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    3. Faster R-CNN的安装及测试（Python版本和Matlab版本）

    Faster R-CNN的安装及测试（Python版本和Matlab版本）

    xiaoxiao2021-03-25  75

    rbg的Python版本

    一、拉取源码

    git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git

    拉取完成后，在/home/cmwang/目录下增加了py-faster-rcnn文件夹【cmwang是我的ubuntu用户名】

    二、安装依赖

    sudo apt-get install python-opencv sudo pip install cython easydict

    三、修改Makefile文件

    终端输入

    cd /home/cmwang/py-faster-rcnn/caffe-fast-rcnn/ cp Makefile.config.example Makefile.config #备份Makefile gedit Makefile.config

    使用python层 将# WITH_PYTHON_LAYER := 1修改为 WITH_PYTHON_LAYER := 1

    调用matlab 将#MATLAB_DIR := / usr/local/MATLAB/R2015b 中的#去掉。

    使用cudnn加速 将# USE_CUDNN := 1修改为USE_CUDNN := 1

    保留# CPU_ONLY := 1不变，使用GPU运行faster r-cnn

    四、编译Cython模块

    终端输入

    cd ~/py-faster-rcnn/lib/ make

    五、编译caffe和pycaffe & matcaffe

    终端输入

    cd ~/py-faster-rcnn/caffe-fast-rcnn/ make -j8 && make pycaffe && make matcaffe

    六、下载模型

    终端输入

    cd ~/py-faster-rcnn/ ./data/scripts/fetch_faster_rcnn_models.sh

    七、faster r-cnn测试

    cd ~/py-faster-rcnn/ ./tools/demo.py

    Shaoqing Ren的MATLAB版本

    一、拉取源码

    下载 faster_rcnn-master.zip

    拉取完成后，在/home/cmwang/目录下增加了faster_rcnn_master文件夹【cmwang是我的ubuntu用户名】

    二、下载补充文件夹

    解压faster_rcnn-master.zip后，发现faster_rcnn-master/external/caffe 是空文件。 下载 caffe-062f2431162165c658a42d717baf8b74918aa18e.zip

    unzip caffe-062f2431162165c658a42d717baf8b74918aa18e.zip cp ./caffe-062f2431162165c658a42d717baf8b74918aa18e/ ./faster_rcnn-master/external/caffe/

    三、修改Makefile文件

    终端输入

    cd /home/cmwang/faster_rcnn-master/external/caffe cp Makefile.config.example Makefile.config #备份Makefile gedit Makefile.config

    使用python层 将# WITH_PYTHON_LAYER := 1修改为 WITH_PYTHON_LAYER := 1

    调用matlab 将#MATLAB_DIR := / usr/local/MATLAB/R2015b 中的#去掉。

    使用cudnn加速 将# USE_CUDNN := 1修改为USE_CUDNN := 1

    保留# CPU_ONLY := 1不变，使用GPU运行faster r-cnn

    四、编译caffe和pycaffe & matcaffe

    终端输入

    cd ~faster_rcnn-master/external/caffe make all -j8 && make test -j8 && make runtest -j8 && make pycaffe && make matcaffe

    五、下载模型

    终端输入

    cd ~/faster_rcnn-master/ run fetch_data/fetch_model_VGG16.m

    六、运行

    cd ~/faster_rcnn-master/ run faster_rcnn_build.m run startup.m

    七、faster r-cnn测试

    cd ~/faster_rcnn-master/ run ./experiments/script_faster_rcnn_demo.m

    备注： Matlab里面有内置的

    Object Detection Using Faster R-CNN Deep Learning

    常出现的问题1

    这是由于当前版本的caffe中cudnn实现与系统所安装的cudnn的版本不一致引起，rbgirshick的py-faster-rcnn其cudnn实现为旧版本的实现，所有出现了以上问题。

    cudnn-7.0-linux-x64-v4.0-prod.tgz不会出现此问题 cudnn-7.5-linux-x64-v5.1.tgz会出现同样问题 cudnn-8.0-linux-x64-v5.1.tgz会出现同样问题

    解决办法

    ①将/home/cmwang/py-faster-rcnn/caffe-fast-rcnn/include/caffe/util/cudnn.hpp 换成最新版caffe里的相应目录下的cudnn.hpp； ②将/home/cmwang/py-faster-rcnn/caffe-fast-rcnn/include/caffe/layers/下所有cudnn开头的文件都替换为最新版caffe里相应目录下的同名文件； ③将/home/cmwang/py-faster-rcnn/caffe-fast-rcnn/src/caffe/layer下所有cudnn开头的文件都替换为最新版caffe里相应目录下的同名文件

