windows10上搭建caffe以及踩到的坑

对动作捕捉的几篇论文感兴趣，想复现一下，需要caffe环境就折腾了下！转模型需要python 2.7环境，我顺便也弄了！！！

1. 环境

Windows10
RTX2080TI 11G
Anaconda Python2.7
visual studio 2013
cuda 11.1
cudnn 8.2.0
cmake 3.26.1
git

2. 具体步骤

2.1 安装cuda和cudnn

略 这部分看其他博客
cuda_11.1.0_456.43_win10.exe
cudnn-11.3-windows-x64-v8.2.0.53.zip

2.2 安装caffe

1.下载源码

git clone https://github.com/BVLC/caffe.git
cd caffe
git checkout windows

2.修改build_win.cmd 这一步决定着后面能不能编译成功
path/caffe/scripts/build_win.cmd

a.需要确定visual studio的版本
MSVC版本号对应关系 我在这边踩了坑，因为我看的那个博客这边弄错了。这个链接没问题。MSVC_VERSION = 12表示VS2013，MSVC_VERSION = 14表示VS2015
b.确定GPU的显卡架构 架构表 一定要看

下面开始修改文件:
1.修改caffe源码中./scripts/build_win.cmd



修改caffe源码中./cmake/Cuda.cmake:


我的显卡是2080ti，对应着75，我只要保证有75就行！

这里的80需要根据我的显卡情况改成75



这里面是一定要改的，不然找不到cudnn！因为caffe之类的代码很久不更新了，只支持到了使用cudnn7.x，在使用了cudnn8的环境下编译caffe时，会在src/caffe/layers/cudnn_conv_layer.cpp等文件里出错！
报错信息是这样的：

error: identifier "CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT" is undefined
error: identifier "cudnnGetConvolutionForwardAlgorithm" is undefined

这是因为cudnn8里没有cudnnGetConvolutionForwardAlgorithm()这个函数了，改成了cudnnGetConvolutionForwardAlgorithm_v7()，也没了CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT这个宏定义，这些都是API不兼容，但是NVIDIA声明cudnn8不支持了，caffe的代码也没人去更新了，所以不能指望NVIDIA或者berkeley，只能自行修改。将cudnn_conv_layer.cpp文件替换成如下:

