라즈베리파이 darknet python

libdarknet.so 파일 CDLL 로드시 에러 발생

I'm also very curious about how to use the CDLL functionality of the original libdarknet.so but with added NNPACK optimization. One option that seems reasonable is to use the latest upstream darknet commit and update it with NNPACK calls.

I also a tried adding in LDFLAGS to the Makefile for darknet-nnpack so that the library libdarknet.so would be built, but I get the following failure when running make:
gcc -Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC -Ofast -DOPENCV -DNNPACK -DARM_NEON -mfpu=neon-vfpv4 -funsafe-math-optimizations -ftree-vectorize -shared obj/gemm.o obj/utils.o obj/cuda.o obj/deconvolutional_layer.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/detection_layer.o obj/route_layer.o obj/box.o obj/normalization_layer.o obj/avgpool_layer.o obj/layer.o obj/local_layer.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/crnn_layer.o obj/demo.o obj/batchnorm_layer.o obj/region_layer.o obj/reorg_layer.o obj/tree.o obj/lstm_layer.o -o libdarknet.so -lm -pthread `pkg-config --libs opencv` -lnnpack -lpthreadpool /usr/bin/ld: /usr/lib/gcc/arm-linux-gnueabihf/4.8/../../../libnnpack.a(softmax.c.o): relocation R_ARM_MOVW_ABS_NC against `a local symbol' can not be used when making a shared object; recompile with -fPIC /usr/lib/gcc/arm-linux-gnueabihf/4.8/../../../libnnpack.a: error adding symbols: Bad value collect2: error: ld returned 1 exit status Makefile:96: recipe for target 'libdarknet.so' failed make: *** [libdarknet.so] Error 1

I will poke around a little with the error. Your suggestion about a static link makes since considering the error I'm seeing: https://stackoverflow.com/questions/13812185/how-to-recompile-with-fpic
I'm trying to recompile NNPACK-darknet with an added -fPIC flag in the build.ninja file (prior to running ninja)
see: Maratyszcza/NNPACK#5

My current Makefile is:

GPU=0
CUDNN=0
OPENCV=1
NNPACK=1
ARM_NEON=1
OPENMP=0
DEBUG=0

ARCH= -gencode arch=compute_30,code=sm_30 \
      -gencode arch=compute_35,code=sm_35 \
      -gencode arch=compute_50,code=[sm_50,compute_50] \
      -gencode arch=compute_52,code=[sm_52,compute_52]
#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify
# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/

CC=gcc
NVCC=nvcc 
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread 
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC

ifeq ($(OPENMP), 1) 
CFLAGS+= -fopenmp
endif

ifeq ($(DEBUG), 1) 
OPTS=-O0 -g
endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), 1) 
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv` 
COMMON+= `pkg-config --cflags opencv` 
endif

ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

ifeq ($(CUDNN), 1) 
COMMON+= -DCUDNN 
CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif

ifeq ($(NNPACK), 1)
COMMON+= -DNNPACK
CFLAGS+= -DNNPACK
LDFLAGS+= -lnnpack -lpthreadpool
endif

ifeq ($(ARM_NEON), 1)
COMMON+= -DARM_NEON
CFLAGS+= -DARM_NEON -mfpu=neon-vfpv4 -funsafe-math-optimizations -ftree-vectorize
endif

OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o attention.o darknet.o

ifeq ($(GPU), 1) 
LDFLAGS+= -lstdc++ 
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o
endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)
all: obj  results $(SLIB) $(ALIB) $(EXEC)


$(EXEC): $(EXECOBJ) $(ALIB)
	$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)
	$(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)
	$(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)
	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)
	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:
	mkdir -p obj
backup:
	mkdir -p backup
results:
	mkdir -p results

.PHONY: clean

clean:
	rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ)

Beyond that, please update if you figure out how to get fast (1 frame/sec) detection through a python interface (pyyolo) or are able to get the library to build with nnpack.

==============

Update: adding the -fPIC flag to cflags within the NNPACK-darknet build.ninja options worked. Now when running the Makefile for darknet-nnpack, I get a shared library libdarknet.so that allows loading within Python. Detection times are around 600-900 ms.
My issue now is that I'm not getting any detections as I would through the regular darknet call. All my arrays are returned empty (no detections!) Any ideas? I think I saw other people were having similar issues.

Let me know if you'd like more details.

다음과 같이 해결했음

NNPACK-darknet 빌드 전에 build.ninja 옵션에서 cflags에 -fPIC 옵션추가하고, 재 설치

Update: adding the -fPIC flag to cflags within the NNPACK-darknet build.ninja options worked

그리고 darknet 다시 빌드하면 CDLL로 로드 성공

인식 테스트까지 완료!!@@