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](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#load-model). ### 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](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#strip-visible-string) * Cannot identify blobs by string name when calling `Extractor::input / extract`, but can identify them by enum value in `id.h`. Read more [here](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#input-and-output). ### disable NCNN_BF16 ``` cmake -DNCNN_BF16=OFF .. ``` * Cannot use bf16 storage type in inference. ### 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 transferring 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.