build minimal library

For some reason, if you’re not happy with the binary size of the ncnn library, then here is the cheatsheet that helps you to build a minimal ncnn :P

disable c++ rtti and exceptions

cmake -DNCNN_DISABLE_RTTI=ON -DNCNN_DISABLE_EXCEPTION=ON ..

  • Cannot use RTTI and Exceptions when ncnn functions are called.

disable vulkan support

cmake -DNCNN_VULKAN=OFF ..

  • Cannot use GPU acceleration.

disable NCNN_STDIO

cmake -DNCNN_STDIO=OFF ..

  • Cannot load model from files, but can load model from memory or by Android Assets.

    Read more here.

disable NCNN_STRING

cmake -DNCNN_STRING=OFF ..

  • Cannot load human-readable param files with visible strings, but can load binary param.bin files.

    Read more here

  • Cannot identify blobs by string name when calling Extractor::input / extract, but can identify them by enum value in id.h.

    Read more here.

disable NCNN_INT8

cmake -DNCNN_INT8=OFF ..

  • Cannot use quantized int8 inference.

drop pixel drawing functions

cmake -DNCNN_PIXEL_DRAWING=OFF ..

  • Cannot use functions doing drawing basic shape and text like ncnn::draw_rectangle_xx / ncnn::draw_circle_xx / ncnn::draw_text_xx, but functions like Mat::from_pixels / from_pixels_resize are still available.

drop pixel rotate and affine functions

cmake -DNCNN_PIXEL_ROTATE=OFF -DNCNN_PIXEL_AFFINE=OFF ..

  • Cannot use functions doing rotatation and affine transformation like ncnn::kanna_rotate_xx / ncnn::warpaffine_bilinear_xx, but functions like Mat::from_pixels / from_pixels_resize are still available.

drop pixel functions

cmake -DNCNN_PIXEL=OFF ..

  • Cannot use functions transfering from image to pixels like Mat::from_pixels / from_pixels_resize / to_pixels / to_pixels_resize, and need create a Mat and fill in data by hand.

disable openmp

cmake -DNCNN_OPENMP=OFF ..

  • Cannot use openmp multi-threading acceleration. If you want to run a model in single thread on your target machine, it is recommended to close the option.

disable avx2 and arm82 optimized kernel

cmake -DNCNN_AVX2=OFF -DNCNN_ARM82=OFF ..

  • Do not compile optimized kernels using avx2 / arm82 instruction set extensions. If your target machine does not support some of them, it is recommended to close the related options.

disable runtime cpu instruction dispatch

cmake -DNCNN_RUNTIME_CPU=OFF ..

  • Cannot check supported cpu instruction set extensions and use related optimized kernels in runtime.

  • If you know which instruction set extensions are supported on your target machine like avx2 / arm82, you can open related options like -DNCNN_AVX2=ON / -DNCNN_ARM82=ON by hand and then sse2 / arm8 version kernels will not be compiled.

drop layers not used

cmake -DWITH_LAYER_absval=OFF -DWITH_LAYER_bnll=OFF ..

  • If your model does not include some layers, taking absval / bnll as a example above, you can drop them.

  • Some key or dependency layers should not be dropped, like convolution / innerproduct, their dependency like padding / flatten, and activation like relu / clip.

disable c++ stl

cmake -DNCNN_SIMPLESTL=ON ..

  • STL provided by compiler is no longer depended on, and use simplestl provided by ncnn as a replacement. Users also can only use simplestl when ncnn functions are called.

  • Usually with compiler parameters -nodefaultlibs -fno-builtin -nostdinc++ -lc

  • Need cmake parameters cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake -DANDROID_STL=system to avoid STL conflict when compiling to Android.

drop optimized kernel not used

  • Modify the source code under ncnn/src/layer/arm/ to delete unnecessary optimized kernels or replace them with empty functions.

  • You can also drop layers and related optimized kernels by -DWITH_LAYER_absval=OFF as mentioned above.

drop operators from BinaryOp UnaryOp

  • Modify ncnn/src/layer/binaryop.cpp unaryop.cpp and ncnn/src/layer/arm/binaryop.cpp unaryop_arm.cpp by hand to delete unnecessary operators.