    常出现的问题2

    出现找不到hdf5.h错误；

    解决办法：终端输入

    cd /home/cmwang/py-faster-rcnn/caffe-fast-rcnn/ gedit Makefile.config 在INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include 后面打上一个空格，然后添加/usr/include/hdf5/serial

    如果没有这一句可能会报一个找不到hdf5.h的错误。

    常出现的问题3

    CXX .build_release/src/caffe/proto/caffe.pb.cc CXX src/caffe/common.cpp CXX src/caffe/net.cpp CXX src/caffe/internal_thread.cpp CXX src/caffe/layers/cudnn_softmax_layer.cpp CXX src/caffe/layers/hinge_loss_layer.cpp CXX src/caffe/layers/relu_layer.cpp CXX src/caffe/layers/contrastive_loss_layer.cpp CXX src/caffe/layers/elu_layer.cpp CXX src/caffe/layers/hdf5_output_layer.cpp CXX src/caffe/layers/cudnn_pooling_layer.cpp CXX src/caffe/layers/deconv_layer.cpp CXX src/caffe/layers/memory_data_layer.cpp CXX src/caffe/layers/eltwise_layer.cpp CXX src/caffe/layers/softmax_loss_layer.cpp CXX src/caffe/layers/silence_layer.cpp CXX src/caffe/layers/power_layer.cpp CXX src/caffe/layers/exp_layer.cpp CXX src/caffe/layers/threshold_layer.cpp CXX src/caffe/layers/tanh_layer.cpp CXX src/caffe/layers/cudnn_lrn_layer.cpp CXX src/caffe/layers/data_layer.cpp CXX src/caffe/layers/base_conv_layer.cpp CXX src/caffe/layers/lstm_unit_layer.cpp CXX src/caffe/layers/accuracy_layer.cpp CXX src/caffe/layers/softmax_layer.cpp CXX src/caffe/layers/im2col_layer.cpp CXX src/caffe/layers/lstm_layer.cpp CXX src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp CXX src/caffe/layers/cudnn_relu_layer.cpp CXX src/caffe/layers/image_data_layer.cpp CXX src/caffe/layers/reshape_layer.cpp CXX src/caffe/layers/window_data_layer.cpp CXX src/caffe/layers/split_layer.cpp CXX src/caffe/layers/hdf5_data_layer.cpp CXX src/caffe/layers/flatten_layer.cpp CXX src/caffe/layers/cudnn_lcn_layer.cpp CXX src/caffe/layers/input_layer.cpp CXX src/caffe/layers/rnn_layer.cpp CXX src/caffe/layers/recurrent_layer.cpp CXX src/caffe/layers/inner_product_layer.cpp CXX src/caffe/layers/reduction_layer.cpp CXX src/caffe/layers/multinomial_logistic_loss_layer.cpp CXX src/caffe/layers/infogain_loss_layer.cpp CXX src/caffe/layers/bias_layer.cpp CXX src/caffe/layers/filter_layer.cpp CXX src/caffe/layers/batch_reindex_layer.cpp CXX src/caffe/layers/slice_layer.cpp CXX src/caffe/layers/argmax_layer.cpp CXX src/caffe/layers/prelu_layer.cpp CXX src/caffe/layers/dropout_layer.cpp CXX src/caffe/layers/absval_layer.cpp CXX src/caffe/layers/bnll_layer.cpp CXX src/caffe/layers/sigmoid_layer.cpp CXX src/caffe/layers/concat_layer.cpp CXX src/caffe/layers/dummy_data_layer.cpp CXX src/caffe/layers/base_data_layer.cpp CXX src/caffe/layers/log_layer.cpp CXX src/caffe/layers/crop_layer.cpp CXX src/caffe/layers/cudnn_tanh_layer.cpp CXX src/caffe/layers/pooling_layer.cpp CXX src/caffe/layers/parameter_layer.cpp CXX src/caffe/layers/euclidean_loss_layer.cpp CXX src/caffe/layers/mvn_layer.cpp CXX src/caffe/layers/lrn_layer.cpp CXX src/caffe/layers/loss_layer.cpp CXX src/caffe/layers/conv_layer.cpp CXX src/caffe/layers/cudnn_sigmoid_layer.cpp CXX src/caffe/layers/scale_layer.cpp CXX src/caffe/layers/tile_layer.cpp CXX src/caffe/layers/embed_layer.cpp CXX src/caffe/layers/neuron_layer.cpp CXX src/caffe/layers/cudnn_conv_layer.cpp CXX src/caffe/layers/batch_norm_layer.cpp CXX src/caffe/layers/spp_layer.cpp CXX src/caffe/layer_factory.cpp CXX src/caffe/blob.cpp CXX src/caffe/layer.cpp CXX src/caffe/data_transformer.cpp CXX src/caffe/syncedmem.cpp CXX src/caffe/util/blocking_queue.cpp CXX src/caffe/util/db_lmdb.cpp CXX src/caffe/util/benchmark.cpp CXX src/caffe/util/io.cpp CXX src/caffe/util/insert_splits.cpp CXX src/caffe/util/hdf5.cpp CXX src/caffe/util/cudnn.cpp CXX src/caffe/util/signal_handler.cpp CXX src/caffe/util/db.cpp CXX src/caffe/util/math_functions.cpp CXX src/caffe/util/upgrade_proto.cpp CXX src/caffe/util/im2col.cpp CXX src/caffe/util/db_leveldb.cpp CXX src/caffe/solvers/adam_solver.cpp CXX src/caffe/solvers/adadelta_solver.cpp CXX src/caffe/solvers/adagrad_solver.cpp CXX src/caffe/solvers/sgd_solver.cpp CXX src/caffe/solvers/rmsprop_solver.cpp CXX src/caffe/solvers/nesterov_solver.cpp CXX src/caffe/data_reader.cpp CXX src/caffe/parallel.cpp CXX src/caffe/solver.cpp AR -o .build_release/lib/libcaffe.a LD -o .build_release/lib/libcaffe.so.1.0.0-rc3 /usr/bin/ld: cannot find -lhdf5_hl /usr/bin/ld: cannot find -lhdf5 collect2: error: ld returned 1 exit status Makefile:566: recipe for target '.build_release/lib/libcaffe.so.1.0.0-rc3' failed make: *** [.build_release/lib/libcaffe.so.1.0.0-rc3] Error 1

