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# $FreeBSD$
PORTNAME= mnn
DISTVERSION= 1.0.0
PORTREVISION= 3
CATEGORIES= misc # machine-learning
MAINTAINER= yuri@FreeBSD.org
COMMENT= Lightweight deep neural network inference engine
LICENSE= APACHE20
BROKEN_i386= always_inline function '_mm_set1_ps' requires target feature 'sse', but would be inlined into function 'Vec4' that is compiled without support for 'sse'
BUILD_DEPENDS= bash:shells/bash
USES= cmake compiler:c++11-lang shebangfix
USE_GITHUB= yes
GH_ACCOUNT= alibaba
GH_PROJECT= MNN
SHEBANG_GLOB= *.sh
USE_LDCONFIG= yes
CXXFLAGS+= -pthread
LDFLAGS+= -pthread
CFLAGS+= -fPIC # workaround for https://github.com/alibaba/MNN/issues/553
CXXFLAGS+= -fPIC
OPTIONS_DEFINE= APPS CONVERTER HARD_FP
OPTIONS_DEFAULT= CONVERTER HARD_FP
OPTIONS_SUB= yes
APPS_DESC= Build ML training, quantization tools
APPS_CMAKE_BOOL= MNN_BUILD_QUANTOOLS MNN_BUILD_TOOLS MNN_BUILD_TRAIN
CONVERTER_DESC= Build the model converter tool
CONVERTER_CMAKE_BOOL= MNN_BUILD_CONVERTER
CONVERTER_LIB_DEPENDS= libprotobuf.so:devel/protobuf
HARD_FP_DESC= Hard floating point numbers
HARD_FP_CMAKE_BOOL= MNN_BUILD_HARD
.include <bsd.port.pre.mk>
.if ${ARCH} != amd64 && ${ARCH} != i386
CMAKE_ARGS+= -DMNN_USE_AVX:BOOL=OFF \
-DMNN_USE_SSE:BOOL=OFF
.endif
pre-build: # https://github.com/alibaba/MNN/issues/648
@cd ${WRKSRC}/schema && ./generate.sh
do-install-APPS-on:
.for f in MNNConvert MNNV2Basic.out mobilenetTest.out backendTest.out testModel.out testModelWithDescrisbe.out getPerformance.out checkInvalidValue.out timeProfile.out quantized.out \
transformer.out train.out rawDataTransform.out dataTransformer.out runTrainDemo.out
${INSTALL_PROGRAM} ${BUILD_WRKSRC}/${f} ${STAGEDIR}${PREFIX}/bin
.endfor
do-install-CONVERTER-on:
${INSTALL_PROGRAM} ${BUILD_WRKSRC}/MNNConvert ${STAGEDIR}${PREFIX}/bin
do-test:
@cd ${BUILD_WRKSRC} && \
${SETENV} ${CONFIGURE_ENV} ${CMAKE_BIN} ${CMAKE_ARGS} -DMNN_BUILD_TEST:BOOL=ON ${CMAKE_SOURCE_PATH} && \
${SETENV} ${MAKE_ENV} ${MAKE_CMD} ${MAKE_ARGS} ${ALL_TARGET} && \
cd ${BUILD_WRKSRC} && ./run_test.out
.include <bsd.port.post.mk>
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