sherpa-ncnn/cmake/ncnn.cmake

function(download_ncnn)
  if(CMAKE_VERSION VERSION_LESS 3.11)
    # FetchContent is available since 3.11,
    # we've copied it to ${CMAKE_SOURCE_DIR}/cmake/Modules
    # so that it can be used in lower CMake versions.
    message(STATUS "Use FetchContent provided by sherpa-ncnn")
    list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
  endif()

  include(FetchContent)

  set(ncnn_URL  "https://github.com/csukuangfj/ncnn/archive/refs/tags/sherpa-0.5.tar.gz")
  set(ncnn_HASH "SHA256=73ddc84406bc8fd8aa98fc05284534e3fdfbee39ee2ec8592dfbbfad28717bfd")

  FetchContent_Declare(ncnn
    URL               ${ncnn_URL}
    URL_HASH          ${ncnn_HASH}
  )

  set(NCNN_INSTALL_SDK OFF CACHE BOOL "" FORCE)
  set(NCNN_PIXEL OFF CACHE BOOL "" FORCE)
  set(NCNN_PIXEL_ROTATE OFF CACHE BOOL "" FORCE)
  set(NCNN_PIXEL_AFFINE OFF CACHE BOOL "" FORCE)
  set(NCNN_PIXEL_DRAWING OFF CACHE BOOL "" FORCE)
  set(NCNN_BUILD_BENCHMARK OFF CACHE BOOL "" FORCE)

  set(NCNN_INT8 OFF CACHE BOOL "" FORCE) # TODO(fangjun): enable it
  set(NCNN_BF16 OFF CACHE BOOL "" FORCE) # TODO(fangjun): enable it

  set(NCNN_BUILD_TOOLS OFF CACHE BOOL "" FORCE)
  set(NCNN_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
  set(NCNN_BUILD_TESTS OFF CACHE BOOL "" FORCE)

  # For RNN-T with ScaledLSTM, the following operators are not sued,
  # so we keep them from compiling.
  #
  # CAUTION: If you switch to a different model, please change
  # the following disabled layers accordingly; otherwise, you
  # will get segmentation fault during runtime.
  set(disabled_layers
    AbsVal
    ArgMax
    BatchNorm
    Bias
    BNLL
    # Concat
    # Convolution
    # Crop
    Deconvolution
    # Dropout
    Eltwise
    ELU
    # Embed
    Exp
    # Flatten  # needed by innerproduct
    # InnerProduct
    # Input
    Log
    LRN
    MemoryData
    MVN
    Pooling
    Power
    PReLU
    Proposal
    # Reduction
    # ReLU
    # Reshape
    ROIPooling
    Scale
    # Sigmoid
    Slice
    Softmax
    # Split
    SPP
    # TanH
    Threshold
    Tile
    RNN
    LSTM
    # BinaryOp
    # UnaryOp
    ConvolutionDepthWise
    # Padding # required by innerproduct and convolution
    Squeeze
    # ExpandDims
    Normalize
    # Permute
    PriorBox
    DetectionOutput
    Interp
    DeconvolutionDepthWise
    ShuffleChannel
    InstanceNorm
    Clip
    Reorg
    YoloDetectionOutput
    Quantize
    Dequantize
    Yolov3DetectionOutput
    PSROIPooling
    ROIAlign
    Packing
    Requantize
    # Cast  # needed InnerProduct
    HardSigmoid
    SELU
    HardSwish
    Noop
    PixelShuffle
    DeepCopy
    Mish
    StatisticsPooling
    Swish
    Gemm
    GroupNorm
    LayerNorm
    Softplus
    GRU
    MultiHeadAttention
    GELU
    Convolution1D
    Pooling1D
    # ConvolutionDepthWise1D
    Convolution3D
    ConvolutionDepthWise3D
    Pooling3D
    MatMul
    Deconvolution1D
    DeconvolutionDepthWise1D
    Deconvolution3D
    DeconvolutionDepthWise3D
    Einsum
    DeformableConv2D
    RelPositionalEncoding
    MakePadMask
    RelShift
    GLU
  )
  foreach(layer IN LISTS disabled_layers)
    string(TOLOWER ${layer} name)
    set(WITH_LAYER_${name} OFF CACHE BOOL "" FORCE)
  endforeach()

  FetchContent_GetProperties(ncnn)
  if(NOT ncnn_POPULATED)
    message(STATUS "Downloading ncnn ${ncnn_URL}")
    FetchContent_Populate(ncnn)
  endif()
  message(STATUS "ncnn is downloaded to ${ncnn_SOURCE_DIR}")
  message(STATUS "ncnn's binary dir is ${ncnn_BINARY_DIR}")

  add_subdirectory(${ncnn_SOURCE_DIR} ${ncnn_BINARY_DIR} EXCLUDE_FROM_ALL)
endfunction()

download_ncnn()