NCNN 1.3.0

pts/ncnn-1.3.0 - 27 July 2021 - Update against NCNN 2021-07-20 upstream, fix possible Vulkan build issue by including glslang source tree.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.4.0-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/Tencent/ncnn/archive/refs/tags/20210720.tar.gz</URL>
      <MD5>c65846784f5b8b615267f6e3c8449d71</MD5>
      <SHA256>a3f356a4efbdd26bac3f36229efb39d3d87e7d82b3bd855dbd2316152ce27024</SHA256>
      <FileName>ncnn-20210720.tar.gz</FileName>
      <FileSize>11727894</FileSize>
    </Package>
    <Package>
      <URL>https://github.com/KhronosGroup/glslang/archive/refs/tags/11.5.0.tar.gz</URL>
      <MD5>6c86e1a5d65815a707af3bead744804c</MD5>
      <SHA256>fd0b5e3bda591bb08bd3049655a99a0a55f0de4059b9c8f7b397e4b19cf5d51f</SHA256>
      <FileName>glslang-11.5.0.tar.gz</FileName>
      <FileSize>3373042</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf ncnn-20210720.tar.gz

tar -xf glslang-11.5.0.tar.gz
cp -va glslang-11.5.0/* ncnn-20210720/glslang/

cd ncnn-20210720

# remove int8 tests
sed -i -e "/benchmark(\".*_int8\"/d" benchmark/benchncnn.cpp

mkdir build
cd build

cmake -DNCNN_VULKAN=ON  -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF ..
# try to build cpu-only test on system without vulkan development files
is_cmake_ok=$?
if [ $is_cmake_ok -ne 0 ]; then
    cmake -DNCNN_VULKAN=OFF -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF ..
fi
make -j $NUM_CPU_CORES
echo $? > ~/install-exit-status

cp ../benchmark/*.param benchmark/

cd ~/
cat>ncnn<<EOT
#!/bin/sh
cd ncnn-20210720/build/benchmark
./benchncnn 250 \$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 v10.4.0-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>           mobilenet  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>           mobilenet  </LineHint>
    <AppendToArgumentsDescription>Model: mobilenet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>        mobilenet_v2  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>        mobilenet_v2  </LineHint>
    <AppendToArgumentsDescription>Model: mobilenet-v2</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: shufflenet-v2</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>             mnasnet  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>             mnasnet  </LineHint>
    <AppendToArgumentsDescription>Model: mnasnet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>     efficientnet_b0  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>     efficientnet_b0  </LineHint>
    <AppendToArgumentsDescription>Model: efficientnet-b0</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  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>           googlenet  </LineHint>
    <AppendToArgumentsDescription>Model: googlenet</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>               vgg16  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>               vgg16  </LineHint>
    <AppendToArgumentsDescription>Model: vgg16</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>            resnet18  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>            resnet18  </LineHint>
    <AppendToArgumentsDescription>Model: resnet18</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  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>            resnet50  </LineHint>
    <AppendToArgumentsDescription>Model: resnet50</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>
  <ResultsParser>
    <OutputTemplate>         yolov4-tiny  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>         yolov4-tiny  </LineHint>
    <AppendToArgumentsDescription>Model: yolov4-tiny</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          squeezenet_ssd  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>          squeezenet_ssd  </LineHint>
    <AppendToArgumentsDescription>Model: squeezenet_ssd</AppendToArgumentsDescription>
  </ResultsParser>
  <ResultsParser>
    <OutputTemplate>          regnety_400m  min =    #_MIN_RESULT_#  max =   #_MAX_RESULT_#  avg =    #_RESULT_#</OutputTemplate>
    <LineHint>          regnety_400m  </LineHint>
    <AppendToArgumentsDescription>Model: regnety_400m</AppendToArgumentsDescription>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.4.0-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>NCNN</Title>
    <AppVersion>20210720</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>1.3.0</Version>
    <SupportedPlatforms>Linux, MacOSX</SupportedPlatforms>
    <SoftwareType>Scientific</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>cmake, build-utilities, vulkan-development</ExternalDependencies>
    <EnvironmentSize>172</EnvironmentSize>
    <ProjectURL>https://github.com/Tencent/ncnn</ProjectURL>
    <RepositoryURL>https://github.com/Tencent/ncnn</RepositoryURL>
    <Maintainer>Michael Larabel</Maintainer>
    <SystemDependencies>glslang/Include/Common.h, glslangValidator</SystemDependencies>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Target</DisplayName>
      <Identifier>target</Identifier>
      <Menu>
        <Entry>
          <Name>CPU</Name>
          <Value>-1</Value>
        </Entry>
        <Entry>
          <Name>Vulkan GPU</Name>
          <Value>0</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>