使用VSCode 调试Apollo无人车代码

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本文链接地址: 使用VSCode 调试Apollo无人车代码

深入研究Apollo的代码是学习自动驾驶的很好途径。很多前沿科技，比如图像识别，激光雷达，多传感器融合，路径规划都可以直接完整的学习。能够直接调试代码是比读代码更能加深理解。本文就介绍怎么去调试Apollo的代码。

Content:
  1. 下载代码
  2. 安装Docker
  3. 安装无线网卡驱动（可选）
  4. 安装Nvidia驱动
  5. 禁止自动更新内核来避免重新安装驱动
  6. 安装Nvidia Docker
  7. 启动Apollo项目
  8. 调试一个车道线识别程序
  9. Windows下使用VS Code Dev Container

下载代码

系统使用Ubuntu 18.04版本。
https://gitee.com/ApolloAuto/apollo.git
比如目录为~/apollo。

安装Docker
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo   "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
 
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
systemctl daemon-reload
systemctl restart docker
安装无线网卡驱动（可选）

因为笔者的网卡Ubuntu没有自带，需要自行安装驱动。
下载驱动：https://codeload.github.com/gnab/rtl8812au/zip/refs/heads/master

make dkms_install
echo 8812au | sudo tee -a /etc/modules
insmod 8812au.ko
安装Nvidia驱动

本人的显卡为GTX1060。Apollo项目需要Nvidia显卡，否则大部分模块无法编译运行。
如下为安装显卡驱动的脚步程序。cuda安装后的路径可能有所不同。

sudo apt-get install linux-headers-$(uname -r)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-$distribution.pin
sudo mv cuda-$distribution.pin /etc/apt/preferences.d/cuda-repository-pin-600
#key的id可以会更新 https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/7fa2af80.pub
echo "deb http://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
sudo apt-get update
sudo apt-get -y install cuda-drivers
export PATH=$PATH:/usr/local/cuda-11.2/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/lib64:/usr/local/cuda/cuda/lib64

安装完成后，如下命令可以查看显卡信息。请确保cuda已正确安装。

nvidia-smi
禁止自动更新内核来避免重新安装驱动

1 查看自己使用的内核

derek@ubuntu:~$ uname -a
Linux ubuntu 5.4.0-150-generic #167~18.04.1-Ubuntu SMP Wed May 24 00:51:42 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

2 查看目前安装的内核，并锁定它们

derek@ubuntu:~$ dpkg --get-selections|grep linux|grep 150
linux-headers-5.4.0-150-generic			install
linux-hwe-5.4-headers-5.4.0-150			install
linux-image-5.4.0-150-generic			install
linux-modules-5.4.0-150-generic			install
linux-modules-extra-5.4.0-150-generic		install
 
derek@ubuntu:~$ sudo apt-mark hold linux-headers-5.4.0-150-generic
linux-headers-5.4.0-150-generic set on hold.
derek@ubuntu:~$ sudo apt-mark hold linux-hwe-5.4-headers-5.4.0-150
linux-hwe-5.4-headers-5.4.0-150 set on hold.
derek@ubuntu:~$ sudo apt-mark hold linux-image-5.4.0-150-generic
linux-image-5.4.0-150-generic set on hold.
derek@ubuntu:~$ sudo apt-mark hold linux-modules-5.4.0-150-generic
linux-modules-5.4.0-150-generic set on hold.
derek@ubuntu:~$ sudo apt-mark hold linux-modules-extra-5.4.0-150-generic
linux-modules-extra-5.4.0-150-generic set on hold
 
derek@ubuntu:~$ dpkg --get-selections|grep linux|grep 150
linux-headers-5.4.0-150-generic			hold
linux-hwe-5.4-headers-5.4.0-150			hold
linux-image-5.4.0-150-generic			hold
linux-modules-5.4.0-150-generic			hold
linux-modules-extra-5.4.0-150-generic		hold

3 修改软件自动更新
修改如下2项

安装Nvidia Docker

Apollo需要运行在Docker预安装环境，以加快开发运行环境的统一性。

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

安装完成后，可启动示例Dockers程序查看。

sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
启动Apollo项目

在源码目录~/apollo中，执行如下命令，进入Apollo Docker：
脚本会把源文件里面的目录以volumn的形式挂载进Docker环境。

./docker/scripts/dev_start.sh 
./docker/scripts/dev_into.sh

以后进入，直接启动已有的Docker即可：

docker ps -a
docker start 671567b64765
./docker/scripts/dev_into.sh
调试一个车道线识别程序

创建一个Bazel编译配置：
~/apollo/modules/perception/camera/test/BUILD
内容为：

load("@rules_cc//cc:defs.bzl", "cc_library", "cc_test")
load("//tools:cpplint.bzl", "cpplint")
 
package(default_visibility = ["//visibility:public"])
 
cc_test(
    name = "camera_lib_lane_detector_darkscnn_lane_detector_test",
    size = "medium",
    srcs = ["camera_lib_lane_detector_darkscnn_lane_detector_test.cc"],
    deps = [
        "//cyber",
        "//modules/perception/base",
        "//modules/perception/camera/lib/lane/detector/darkSCNN:darkSCNN_lane_detector",
        "//modules/perception/common/io:io_util",
        "@com_google_googletest//:gtest_main",
        "@opencv//:core",
    ],
)
 
cpplint()