    解决方法

    修改Makefile 文件（注意不是）Makefile.config 将里面的 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

    常出现的问题4

    CXX/LD -o .build_release/tools/convert_imageset.bin .build_release/lib/libcaffe.so: undefined reference to cv::imread(cv::String const&, int)' .build_release/lib/libcaffe.so: undefined reference tocv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator >&, std::vector<int, std::allocator > const&)' .build_release/lib/libcaffe.so: undefined reference to `cv::imdecode(cv::_InputArray const&, int)' collect2: error: ld returned 1 exit status make: *** [.build_release/tools/convert_imageset.bin] Error 1 CXX/LD -o .build_release/test/test_all.testbin src/caffe/test/test_caffe_main.cpp LD .build_release/src/caffe/test/test_threshold_layer.o .build_release/lib/libcaffe.so: undefined reference to cv::imread(cv::String const&, int)' .build_release/lib/libcaffe.so: undefined reference tocv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator >&, std::vector<int, std::allocator > const&)' .build_release/lib/libcaffe.so: undefined reference to `cv::imdecode(cv::_InputArray const&, int)' collect2: error: ld returned 1 exit status make: *** [.build_release/test/test_threshold_layer.testbin] Error 1

    Maybe it is the related with OpenCv.

    解决方法

    add "opencv_imgcodecs" in Makefile.(LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs)

    常出现的问题5

    执行make runtest这一命令之后，遇到了奇葩的问题”Unknown V1LayerParameter Layer type: 40”。如下图：

    解决方案

    在这个文件上面(external/caffe/src/caffe/util/upgrade_proto.cpp)。我们需要补上Reshape，ROIPooling以及SmoothL1Loss这三个层，具体见下：

    case V1LayerParameter_LayerType_THRESHOLD: return "Threshold"; case V1LayerParameter_LayerType_RESHAPE: return "Reshape"; case V1LayerParameter_LayerType_ROIPOOLING: return "ROIPooling";` case V1LayerParameter_LayerType_SMOOTH_L1_LOSS: return "SmoothL1Loss";

    若还有其他的问题敬请查看

    Faste R-CNN的安装及测试

    参考文献：

    安装Caffe.build_release/lib/libcaffe.so.1.0.0-rc3’ failed

    Faster R-CNN的安装、测试经历

    Error: ‘make all’ ‘make test’

    Faster R-CNN教程

    faster rcnn配置方法MATLAB 版本

    Object Detection Using Faster R-CNN Deep Learning

    转载请注明原文地址: https://ju.6miu.com/read-15660.html

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