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  • Autoware 笔记No.8 ENet 障碍物识别(vision segment ENet detect)

    一. 前言

    人人为我,我为人人

    我不喜欢讲一些网络上抄的网络模型,全是干货,让大家直接上手干,如果想探讨可以联系我。

    请大家按照我的Autoware 1.14安装

    二. 安装

    (1)下载ENet,一定要安装在home目录下,否则vision_segment_enet_detect.launch文件中的network_definition_file和pretrained_model_file路径会有变化。

    $ cd ~
    $ git clone --recursive https://github.com/TimoSaemann/ENet.git
    $ cd ENet/caffe-enet

    (2)修改Makefile.config,我用的CUDA10.0,直接贴出我的Makefile.config

    ## Refer to http://caffe.berkeleyvision.org/installation.html
    # Contributions simplifying and improving our build system are welcome!
    
    # cuDNN acceleration switch (uncomment to build with cuDNN).
    USE_CUDNN := 1
    
    # CPU-only switch (uncomment to build without GPU support).
    # CPU_ONLY := 1
    
    # uncomment to disable IO dependencies and corresponding data layers
    USE_OPENCV := 1
    USE_LEVELDB := 1
    USE_LMDB := 1
    
    # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
    #    You should not set this flag if you will be reading LMDBs with any
    #    possibility of simultaneous read and write
    # ALLOW_LMDB_NOLOCK := 1
    
    # Uncomment if you're using OpenCV 3
    OPENCV_VERSION := 3
    
    # To customize your choice of compiler, uncomment and set the following.
    # N.B. the default for Linux is g++ and the default for OSX is clang++
    # CUSTOM_CXX := g++
    
    # CUDA directory contains bin/ and lib/ directories that we need.
    CUDA_DIR := /usr/local/cuda-10.0
    # On Ubuntu 14.04, if cuda tools are installed via
    # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
    # CUDA_DIR := /usr
    
    # CUDA architecture setting: going with all of them.
    # For CUDA < 6.0, comment the *_50 lines for compatibility.
    # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
    # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
    CUDA_ARCH := -gencode arch=compute_61,code=compute_61
    
    # BLAS choice:
    # atlas for ATLAS (default)
    # mkl for MKL
    # open for OpenBlas
    BLAS := open
    # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
    # Leave commented to accept the defaults for your choice of BLAS
    # (which should work)!
    BLAS_INCLUDE := /usr/local/cuda-10.0/targets/x86_64-linux/include
    BLAS_LIB := /usr/local/cuda-10.0/targets/x86_64-linux/lib
    
    # Homebrew puts openblas in a directory that is not on the standard search path
    # BLAS_INCLUDE := $(shell brew --prefix openblas)/include
    # BLAS_LIB := $(shell brew --prefix openblas)/lib
    
    # This is required only if you will compile the matlab interface.
    # MATLAB directory should contain the mex binary in /bin.
    # MATLAB_DIR := /usr/local
    # MATLAB_DIR := /Applications/MATLAB_R2012b.app
    
    # NOTE: this is required only if you will compile the python interface.
    # We need to be able to find Python.h and numpy/arrayobject.h.
    PYTHON_INCLUDE := /usr/include/python2.7 
            /usr/lib/python2.7/dist-packages/numpy/core/include
    # Anaconda Python distribution is quite popular. Include path:
    # Verify anaconda location, sometimes it's in root.
    # ANACONDA_HOME := $(HOME)/anaconda
    # PYTHON_INCLUDE := $(ANACONDA_HOME)/include 
            # $(ANACONDA_HOME)/include/python2.7 
            # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include 
    
    # Uncomment to use Python 3 (default is Python 2)
    # PYTHON_LIBRARIES := boost_python3 python3.5m
    # PYTHON_INCLUDE := /usr/include/python3.5m 
    #                 /usr/lib/python3.5/dist-packages/numpy/core/include
    
