ubuntu系統下配置caffe的GPU環境，訓練mobileNet-SSD并在EAIDK-310嵌入式上推理

前言
一、NVIDIA顯卡驅動及KUDA的安裝
- 1.安裝gpu驅動
- 2.安裝cuda和cudnn
二、ubuntu安裝GPU版caffe
- (一) 安裝依賴庫:
- (二) 下載原始碼并切換到 ssd 分支
- (三) 修改組態檔
- - 1.修改Makefile.config檔案
  - 2.修改Makefile檔案
- (四) 編譯及測驗caffe
三、anaconda3環境下安裝gpu版caffe
- (一) ubuntu系統安裝anaconda3
- - 1.下載及安裝
  - 2.創建虛擬環境
- (二) 虛擬環境下安裝caffe
四、caffe環境下訓練ＭobileNet—SSD

前言

如果沒有安裝好ubuntu系統或者系統崩潰需要重裝的可以參考上一篇博客：ubuntu+win10雙系統安裝，

一、NVIDIA顯卡驅動及KUDA的安裝

1.安裝gpu驅動

這里推薦一個簡單的安裝方法，打開選單，找到軟體和更新，在這里插入圖片描述
找到附加驅動，這里會自動推薦適合GPU的驅動，
選擇使用NVIDIA驅動，點擊右下角的應用更改，系統會自動安裝顯卡驅動并重啟，
若此方法失敗，則可參考網上安裝gpu驅動的教程，教程有很多，這里不再贅述，
驗證是否成功安裝：nvidia-smi

nvidia-smi

顯示如下圖則說明安裝成功，注意版本，我這里是Driver Version: 384.130 在這里插入圖片描述

2.安裝cuda和cudnn

版本問題
cada: cuda_8.0.44_linux.run
cudnn:cudnn-8.0-linux-x64-v5.1.tgz

如沒有使用此版本以及上面的顯卡驅動版本，不保證能配置成功，本人經過長達兩周的時間摸索此對應的版本，推薦CUDA官網，cudnn的安裝下載，若有需要，可參考百度云，
至于安裝教程，網上很多，這里推薦一篇教程，
查看是否安裝成功：nvcc -V

二、ubuntu安裝GPU版caffe

(一) 安裝依賴庫:

1.安裝指令如下：

sudo apt-get install libprotobuf-dev //（16.04默認版本2.6.1，不能升級）
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install python-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev

2.特別注意
本人安裝時python版本是Ubuntu16.04自帶的Python2.7，如果需要其他python版本，建議參考后面第三節anaconda虛擬環境下的安裝，
安裝完成后確認python版本,直接輸入python：

python

在這里插入圖片描述
這里我只有用opencv2.4.9.1才編譯成功，及Ubuntu16.04默認安裝的版本，
安裝完成后確認opencv版本,接著輸入：

import cv2
cv2.__version__

opencv版本

(二) 下載原始碼并切換到 ssd 分支

1.下載碼源
在主目錄下(我的是/home/xxx,xxx為用戶名)打開命令視窗，依次輸入：
下載github上的caffe專案：

git clone https://github.com/weiliu89/caffe.git

若后面編譯不成功，也可以下載這里編譯好的caffe，
切換到caffe檔案夾：

cd caffe

2.切換到 ssd 分支

git checkout ssd

3.安裝python依賴庫
切換到 caffe 下的 python 目錄下，下載 python 依賴庫(先安裝 pip)

cd python
sudo apt-get install python-pip
for req in $(catpython requirements.txt); do pip install $req; done

(三) 修改組態檔

（針對python2.7,opencv2.4.9.1d的環境修改）

1.修改Makefile.config檔案

先將Makefile.config.example復制并重命名為Makefile.config

復制指令如下：

cp Makefile.config.example Makefile.config

然后修改Makefile.config檔案，修改后如下：

## 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

# 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++

# 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 lines after *_35 for compatibility.
CUDA_ARCH := -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_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
# BLAS := atlas
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/local/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)/anaconda2
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python2.7 \
		$(ANACONDA_HOME)/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)/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/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib \
		/usr/lib/x86_64-linux-gnu/hdf5/serial

# 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

# 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 ?= @

2.修改Makefile檔案

（這里代碼有點多，避免一行行的去查找修改，這里直接給出修改好的Makefile檔案，）

(四) 編譯及測驗caffe

make pycaffe -j8
make all -j8
make test -j8
make runtest -j8

時間較長，請耐心等待，-j8是指多核同時編譯，可節省時間，根據自己電腦設定-j**，出現pass則編譯成功，

測驗：
添加環境變數，注意caffe的路徑：

cd caffe/python
export PYTHONPATH=~/caffe/python:$PYTHONPATH

進入 python 環境，輸入：import caffe 如果沒有報錯，證明安裝成功，若編譯通過但是匯入失敗，則make pycaffe一下，

匯入環境變數：

sudo echo export PYTHONPATH=“~/caffe/python" >>~/.bashrc
source ~/.bashrc #使環境變數生效

