2021-01-05

参考https://github.com/awai54st/PYNQ-Classification/blob/master/MANUAL_INSTALL.md一.安装caffe1.安装依赖sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobu

youzjuer

373人浏览 · 2021-01-05 15:17:55

youzjuer · 2021-01-05 15:17:55 发布

参考https://github.com/awai54st/PYNQ-Classification/blob/master/MANUAL_INSTALL.md

一.安装caffe

1.安装依赖

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-dev sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

2.安装Protobuf 3

pip install protobuf

3.安装caffe

cd /home/xilinx git clone https://github.com/BVLC/caffe.git

4.拷贝makefile并修改

（1）Copy the PYNQ version of Makefile.config (provided under PYNQ-Classification/tools/CAFFE_ON_PYNQ) to caffe root directory

（2）按照下述方式修改，需要更改的红色字体标出

## 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 *_50 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_50,code=compute_50

# 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 := $(HOME)/anaconda

# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \

# $(ANACONDA_HOME)/include/python2.7 \

# $(ANACONDA_HOME)/lib/python3/site-packages/numpy/core/include \

# Uncomment to use Python 3 (default is Python 2)，下面是自己的python路径，蓝色

的路径比较特殊，是在pyhon3下，不是再pyhon3.6下

PYTHON_LIBRARIES := boost_python3 python3.6

PYTHON_INCLUDE := /usr/include/python3.6 \

/usr/include \

/usr/local/lib/python3/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.

PYTHON_LIB := /usr/lib/python3.6/config-3.6m-arm-linux-gnueabihf

# 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/arm-linux-gnueabihf/hdf5/serial /usr/lib/arm-linux-gnueabihf/hdf5

# 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 ?= @

5. 编译

make all make test make runtest

运行make runtest的时候会出现

则需要

一定要运行ldconfig不要忘了。

6.需要提前安装好opencv

我安装的是opencv3.4.6,可以使用。

二.安装pycaffe

参考https://www.cnblogs.com/lyyang/p/6573846.html

1.确保安装有pip

注意要使用对应的pip，如果linux中安装有多个python，则使用对应的pip版本，我使用的事pip3

2.安装依赖库

（1）在caffe的目录下，cd python

（2）输入for req in $(cat requirements.txt); do pip3 install $req; done

执行上述代码，可以自动安装所需要的库。但会在第一次执行时出错了，说是好多引用找不到。后来，打开了sudo vim /etc/profile 在后面加了个几个路径，再执行就可以正常安装了，路径如下：

红色为自己的caffe目录

export PYTHONPATH=${HOME}/caffe/python:$PYTHONPATH

export LD_LIBRARY_PATH=${HOME}/caffe/build/lib:$LD_LIBRARY_PATH

export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH

然后执行 source /etc/profile

（3）运行上面这个命令可能会有下面的提示:matplotlib 3.0.0 has requirement python-dateutil>=2.1, but you'll have python-dateutil 1.5 which is incompatible. pandas 0.23.4 has requirement python-dateutil>=2.5.0, but you'll have python-dateutil 1.5 which is incompatible

这个时候可以考虑将requirements.txt里面的;

python-dateutil>=1.4,<2

改为:

python-dateutil>=2.5

（4）编译

make pycaffe make distribute

（5）验证

python

import caffe

如果不报错，就是编译成功了。

三.安装Theano

参考https://github.com/Lasagne/Lasagne

（1）In short, you can install a known compatible version of Theano and the latest Lasagne development version via:

sudo pip3 install -r https://raw.githubusercontent.com/Lasagne/Lasagne/master/requirements.txt pip install https://github.com/Lasagne/Lasagne/archive/master.zip

注意：网速可能不太够，需要链接vpn

（2）sudo pip3 install Lasagne==0.1

四.运行历程出现的问题

（1）No module named 'google'

solution：

参考：https://stackoverflow.com/questions/36183486/importerror-no-module-named-google

试着都装一遍，但是要注意要在这些命令前+sudo(需要超级用户权限)

（2）ImportError: cannot import name 'downsample'

（3）无法导入.pyx文件

参考https://www.cnblogs.com/ZhengPeng7/p/8706657.html

这是由cpython生成的文件，所以要

①安装cpython