可以参考benchmark_tools,推荐一键benchmark

测试环境

  • 测试模型
    • fp32模型
      • mobilenet_v1
      • mobilenet_v2
      • squeezenet_v1.1
      • mnasnet
      • shufflenet_v2
    • int8模型
      • mobilenet_v1
      • mobilenet_v2
      • resnet50
  • 测试机器(android ndk ndk-r17c)
    • 骁龙855
      • xiaomi mi9, snapdragon 855
      • 4xA76(1@2.84GHz + 3@2.4GHz) + 4xA55@1.78GHz
    • 骁龙845
      • xiaomi mi8, 845
      • 2.8GHz(大四核),1.7GHz(小四核)
    • 骁龙835
      • xiaomi mix2, snapdragon 835
      • 2.45GHz(大四核),1.9GHz(小四核)
    • 骁龙625
      • oppo R9s, snapdragon625
      • A53 x 8, big core@2.0GHz
    • 骁龙653
      • 360 N5, snapdragon 653
      • 4 x A73@2.0GHz + 4 x A53@1.4GHz
    • 麒麟970
      • HUAWEI Mate10
  • 测试说明
    • branch: release/2.0.0
    • warmup=10, repeats=30,统计平均时间,单位是ms
    • 当线程数为1时,DeviceInfo::Global().SetRunMode设置LITE_POWER_HIGH,否者设置LITE_POWER_NO_BIND
    • 模型的输入图像的维度是{1, 3, 224, 224},输入图像的每一位数值是1

测试数据

fp32模型测试数据

paddlepaddle model

骁龙855 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 32.19 18.81 10.90 30.92 18.31 10.15
mobilenet_v2 22.91 13.75 8.64 21.15 12.79 7.84
shufflenet_v2 4.67 3.37 2.65 4.43 3.15 2.66
squeezenet_v1.1 25.10 15.93 9.68 23.28 14.61 8.71
mnasnet 21.84 13.14 7.96 19.61 11.88 7.55
骁龙835 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 94.13 52.17 30.68 88.28 47.58 26.64
mobilenet_v2 61.24 34.64 22.36 56.66 32.19 19.63
shufflenet_v2 10.87 6.92 5.12 10.41 6.76 4.97
squeezenet_v1.1 73.61 42.25 24.44 64.87 38.43 23.06
mnasnet 58.22 33.43 20.44 53.43 30.20 18.09
麒麟980 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 55.11 28.24 13.27 34.24 17.74 12.41
mobilenet_v2 37.03 19.80 51.94 23.64 12.98 9.38
shufflenet_v2 7.26 4.94 15.06 5.32 3.33 2.82
squeezenet_v1.1 42.73 23.66 57.39 26.03 14.53 13.66
mnasnet 36.87 20.15 46.04 21.85 12.06 8.68
麒麟970 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 97.80 52.64 34.46 94.51 49.36 28.43
mobilenet_v2 66.55 38.52 23.19 62.89 34.93 21.53
shufflenet_v2 13.78 8.11 5.93 11.95 7.90 5.91
squeezenet_v1.1 77.64 43.67 25.72 69.91 40.66 24.62
mnasnet 61.86 34.62 22.68 59.61 32.79 19.56

caffe model

骁龙855 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 32.42 18.68 10.86 30.92 18.35 10.07
mobilenet_v2 29.53 17.76 10.89 27.19 16.53 9.75
shufflenet_v2 4.61 3.29 2.61 4.36 3.11 2.51
骁龙835 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 92.52 52.34 30.37 88.31 49.75 27.29
mobilenet_v2 79.50 45.67 28.79 76.13 44.01 26.13
shufflenet_v2 10.94 7.08 5.16 10.64 6.83 5.01
麒麟980 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 55.36 28.18 13.31 34.42 17.93 12.52
mobilenet_v2 49.17 26.10 65.49 30.50 16.66 11.72
shufflenet_v2 8.45 5.00 15.65 4.58 3.14 2.83
麒麟970 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 97.85 53.38 33.85 94.29 49.42 28.29
mobilenet_v2 87.40 50.25 31.85 85.55 48.11 28.24
shufflenet_v2 12.16 8.39 6.21 12.21 8.33 6.32

int8量化模型测试数据

骁龙855 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 36.80 21.58 11.12 14.01 8.13 4.32
mobilenet_v2 28.72 19.08 12.49 17.24 11.55 7.82
骁龙835 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 60.76 32.25 16.66 56.57 29.84 15.24
mobilenet_v2 49.38 31.10 22.07 47.52 28.18 19.24
麒麟970 armv7     armv8    
threads num 1 2 4 1 2 4
mobilenet_v1 65.95 34.39 18.68 60.86 30.98 16.31
mobilenet_v2 68.87 39.39 24.43 65.57 37.31 20.87