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Makefile
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cc := g++
nvcc = ${lean_cuda}/bin/nvcc
cpp_srcs := $(shell find src -name "*.cpp")
cpp_objs := $(cpp_srcs:.cpp=.o)
cpp_objs := $(cpp_objs:src/%=objs/%)
cpp_mk := $(cpp_objs:.o=.mk)
cu_srcs := $(shell find src -name "*.cu")
cu_objs := $(cu_srcs:.cu=.cuo)
cu_objs := $(cu_objs:src/%=objs/%)
cu_mk := $(cu_objs:.cuo=.cumk)
# 配置你的库路径
# 1. cudnn8.2.2.26(请自行下载)
# runtime的tar包,runtime中包含了lib、so文件
# develop的tar包,develop中包含了include、h等文件
# 2. tensorRT-8.0.1.6-cuda10.2(请自行下载)
# tensorRT下载GA版本(通用版、稳定版),EA(尝鲜版本)不要
# 3. cuda10.2,也可以是11.x看搭配(请自行下载安装)
# lean_tensor_rt := /data/sxai/lean/TensorRT-8.0.1.6-cuda10.2-cudnn8.2
# lean_cudnn := /data/sxai/lean/cudnn8.2.2.26
# lean_opencv := /data/sxai/lean/opencv4.2.0
# lean_cuda := /data/sxai/lean/cuda-10.2
lean_tensor_rt := /datav/lean/TensorRT-8.2.3.0-cuda11.4-cudnn8.2
lean_cudnn := /datav/lean/cudnn8.2.4.15-cuda11.4
lean_opencv := /datav/lean/opencv-4.2.0
lean_cuda := /datav/lean/cuda-11.2
# lean_tensor_rt := /data/sxai/lean/TensorRT-7.0.0.11
# lean_cudnn := /data/sxai/lean/cudnn7.6.5.32-cuda10.2
# lean_opencv := /data/sxai/lean/opencv4.2.0
# lean_cuda := /data/sxai/lean/cuda-10.2
# lean_tensor_rt := /data/sxai/lean/TensorRT-7.2.1.6
# lean_cudnn := /data/sxai/lean/cudnn8.2.2.26
# lean_opencv := /data/sxai/lean/opencv4.2.0
# lean_cuda := /data/sxai/lean/cuda-11.1
include_paths := src \
$(lean_opencv)/include/opencv4 \
$(lean_tensor_rt)/include \
$(lean_cuda)/include \
$(lean_cudnn)/include
library_paths := $(lean_tensor_rt)/lib \
$(lean_opencv)/lib \
$(lean_cuda)/lib64 \
$(lean_cudnn)/lib
link_librarys := opencv_core opencv_imgproc opencv_videoio opencv_imgcodecs \
nvinfer nvonnxparser \
cuda cublas cudart cudnn \
stdc++ dl
empty :=
export_path := $(subst $(empty) $(empty),:,$(library_paths))
paths := $(foreach item,$(library_paths),-Wl,-rpath=$(item))
include_paths := $(foreach item,$(include_paths),-I$(item))
library_paths := $(foreach item,$(library_paths),-L$(item))
link_librarys := $(foreach item,$(link_librarys),-l$(item))
# 如果是其他显卡,请修改-gencode=arch=compute_75,code=sm_75为对应显卡的能力
# 显卡对应的号码参考这里:https://developer.nvidia.com/zh-cn/cuda-gpus#compute
# 如果是 jetson nano,提示找不到-m64指令,请删掉 -m64选项。不影响结果
cpp_compile_flags := -std=c++11 -fPIC -m64 -g -fopenmp -w -O0
cu_compile_flags := -std=c++11 -m64 -Xcompiler -fPIC -g -w -O0
link_flags := -pthread -fopenmp -Wl,-rpath='$$ORIGIN'
cpp_compile_flags += $(include_paths)
cu_compile_flags += $(include_paths)
link_flags += $(library_paths) $(link_librarys) $(paths)
ifneq ($(MAKECMDGOALS), clean)
-include $(cpp_mk) $(cu_mk)
endif
pro : workspace/pro
workspace/pro : $(cpp_objs) $(cu_objs)
@echo Link $@
@mkdir -p $(dir $@)
@g++ $^ -o $@ $(link_flags)
python/pytrt/libpytrtc.so : $(cpp_objs) $(cu_objs)
@echo Link $@
@mkdir -p $(dir $@)
@g++ -shared $^ -o $@ $(link_flags)
objs/%.o : src/%.cpp
@echo Compile CXX $<
@mkdir -p $(dir $@)
@g++ -c $< -o $@ $(cpp_compile_flags)
objs/%.cuo : src/%.cu
@echo Compile CUDA $<
@mkdir -p $(dir $@)
@${nvcc} -c $< -o $@ $(cu_compile_flags)
objs/%.mk : src/%.cpp
@echo Compile depends CXX $<
@mkdir -p $(dir $@)
@g++ -M $< -MF $@ -MT $(@:.mk=.o) $(cpp_compile_flags)
objs/%.cumk : src/%.cu
@echo Compile depends CUDA $<
@mkdir -p $(dir $@)
@${nvcc} -M $< -MF $@ -MT $(@:.cumk=.cuo) $(cu_compile_flags)
run : workspace/pro
@cd workspace && ./pro
debug :
@echo $(export_path)
clean :
@rm -rf objs workspace/pro build
.PHONY : clean yolo alphapose fall debug
# 导出符号,使得运行时能够链接上
export LD_LIBRARY_PATH:=$(export_path):$(LD_LIBRARY_PATH)