Mobile Neural Network 2.1.0

pts/mnn-2.1.0 - 31 August 2022 - Update against MNN 2.1 upstream.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/alibaba/MNN/archive/refs/tags/2.1.0.tar.gz</URL>
      <MD5>6a5e963d9f7b5a70d2c9959b138e167c</MD5>
      <SHA256>2e4c337208c1ed4c60be23fd699dce5c23dade6ac6f509e35ef8ab5b4a81b2fa</SHA256>
      <FileName>MNN-2.1.0.tar.gz</FileName>
      <FileSize>18970119</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/bash

rm -rf MNN-2.1.0
tar -xf MNN-2.1.0.tar.gz
cd MNN-2.1.0
cd schema
./generate.sh
cd ..
mkdir build
cd build

EXTRA_CMAKE_FLAGS=""
if [ $OS_TYPE = "Linux" ]
then
    if grep avx512 /proc/cpuinfo > /dev/null
    then
	EXTRA_CMAKE_FLAGS="$EXTRA_CMAKE_FLAGS -DMNN_AVX512=ON"
    fi
    if grep avx512_vnni /proc/cpuinfo > /dev/null
    then
	EXTRA_CMAKE_FLAGS="$EXTRA_CMAKE_FLAGS -DMNN_AVX512_VNNI=ON"
    fi
fi

cmake .. -DMNN_BUILD_BENCHMARK=true -DCMAKE_BUILD_TYPE=Release -DMNN_OPENMP=ON $EXTRA_CMAKE_FLAGS
make -j $NUM_CPU_CORES
echo $? > ~/install-exit-status

cd ~/
cat>mnn<<EOT
#!/bin/sh
cd MNN-2.1.0/build
./benchmark.out ../benchmark/models/ 2000 100 0 \$NUM_CPU_CORES > \$LOG_FILE
echo \$? > ~/test-exit-status
EOT
chmod +x mnn

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>[ - ] nasnet.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>nasnet.mnn</LineHint>
    <ArgumentsDescription>Model: nasnet</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] mobilenetV3.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>mobilenetV3.mnn</LineHint>
    <ArgumentsDescription>Model: mobilenetV3</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] squeezenetv1.1.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>squeezenetv1.1.mnn</LineHint>
    <ArgumentsDescription>Model: squeezenetv1.1</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] resnet-v2-50.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>resnet-v2-50.mnn</LineHint>
    <ArgumentsDescription>Model: resnet-v2-50</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] SqueezeNetV1.0.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>SqueezeNetV1.0.mnn</LineHint>
    <ArgumentsDescription>Model: SqueezeNetV1.0</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] MobileNetV2_224.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>MobileNetV2_224.mnn</LineHint>
    <ArgumentsDescription>Model: MobileNetV2_224</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] mobilenet-v1-1.0.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>mobilenet-v1-1.0.mnn</LineHint>
    <ArgumentsDescription>Model: mobilenet-v1-1.0</ArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>[ - ] inception-v3.mnn                  max =  #_MAX_RESULT_# ms  min =   #_MIN_RESULT_# ms  avg =   #_RESULT_# ms</OutputTemplate>
    <LineHint>inception-v3.mnn</LineHint>
    <ArgumentsDescription>Model: inception-v3</ArgumentsDescription>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>Mobile Neural Network</Title>
    <AppVersion>2.1</AppVersion>
    <Description>MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions.</Description>
    <ResultScale>ms</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>2.1.0</Version>
    <SupportedPlatforms>Linux, MacOSX</SupportedPlatforms>
    <SoftwareType>Scientific</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>cmake, build-utilities</ExternalDependencies>
    <EnvironmentSize>2700</EnvironmentSize>
    <ProjectURL>https://www.mnn.zone/</ProjectURL>
    <RepositoryURL>https://github.com/alibaba/MNN/</RepositoryURL>
    <Maintainer>Michael Larabel</Maintainer>
  </TestProfile>
</PhoronixTestSuite>