    # We need to be able to find libpythonX.X.so or .dylib.
    PYTHON_LIB := /usr/lib
    # PYTHON_LIB := $(ANACONDA_HOME)/lib
    
    # Homebrew installs numpy in a non standard path (keg only)
    # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
    # PYTHON_LIB += $(shell brew --prefix numpy)/lib
    
    # Uncomment to support layers written in Python (will link against Python libs)
    # WITH_PYTHON_LAYER := 1
    
    # Whatever else you find you need goes here.
    INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu
    
    # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
    # INCLUDE_DIRS += $(shell brew --prefix)/include
    # LIBRARY_DIRS += $(shell brew --prefix)/lib
    
    # Uncomment to use `pkg-config` to specify OpenCV library paths.
    # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
    # USE_PKG_CONFIG := 1
    
    # N.B. both build and distribute dirs are cleared on `make clean`
    BUILD_DIR := build
    DISTRIBUTE_DIR := distribute
    
    # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
    # DEBUG := 1
    
    # The ID of the GPU that 'make runtest' will use to run unit tests.
    TEST_GPUID := 0
    
    # enable pretty build (comment to see full commands)
    Q ?= @

    (3)修改Makefile

    打开Makefile,查找LIBRARIES,修改为:

    LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

    (4)编译ENet

    make && make distribute

    如果出现错误如下:

    ./include/caffe/util/cudnn.hpp:113:70: error: too few arguments to function ‘cudnnStatus_t cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t, int, int, int, int, int, int, cudnnConvolutionMode_t, cudnnDataType_t)’
           pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));

    修改/home/xxx/ENet/caffe-enet/include/caffe/util/cudnn.hpp为如下代码。

    #ifndef CAFFE_UTIL_CUDNN_H_
    #define CAFFE_UTIL_CUDNN_H_
    #ifdef USE_CUDNN
    
    #include <cudnn.h>
    
    #include "caffe/common.hpp"
    #include "caffe/proto/caffe.pb.h"
    
    #define CUDNN_VERSION_MIN(major, minor, patch) 
        (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
    
    #define CUDNN_CHECK(condition) 
      do { 
        cudnnStatus_t status = condition; 
        CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "
          << cudnnGetErrorString(status); 
      } while (0)
    
    inline const char* cudnnGetErrorString(cudnnStatus_t status) {
      switch (status) {
        case CUDNN_STATUS_SUCCESS:
          return "CUDNN_STATUS_SUCCESS";
        case CUDNN_STATUS_NOT_INITIALIZED:
          return "CUDNN_STATUS_NOT_INITIALIZED";
        case CUDNN_STATUS_ALLOC_FAILED:
          return "CUDNN_STATUS_ALLOC_FAILED";
        case CUDNN_STATUS_BAD_PARAM:
          return "CUDNN_STATUS_BAD_PARAM";
        case CUDNN_STATUS_INTERNAL_ERROR:
          return "CUDNN_STATUS_INTERNAL_ERROR";
        case CUDNN_STATUS_INVALID_VALUE:
          return "CUDNN_STATUS_INVALID_VALUE";
        case CUDNN_STATUS_ARCH_MISMATCH:
          return "CUDNN_STATUS_ARCH_MISMATCH";
        case CUDNN_STATUS_MAPPING_ERROR:
          return "CUDNN_STATUS_MAPPING_ERROR";
        case CUDNN_STATUS_EXECUTION_FAILED:
          return "CUDNN_STATUS_EXECUTION_FAILED";
        case CUDNN_STATUS_NOT_SUPPORTED:
          return "CUDNN_STATUS_NOT_SUPPORTED";
        case CUDNN_STATUS_LICENSE_ERROR:
          return "CUDNN_STATUS_LICENSE_ERROR";
    #if CUDNN_VERSION_MIN(6, 0, 0)
        case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
          return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
    #endif
    #if CUDNN_VERSION_MIN(7, 0, 0)
        case CUDNN_STATUS_RUNTIME_IN_PROGRESS:
          return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
        case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
          return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
    #endif
      }
      return "Unknown cudnn status";
    }
    