现在可以编译Apollo程序了：

./apollo.sh build_dbg

启动GDB Server，供Docker外面的VS Code进行远程调试：

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/libtorch_gpu/lib/
apt update
apt install gdbserver
gdbserver 127.0.0.1:2222 bazel-bin/modules/perception/camera/test/camera_lib_lane_detector_darkscnn_lane_detector_test

启动VS Code，安装C++插件，然后点击Debug，添加如下配置即可调试Docker里面刚才启动的程序：(注意修改program的路径)

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "gdb Remote camera_lib_lane_postprocessor_darkscnn_lane_postprocessor_test",
            "type": "cppdbg",
            "request": "launch",
            "program": "~/apollo/.cache/bazel/540135163923dd7d5820f3ee4b306b32/execroot/apollo/bazel-out/k8-dbg/bin/modules/perception/camera/test/camera_lib_lane_detector_darkscnn_lane_detector_test",
            "args": ["myarg1", "myarg2", "myarg3"],
            "stopAtEntry": true,
            "environment": [],
            "externalConsole": false,
            "MIMode": "gdb",
            "miDebuggerPath": "gdb",
            "miDebuggerArgs": "gdb",
            "linux": {
                "MIMode": "gdb",
                "miDebuggerPath": "/usr/bin/gdb",
                "miDebuggerServerAddress": "127.0.0.1:2222",
            },
            "logging": {
                "moduleLoad": false,
                "engineLogging": false,
                "trace": false
            },
            "setupCommands": [
                {
                    "description": "Enable pretty-printing for gdb",
                    "text": "-enable-pretty-printing",
                    "ignoreFailures": true
                }
            ],
            "cwd": "${workspaceFolder}",
        }
    ]
}
Windows下使用VS Code Dev Container

Windows下使用VS Code Dev Container来调试也是比较方便的，Dev Container可以让你在Windows下的操作如同在Container里面。

在项目根目录创建文件：.devcontainer/Dockerfile

FROM registry.baidubce.com/apolloauto/apollo:dev-x86_64-18.04-20210914_1336
 
ENV SHELL /bin/bash

在项目根目录创建文件：.devcontainer/devcontainer.json

{
    "name": "Apollo Dev Container",
    "build": {
      "dockerfile": "./Dockerfile"
    },
    "remoteUser": "root",
    //docker run -v apollo_map_volume-sunnyvale_big_loop_root:/apollo/modules/map/data/sunnyvale_big_loop --rm registry.baidubce.com/apolloauto/apollo:map_volume-sunnyvale_big_loop-latest true
    //docker run -v apollo_map_volume-sunnyvale_loop-latest:/apollo/modules/map/data/sunnyvale_loop --rm registry.baidubce.com/apolloauto/apollo:map_volume-sunnyvale_loop-latest true
    //docker run -v apollo_audio_volume_root:/apollo/modules/audio/data/ --rm registry.baidubce.com/apolloauto/apollo:data_volume-audio_model-x86_64-latest true
    "runArgs": [
        "--privileged",
        "--net=host",
        "-v", "apollo_map_volume-sunnyvale_big_loop_root:/apollo/modules/map/data/sunnyvale_big_loop",
        "-v", "apollo_map_volume-sunnyvale_loop-latest:/apollo/modules/map/data/sunnyvale_loop",
        "-v", "apollo_audio_volume_root:/apollo/modules/audio/data/",
        "--env", "CROSS_PLATFORM=0",
        "--env", "USER=root",
        "--env", "DOCKER_USER=root",
        "--env", "DOCKER_USER_ID=0",
        "--env", "DOCKER_GRP=0",
        "--env", "DOCKER_GRP_ID=0",
        "--env", "DOCKER_IMG=apolloauto/apollo:dev-x86_64-18.04-20210914_1336",
        "--env", "USE_GPU_HOST=false", // Set to 'true' or 'false' depending on the script
        "--env", "NVIDIA_VISIBLE_DEVICES=all",
        "--env", "NVIDIA_DRIVER_CAPABILITIES=compute,video,graphics,utility",
        "--workdir", "/apollo",
        "--add-host", "in-dev-docker:127.0.0.1",
        "--add-host", "localhost:127.0.0.1",
        "--hostname", "in-dev-docker"
    ],
    "workspaceMount": "type=bind,source=${localWorkspaceFolder},target=/apollo",
    "customizations": {
      "vscode": {
        "settings.json": {
          "terminal.integrated.profiles.linux": { "bash": { "path": "/bin/bash" } }
        }
      }
    }
}

最后，安装上VS Code 的 Dev Container 插件后就可以 Open In Container 了。

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