深海游弋的鱼 – 默默的点滴

ubuntu 22.04系统Docker和Nvidia-docker的安装、测试，及运行GUI应用

快速搭建所需开发环境

Docker文档：https://docs.docker.com/，Docker安装指南： Install Docker Engine on Ubuntu

Dokcer安装

Uninstall old versions
 
~$ sudo apt-get remove docker docker-engine docker.io containerd runc

~$ sudo apt-get install curl
 
Install using the repository

~$ curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker
 
Verify that Docker Engine is installed correctly by running the hello-world image.
~$ sudo docker run hello-world

Docker测试

# 启动docker服务
$ sudo service docker start

# Docker: hello-world
$ sudo docker run hello-world

其他Docker命令：

Usage: service docker {start|stop|restart|status}

查看镜像
$ sudo docker images

查看容器
$ sudo docker container ls -a

Tips：Docker中一般Crtl+C退出，传送门：停止、删除所有的 docker 容器和镜像。

Nvidia-docker安装

查看nvidia版本

$ nvidia-smi
Thu Nov 26 10:34:37 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 2060    Off  | 00000000:01:00.0  On |                  N/A |
|  0%   37C    P8     9W / 190W |    301MiB /  5931MiB |      2%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A       942      G   /usr/lib/xorg/Xorg                 35MiB |
|    0   N/A  N/A      2278      G   /usr/lib/xorg/Xorg                 96MiB |
|    0   N/A  N/A      2404      G   /usr/bin/gnome-shell              150MiB |
|    0   N/A  N/A      4051      G   /usr/lib/firefox/firefox            3MiB |
+-----------------------------------------------------------------------------+

参考链接：官网 installation guide
Github：NVIDIA/nvidia-docker

#
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
 
$ sudo apt-get update

$ sudo apt-get install -y nvidia-container-toolkit

$ sudo nvidia-ctk runtime configure --runtime=docker
 
$ sudo systemctl restart docker

$ docker pull nvidia/cudagl:11.0-base
 
# 测试
$ docker run --rm --gpus all nvidia/cudagl:11.0-base nvidia-smi
 
Thu Nov 26 02:30:34 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 2060    Off  | 00000000:01:00.0  On |                  N/A |
|  0%   37C    P8    10W / 190W |    307MiB /  5931MiB |     13%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

测试

$ sudo docker run --runtime=nvidia --rm nvidia/cudagl:11.0-base nvidia-smi

Docker 容器 GUI

$ sudo apt-get install x11-xserver-utils

# 关闭权限控制，允许其他X客户端绘制
$ xhost +
access control disabled, clients can connect from any host

$ docker run -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix --rm -it container-name-or-id
若遇到X Error时，添加参数：--ipc=host 或 --env="QT_X11_NO_MITSHM=1"，参考链接：
How to fix X Error: BadAccess, BadDrawable, BadShmSeg while running graphical application using Docker?
Docker: gazebo: cannot connect to X server
若遇到 libGL error: No matching fbConfigs or visuals found libGL error... ，参考链接：
使用docker时出现libGL error: No matching fbConfigs or visuals found libGL error: failed to load driver...
已成功测试上述链接中的 pull image 方式
使用nvidia-smi查看nvidia driver和cuda版本，根据 nvidia/cudagl ，选择合适的TAG
$ sudo apt-get install x11-xserver-utils

$ nvidia-smi

$ docker pull nvidia/cudagl:11.0-base

# 关闭权限控制，允许其他X客户端绘制
$ xhost +
access control disabled, clients can connect from any host

$ sudo docker run --rm --runtime=nvidia -it -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix nvidia/cudagl:11.0-base

$ apt-get update

$ apt-get install mesa-utils

$ glxgears

创建新的长期镜像：

$ sudo apt-get install x11-xserver-utils

$ nvidia-smi

$ docker pull nvidia/cudagl:10.2-base

# 关闭权限控制，允许其他X客户端绘制
$ xhost +
access control disabled, clients can connect from any host

$ sudo docker run -it --name isaac --runtime=nvidia -it -e DISPLAY=$DISPLAY -e GDK_SCALE -e GDK_DPI_SCAL -v /tmp/.X11-unix:/tmp/.X11-unix -v /data/isaac:/data/isaac nvidia/cudagl:10.2-base

$ apt-get update

$ apt-get install mesa-utils

$ glxgears

$ exit

$ sudo docker ps -a

$ sudo docker start isaac

$ sudo docker attach isaac

如果遇到如下报错：

Nvidia-container-cli: initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory

