使用anaconda3環境下的python2.7, 機器macos mojave 10.14
1.安裝Xcode
首先現在app store中安裝Xcode：
不然會有” framework not found vecLib “的問題出現
2.相應包安裝
1.首先要安裝homebrew包管理工具，在終端運行下面命令：
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
2.然后利用該管理工具安裝caffe依賴包
brew install -vd snappy leveldb gflags glog szip lmdb brew install hdf5 opencv
brew install openblas
3.安裝boost
brew install boost@1.59 boost-python@1.59 brew link boost@1.59 --force brew link boost-python@1.59 --force
4. 安裝2.6.1版本的protobuf
一開始的是安裝3.5.1版本的protobuf，但是后面出現問題，所以還是安裝2.6.1版本
下面只是記錄一下：
cd ~/Downloads wget https://github.com/protocolbuffers/protobuf/archive/v3.5.1.zip unzip v3.5.1.zip cd protobuf-3.5.1 ./autogen.sh ./configure make make install
運行./autogen.sh過程出錯：
+ autoreconf -f -i -Wall,no-obsolete ./autogen.sh: line 50: autoreconf: command not found
解決方案是安裝protobuf的依賴項：
(deeplearning2) userdeMacBook-Pro:protobuf-3.5.1 user$ brew install autoconf automake libtool
再重新運行./autogen.sh及之后命令
成功后查看版本：
(deeplearning2) userdeMacBook-Pro:protobuf-3.5.1 user$ protoc --version libprotoc 3.5.1
這里在下面caffe文件夾中make all之前忘記make clean了，可能是這個原因沒成功也不一定，但是2.6.0版本的的確成功了，直接用該版本即可
后面發現還是不行，就去下了2.6.1版本的：
/Library/Developer/CommandLineTools/usr/bin/make check-TESTS PASS: protobuf-test PASS: protobuf-lazy-descriptor-test PASS: protobuf-lite-test PASS: google/protobuf/compiler/zip_output_unittest.sh PASS: google/protobuf/io/gzip_stream_unittest.sh ============================================================================ Testsuite summary for Protocol Buffers 2.6.1 ============================================================================ # TOTAL: 5 # PASS: 5 # SKIP: 0 # XFAIL: 0 # FAIL: 0 # XPASS: 0 # ERROR: 0 ============================================================================
版本查看：
(deeplearning2) userdeMacBook-Pro:~ user$ protoc --version libprotoc 2.6.1
3.下載caffe源碼
(deeplearning2) userdeMacBook-Pro:~ user$ git clone https://github.com/bvlc/caffe.git Cloning into 'caffe'... remote: Enumerating objects: 65269, done.
下載好后：
(deeplearning2) userdeMacBook-Pro:~ user$ cd caffe
(deeplearning2) userdeMacBook-Pro:caffe user$ cp Makefile.config.example Makefile.config
4.更改Makefile.config文件
打開CPU_ONLY選項，保存
# CPU-only switch (uncomment to build without GPU support). CPU_ONLY := 1
即去掉前面的#號，表示caffe編譯時僅支持CPU，不支持GPU
取消CPU_ONLY前面的注釋
取消USE_OPENCV前面的注釋
取消USE_HDF5前面的注釋，並且改成1
更改CUSTOM_CXX
注釋掉CUDA_DIR那一行
將BLAS改成open
設置blas的路徑，取消下面兩行的注釋
# uncomment to disable IO dependencies and corresponding data layers USE_OPENCV := 0 # This code is taken from https://github.com/sh1r0/caffe-android-lib USE_HDF5 := 1 # Uncomment if you're using OpenCV 3 OPENCV_VERSION := 3