或者：

echo "export CAFFE_ROOT=~/caffe" >> ~/.bashrc
echo "export PYTHONPATH=${CAFFE_ROOT}/python:$PYTHONPATH" >> ~/.bashrc
source ~/.bashrc

如出錯參考鏈接，

重新打開一個終端視窗，輸入python，再輸入caffe，不出錯則添加環境變數成功，

三、anaconda3環境下安裝gpu版caffe

(一) ubuntu系統安裝anaconda3

（因為這里主要介紹anaconda3下的caffe環境配置，至于anaconda3的安裝，不做過多的贅述，）

1.下載及安裝

下載：anaconda安裝包下載，推薦官網，或者百度云，提取碼：zu0i，

安裝：網上很多教程，這里推薦一篇，

是否安裝成功：安裝完成后需要添加環境變數，查看環境變數終端輸入：

sudo gedit ~/.bashrc

打開.bashrc后看最后是否有如下字樣代碼

## >>> conda initialize >>>
## !! Contents within this block are managed by 'conda init' !!

__conda_setup="$('/home/×××/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
    eval "$__conda_setup"
else
    if [ -f "/home/×××/anaconda3/etc/profile.d/conda.sh" ]; then
        . "/home/×××/anaconda3/etc/profile.d/conda.sh"
    else
       export PATH="/home/×××/anaconda3/bin:$PATH"
    fi
fi
unset __conda_setup
##    <<< conda initialize <<<

若沒有則復制進去，注意×××是自己的用戶名，
安裝完成后，終端命令列前出現默認環境(base)，輸入conda不報錯說明安裝成功，

2.創建虛擬環境

創建名為caffe的python2.7的虛擬環境并切換至該環境：

conda create -n caffe python=2.7
conda activate caffe

(二) 虛擬環境下安裝caffe

安裝依賴檔案如第二節所示，但是我遇到的問題是sudo apt-get安裝的opencv庫在conda虛擬環境下無法呼叫，重新編譯安裝的opencv庫與我想安裝的caffe環境又有沖突，導致caffe編譯失敗，
這里給出我的解決方法：
將usr/lib/python2.7/dist-packages目錄下的cv.py,cv.pyc和cv2.x86_64-linux-gnu.so三個檔案復制到anaconda3/envs/caffe/lib/python2.7/dist-packages檔案夾下，并將cv2.x86_64-linux-gnu.so改名為cv2.so

至此，caffe下的cv2版本才符合要求，如圖所示：
下載碼源（與前面一致）
在主目錄下(我的是/home/xxx/my_code,xxx為用戶名)打開命令視窗，依次輸入：
下載github上的caffe專案：
```
git clone https://github.com/weiliu89/caffe.git
```
切換到caffe檔案夾：
```
cd caffe
```
切換到 ssd 分支

git checkout ssd

安裝python依賴庫
切換到 caffe 下的 python 目錄下，下載 python 依賴庫(先安裝 pip)

cd python
sudo apt-get install python-pip
for req in $(catpython requirements.txt); do pip install $req; done

修改組態檔
首先是Makefile.config檔案，將python的路徑改成anaconda下虛擬環境的python，其他的不變，
修改后如下：

## 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

# 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++

# 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 lines after *_35 for compatibility.
CUDA_ARCH := -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_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
# BLAS := atlas
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/local/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/lf/anaconda3/envs/caffe
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python2.7 \
		$(ANACONDA_HOME)/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 := $(ANACONDA_HOME)/lib
LINKFLAGS := -Wl,-rpath,$(ANACONDA_HOME)/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/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib \
		/usr/lib/x86_64-linux-gnu/hdf5/serial

# 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

# 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 ?= @

其次是Makefile檔案，代碼比較多，避免一行行的去查找修改，這里直接給出修改好的Makefile檔案，）

剩下的編譯程序也是完全一樣，請參考前面部分，

至此，anconda下的caffe環境就安裝好了，

四、caffe環境下訓練ＭobileNet—SSD

本人安裝ubuntu下的caffe環境主要是利用自己的資料集訓練ＭobileNetSSD model，這部分可以參考鏈接，
最后，將訓練的模型.caffemodel模型檔案轉化成.tmfile檔案，在eaidk-310平臺上做推理，這部分內容可以參考下一篇博客，

轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/135162.html

標籤：python

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ubuntu16.04系統下配置caffe的GPU環境，訓練mobileNet-SSD并在EAIDK-310嵌入式上推理