TensorFlow Lite 1.1.0

pts/tensorflow-lite-1.1.0 - 19 May 2022 - Update against latest upstream nightly.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.3-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>http://www.phoronix-test-suite.com/benchmark-files/tf-lite-20220518.tar.xz</URL>
      <MD5>dd5f65466ca96c640370064cd61f6ff9</MD5>
      <SHA256>f7f8c0ee4fa5b78b0943b7d10f9141fbc714dc16a7cdca54b42ea81ec8b9d590</SHA256>
      <FileName>tf-lite-20220518.tar.xz</FileName>
      <FileSize>2437736</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz</URL>
      <MD5>bc1d3a910cc3f70d94e5d608fc5e9590</MD5>
      <SHA256>2fadeabb9968ec6833bee903900dda6e61b3947200535874ce2fe42a8493abc0</SHA256>
      <FileName>mobilenet_v1_1.0_224.tgz</FileName>
      <FileSize>94321559</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz</URL>
      <MD5>36af340c00e60291931cb30ce32d4e86</MD5>
      <SHA256>d32432d28673a936b2d6281ab0600c71cf7226dfe4cdcef3012555f691744166</SHA256>
      <FileName>mobilenet_v1_1.0_224_quant.tgz</FileName>
      <FileSize>35069912</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz</URL>
      <MD5>398345d7af082c173d90989d44d856db</MD5>
      <SHA256>b3a3c5471f23f165e49fe0c2e56a3c503eeaa6f85d97f22369e3c36088e127c5</SHA256>
      <FileName>nasnet_mobile_2018_04_27.tgz</FileName>
      <FileSize>39558480</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz</URL>
      <MD5>8effe92b879970cad0953868401cb2b2</MD5>
      <SHA256>75fc495b2792db6edccdc3a6f1bced19622b59dcc8835f386ab39f481ca1db9c</SHA256>
      <FileName>squeezenet_2018_04_27.tgz</FileName>
      <FileSize>9298056</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz</URL>
      <MD5>59d4080bd81c1b675d2124672d8afe4c</MD5>
      <SHA256>fb16b93ff2b2bcda0da5cdfd25a8d5b8b74438943dae738db659bad0d3d48ff1</SHA256>
      <FileName>inception_resnet_v2_2018_04_27.tgz</FileName>
      <FileSize>225882079</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz</URL>
      <MD5>97da95494e4a4d755cf79d636c726bcb</MD5>
      <SHA256>305e45035c690f7a064b5babe27ea68a4e6da5819147c7c94319963c6f256467</SHA256>
      <FileName>inception_v4_2018_04_27.tgz</FileName>
      <FileSize>317324155</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf tf-lite-20220518.tar.xz

tar -xf mobilenet_v1_1.0_224.tgz
tar -xf mobilenet_v1_1.0_224_quant.tgz
tar -xf nasnet_mobile_2018_04_27.tgz
tar -xf squeezenet_2018_04_27.tgz
tar -xf inception_resnet_v2_2018_04_27.tgz
tar -xf inception_v4_2018_04_27.tgz

echo "#!/bin/sh
if [ \$OS_ARCH = \"aarch64\" ]
then
	./linux_aarch64_benchmark_model --num_threads=\$NUM_CPU_CORES \$@ > \$LOG_FILE 2>&1
else
	./linux_x86-64_benchmark_model --num_threads=\$NUM_CPU_CORES \$@ > \$LOG_FILE 2>&1
fi

echo \$? > ~/test-exit-status" > tensorflow-lite
chmod +x tensorflow-lite

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.3-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>Inference timings in us: Init: 317, First inference: 269835, Warmup (avg): 241235, Inference (avg): #_RESULT_#</OutputTemplate>
    <LineHint>Inference timings in us</LineHint>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.3-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>TensorFlow Lite</Title>
    <AppVersion>2022-05-18</AppVersion>
    <Description>This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time.</Description>
    <ResultScale>Microseconds</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.1.0</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Benchmark</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <EnvironmentSize>1500</EnvironmentSize>
    <ProjectURL>https://www.tensorflow.org/lite/performance/measurement</ProjectURL>
    <RepositoryURL>https://github.com/tensorflow/tensorflow</RepositoryURL>
    <InternalTags>SMP</InternalTags>
    <Maintainer>Michael Larabel</Maintainer>
  </TestProfile>
  <TestSettings>
    <Default>
      <Arguments>--warmup_runs=5  --num_runs=50 --min_secs=60</Arguments>
    </Default>
    <Option>
      <DisplayName>Model</DisplayName>
      <Identifier>model</Identifier>
      <ArgumentPrefix>--graph=</ArgumentPrefix>
      <Menu>
        <Entry>
          <Name>Mobilenet Float</Name>
          <Value>mobilenet_v1_1.0_224.tflite</Value>
        </Entry>
        <Entry>
          <Name>Mobilenet Quant</Name>
          <Value>mobilenet_v1_1.0_224_quant.tflite</Value>
        </Entry>
        <Entry>
          <Name>NASNet Mobile</Name>
          <Value>nasnet_mobile.tflite</Value>
        </Entry>
        <Entry>
          <Name>SqueezeNet</Name>
          <Value>squeezenet.tflite</Value>
        </Entry>
        <Entry>
          <Name>Inception ResNet V2</Name>
          <Value>inception_resnet_v2.tflite</Value>
        </Entry>
        <Entry>
          <Name>Inception V4</Name>
          <Value>inception_v4.tflite</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>