# CUDA directory contains bin/ and lib/ directories that we need.
#CUDA_DIR := /usr/local/cuda
BLAS := open # Homebrew puts openblas in a directory that is not on the standard search path BLAS_INCLUDE := $(shell brew --prefix openblas)/include BLAS_LIB := $(shell brew --prefix openblas)/lib
# Homebrew puts openblas in a directory that is not on the standard search path
BLAS_INCLUDE := $(shell brew --prefix openblas)/include
BLAS_LIB := $(shell brew --prefix openblas)/lib
下面就是設置路徑，使用的是anaconda3，這個根據你自己的路徑設置
注釋掉：
# 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
更改為：
# Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it's in root. ANACONDA_HOME := /anaconda3 PYTHON_INCLUDE := $(ANACONDA_HOME)/envs/deeplearning2/include \ $(ANACONDA_HOME)/envs/deeplearning2/include/python2.7 \ $(ANACONDA_HOME)/envs/deeplearning2/lib/python2.7/site-packages/numpy/core/include
下面這個也更改為：
# We need to be able to find libpythonX.X.so or .dylib.
# PYTHON_LIB := /usr/lib PYTHON_LIB := $(ANACONDA_HOME)/envs/deeplearning2/lib
然后添加一些include和lib路徑：
# Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/local/opt/boost@1.59/include /usr/local/opt/hdf5/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/opt/boost-python@1.59/lib /usr/local/opt/hdf5/lib
最終版本為：
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome! # cuDNN acceleration switch (uncomment to build with cuDNN). # USE_CUDNN := 1 # CPU-only switch (uncomment to build without GPU support). CPU_ONLY := 1 # uncomment to disable IO dependencies and corresponding data layers USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 0 # This code is taken from https://github.com/sh1r0/caffe-android-lib USE_HDF5 := 1 # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) # You should not set this flag if you will be reading LMDBs with any # possibility of simultaneous read and write # ALLOW_LMDB_NOLOCK := 1 # Uncomment if you're using OpenCV 3 # OPENCV_VERSION := 3 # 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++ # CUSTOM_CXX := g++ CUSTOM_CXX := clang++ -std=c++11 # CUDA directory contains bin/ and lib/ directories that we need. 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: # CUDA_DIR := /usr # CUDA architecture setting: going with all of them. # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility. # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility. # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility. CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \ -gencode arch=compute_20,code=sm_21 \ -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=sm_50 \ -gencode arch=compute_52,code=sm_52 \ -gencode arch=compute_60,code=sm_60 \ -gencode arch=compute_61,code=sm_61 \ -gencode arch=compute_61,code=compute_61 # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := open # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # Leave commented to accept the defaults for your choice of BLAS # (which should work)! # BLAS_INCLUDE := /path/to/your/blas # BLAS_LIB := /path/to/your/blas # Homebrew puts openblas in a directory that is not on the standard search path BLAS_INCLUDE := $(shell brew --prefix openblas)/include BLAS_LIB := $(shell brew --prefix openblas)/lib # This is required only if you will compile the matlab interface. # MATLAB directory should contain the mex binary in /bin. # MATLAB_DIR := /usr/local # MATLAB_DIR := /Applications/MATLAB_R2012b.app # 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 := /anaconda3 PYTHON_INCLUDE := $(ANACONDA_HOME)/envs/deeplearning2/include \ $(ANACONDA_HOME)/envs/deeplearning2/include/python2.7 \ $(ANACONDA_HOME)/envs/deeplearning2/lib/python2.7/site-packages/numpy/core/include # Uncomment to use Python 3 (default is Python 2) # PYTHON_LIBRARIES := boost_python3 python3.5m # PYTHON_INCLUDE := /usr/include/python3.5m \ # /usr/lib/python3.5/dist-packages/numpy/core/include # We need to be able to find libpythonX.X.so or .dylib. # PYTHON_LIB := /usr/lib PYTHON_LIB := $(ANACONDA_HOME)/envs/deeplearning2/lib # Homebrew installs numpy in a non standard path (keg only) # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include # PYTHON_LIB += $(shell brew --prefix numpy)/lib # Uncomment to support layers written in Python (will link against Python libs) WITH_PYTHON_LAYER := 1 # Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/local/opt/boost@1.59/include /usr/local/opt/hdf5/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/opt/boost-python@1.59/lib /usr/local/opt/hdf5/lib # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies # INCLUDE_DIRS += $(shell brew --prefix)/include # LIBRARY_DIRS += $(shell brew --prefix)/lib # NCCL acceleration switch (uncomment to build with NCCL) # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0) # USE_NCCL := 1 # 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 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://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 ?= @
如果你在運行make test命令出下面類似的錯誤：
In file included from /usr/local/include/leveldb/iterator.h:20: /usr/local/include/leveldb/status.h:27:11: error: expected ';' at end of declaration list Status() noexcept : state_(nullptr) {} ^ ; /usr/local/include/leveldb/status.h:33:16: warning: rvalue references are a C++11 extension [-Wc++11-extensions] Status(Status&& rhs) noexcept : state_(rhs.state_) { rhs.state_ = nullptr; } ^ /usr/local/include/leveldb/status.h:33:23: error: expected ';' at end of declaration list Status(Status&& rhs) noexcept : state_(rhs.state_) { rhs.state_ = nullptr; } ^ ; /usr/local/include/leveldb/status.h:103:16: error: 'Status' is missing exception specification 'throw()' inline Status::Status(const Status& rhs) { ^ throw() /usr/local/include/leveldb/status.h:24:22: note: previous declaration is here class LEVELDB_EXPORT Status { ^ /usr/local/include/leveldb/status.h:115:40: warning: rvalue references are a C++11 extension [-Wc++11-extensions] inline Status& Status::operator=(Status&& rhs) noexcept { ^ /usr/local/include/leveldb/status.h:115:48: error: expected function body after function declarator inline Status& Status::operator=(Status&& rhs) noexcept { ^ In file included from src/caffe/util/db.cpp:2:
之前出這樣的內容以為是leveldb版本的問題，但是后面發現設置該為：
# 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++ # CUSTOM_CXX := g++ CUSTOM_CXX := clang++ -std=c++11
需要添加-std=c++11才能正常編譯,就沒有上面的問題了，跟着上面的配置走應該就不會有問題
想要讀取python寫的layer，就要取消下面行的注釋：
# Uncomment to support layers written in Python (will link against Python libs) WITH_PYTHON_LAYER := 1
5.make編譯：
make all
make test
make runtest
make pycaffe
⚠️注意:過程中可能會出現很多warning，只要不出現error，那都可以忽略
下面的問題基本上都使用更改配置的方法解決了，所以如果你跟着我上面的配置走，下面的一些問題你應該不會遇見
下面只是記錄一下
⚠️運行make命令時，出了任何錯，更改過后都要記得make clean一次，然后再從頭make all進行編譯
make all問題：
bool OSAtomicCompareAndSwap64Barrier( int64_t __oldValue, int64_t __newValue, ^ src/caffe/net.cpp:774:7: error: use of undeclared identifier 'H5Fis_hdf5' if (H5Fis_hdf5(trained_filename.c_str())) { ^ 11 warnings and 1 error generated. make: *** [.build_release/src/caffe/net.o] Error 1
解決，在配置中添加boost路徑：
# Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/local/opt/boost@1.59/include /usr/local/opt/hdf5/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/opt/boost-python@1.59/lib /usr/local/opt/hdf5/lib
make runtest問題：
(deeplearning2) userdeMacBook-Pro:caffe user$ make runtest .build_release/tools/caffe dyld: Library not loaded: /usr/local/opt/gcc/lib/gcc/8/libgfortran.5.dylib Referenced from: /usr/local/opt/openblas/lib/libopenblasp-r0.3.5.dylib Reason: image not found make: *** [runtest] Abort trap: 6
原因:
因為我本地安裝的gcc是9版本，只有/usr/local/opt/gcc/lib/gcc/9，沒有8的路徑
解決辦法就是安裝了個8的版本：
(deeplearning2) userdeMacBook-Pro:caffe user$ brew install gcc@8 ==> Downloading https://homebrew.bintray.com/bottles/gcc@8-8.3.0.mojave.bottle.tar.gz ==> Downloading from https://akamai.bintray.com/b1/b1e150c72b4c3b7f3493371d71cdb668f691bfee2e998e5b0bf570eed28254d6?__gda__=exp=1565346562~hmac=81794cd252808740593fa500900f13dbd ######################################################################## 100.0% ==> Pouring gcc@8-8.3.0.mojave.bottle.tar.gz 🍺 /usr/local/Cellar/gcc@8/8.3.0: 1,413 files, 288.5MB
然后到/usr/local/Cellar/gcc@8/8.3.0/lib/gcc/8將該文件夾復制一份到/usr/local/opt/gcc/lib/gcc/位置下
然后在make runtest,又出錯:
Bye... *** Aborted at 1565349441 (unix time) try "date -d @1565349441" if you are using GNU date *** PC: @ 0x0 (unknown) *** SIGABRT (@0x7fff62e09b86) received by PID 73394 (TID 0x1142795c0) stack trace: *** @ 0x7fff62eb4b3d _sigtramp make: *** [runtest] Abort trap: 6
類似這樣的錯誤，將hdf5移除：
conda remove hdf5
再運行make runtest即可：
...
