学习网址记录一下：
Python安装和环境配置
Python数据结构
C++教程
Python安装和环境配置
Git基础概念
https://www.youtube.com/watch?v=qWl9idsCuLQ
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://www.youtube.com/watch?v=rB1BmBOkKTw&feature=youtu.be
Pyramid Scene Parsing Network (CVPR 2017)
https://www.youtube.com/watch?v=BNE1hAP6Qho
CASENet: Deep Category-Aware Semantic Edge Detection
RefineNet Results on the CityScapes Dataset
https://www.youtube.com/watch?v=L0V6zmGP_oQ
DeepLab v2
Introduction
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.
It combines (1) atrous convolution to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks, (2) atrous spatial pyramid pooling to robustly segment objects at multiple scales with filters at multiple sampling rates and effective fields-of-views, and (3) densely connected conditional random fields (CRF) as post processing.
This distribution provides a publicly available implementation for the key model ingredients reported in our latest arXiv paper. This version also supports the experiments (DeepLab v1) in our ICLR’15. You only need to modify the old prototxt files. For example, our proposed atrous convolution is called dilated convolution in CAFFE framework, and you need to change the convolution parameter “hole” to “dilation” (the usage is exactly the same). For the experiments in ICCV’15, there are some differences between our argmax and softmax_loss layers and Caffe’s. Please refer to DeepLabv1 for details.
DeepLab是一个先进的深度学习系统，用于在Caffe之上构建语义图像分割。它结合了 (1) 无限卷积atrous convolution 以明确控制在深度卷积神经网络内计算特征响应的分辨率， (2) atrous spatial空间金字塔积累池，以多个采样率和有效场的滤波器在多个尺度上鲁棒地分割对象 - （3）densely connected conditional random fields密集连接的条件随机场（CRF）作为后处理。
这是CSDN上关于deeplab-v2的配置的参考链接：
【1】DeepLab V2安装配置
【2】图像语义分割：从头开始训练deeplab v2系列之二【VOC2012数据集】
【3】Deeplab v2 调试全过程（Ubuntu 16.04+cuda8.0）
【4】语义分割 - 数据集准备
下面是自己根据以上链接教程学习后的配置过程,使用的环境是ubuntu14.04+cuda8.0+cudnn5.1
1、安装Matio
下载 matio
我下载的是matio-1.5.12
进入matio-1.5.12文件夹：
cd matio-1.5.12 chmod a+x configure ./configure make make check sudo make install sudo ldconfig2、安装wget
$ sudo pip install wget Downloading/unpacking wget Downloading wget-3.2.zip Running setup.py (path:/tmp/pip_build_root/wget/setup.py) egg_info for package wget Installing collected packages: wget Running setup.py install for wget Could not find .egg-info directory in install record for wget Successfully installed wget Cleaning up...3、安装deeplab-v2
这里安装的过程跟caffe的配置过程类似
进入deeplab-v2文件夹
将Makefile.config.example根据自己的需要更改下，更改完重命名为Makefile.config
在Makefile.config文件中可以选择开启cuda加速，定义python等的路径，指定opencv版本，如果有opencv2和opencv3多版本安装caffe等，也可以通过pkd-config来指定opencv的版本完成编译；
make all -j4 #这里 -j 视自己的主机而定 make test -j4 make runtest -j4 make pycaffe#上述编译过程有问题可以自行查询caffe编译-csdn上的博客 #然后添加环境变量 sudo gedit /etc/profile #添加 export PYTHONPATH=/home/relaybot/mumu/slam/deeplab-v2/python:$PYTHONPATH source /etc/profile #使环境变量生效 然后进入python文件夹 python >>>import caffe >>> #无反应说明配置成功 >>>exit() #退出4、安装PSPNet出现的问题
issues:https://github.com/hszhao/PSPNet/issues/10
relaybot@ubuntu:~/mumu/slam/PSPNet$ make all -j4 CXX .build_release/src/caffe/proto/caffe.pb.cc CXX src/caffe/syncedmem.cpp CXX src/caffe/parallel.cpp CXX src/caffe/internal_thread.cpp In file included from ./include/caffe/util/device_alternate.hpp:40:0, from ./include/caffe/common.hpp:19, from src/caffe/syncedmem.cpp:1: ./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::createPoolingDesc(cudnnPoolingStruct**, caffe::PoolingParameter_PoolMethod, cudnnPoolingMode_t*, int, int, int, int, int, int)’: ./include/caffe/util/cudnn.hpp:127:41: error: too few arguments to function ‘cudnnStatus_t cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t, cudnnPoolingMode_t, cudnnNanPropagation_t, int, int, int, int, int, int)’ pad_h, pad_w, stride_h, stride_w)); ^ ./include/caffe/util/cudnn.hpp:15:28: note: in definition of macro ‘CUDNN_CHECK’ cudnnStatus_t status = condition; \ ^ In file included from ./include/caffe/util/cudnn.hpp:5:0, from ./include/caffe/util/device_alternate.hpp:40, from ./include/caffe/common.hpp:19, from src/caffe/syncedmem.cpp:1: /usr/local/cuda/include/cudnn.h:803:27: note: declared here cudnnStatus_t CUDNNWINAPI cudnnSetPooling2dDescriptor( ^ make: *** [.build_release/src/caffe/syncedmem.o] Error 1 make: *** Waiting for unfinished jobs.... In file included from ./include/caffe/util/device_alternate.hpp:40:0, from ./include/caffe/common.hpp:19, from ./include/caffe/blob.hpp:8, from ./include/caffe/caffe.hpp:7, from src/caffe/parallel.cpp:12: ./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::createPoolingDesc(cudnnPoolingStruct**, caffe::PoolingParameter_PoolMethod, cudnnPoolingMode_t*, int, int, int, int, int, int)’: ./include/caffe/util/cudnn.hpp:127:41: error: too few arguments to function ‘cudnnStatus_t cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t, cudnnPoolingMode_t, cudnnNanPropagation_t, int, int, int, int, int, int)’ pad_h, pad_w, stride_h, stride_w)); ^ ./include/caffe/util/cudnn.hpp:15:28: note: in definition of macro ‘CUDNN_CHECK’ cudnnStatus_t status = condition; \ ^ In file included from ./include/caffe/util/cudnn.hpp:5:0, from ./include/caffe/util/device_alternate.hpp:40, from ./include/caffe/common.hpp:19, from ./include/caffe/blob.hpp:8, from ./include/caffe/caffe.hpp:7, from src/caffe/parallel.cpp:12: /usr/local/cuda/include/cudnn.h:803:27: note: declared here cudnnStatus_t CUDNNWINAPI cudnnSetPooling2dDescriptor( ^ make: *** [.build_release/src/caffe/parallel.o] Error 1 In file included from ./include/caffe/util/device_alternate.hpp:40:0, from ./include/caffe/common.hpp:19, from ./include/caffe/internal_thread.hpp:4, from src/caffe/internal_thread.cpp:4: ./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::createPoolingDesc(cudnnPoolingStruct**, caffe::PoolingParameter_PoolMethod, cudnnPoolingMode_t*, int, int, int, int, int, int)’: ./include/caffe/util/cudnn.hpp:127:41: error: too few arguments to function ‘cudnnStatus_t cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t, cudnnPoolingMode_t, cudnnNanPropagation_t, int, int, int, int, int, int)’ pad_h, pad_w, stride_h, stride_w)); ^ ./include/caffe/util/cudnn.hpp:15:28: note: in definition of macro ‘CUDNN_CHECK’ cudnnStatus_t status = condition; \ ^ In file included from ./include/caffe/util/cudnn.hpp:5:0, from ./include/caffe/util/device_alternate.hpp:40, from ./include/caffe/common.hpp:19, from ./include/caffe/internal_thread.hpp:4, from src/caffe/internal_thread.cpp:4: /usr/local/cuda/include/cudnn.h:803:27: note: declared here cudnnStatus_t CUDNNWINAPI cudnnSetPooling2dDescriptor( ^ make: *** [.build_release/src/caffe/internal_thread.o] Error 1 #这里是替换cudnn的一些相关文件 relaybot@ubuntu:~/mumu/slam/PSPNet$ cp ../deeplab-v2/include/caffe/util/cudnn.hpp include/caffe/util/cudnn.hpp relaybot@ubuntu:~/mumu/slam/PSPNet$ cp ../deeplab-v2/include/caffe/layers/cudnn_* include/caffe/layers/ relaybot@ubuntu:~/mumu/slam/PSPNet$ cp ../deeplab-v2/src/caffe/layers/cudnn_* src/caffe/layers/ **issues:error: function "atomicAdd(double *, double)" has already been defined
