ICnet pspnet编译过程

下载ICnet，想评估一下，编译的过程相当伤神。总结一下：

这个caffe不是原装的，而且不支持cuda8，于是百度无数，才得以编译成功，以资纪念。感谢这个哥们：
http://blog.csdn.net/u010733679/article/details/52221404
1. 用最新caffe源码的以下文件替换掉faster rcnn 的对应文件

include/caffe/layers/cudnn_relu_layer.hpp, src/caffe/layers/cudnn_relu_layer.cpp, src/caffe/layers/cudnn_relu_layer.cu

include/caffe/layers/cudnn_sigmoid_layer.hpp, src/caffe/layers/cudnn_sigmoid_layer.cpp, src/caffe/layers/cudnn_sigmoid_layer.cu

include/caffe/layers/cudnn_tanh_layer.hpp, src/caffe/layers/cudnn_tanh_layer.cpp, src/caffe/layers/cudnn_tanh_layer.cu

2. 用caffe源码中的这个文件替换掉faster rcnn 对应文件

include/caffe/util/cudnn.hpp

3. 将 faster rcnn 中的 src/caffe/layers/cudnn_conv_layer.cu 文件中的所有

cudnnConvolutionBackwardData_v3 函数名替换为 cudnnConvolutionBackwardData

cudnnConvolutionBackwardFilter_v3函数名替换为 cudnnConvolutionBackwardFilter

但是第三步我没做。还有这一篇文章： http://blog.csdn.net/zziahgf/article/details/72900948

问题28 - matio.h no such file or directory / matio 安装

$ sudo apt-get install libmatio-dev
或源码安装：
# 下载 matio (https://sourceforge.net/projects/matio/)
$ tar zxf matio-X.Y.Z.tar.gz
$ cd matio-X.Y.Z
$ ./configure
$ make
$ make check
$ make install  # 安装
$ export LD_LIBRARY_PATH=/path/to/libmatio.so.2

# 在caffe 的 Makefile.config 中的INCLUDE_DIRS 中添加 matio 的 src路径， LIBRARY_DIRS 中添加 src/.libs，如：
#   INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include  /path/to/matio-1.5.2/src
#   LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /path/to/matio-1.5.2/src/.libs

# 参考： http://blog.csdn.net/houqiqi/article/details/46469981[/code] 
另外一篇网文，修改common.cuh：

#ifndef CAFFE_COMMON_CUH_  

#define CAFFE_COMMON_CUH_  

  

  

#include <cuda.h>  

#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600  

#else  

// CUDA: atomicAdd is not defined for doubles  

static __inline__ __device__ double atomicAdd(double *address, double val) {  

   unsigned long long int* address_as_ull = (unsigned long long int*)address;  

   unsigned long long int old = *address_as_ull, assumed;  

   if (val==0.0)  

     return __longlong_as_double(old);  

   do {  

     assumed = old;  

     old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val +__longlong_as_double(assumed)));  

   } while (assumed != old);  

   return __longlong_as_double(old);  

 }  

#endif  

#endif  

///必须这样改，我删空不能成功

另外附上我的Makefile.config and Makefile,用在我试用的豪华云服务器上（8个titanxp）
Makefile改动部分：

LIBRARIES += matio glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

Makefile.config:

## 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-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.

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

# 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 ?= @