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
- 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 |