#ifdef USE_CUDNN
#include <algorithm>
#include <vector>#include "caffe/layers/cudnn_conv_layer.hpp"namespace caffe {// Set to three for the benefit of the backward pass, which
// can use separate streams for calculating the gradient w.r.t.
// bias, filter weights, and bottom data for each group independently
#define CUDNN_STREAMS_PER_GROUP 3/*** TODO(dox) explain cuDNN interface*/
template <typename Dtype>
void CuDNNConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);// Initialize CUDA streams and cuDNN.stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];// Initialize algorithm arraysfwd_algo_       = new cudnnConvolutionFwdAlgo_t[bottom.size()];bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];bwd_data_algo_  = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];// initialize size arraysworkspace_fwd_sizes_ = new size_t[bottom.size()];workspace_bwd_filter_sizes_ = new size_t[bottom.size()];workspace_bwd_data_sizes_ = new size_t[bottom.size()];// workspace dataworkspaceSizeInBytes = 0;workspaceData = NULL;workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP];for (size_t i = 0; i < bottom.size(); ++i) {// initialize all to default algorithmsfwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;// default algorithms don't require workspaceworkspace_fwd_sizes_[i] = 0;workspace_bwd_data_sizes_[i] = 0;workspace_bwd_filter_sizes_[i] = 0;}for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {CUDA_CHECK(cudaStreamCreate(&stream_[g]));CUDNN_CHECK(cudnnCreate(&handle_[g]));CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));workspace[g] = NULL;}// Set the indexing parameters.bias_offset_ = (this->num_output_ / this->group_);// Create filter descriptor.const int* kernel_shape_data = this->kernel_shape_.cpu_data();const int kernel_h = kernel_shape_data[0];const int kernel_w = kernel_shape_data[1];cudnn::createFilterDesc<Dtype>(&filter_desc_,this->num_output_ / this->group_, this->channels_ / this->group_,kernel_h, kernel_w);// Create tensor descriptor(s) for data and corresponding convolution(s).for (int i = 0; i < bottom.size(); i++) {cudnnTensorDescriptor_t bottom_desc;cudnn::createTensor4dDesc<Dtype>(&bottom_desc);bottom_descs_.push_back(bottom_desc);cudnnTensorDescriptor_t top_desc;cudnn::createTensor4dDesc<Dtype>(&top_desc);top_descs_.push_back(top_desc);cudnnConvolutionDescriptor_t conv_desc;cudnn::createConvolutionDesc<Dtype>(&conv_desc);conv_descs_.push_back(conv_desc);}// Tensor descriptor for bias.if (this->bias_term_) {cudnn::createTensor4dDesc<Dtype>(&bias_desc_);}handles_setup_ = true;
}template <typename Dtype>
void CuDNNConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {ConvolutionLayer<Dtype>::Reshape(bottom, top);CHECK_EQ(2, this->num_spatial_axes_)<< "CuDNNConvolution input must have 2 spatial axes "<< "(e.g., height and width). "<< "Use 'engine: CAFFE' for general ND convolution.";bottom_offset_ = this->bottom_dim_ / this->group_;top_offset_ = this->top_dim_ / this->group_;const int height = bottom[0]->shape(this->channel_axis_ + 1);const int width = bottom[0]->shape(this->channel_axis_ + 2);const int height_out = top[0]->shape(this->channel_axis_ + 1);const int width_out = top[0]->shape(this->channel_axis_ + 2);const int* pad_data = this->pad_.cpu_data();const int pad_h = pad_data[0];const int pad_w = pad_data[1];const int* stride_data = this->stride_.cpu_data();const int stride_h = stride_data[0];const int stride_w = stride_data[1];
#if CUDNN_VERSION_MIN(8, 0, 0)int RetCnt;bool found_conv_algorithm;size_t free_memory, total_memory;cudnnConvolutionFwdAlgoPerf_t     fwd_algo_pref_[4];cudnnConvolutionBwdDataAlgoPerf_t bwd_data_algo_pref_[4];//get memory sizescudaMemGetInfo(&free_memory, &total_memory);
#else// Specify workspace limit for kernels directly until we have a// planning strategy and a rewrite of Caffe's GPU memory mangagementsize_t workspace_limit_bytes = 8*1024*1024;
#endiffor (int i = 0; i < bottom.size(); i++) {cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],this->num_,this->channels_ / this->group_, height, width,this->channels_ * height * width,height * width, width, 1);cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],this->num_,this->num_output_ / this->group_, height_out, width_out,this->num_output_ * this->out_spatial_dim_,this->out_spatial_dim_, width_out, 1);cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],filter_desc_, pad_h, pad_w,stride_h, stride_w);#if CUDNN_VERSION_MIN(8, 0, 0)// choose forward algorithm for filter// in forward filter the CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED is not implemented in cuDNN 8CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm_v7(handle_[0],bottom_descs_[i],filter_desc_,conv_descs_[i],top_descs_[i],4,&RetCnt,fwd_algo_pref_));found_conv_algorithm = false;for(int n=0;n<RetCnt;n++){if (fwd_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&fwd_algo_pref_[n].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED &&fwd_algo_pref_[n].memory < free_memory){found_conv_algorithm = true;fwd_algo_[i]                   = fwd_algo_pref_[n].algo;workspace_fwd_sizes_[i]        = fwd_algo_pref_[n].memory;break;}}if(!found_conv_algorithm) LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";else{// choose backward algorithm for filter// for better or worse, just a fixed constant due to the missing // cudnnGetConvolutionBackwardFilterAlgorithm in cuDNN version 8.0bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;//twice the amount of the forward search to be save     workspace_bwd_filter_sizes_[i] = 2*workspace_fwd_sizes_[i];}// choose backward algo for dataCUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm_v7(handle_[0],filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],4,&RetCnt,bwd_data_algo_pref_));found_conv_algorithm = false;for(int n=0;n<RetCnt;n++){if (bwd_data_algo_pref_[n].status == CUDNN_STATUS_SUCCESS &&bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD &&bwd_data_algo_pref_[n].algo != CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED &&bwd_data_algo_pref_[n].memory < free_memory){found_conv_algorithm = true;bwd_data_algo_[i]              = bwd_data_algo_pref_[n].algo;workspace_bwd_data_sizes_[i]   = bwd_data_algo_pref_[n].memory;break;}}if(!found_conv_algorithm) LOG(ERROR) << "cuDNN did not return a suitable algorithm for convolution.";
#else// choose forward and backward algorithms + workspace(s)CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0],bottom_descs_[i],filter_desc_,conv_descs_[i],top_descs_[i],CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,workspace_limit_bytes,&fwd_algo_[i]));CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0],bottom_descs_[i],filter_desc_,conv_descs_[i],top_descs_[i],fwd_algo_[i],&(workspace_fwd_sizes_[i])));// choose backward algorithm for filterCUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0],bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,workspace_limit_bytes, &bwd_filter_algo_[i]) );// get workspace for backwards filter algorithmCUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0],bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i]));// choose backward algo for dataCUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0],filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,workspace_limit_bytes, &bwd_data_algo_[i]));// get workspace sizeCUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0],filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]) );
#endif}// reduce over all workspace sizes to get a maximum to allocate / reallocatesize_t total_workspace_fwd = 0;size_t total_workspace_bwd_data = 0;size_t total_workspace_bwd_filter = 0;for (size_t i = 0; i < bottom.size(); i++) {total_workspace_fwd        = std::max(total_workspace_fwd,workspace_fwd_sizes_[i]);total_workspace_bwd_data   = std::max(total_workspace_bwd_data,workspace_bwd_data_sizes_[i]);total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,workspace_bwd_filter_sizes_[i]);}// get max over all operationssize_t max_workspace = std::max(total_workspace_fwd,total_workspace_bwd_data);max_workspace = std::max(max_workspace, total_workspace_bwd_filter);// ensure all groups have enough workspacesize_t total_max_workspace = max_workspace *(this->group_ * CUDNN_STREAMS_PER_GROUP);// this is the total amount of storage needed over all groups + streamsif (total_max_workspace > workspaceSizeInBytes) {DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;workspaceSizeInBytes = total_max_workspace;// free the existing workspace and allocate a new (larger) onecudaFree(this->workspaceData);cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);if (err != cudaSuccess) {// force zero memory pathfor (int i = 0; i < bottom.size(); i++) {workspace_fwd_sizes_[i] = 0;workspace_bwd_filter_sizes_[i] = 0;workspace_bwd_data_sizes_[i] = 0;fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;}// NULL out all workspace pointersfor (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {workspace[g] = NULL;}// NULL out underlying dataworkspaceData = NULL;workspaceSizeInBytes = 0;}// if we succeed in the allocation, set pointer aliases for workspacesfor (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {workspace[g] = reinterpret_cast<char *>(workspaceData) + g*max_workspace;}}// Tensor descriptor for bias.if (this->bias_term_) {cudnn::setTensor4dDesc<Dtype>(&bias_desc_,1, this->num_output_ / this->group_, 1, 1);}
}template <typename Dtype>
CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {// Check that handles have been setup before destroying.if (!handles_setup_) { return; }for (int i = 0; i < bottom_descs_.size(); i++) {cudnnDestroyTensorDescriptor(bottom_descs_[i]);cudnnDestroyTensorDescriptor(top_descs_[i]);cudnnDestroyConvolutionDescriptor(conv_descs_[i]);}if (this->bias_term_) {cudnnDestroyTensorDescriptor(bias_desc_);}cudnnDestroyFilterDescriptor(filter_desc_);for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {cudaStreamDestroy(stream_[g]);cudnnDestroy(handle_[g]);}cudaFree(workspaceData);delete [] stream_;delete [] handle_;delete [] fwd_algo_;delete [] bwd_filter_algo_;delete [] bwd_data_algo_;delete [] workspace_fwd_sizes_;delete [] workspace_bwd_data_sizes_;delete [] workspace_bwd_filter_sizes_;
}INSTANTIATE_CLASS(CuDNNConvolutionLayer);}   // namespace caffe
#endif

