blob: 613e9f97c38942f20d3ca44cdc69e72b35c8608f (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
book_path: /mobile/_book.yaml
project_path: /mobile/_project.yaml
# Performance
This document lists TensorFlow Lite performance benchmarks when running well
known models on some Android and iOS devices.
These performance benchmark numbers were generated with the
[Android TFLite benchmark binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark)
and the [iOS benchmark app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios).
# Android performance benchmarks
For Android benchmarks, the CPU affinity is set to use big cores on the device to
reduce variance (see [details](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#reducing-variance-between-runs-on-android)).
It assumes that models were download and unzipped to the
`/data/local/tmp/tflite_models` directory. The benchmark binary is built
using [these instructions](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#on-android)
and assumed in the `/data/local/tmp` directory.
To run the benchmark:
```
adb shell taskset ${CPU_MASK} /data/local/tmp/benchmark_model \
--num_threads=1 \
--graph=/data/local/tmp/tflite_models/${GRAPH} \
--warmup_runs=1 \
--num_runs=50 \
--use_nnapi=false
```
Here, `${GRAPH}` is the name of model and `${CPU_MASK}` is the CPU affinity
chosen according to the following table:
Device | CPU_MASK |
-------| ----------
Pixel 2 | f0 |
Pixel xl | 0c |
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Device </th>
<th>Mean inference time (std dev)</th>
</tr>
</thead>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>Pixel 2 </td>
<td>166.5 ms (2.6 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>122.9 ms (1.8 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz">Mobilenet_1.0_224 (quant)</a>
</td>
<td>Pixel 2 </td>
<td>69.5 ms (0.9 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>78.9 ms (2.2 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz">NASNet mobile</a>
</td>
<td>Pixel 2 </td>
<td>273.8 ms (3.5 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>210.8 ms (4.2 ms)</td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz">SqueezeNet</a>
</td>
<td>Pixel 2 </td>
<td>234.0 ms (2.1 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>158.0 ms (2.1 ms)</td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz">Inception_ResNet_V2</a>
</td>
<td>Pixel 2 </td>
<td>2846.0 ms (15.0 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>1973.0 ms (15.0 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz">Inception_V4</a>
</td>
<td>Pixel 2 </td>
<td>3180.0 ms (11.7 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>2262.0 ms (21.0 ms) </td>
</tr>
</table>
# iOS benchmarks
To run iOS benchmarks, the [benchmark
app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios)
was modified to include the appropriate model and `benchmark_params.json` was
modified to set `num_threads` to 1.
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Device </th>
<th>Mean inference time (std dev)</th>
</tr>
</thead>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>iPhone 8 </td>
<td>32.2 ms (0.8 ms)</td>
</tr>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)">Mobilenet_1.0_224 (quant)</a>
</td>
<td>iPhone 8 </td>
<td>24.4 ms (0.8 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz">NASNet mobile</a>
</td>
<td>iPhone 8 </td>
<td>60.3 ms (0.6 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz">SqueezeNet</a>
</td>
<td>iPhone 8 </td>
<td>44.3 (0.7 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz">Inception_ResNet_V2</a>
</td>
<td>iPhone 8</td>
<td>562.4 ms (18.2 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz">Inception_V4</a>
</td>
<td>iPhone 8 </td>
<td>661.0 ms (29.2 ms)</td>
</tr>
</table>
|
