137 lines
4.4 KiB
Markdown
137 lines
4.4 KiB
Markdown
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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
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### disable c++ rtti and exceptions
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```
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cmake -DNCNN_DISABLE_RTTI=ON -DNCNN_DISABLE_EXCEPTION=ON ..
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```
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* Cannot use RTTI and Exceptions when ncnn functions are called.
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### disable vulkan support
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```
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cmake -DNCNN_VULKAN=OFF ..
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```
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* Cannot use GPU acceleration.
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### disable NCNN_STDIO
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```
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cmake -DNCNN_STDIO=OFF ..
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```
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* Cannot load model from files, but can load model from memory or by Android Assets.
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Read more [here](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#load-model).
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### disable NCNN_STRING
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```
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cmake -DNCNN_STRING=OFF ..
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```
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* Cannot load human-readable param files with visible strings, but can load binary param.bin files.
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Read more [here](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#strip-visible-string)
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* Cannot identify blobs by string name when calling `Extractor::input / extract`, but can identify them by enum value in `id.h`.
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Read more [here](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md#input-and-output).
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### disable NCNN_BF16
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```
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cmake -DNCNN_BF16=OFF ..
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```
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* Cannot use bf16 storage type in inference.
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### disable NCNN_INT8
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```
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cmake -DNCNN_INT8=OFF ..
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```
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* Cannot use quantized int8 inference.
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### drop pixel drawing functions
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```
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cmake -DNCNN_PIXEL_DRAWING=OFF ..
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```
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* 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.
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### drop pixel rotate and affine functions
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```
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cmake -DNCNN_PIXEL_ROTATE=OFF -DNCNN_PIXEL_AFFINE=OFF ..
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```
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* 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.
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### drop pixel functions
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```
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cmake -DNCNN_PIXEL=OFF ..
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```
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* 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.
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### disable openmp
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```
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cmake -DNCNN_OPENMP=OFF ..
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```
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* 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.
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### disable avx2 and arm82 optimized kernel
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```
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cmake -DNCNN_AVX2=OFF -DNCNN_ARM82=OFF ..
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```
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* 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.
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### disable runtime cpu instruction dispatch
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```
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cmake -DNCNN_RUNTIME_CPU=OFF ..
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```
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* Cannot check supported cpu instruction set extensions and use related optimized kernels in runtime.
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* 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.
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### drop layers not used
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```
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cmake -DWITH_LAYER_absval=OFF -DWITH_LAYER_bnll=OFF ..
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```
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* If your model does not include some layers, taking absval / bnll as a example above, you can drop them.
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* Some key or dependency layers should not be dropped, like convolution / innerproduct, their dependency like padding / flatten, and activation like relu / clip.
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### disable c++ stl
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```
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cmake -DNCNN_SIMPLESTL=ON ..
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```
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* 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.
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* Usually with compiler parameters `-nodefaultlibs -fno-builtin -nostdinc++ -lc`
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* 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.
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### drop optimized kernel not used
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* Modify the source code under `ncnn/src/layer/arm/` to delete unnecessary optimized kernels or replace them with empty functions.
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* You can also drop layers and related optimized kernels by `-DWITH_LAYER_absval=OFF` as mentioned above.
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### drop operators from BinaryOp UnaryOp
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* Modify `ncnn/src/layer/binaryop.cpp unaryop.cpp` and `ncnn/src/layer/arm/binaryop.cpp unaryop_arm.cpp` by hand to delete unnecessary operators.
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