    namespace caffe {
    
    namespace cudnn {
    
    template <typename Dtype> class dataType;
    template<> class dataType<float>  {
     public:
      static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
      static float oneval, zeroval;
      static const void *one, *zero;
    };
    template<> class dataType<double> {
     public:
      static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
      static double oneval, zeroval;
      static const void *one, *zero;
    };
    
    template <typename Dtype>
    inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
      CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
    }
    
    template <typename Dtype>
    inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
        int n, int c, int h, int w,
        int stride_n, int stride_c, int stride_h, int stride_w) {
      CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
            n, c, h, w, stride_n, stride_c, stride_h, stride_w));
    }
    
    template <typename Dtype>
    inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
        int n, int c, int h, int w) {
      const int stride_w = 1;
      const int stride_h = w * stride_w;
      const int stride_c = h * stride_h;
      const int stride_n = c * stride_c;
      setTensor4dDesc<Dtype>(desc, n, c, h, w,
                             stride_n, stride_c, stride_h, stride_w);
    }
    
    template <typename Dtype>
    inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
        int n, int c, int h, int w) {
      CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
    #if CUDNN_VERSION_MIN(5, 0, 0)
      CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
          CUDNN_TENSOR_NCHW, n, c, h, w));
    #else
      CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
          CUDNN_TENSOR_NCHW, n, c, h, w));
    #endif
    }
    
    template <typename Dtype>
    inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
      CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
    }
    
    template <typename Dtype>
    inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
        cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
        int pad_h, int pad_w, int stride_h, int stride_w) {
    #if CUDNN_VERSION_MIN(6, 0, 0)
      CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
          pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
          dataType<Dtype>::type));
    #else
        CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
          pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
    #endif
    }
    
    template <typename Dtype>
    inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
        PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
        int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
      switch (poolmethod) {
      case PoolingParameter_PoolMethod_MAX:
        *mode = CUDNN_POOLING_MAX;
        break;
      case PoolingParameter_PoolMethod_AVE:
        *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
        break;
      default:
        LOG(FATAL) << "Unknown pooling method.";
      }
      CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
    #if CUDNN_VERSION_MIN(5, 0, 0)
      CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
            CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    #else
      CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
            CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
    #endif
    }
    
    template <typename Dtype>
    inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
        cudnnActivationMode_t mode) {
      CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
      CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
                                               CUDNN_PROPAGATE_NAN, Dtype(0)));
    }
    
    }  // namespace cudnn
    
    }  // namespace caffe
    
    #endif  // USE_CUDNN
    #endif  // CAFFE_UTIL_CUDNN_H_

    编译成功后,进入/home/xxx/ENet/enet_weights_zoo/,执行

    $ sudo chmod a+x cityscapes_weights.sh
    $ sh cityscapes_weights.sh

    得到cityscapes_weights.caffemodel和cityscapes_weights_before_bn_merge.caffemodel。

    (5)执行vision_segment_enet_detect节点

    $ cd ~/autoware.ai
    $ source install/setup.bash
    $ roslaunch vision_segment_enet_detect vision_segment_enet_detect.launch

     如果没有image_segmenter_enet.launch文件,删除build和install里面的image_segmenter_enet,单独编译vision_segment_enet_detect。

    $ cd ~/autoware.ai 
    $ AUTOWARE_COMPILE_WITH_CUDA=1 colcon build --cmake-args -DCMAKE_BUILD_TYPE=Release --packages-select vision_segment_enet_detect

    打开moriyama数据集测试。

    如果出现:libcaffe.so.1.0.0-rc3: cannot open shared object file: No such file or directory错误

    需要执行

    $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:~/ENet/caffe-enet/distribute/lib
    $ roslaunch vision_segment_enet_detect vision_segment_enet_detect.launch

    原创博文,转载请标明出处。

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  • 原文地址:https://www.cnblogs.com/hgl0417/p/14618353.html
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