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FAST-RCNN makefile.config 参考配置

2019-07-13 18:59发布

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# Contributions simplifying and improving our build system are welcome! #CUDNN  的加速开关，如果为1就说明要用GPU  英伟达的CUDNN库加速。 # cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1 # CPU-only switch (uncomment to build without GPU support). #这个是专门针对没有GPU的用户的。没有GPU用CPU_ONLY=1;
# CPU_ONLY := 1   # To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++ #用于指定你的MAKE过程中的编译器，以便更快速的编译。 CUSTOM_CXX := g++-4.7 # CUDA directory contains bin/ and lib/ directories that we need. #CUDA的路径。
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead: #专门针对 的 Ubuntu 14.04,如果你直接APT安装的，那么你的路径要改一下。切记
# CUDA_DIR := /usr # CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility. #CUDA的架构，如果你的GPU性能比较低，然后你的CUDA<0.6.那么你要用*—50，兼容。
CUDA_ARCH := -gencode arch=compute_35,code=sm_35
  -gencode arch=compute_50,code=sm_50
  -gencode arch=compute_50,code=compute_50 # -gencode arch=compute_20,code=sm_20
#  -gencode arch=compute_20,code=sm_21
#  -gencode arch=compute_30,code=sm_30 # BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas #各种数学库的选择，超难搞。自己填坑吧。
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)! #自己安装的数学库的选择，一定要清楚自己的安装路径，不然会出来各种莫名其妙的错误。头大！
BLAS_INCLUDE := /opt/OpenBLAS/include
BLAS_LIB := /opt/OpenBLAS/lib # This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin. #MATLAB的安装路径，说明CAFFE框架是可以与MATLAB提供接口的。
MATLAB_DIR := /opt/matlab/r2014a # NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
# PYTHON_INCLUDE := /usr/include/python2.7
#   /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda
PYTHON_INCLUDE := $(ANACONDA_HOME)/include
  $(ANACONDA_HOME)/include/python2.7
  $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include # We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib # Uncomment to support layers written in Python (will link against Python libs)
# This will require an additional dependency boost_regex provided by boost.
WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib # Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1 BUILD_DIR := build
DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due tohttps://github.com/BVLC/caffe/issues/171
# DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0 # enable pretty build (comment to see full commands)
Q ?= @

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