ubantu install caffe when after install cuda and cudnn 1.caffe编译出现connot find -lopencv_imgcodecs的解决方式 2.[caffe]python3编译caffe错误:cannot find -lboost_python3

1.makeconfig file modify

1.应用 opencv 版本

将
#OPENCV_VERSION := 3 
修改为： 
OPENCV_VERSION := 3

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2.使用 python 接口

将
#WITH_PYTHON_LAYER := 1 
修改为 
WITH_PYTHON_LAYER := 1

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3.修改 python 路径

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 
修改为： 
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 /usr/lib/x86_64-linux-gnu/hdf5/serial 

4.修改 python  version

all content is:

## 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
USE_OPENCV := 1
#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-8.0
# 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 := atlas
# 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/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 /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

# 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 ?= @

2.modify makefile

然后修改 caffe 目录下的 Makefile 文件：

将：
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
替换为：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

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将：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
改为：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

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然后修改 /usr/local/cuda/include/host_config.h 文件 :

将
#error-- unsupported GNU version! gcc versions later than 4.9 are not supported!
改为
//#error-- unsupported GNU version! gcc versions later than 4.9 are not supported!

OK ，可以开始编译了，在 caffe 目录下执行 ：

make all -j8

编译成功后测试命令

sudo make runtest -j8

error:

1.caffe编译出现connot find -lopencv_imgcodecs的解决方式

先将Makefile.config文件中

OPENCV_VERSION :=3 注释掉，只修改USE_OPENCV := 1

修改后的结果：

USE_OPENCV := 1

#OPENCV_VERSION := 3

在caffe根目录下，找到Makefile文件，打开文件

查找“Derive include and lib directories”一节，修改“LIBRARIES +=”的最后一行（LIBRARIES +=opencv_imgcodecs ），增加opencv_imgcodecs

opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs

（此时应该变为LIBRARIES += opencv_imgcodecs  opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs）

保存，退出。

注意：在做前边的基础工作是将Makefile.config文件中

2.[caffe]python3编译caffe错误:cannot find -lboost_python3

针对Python3编译caffe时make all的时候出现了如下错误

cannot find -lboost_python3

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首先去/usr/lib/x86_64-linux-gnu目录下查看是否有python3版本的libboost，如果有类似libboost_python35.so但是没有libboost_python3.so则需要手动建立连接。
方法为：sudo ln -s libboost_python-py35.so libboost_python3.so


CTPN caffe 版本不同造成的问题：

[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 387:19: Message type "caffe.LayerParameter" has no field named "transpose_param".
F0519 15:36:28.264559 15846 upgrade_proto.cpp:90] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: /media/yang/3ed2dc8e-c16d-4d0a-bf9d-d783aaa7c4f2/yang/textrecon/sceneReco-master/CTPN/models/deploy.prototxt
*** Check failure stack trace: ***

原因：缺少相关的 transpose,reverse,lstm层。使用老版本却不支持cuda8.0

所以自定义向支持cuda8.0的caffe添加这三层，其实根据老版本向新的版本添加这几个就可以了，具体如下：

（1）在./src/caffe/proto/caffe.proto 中增加对应layer的paramter message；

（2）在./include/caffe/***layers.hpp中增加该layer的类的声明；

（3）在./src/caffe/layers/目录下新建.cpp和.cu（GPU）文件，进行类实现。

（4）在./src/caffe/gtest/中增加layer的测试代码，对所写的layer前传和反传进行测试，测试还包括速度。（可省略，但建议加上）

参考：https://www.cnblogs.com/573177885qq/p/6065625.html

             https://blog.csdn.net/shuzfan/article/details/51322976

例子如下

reverse_layer.hpp

#ifndef CAFFE_REVERSE_LAYER_HPP_
#define CAFFE_REVERSE_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"
namespace caffe {

/**
 * @brief Computes the Reverse function.
 *
 * TODO(dox): thorough documentation for Forward, Backward, and proto params.
 */
template <typename Dtype>
class ReverseLayer : public Layer<Dtype> {
 public:
  explicit ReverseLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "Reverse"; }
  virtual inline int ExactNumBottomBlobs() const { return 1; }
  virtual inline int ExactNumTopBlobs() const { return 1; }

 protected:
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 private:
  ReverseParameter reverse_param_;
  Blob<int> bottom_counts_;
  int axis_;
};
} 

#endif  // CAFFE_REVERSE_LAYER_HPP_

reverse_layer.cpp

#include <algorithm>
#include <vector>
#include "caffe/layers/reverse_layer.hpp"

namespace caffe {
template <typename Dtype>
void reverse_cpu(const int count, const Dtype* from_data, Dtype* to_data, 
	const int* counts, const int axis_count, const int axis) {
	for(int index=0; index<count; index++) {
		int ind=(index/counts[axis])%axis_count;
		int to_index=counts[axis]*(axis_count-2*ind-1)+index;
		*(to_data+to_index)=*(from_data+index);
	}
}
template <typename Dtype>
void ReverseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
	CHECK_NE(bottom[0], top[0])<<this->type()<<" does not support in-place computation.";
	reverse_param_=this->layer_param_.reverse_param();
}

template <typename Dtype>
void ReverseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
	vector<int> shape=bottom[0]->shape();
	axis_=reverse_param_.axis();
	CHECK_GT(shape.size(), 0)<<this->type()<<" does not support 0 axes blob.";
	CHECK_GE(axis_, 0)<<"axis must be greater than or equal to 0.";
	CHECK_LT(axis_, shape.size())<<"axis must be less than bottom's dimension.";
	top[0]->ReshapeLike(*bottom[0]);
	const int dim=shape.size();
	shape.clear();
	shape.push_back(dim);
	bottom_counts_.Reshape(shape);
	int* p=bottom_counts_.mutable_cpu_data();
	for (int i=1; i<dim; i++) {
		*p=bottom[0]->count(i);
		p++;
	}
	*p=1;
}

template <typename Dtype>
void ReverseLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, 
		const vector<Blob<Dtype>*>& top) {
	reverse_cpu<Dtype>(bottom[0]->count(), bottom[0]->cpu_data(), 
		top[0]->mutable_cpu_data(), bottom_counts_.cpu_data(), 
		bottom[0]->shape(axis_), axis_);
}

template <typename Dtype>
void ReverseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    	const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
	if (!propagate_down[0]) {
		return;
	}
	reverse_cpu<Dtype>(bottom[0]->count(), top[0]->cpu_diff(), 
		bottom[0]->mutable_cpu_diff(), bottom_counts_.cpu_data(), 
		bottom[0]->shape(axis_), axis_);
}

#ifdef CPU_ONLY
STUB_GPU(ReverseLayer);
#endif

INSTANTIATE_CLASS(ReverseLayer);
REGISTER_LAYER_CLASS(Reverse);

}  // namespace caffe