Caffe

错误： 采用make方式编译时遇到如下错误

@H_301_46@In file included from /usr/include/boost/python/detail/prefix.hpp:13:0,from /usr/include/boost/python/args.hpp:8,from /usr/include/boost/python.hpp:11,from tools/caffe.cpp:2: /usr/include/boost/python/detail/wrap_python.hpp:50:23: fatal error: pyconfig.h: No such file or directory compilation terminated. Makefile:575: recipe for target '.build_release/tools/caffe.o' Failed make: *** [.build_release/tools/caffe.o] Error 1

解决方案：修改Makefile.config，将

@H_301_46@PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ # $(ANACONDA_HOME)/include/python2.7 \ # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

取消以下2行注释

@H_301_46@PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python2.7 \ $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \ @H_301_46@Note:$(ANACONDA_HOME) #虚拟环境Anaconda2的根目录

Faster-RCNN

问题： 如何编译只采用cpu版本的Faster-RCNN?

解决方案：
在./lib/setup.py中注释以下部分

@H_301_46@... #CUDA = locate_cuda() ... ... #self.set_executable('compiler_so',CUDA['nvcc']) ... ... #Extension('nms.gpu_nms', #[‘nms/nms_kernel.cu','nms/gpu_nms.pyx'], #library_dirs=[CUDA['lib64']], #libraries=['cudart'], #language='c++', #runtime_library_dirs=[CUDA['lib64']], ## this Syntax is specific to this build system ## we're only going to use certain compiler args with nvcc and not with ## gcc the implementation of this trick is in customize_compiler() below #extra_compile_args={'gcc': ["-Wno-unused-function"], #’nvcc': ['-arch=sm_35', #’—ptxas-options=-v', #’-c’, #’—compiler-options', #”’-fPIC'"]}, #include_dirs = [numpy_include,CUDA['include']] #)

问题：运行时，遇到错误：ImportError: No module named cv2

@H_301_46@File "./tools/test_net.py",line 13,in <module> from fast_rcnn.test import test_net File "/home/rtc5/JpHu/pva-faster-rcnn-master/tools/../lib/fast_rcnn/test.py",line 15,in <module> import cv2 ImportError: No module named cv2

解决方案
（1）检查cv2是否存在:
在${HOME}目录下运行

@H_301_46@$find -name cv2

进行查找
（2）如果不存在cv2，安装python-opencv

@H_301_46@sudo apt-get install python-opencv

（3）如果存在cv2,将文件夹cv2所在目录添加到.bashrc最后一行（如我将cv2安装在/home/rtc5/anaconda2/envs/tensorflow/lib/python2.7/site-packages/cv2下）

@H_301_46@export PATHONPATH=$PYTHONPATH:/home/rtc5/anaconda2/envs/tensorflow/lib/python2.7/site-packages/cv2

运行命令

@H_301_46@source ./bashrc #激活

激活./bashrc

问题：编译cpu版本成功后，faster-rcnn运行时，遇到错误ImportError: No module named gpu_nms

@H_301_46@File "./demo.py",line 18,in from fast_rcnn.test import im_detect File ".../py-faster-rcnn-master/tools/../lib/fast_rcnn/test.py",line 17,in from fast_rcnn.nms_wrapper import nms File ".../py-faster-rcnn-master/tools/../lib/fast_rcnn/nms_wrapper.py",line 11,in from nms.gpu_nms import gpu_nms ImportError: No module named gpu_nms

解决方案：
注释${FCNN}/py-faster-rcnn/lib/fast_rcnn/nms_wrapper.py 中有关gpu的代码

@H_301_46@from fast_rcnn.config import cfg #from nms.gpu_nms import gpu_nms from nms.cpu_nms import cpu_nms def nms(dets,thresh,force_cpu=False): """Dispatch to either cpu or GPU NMS implementations.""" if dets.shape[0] == 0: return [] #if cfg.USE_GPU_NMS and not force_cpu: # return gpu_nms(dets,device_id=cfg.GPU_ID) else: return cpu_nms(dets,thresh)

问题：（1）运行vgg16版本的faster-rcnn的./tools/demo.py遇到如下问题

@H_301_46@WARNING: Logging before InitGoogleLogging() is written to STDERR F1207 00:08:31.251930 20944 common.cpp:66] Cannot use GPU in cpu-only Caffe: check mode. @H_249_404@*** Check failure stack trace: *** Aborted (core dumped)