在命令行窗口运行 scripts/build_win.cmd，会下载libraries_v120_x64_py27_1.1.0.tar.bz2文件，最好挂个梯子，我这边下的很快。这个文件是caffe相关的依赖库，此过程中编译的时候会报一个boost相关的错误，对C:\Users\qiao\.caffe\dependencies\libraries_v120_x64_py27_1.1.0\libraries\include\boost-1_61\boost\config\compiler路径下的 nvcc.hpp 作如下修改，因为RTX2080ti的编译器nvcc版本大于7.5：

之后删除之前编译的build文件夹，重新编译一次，编译过程中会出现较多警告可以不用理会，稍等一段时间后，最终会出现：

最后在build文件夹下找到Caffe.sln文件，用VS2013打开，然后右键ALL_BUILD进行生成，等几分钟后编译完，
release版本

debug版本

将caffe源码下中python中的caffe文件夹粘贴到上面配置的python路径中D:\Anaconda3\envs\pycaffe27\Lib\site-packages再将E:\caffe\build\install\bin路径添加到环境变量中，在终端中测试一下caffe命令是否正常，然后pip安装一些必要的库
pip install numpy scipy protobuf six scikit-image pyyaml pydotplus graphviz
最后打开python，测试一下

参考文献:
Windows10 下RTX30系列Caffe安装教程
Windows10下搭建caffe过程记录