v2 min = 13.20 max = 13.41 avg = 13.29 mobilenet_v3 min = 11.39 max = 11.57 avg = 11.48 shufflenet min = 8.07 max = 8.18 avg = 8.11 shufflenet_v2 min = 8.41 max = 8.51 avg = 8.45 mnasnet min = 12.74 max = 12.91 avg = 12.79 proxylessnasnet min = 15.18 max = 15.32 avg = 15.25 efficientnet_b0 min = 26.86 max = 26.96 avg = 26.90 efficientnetv2_b0 min = 35.99 max = 36.15 avg = 36.07 regnety_400m min = 16.81 max = 16.98 avg = 16.87 blazeface min = 4.25 max = 4.37 avg = 4.29 googlenet min = 48.73 max = 48.98 avg = 48.87 googlenet_int8 min = 47.39 max = 47.60 avg = 47.49 resnet18 min = 30.93 max = 31.24 avg = 31.08 resnet18_int8 min = 55.44 max = 55.70 avg = 55.56 alexnet min = 44.19 max = 44.43 avg = 44.33 vgg16 min = 173.94 max = 174.97 avg = 174.46 vgg16_int8 min = 475.10 max = 479.37 avg = 477.33 resnet50 min = 89.50 max = 90.11 avg = 89.80 resnet50_int8 min = 106.77 max = 107.14 avg = 106.96 squeezenet_ssd min = 37.78 max = 38.35 avg = 37.93 squeezenet_ssd_int8 min = 50.48 max = 50.88 avg = 50.74 mobilenet_ssd min = 45.62 max = 46.12 avg = 45.74 mobilenet_ssd_int8 min = 37.77 max = 38.00 avg = 37.88 mobilenet_yolo min = 90.23 max = 90.49 avg = 90.35 mobilenetv2_yolov3 min = 47.27 max = 47.48 avg = 47.33 yolov4-tiny min = 60.41 max = 60.75 avg = 60.57 nanodet_m min = 19.26 max = 19.43 avg = 19.35 yolo-fastest-1.1 min = 8.16 max = 8.31 avg = 8.20 yolo-fastestv2 min = 8.26 max = 8.39 avg = 8.32 i@orin:~/projects/ncnn/benchmark$ ./benchncnn 64 2 0 -1 0 loop_count = 64 num_threads = 2 powersave = 0 gpu_device = -1 cooling_down = 0 squeezenet min = 6.83 max = 6.98 avg = 6.90 squeezenet_int8 min = 7.39 max = 7.50 avg = 7.45 mobilenet min = 10.40 max = 10.50 avg = 10.45 mobilenet_int8 min = 8.92 max = 9.09 avg = 8.99 mobilenet_v2 min = 7.67 max = 7.80 avg = 7.74 mobilenet_v3 min = 6.86 max = 7.01 avg = 6.93 shufflenet min = 6.34 max = 6.44 avg = 6.39 shufflenet_v2 min = 5.71 max = 5.83 avg = 5.76 mnasnet min = 7.47 max = 7.58 avg = 7.53 proxylessnasnet min = 8.73 max = 8.83 avg = 8.78 efficientnet_b0 min = 14.93 max = 15.13 avg = 15.03 efficientnetv2_b0 min = 20.17 max = 20.70 avg = 20.29 regnety_400m min = 12.50 max = 12.62 avg = 12.57 blazeface min = 2.95 max = 3.06 avg = 3.00 googlenet min = 26.25 max = 26.53 avg = 26.37 googlenet_int8 min = 26.54 max = 26.79 avg = 26.66 resnet18 min = 16.69 max = 16.90 avg = 16.80 resnet18_int8 min = 29.70 max = 29.93 avg = 29.81 alexnet min = 22.96 max = 23.12 avg = 23.03 vgg16 min = 88.39 max = 89.16 avg = 88.79 vgg16_int8 min = 245.86 max = 247.55 avg = 246.62 resnet50 min = 46.55 max = 46.86 avg = 46.70 resnet50_int8 min = 56.28 max = 56.63 avg = 56.43 squeezenet_ssd min = 23.65 max = 24.29 avg = 23.81 squeezenet_ssd_int8 min = 30.86 max = 31.27 avg = 30.99 mobilenet_ssd min = 25.17 max = 25.31 avg = 25.24 mobilenet_ssd_int8 min = 21.77 max = 21.97 avg = 21.84 mobilenet_yolo min = 48.03 max = 48.33 avg = 48.14 mobilenetv2_yolov3 min = 26.58 max = 26.81 avg = 26.66 yolov4-tiny min = 35.31 max = 35.53 avg = 35.41 nanodet_m min = 12.93 max = 13.08 avg = 13.01 yolo-fastest-1.1 min = 6.00 max = 6.10 avg = 6.04 yolo-fastestv2 min = 6.46 max = 6.61 avg = 6.52 i@orin:~/projects/ncnn/benchmark$ ./benchncnn 64 4 0 -1 0 loop_count = 64 num_threads = 4 powersave = 0 gpu_device = -1 cooling_down = 0 squeezenet min = 4.54 max = 4.84 avg = 4.61 squeezenet_int8 min = 4.96 max = 5.41 avg = 5.05 mobilenet min = 5.96 max = 6.23 avg = 6.04 mobilenet_int8 min = 5.21 max = 5.50 avg = 5.30 mobilenet_v2 min = 5.05 max = 5.26 avg = 5.15 mobilenet_v3 min = 4.83 max = 5.14 avg = 4.90 shufflenet min = 5.11 max = 5.34 avg = 5.18 shufflenet_v2 min = 4.13 max = 4.44 avg = 4.18 mnasnet min = 4.93 max = 5.27 avg = 5.01 proxylessnasnet min = 5.64 max = 5.89 avg = 5.72 efficient