LiteRT 1.0.0

pts/litert-1.0.0 - 15 October 2024 - Initial commit of LiteRT now that TensorFlow Lite rebranded, fork from pts/tensorflow-lite test profile.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.5-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>http://www.phoronix-test-suite.com/benchmark-files/litert-20241015.tar.xz</URL>
      <MD5>6953575983266715397dcb3f0342a28a</MD5>
      <SHA256>6eb2a6751c613c2d6c2dde347b78406c0df00227443f46943e04ba61af563349</SHA256>
      <FileName>litert-20241015.tar.xz</FileName>
      <FileSize>3626644</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>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip</URL>
      <MD5>3764f289165250252d2323d718c04d83</MD5>
      <SHA256>a809cd290b4d6a2e8a9d5dad076e0bd695b8091974e0eed1052b480b2f21b6dc</SHA256>
      <FileName>coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip</FileName>
      <FileSize>2807218</FileSize>
    </Package>
    <Package>
      <URL>https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/deeplabv3_257_mv_gpu.tflite</URL>
      <MD5>4a24db5a5fb05c47586a1197765e8548</MD5>
      <SHA256>68a539782c2c6a72f8aac3724600124a85ed977162b44e84cbae5db717c933c6</SHA256>
      <FileName>deeplabv3_257_mv_gpu.tflite</FileName>
      <FileSize>2779264</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh
tar -xf litert-20241015.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
unzip -o coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip
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" > litert
chmod +x litert

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.5-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>INFO: Inference timings in us: Init: 6490, First inference: 2195, Warmup (avg): 1528.67, Inference (avg): #_RESULT_#</OutputTemplate>
    <LineHint>Inference timings in us</LineHint>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.5-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>LiteRT</Title>
    <AppVersion>2024-10-15</AppVersion>
    <Description>This is a benchmark of LiteRT (formerly known as TensorFlow Lite) focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This LiteRT benchmark test profile is measuring the average inference time.</Description>
    <ResultScale>Microseconds</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.0.0</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Benchmark</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <EnvironmentSize>1500</EnvironmentSize>
    <ProjectURL>https://ai.google.dev/edge/litert/models/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=50</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>
        <Entry>
          <Name>Quantized COCO SSD MobileNet v1</Name>
          <Value>detect.tflite</Value>
        </Entry>
        <Entry>
          <Name>DeepLab V3</Name>
          <Value>deeplabv3_257_mv_gpu.tflite</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>