解决方案：
采用命令：

@H_301_46@$./tools/demo.py --cpu

Note:运行pvanet示例时，遇到类似问题，则需要将测试文件*.py中set_gpu的相关代码注释

问题：如何编译cpu版本的pvanet

编译caffe，遇到问题：

@H_301_46@src/caffe/layers/proposal_layer.cpp:321:10: error: redefinition of ‘void caffe::ProposalLayer<Dtype>::Backward_gpu(const std::vector<caffe::Blob<Dtype>*>&,const std::vector<bool>&,const std::vector<caffe::Blob<Dtype>*>&)’ STUB_GPU(ProposalLayer); ^ ./include/caffe/util/device_alternate.hpp:17:6: note: in definition of macro ‘STUB_GPU’ void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,\ ^ In file included from src/caffe/layers/proposal_layer.cpp:1:0: ./include/caffe/fast_rcnn_layers.hpp:122:16: note: ‘virtual void caffe::ProposalLayer<Dtype>::Backward_gpu(const std::vector<caffe::Blob<Dtype>*>&,const std::vector<caffe::Blob<Dtype>*>&)’ prevIoUsly declared here virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,^ Makefile:575: recipe for target '.build_release/src/caffe/layers/proposal_layer.o' Failed make: *** [.build_release/src/caffe/layers/proposal_layer.o] Error 1 make: *** Waiting for unfinished jobs....

解决方案：
由于caffe::ProposalLayer<Dtype>::Backward_gpu在./include/caffe/fast_rcnn_layers.hpp和./include/caffe/util/device_alternate.hpp（后者为模板形式）中定义了两次，被系统认为重定义。
解决方法如下：
将./include/caffe/fast_rcnn_layers.hpp的Backward_gpu代码

@H_301_46@virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,const vector<bool>& propagate_down,const vector<Blob<Dtype>*>& bottom){}

修改如下

@H_301_46@virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,const vector<Blob<Dtype>*>& bottom);

由于Backward_cpu只在./include/caffe/fast_rcnn_layers.hpp中定义过一次，所以一定避免对它做如上gpu的修改。

问题：如何只用cpu训练caffe,py-faster-rcnn,pvanet?

*报错：

@H_301_46@smooth_L1_loss_layer Not Implemented Yet

解决方案：*
补充./src/caffe/layers/smooth_L1_loss_layer.cpp函数实体SmoothL1LossLayer::Forward_cpu和SmoothL1LossLayer::Backward_cpu

@H_301_46@`// ------------------------------------------------------------------ // Fast R-CNN // Copyright (c) 2015 Microsoft // Licensed under The MIT License [see fast-rcnn/LICENSE for details] // Written by Ross Girshick // ------------------------------------------------------------------ #include "caffe/fast_rcnn_layers.hpp" namespace caffe { template void SmoothL1LossLayer::LayerSetUp( const vector<Blob>& bottom,const vector<Blob>& top) { SmoothL1LossParameter loss_param = this->layer_param_.smooth_l1_loss_param(); sigma2_ = loss_param.sigma() * loss_param.sigma(); has_weights_ = (bottom.size() >= 3); if (has_weights_) { CHECK_EQ(bottom.size(),4) << "If weights are used,must specify both " "inside and outside weights"; } } template void SmoothL1LossLayer::Reshape( const vector<Blob>& bottom,const vector<Blob>& top) { LossLayer::Reshape(bottom,top); CHECK_EQ(bottom[0]->channels(),bottom[1]->channels()); CHECK_EQ(bottom[0]->height(),bottom[1]->height()); CHECK_EQ(bottom[0]->width(),bottom[1]->width()); if (has_weights_) { CHECK_EQ(bottom[0]->channels(),bottom[2]->channels()); CHECK_EQ(bottom[0]->height(),bottom[2]->height()); CHECK_EQ(bottom[0]->width(),bottom[2]->width()); CHECK_EQ(bottom[0]->channels(),bottom[3]->channels()); CHECK_EQ(bottom[0]->height(),bottom[3]->height()); CHECK_EQ(bottom[0]->width(),bottom[3]->width()); } diff_.Reshape(bottom[0]->num(),bottom[0]->channels(),bottom[0]->height(),bottom[0]->width()); errors_.Reshape(bottom[0]->num(),bottom[0]->width()); // vector of ones used to sum ones_.Reshape(bottom[0]->num(),bottom[0]->width()); for (int i = 0; i < bottom[0]->count(); ++i) { ones_.mutable_cpu_data()[i] = Dtype(1); } } template void SmoothL1LossLayer::Forward_cpu(const vector<Blob>& bottom,const vector<Blob>& top) { // NOT_IMPLEMENTED; int count = bottom[0]->count(); //int num = bottom[0]->num(); const Dtype* in = diff_.cpu_data(); Dtype* out = errors_.mutable_cpu_data(); caffe_set(errors_.count(),Dtype(0),out); caffe_sub( count,bottom[0]->cpu_data(),bottom[1]->cpu_data(),diff_.mutable_cpu_data()); // d := b0 - b1 if (has_weights_) { // apply "inside" weights caffe_mul( count,bottom[2]->cpu_data(),diff_.cpu_data(),diff_.mutable_cpu_data()); // d := w_in * (b0 - b1) } for (int index = 0;index < count; ++index){ Dtype val = in[index]; Dtype abs_val = abs(val); if (abs_val < 1.0 / sigma2_) { out[index] = 0.5 * val * val * sigma2_; } else { out[index] = abs_val - 0.5 / sigma2_; } } if (has_weights_) { // apply "outside" weights caffe_mul( count,bottom[3]->cpu_data(),errors_.cpu_data(),errors_.mutable_cpu_data()); // d := w_out * SmoothL1(w_in * (b0 - b1)) } Dtype loss = caffe_cpu_dot(count,ones_.cpu_data(),errors_.cpu_data()); top[0]->mutable_cpu_data()[0] = loss / bottom[0]->num(); } template void SmoothL1LossLayer::Backward_cpu(const vector<Blob>& top,const vector& propagate_down,const vector<Blob>& bottom) { // NOT_IMPLEMENTED; int count = diff_.count(); //int num = diff_.num(); const Dtype* in = diff_.cpu_data(); Dtype* out = errors_.mutable_cpu_data(); caffe_set(errors_.count(),out); for (int index = 0;index < count; ++index){ Dtype val = in[index]; Dtype abs_val = abs(val); if (abs_val < 1.0 / sigma2_) { out[index] = sigma2_ * val; } else { out[index] = (Dtype(0) < val) - (val < Dtype(0)); } } for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num(); caffe_cpu_axpby( count,// count alpha,// alpha diff_.cpu_data(),// x Dtype(0),// beta bottom[i]->mutable_cpu_diff()); // y if (has_weights_) { // Scale by "inside" weight caffe_mul( count,bottom[i]->cpu_diff(),bottom[i]->mutable_cpu_diff()); // Scale by "outside" weight caffe_mul( count,bottom[i]->mutable_cpu_diff()); } } } } #ifdef cpu_ONLY STUB_GPU(SmoothL1LossLayer); #endif INSTANTIATE_CLASS(SmoothL1LossLayer); REGISTER_LAYER_CLASS(SmoothL1Loss); } // namespace caffe

