NCNN 1.0.1

pts/ncnn-1.0.1 - 18 September 2020 - Increase the run count.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.0.0m2-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/Tencent/ncnn/archive/20200916.tar.gz</URL>
      <MD5>d93c0b057066540f52bae858701f8ee0</MD5>
      <SHA256>a7abc03c9acdaa1b4f85ce3f80722822f9eacc0efefc9dfef1e253fdb23d0f80</SHA256>
      <FileName>ncnn-20200916.tar.gz</FileName>
      <FileSize>11121685</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf ncnn-20200916.tar.gz
cd ncnn-20200916

# Workaround for build issues with current release
echo "cmake_minimum_required(VERSION 3.1) # for CMAKE_CXX_STANDARD
set(CMAKE_CXX_STANDARD 11)

add_subdirectory(caffe)
add_subdirectory(mxnet)
add_subdirectory(onnx)
add_subdirectory(darknet)
add_subdirectory(quantize)
" > tools/CMakeLists.txt

echo "" > tools/caffe/CMakeLists.txt
echo "" > tools/onnx/CMakeLists.txt

mkdir build
cd build

# Vulkan build currently not enabled due to hitting seg fault...
cmake ..
make -j $NUM_CPU_CORES
# sometimes parallel build seems to fail, so do another make in case
make
echo $? > ~/install-exit-status

cp ../benchmark/*.param benchmark/

cd ~/
cat>ncnn<<EOT
#!/bin/sh
cd ncnn-20200916/build/benchmark
./benchncnn 200 \$NUM_CPU_CORES 0 \$@ 0  > \$LOG_FILE 2>&1
echo \$? > ~/test-exit-status
EOT
chmod +x ncnn

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.0.0m2-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>          squeezenet_int8  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>squeezenet_int8</LineHint>
    <AppendToArgumentsDescription>Model: squeezenet_int8</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          mobilenet_v3  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>mobilenet_v3</LineHint>
    <AppendToArgumentsDescription>Model: mobilenet_v3</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          shufflenet_v2  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>shufflenet_v2</LineHint>
    <AppendToArgumentsDescription>Model: squeezenet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          mnasnet  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>mnasnet</LineHint>
    <AppendToArgumentsDescription>Model: mnasnet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          blazeface  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>blazeface</LineHint>
    <AppendToArgumentsDescription>Model: blazeface</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          googlenet_int8  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>googlenet_int8</LineHint>
    <AppendToArgumentsDescription>Model: googlenet_int8</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          vgg16_int8  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>vgg16_int8</LineHint>
    <AppendToArgumentsDescription>Model: vgg16_int8</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          resnet18_int8  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>resnet18_int8</LineHint>
    <AppendToArgumentsDescription>Model: resnet18_int8</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          alexnet  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>alexnet</LineHint>
    <AppendToArgumentsDescription>Model: alexnet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          resnet50_int8  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>resnet50_int8</LineHint>
    <AppendToArgumentsDescription>Model: resnet50_int8</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          mobilenetv2_yolov3  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>mobilenetv2_yolov3</LineHint>
    <AppendToArgumentsDescription>Model: mobilenetv2_yolov3</AppendToArgumentsDescription>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.0.0m2-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>NCNN</Title>
    <AppVersion>20200916</AppVersion>
    <Description>NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent.</Description>
    <ResultScale>ms</ResultScale>
    <Proportion>LIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.0.1</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Scientific</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>cmake, build-utilities, protobuf, opencv</ExternalDependencies>
    <EnvironmentSize>2700</EnvironmentSize>
    <ProjectURL>https://github.com/Tencent/ncnn</ProjectURL>
    <Maintainer>Michael Larabel</Maintainer>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Target</DisplayName>
      <Identifier>target</Identifier>
      <Menu>
        <Entry>
          <Name>CPU</Name>
          <Value>-1</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>