https://stackoverflow.com/questions/39274472/error-function-atomicadddouble-double-has-already-been-defined**
./include/caffe/common.cuh(9): error: function "atomicAdd(double *, double)" has already been defined 1 error detected in the compilation of "/tmp/tmpxft_00001fe1_00000000-5_interp.cpp4.ii". make: *** [.build_release/cuda/src/caffe/util/interp.o] Error 1 make: *** Waiting for unfinished jobs....修改commmon.cuh文件
I finally got it working with the help of @Robert Crovella's comment. I had to modify the file common.cuh from the DeepLab_v2 master branch in the following way: #ifndef CAFFE_COMMON_CUH_ #define CAFFE_COMMON_CUH_ #include <cuda.h> #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600 #else 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安装PSPNet
make all -j4 make pycaffe #添加环境变量 #不要忘记这一步编译matlab-caffe
下载链接：（已经保存到我的云盘空间）
http://yunpan.taobao.com/s/ZFLGQjNABU#/
提取码：dxBxMJ**
数据集：camvid、The Cityscapes
https://blog.csdn.net/u010069760/article/details/77847595
Pyramid Scene Parsing Network ѧϰ0420
https://hszhao.github.io/projects/pspnet/ 主页
http://groups.csail.mit.edu/vision/datasets/ADE20K/ 数据集
制作自己的caffe,cmake
build中出现问题：
In file included from /home/relaybot/mumu/slam/PSPNet/include/caffe/caffe.hpp:7:0, from /home/relaybot/mumu/slam/pspnet_1/src/pspnet.cpp:6: /home/relaybot/mumu/slam/PSPNet/include/caffe/blob.hpp:9:34: fatal error: caffe/proto/caffe.pb.h: No such file or directory #include "caffe/proto/caffe.pb.h" ^寻找自己的PSPNet中的 include/caffe/ 中没有proto文件夹
参照https://github.com/NVIDIA/DIGITS/issues/105
When I ran make all --jobs=4 I got: In file included from ./include/caffe/util/device_alternate.hpp:40:0, from ./include/caffe/common.hpp:19, from ./include/caffe/blob.hpp:8, from ./include/caffe/net.hpp:10, from src/caffe/solver.cpp:7: ./include/caffe/util/cudnn.hpp:8:34: fatal error: caffe/proto/caffe.pb.h: No such file or directory #include "caffe/proto/caffe.pb.h" ^ compilation terminated. I solved this by running: $ protoc src/caffe/proto/caffe.proto --cpp_out=. $ mkdir include/caffe/proto $ mv src/caffe/proto/caffe.pb.h include/caffe/proto 同样参照上述指令，重新编译caffe-PSPNet
make all -j4 make test -j4http://manutdzou.github.io/2016/05/29/master-note.htmlAR -o .build_release/lib/libcaffe.a LD -o .build_release/lib/libcaffe.so.1.0.0-rc3 /usr/bin/ld: 找不到 -lhdf5_serial_hl /usr/bin/ld: 找不到 -lhdf5_serial collect2: error: ld returned 1 exit status make: *** [.build_release/lib/libcaffe.so.1.0.0-rc3] 错误 1 zmz@zmz-inin:~/zhaolin_2018/PSPNET-cudnn5$ cd /usr/lib/x86_64-linux-gnu
\然后根据情况执行下面两句：
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_hl.so
https://blog.csdn.net/madman_z/article/details/70136104
解决方法 在Makefile.config文件的第85行，添加 /usr/include/hdf5/serial/ 到 INCLUDE_DIRS，也就是把下面第一行代码改为第二行代码。 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ 在Makefile文件的第173行，把 hdf5_hl 和hdf5修改为hdf5_serial_hl 和 hdf5_serial，也就是把下面第一行代码改为第二行代码。 LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5 LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial 实际我的解决方法是：
改Makefile:
############################## # Derive include and lib directories ############################## CUDA_INCLUDE_DIR := $(CUDA_DIR)/include CUDA_LIB_DIR := # add <cuda>/lib64 only if it exists ifneq ("$(wildcard $(CUDA_DIR)/lib64)","") CUDA_LIB_DIR += $(CUDA_DIR)/lib64 endif CUDA_LIB_DIR += $(CUDA_DIR)/lib INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include ifneq ($(CPU_ONLY), 1) INCLUDE_DIRS += $(CUDA_INCLUDE_DIR) LIBRARY_DIRS += $(CUDA_LIB_DIR) LIBRARIES := cudart cublas curand endif LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5