Comments (3)
1: 安装CUDA 9.2, cuda9.2版已经带有显卡驱动,默认安装,保持cuda和显卡驱动一致,否则会报cuda runtime is inefficient 35号错误。
2:确认各项目的include、lib和第三方库路径的配置,本人在项目中除CUDA使用绝对路径外,其余的include、lib路径均使用相对路径
caffe项目:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include
....\opensource\boost_1_57_0
....\opensource\opencv\include
....\3rdparty\include
....\include
....\3rdparty\include\lmdb
....\3rdparty\include\hdf5
....\src
....\3rdparty\include\glog
....\3rdparty\include\cudnn
....\src\caffe\proto
....\3rdparty\include\openblas
....\opensource\boost_1_57_0\lib
....\opensource\opencv\lib
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\lib\x64
....\3rdparty\lib
....\tools_bin
libClassfication项目:
..\..\3rdparty\include\openblas
....\opensource\opencv\include
....\opensource\boost_1_57_0
....\3rdparty\include
....\include
....\3rdparty\include\lmdb
....\3rdparty\include\hdf5
....\src
....\3rdparty\include\glog
....\3rdparty\include\cudnn
....\src\caffe\proto
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\lib\x64
....\opensource\boost_1_57_0\lib
....\opensource\opencv\lib
....\3rdparty\lib
....\tools_bin
ocr_test:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\include
....\opensource\boost_1_57_0
....\opensource\opencv\include
..\libClassification
....\include
....\3rdparty\include
....\src
....\3rdparty\include\openblas
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\lib\x64
....\opensource\opencv\lib
....\opensource\boost_1_57_0\lib
....\tools_bin
3:生成时要选择Release,x64格式
4:在项目的cuda生成host代码时,支持一些旧版本的cu伪代码,三个项目都设置成:compute_61,sm_61;compute_52,sm_52;compute_50,sm_50;compute_35,sm_35;compute_30,sm_30,否则会报no kernel image 40号错误
5:整理的3rdparty请使用群文件的3rdparty-20180730.tar
6:在visual studio 2015打开caffe.sln方案时(项目根目录下),如果出现无法加载工程的错误,打开项目根目录下的caffe-vsproj\caffe.sln
7:导入工程后,在ocr_test的项目,右键,选择【生成依赖项】-> 【项目依赖项】,勾选caffe, libClassfication两个项目
8: 项目代码中对路径没有做规范化处理,导致在运行期间报找不到模型文件的错误,为了调试方便,这里把代码中的路径全部修改成绝对路径,如ocr_test.cpp的962行, string modelfolder = "E:\License-Plate-Detect-Recognition\caffe-vsproj\ocr_test\plateCard_test"; 其他的类似修改 但要区分MTCNN模型和ICNNPredict模型的位置
9:启动一个cmd,切换到工程根目录下的tools_bin目录,启动ocr_test.ext
from license-plate-detect-recognition-via-deep-neural-networks-accuracy-up-to-99.9.
@zhubenfu
您好,我在群里下的3rdparty20180726.7z(没有找到您说的3rdparty-20180730.tar)第三方文件,
cuda改成了9.2,cudnn用7.1和3rdparty里边的都说不兼容!
方便的话,您能告知一下用哪个版本的cudnn吗?
十分感谢!
from license-plate-detect-recognition-via-deep-neural-networks-accuracy-up-to-99.9.
链接:https://pan.baidu.com/s/1nX3CgpTckEOjSXlNvaruNw
提取码:8gls
或者加群 群里有更新
from license-plate-detect-recognition-via-deep-neural-networks-accuracy-up-to-99.9.
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