转自： zhouphd 的解答，已验证有效，caffe能够通过编译，并进行训练

问题：运行pvanet时，报错

@H_301_46@Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.

原因：由于之前安装tensorflow时，采用的是anaconda，它独自创建了一个虚拟环境（自行另安装依赖库），但由于anaconda会在~/.bashrc中添加PATH路径。所以执行caffe程序时（在虚拟环境之外），其依赖库也会受到anaconda安装软件的影响。
解决方案：屏蔽anaconda设置的PATH，在~/.bashrc中注释

@H_301_46@#export PATH="/home/cvrsg/anaconda2/bin:$PATH" $source ~/.bashrc #激活.bashrc

注意：重开一个终端，在当前终端，source命令是没有生效的。
如何验证？

@H_301_46@如果在当前终端下输入 sudo echo $PATH 你会发现anaconda2/bin还在PATH中，source未生效 重开终端之后， anaconda2/bin已消失

同样由此可知，当我们需要anaconda2时，我们可以将

@H_301_46@#export PATH="/home/cvrsg/anaconda2/bin:$PATH"

解注释，并source ~/.bashrc激活
不需要时，注释即可。
在上述命令被注释的情况下，运行source activate tensorflow会出现以下错误

@H_301_46@bash: activate: No such file or directory

别着急，解注释，激活就好。

Note-切记：：：
另外，如果我们要用conda安装软件时，一定要切换到相应的虚拟环境下，否则安装的软件很容易和系统软件发生版本冲突，导致程序出错。

在安装pycaffe依赖库时，遇到的问题

利用命令for req in $(cat requirements.txt); do pip install $req; done安装pycaffe相关依赖库遇到问题：ImportError: No module named packaging.version
描述：这是因为采用 sudo apt-get install python-pip安装的pip有问题

@H_301_46@sudo apt-get remove python-pip #删除原有pip wget https://bootstrap.pypa.io/get-pip.py //获取特定pip，并进行安装 sudo python get-pip.py

错误

@H_301_46@F0608 15:36:07.750129 6353 concat_layer.cpp:42] Check Failed: top_shape[j] == bottom[i]->shape(j) (63 vs. 62) All inputs must have the same shape,except at concat_axis. *** Check failure stack trace: *** Aborted (core dumped)

这个似乎是新版本的PVANET的问题，旧版本的PVANET没有该问题。问题出在lib文件的改变。

其他

问题: wget如何避免防火墙的影响？

解决方案：
在命令

@H_301_46@wget xxx #如wget https://www.dropBox.com/s/87zu4y6cvgeu8vs/test.model?dl=0 -O models/pvanet/full/test.model

之后加

@H_301_46@—no-check-certificate 原文链接：https://www.f2er.com/ubuntu/355699.html