[ RUN ] NetTest/1.TestUnsharedWeightsDiffNet [ OK ] NetTest/1.TestUnsharedWeightsDiffNet (0 ms) [ RUN ] NetTest/1.TestSkipPropagateDown [ OK ] NetTest/1.TestSkipPropagateDown (1 ms) [ RUN ] NetTest/1.TestSharedWeightsUpdate [ OK ] NetTest/1.TestSharedWeightsUpdate (1 ms) [ RUN ] NetTest/1.TestAllInOneNetVal [ OK ] NetTest/1.TestAllInOneNetVal (1 ms) [ RUN ] NetTest/1.TestParamPropagateDown [ OK ] NetTest/1.TestParamPropagateDown (1 ms) [ RUN ] NetTest/1.TestGetLayerByName [ OK ] NetTest/1.TestGetLayerByName (1 ms) [ RUN ] NetTest/1.TestHasBlob [ OK ] NetTest/1.TestHasBlob (0 ms) [----------] 26 tests from NetTest/1 (25 ms total) [----------] Global test environment tear-down [==========] 1058 tests from 143 test cases ran. (36250 ms total) [ PASSED ] 1058 tests.
如果遇見問題：
make: .build_release/tools/caffe: No such file or directory
那是因為make clean后沒有從make all重新運行
這個時候如果就嘗試import caffe還是不能成功的，還要make pycaffe
make runtest成功后，運行make pycaffe出現問題：
userdeMacBook-Pro:caffe user$ make pycaffe make: Nothing to be done for `pycaffe'.
這是因為我之前運行過該命令一次，所以要運行一遍make clean，再從頭make all再來一遍即可
make pycaffe返回結尾為：
... touch python/caffe/proto/__init__.py PROTOC (python) src/caffe/proto/caffe.proto
這就表示編譯成功了
6.測試
這時候測試python能不能導入，發現還是沒成功，還需要將路徑寫入：
sudo vim ~/.bash_profile
在文件末尾寫入：
export PYTHONPATH=~/caffe/python:$PYTHONPATH
根據你自己的caffe安裝路徑來
然后再打開新的窗口查看即成功了：
(base) userdeMacBook-Pro:~ user$ conda activate deeplearning2 (deeplearning2) userdeMacBook-Pro:~ user$ python -c 'import caffe' (deeplearning2) userdeMacBook-Pro:~ user$
中間還做過操作，但我覺得應該是沒用的：
source /etc/profile
如果你上面更改路徑后測試還是沒成功，試試再運行該命令
到這里終於安裝成功了，不容易
7.使用
在這里我要使用caffe實現的一件事就是將caffe model模型轉為pytorch格式的模型，可見：
DEX-6-caffe模型轉成pytorch模型辦法(其中的4.下載對應的caffemodel模型並轉換)