mindspore lite端侧性能，初测情况统计 #35
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835/armv8/CPU tf_v1: 104 835/armv8/GPU tf_v1: 55 855/armv8/cpu tf_v1: 18 855/armv8/gpu tf_v1: 37 980/armv8/CPU tf_v1: 21ms 980/armv8/GPU tf_v1: 31ms 990-cpu-armv8 armv8-CPU 990 armv8-GPU 845 armv8-CPU 845
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2021年2月测试mindspore lite v1.1.0mindspore lite的release页面不提供编译好的binary，因此需要从源码编译，后来在官网文档发现有编译好的。具体的依赖在官网有详细的说明和链接跳转：https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/build.html。mindspore lite库还好说，但是converter依赖的东西就较多。之类简单罗列： 模型转换工具的编译安装
安装完上述依赖后，便可以开始编译converter，默认编译X86就有模型转换工具，而编译端侧库是没有的，执行下面命令开始编译x86_64架构Release版本，编译模型转换、基准测试和库裁剪工具： bash build.sh -I x86_64
编译成功显示如下日志
[100%] Built target lite-test
Run CPack packaging tool...
CPack: Create package using TGZ
CPack: Install projects
CPack: - Run preinstall target for: Lite
CPack: - Install project: Lite []
CPack: - Install component: inference-linux-x64
CPack: - Install component: converter-linux-x64
CPack: Create package
CPack: - package: /home/xxxx/code/mindspore/output/tmp/mindspore-lite-1.1.0-converter-linux-x64.tar.gz generated.
CPack: - checksum file: /home/xxxx/code/mindspore/output/tmp/mindspore-lite-1.1.0-converter-linux-x64.tar.gz.sha256 generated.
CPack: - package: /home/xxxx/code/mindspore/output/tmp/mindspore-lite-1.1.0-inference-linux-x64.tar.gz generated.
CPack: - checksum file: /home/xxxx/code/mindspore/output/tmp/mindspore-lite-1.1.0-inference-linux-x64.tar.gz.sha256 generated.
---------------- mindspore lite: build success ----------------但在运行converter时会提示如下报错： ./converter: error while loading shared libraries: libglog.so.0: cannot open shared object file: No such file or directory将output目录下的mindspore-lite-1.1.0-converter-linux-x64.tar.gz解压，然后进入mindspore-lite-1.1.0-converter-linux-x64/converter，直接执行 下面将caffe和tflite格式的mobilenetv1和v2转换为mindspore lite模型格式： # note: https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/converter_tool.html
prefix_path="/home/yuanshuai/code/tmp/embedded-ai.bench/"
# caffe
./converter_lite --fmk=CAFFE --modelFile=${prefix_path}/models/caffe_mobilenetv1.prototxt --weightFile=${prefix_path}/models/caffe_mobilenetv1.caffemodel --outputFile=caffe_mobilenetv1
./converter_lite --fmk=CAFFE --modelFile=${prefix_path}/models/caffe_mobilenetv2.prototxt --weightFile=${prefix_path}/models/caffe_mobilenetv2.caffemodel --outputFile=caffe_mobilenetv2
# tflite
./converter_lite --fmk=TFLITE --modelFile=${prefix_path}/models/tf_mobilenetv1/tf_mobilenetv1.tflite --outputFile=tf_mobilenetv1
./converter_lite --fmk=TFLITE --modelFile=${prefix_path}/models/tf_mobilenetv2/tf_mobilenetv2.tflite --outputFile=tf_mobilenetv2转换成功得到下面4个模型：
编译GPU库默认编译GPU是带有CPU算子的，执行下面命令编译GPU mindspore lite库： bash build.sh -I arm64 -e gpu
bash build.sh -I arm32 -e gpu编译完成会在output目录下显示两个文件夹：
benchmark解压上面两个压缩包后，里面有一个可执行文件bin，以及mindspore_lite.so文件，将其adb push到手机上，顺带前面转好的模型，执行跑一下benchmark： $ADB mkdir -p /data/local/tmp/mindspore
$ADB push ./${prefix_path}/benchmark/benchmark /data/local/tmp/mindspore/benchmark
$ADB push ./${prefix_path}/lib/libmindspore-lite.so /data/local/tmp/mindspore/libmi
$ADB push ./${prefix_path}/lib/liboptimize.so /data/local/tmp/mindspore/liboptimize
$ADB push ./${prefix_path}/lib/libc++_shared.so /data/local/tmp/mindspore/libc++_sh
$ADB push ./tf_mobilenetv1.ms /data/local/tmp/mindspore/tf_mobilenetv1.ms
$ADB push ./tf_mobilenetv2.ms /data/local/tmp/mindspore/tf_mobilenetv2.ms
$ADB push ./caffe_mobilenetv1.ms /data/local/tmp/mindspore/caffe_mobilenetv1.ms
$ADB push ./caffe_mobilenetv2.ms /data/local/tmp/mindspore/caffe_mobilenetv2.ms
$ADB shell chmod +x /data/local/tmp/mindspore/benchmark
$ADB shell /data/local/tmp/mindspore/benchmark
# https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/evaluating_the_model.html
#$ADB shell "export LD_LIBRARY_PATH=/data/local/tmp/mindspore/; \
# /data/local/tmp/mindspore/benchmark \
# --bleFp16mbleFp16odelPath=<xxx> \
# --device=<CPU|GPU> \
# --cpuBindMode=<-1:midcore, 1: bigcore, 0:nobind> \
# --numThreads=<2> \
# --loopCount=10 \
# --warmUpLoopCount=3 \
# --enableFp16=<false|true>"
$ADB shell "export LD_LIBRARY_PATH=/data/local/tmp/mindspore/; \
/data/local/tmp/mindspore/benchmark \
--modelFile=/data/local/tmp/mindspore/tf_mobilenetv1.ms \
--device=GPU \
--cpuBindMode=1 \
--numThreads=1 \
--loopCount=1000 \
--warmUpLoopCount=20 \
--enableFp16=true" |
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