上述报错目前没有找到很好的解决方法，应该是某个安装包意外修改了系统配置，导致出现问题，重装系统可以顺利解决此问题。

参考链接

Isaac教程--基于Elbrus立体视觉 VSLAM 的定位

基于Elbrus立体视觉 VSLAM 的定位

继续阅读Isaac教程--基于Elbrus立体视觉 VSLAM 的定位

像素坐标转世界坐标的计算

原理

下图表示了小孔成像模型（图片及公式参考 OpenCV官方资料）

这个图里涉及4个坐标系：

世界坐标系：其坐标原点可视情况而定，可以表示空间的物体，单位为长度单位，比如MM（毫米），用矩阵$\begin{bmatrix} X_W \\ Y_W \\Z_W \end{bmatrix}$表示；
相机坐标系：以摄像机光心为原点（在针孔模型中也就是针孔为中心），z轴与光轴重合，也就是z轴指向相机的前方（与成像平面垂直），x轴与y轴的正方向与世界坐标系平行，单位为长度单位，比如MM（毫米），用矩阵$\begin{bmatrix}X_c \\ Y_c \\ Z_c\end{bmatrix}$表示；
图像物理坐标系（也叫成像平面坐标系）：用物理长度单位表示像素的位置，坐标原点为摄像机光轴与图像物理坐标系的交点位置，单位为长度单位，比如MM（毫米），用矩阵$\begin{bmatrix}x \\ y \end{bmatrix}$表示。
像素坐标系：坐标原点在左上角，以像素为单位，有明显的范围限制，即用于表示全画面的像素长和像素长宽，矩阵$\begin{bmatrix}u \\ v \end{bmatrix}$表示。

以下公式描述了$\begin{bmatrix}u & v \end{bmatrix}^T$、$\begin{bmatrix}x & y \end{bmatrix}^T$、$\begin{bmatrix}X_c & Y_c & Z_c\end{bmatrix}^T$和$\begin{bmatrix}X_W & Y_W & Z_W \end{bmatrix}^T$之间的转换关系。

$z\begin{bmatrix}u \\ v\\ 1 \end{bmatrix}= \begin{bmatrix}1/d_x&0&c_x\\0&1/d_y&c_y\\0&0&1 \end{bmatrix} \begin{bmatrix}f&0&0\\ 0&f&0\\ 0&0&1 \end{bmatrix} \begin{bmatrix}r11&r12&r13&t1\\ r21&r22&r23&t2\\ r31&r32&r33&t3 \end{bmatrix} \begin{bmatrix}X_W \\ Y_W \\Z_W \\ 1\end{bmatrix}$

以上公式中，$d_x$和$d_y$表示1个像素有多少长度，即用传感器的尺寸除以像素数量，比如2928.384um * 2205.216um的传感的分辨率为2592 * 1944，每个像素的大小即约1.12um。

由于相机与物体的视角来看，都是三维坐标，因此两者之间的变换只需要进行矩阵的旋转、平移即可达到坐标系转换的目的（不同坐标系中，物体的绝对大小并不会随着坐标系的变化而变化，因此不涉及缩放处理）。对于变换矩阵 $\begin{bmatrix}r11&r12&r13&t1\\ r21&r22&r23&t2\\ r31&r32&r33&t3 \end{bmatrix}$ 需要理解，矩阵是由 3*3 的旋转矩阵 r (rotation) 和 3*1的平移向量 t (translation)组成。

$f$表示焦距，在上图中，根据相似三角形，P点和p点具有以下关系：

$\frac{X_c}{x} = \frac{Y_c}{y} = \frac{Z_c}{f}$ 即$x=X_c/(\frac{Z_c}{f})$ $y=Y_c/(\frac{Z_c}{f})$，可见：$f$越大，$x$和$y$越大，$Z_c$越大，$x$和$y$越小。

$c_x$和$c_y$表示中心点在像素坐标系中的位置。

要求像素坐标系中某像素点对应在世界坐标系中的位置，需要知道相机的内参、外参，相机的内参可以通过标定获得，外参可以人为设定。

第一步，将像素坐标变换到相机坐标系：

$z\begin{bmatrix}u \\ v\\ 1 \end{bmatrix} = \begin{bmatrix}f_x&0&c_x\\0&f_y&c_y\\0&0&1 \end{bmatrix} \begin{bmatrix}x \\ y\\ 1 \end{bmatrix} = K\begin{bmatrix}x \\ y\\ 1 \end{bmatrix}$

两边乘以K的逆后推导出：

$\begin{bmatrix}x \\ y\\ z \end{bmatrix}=K^{-1} \begin{bmatrix}u \\ v\\ 1 \end{bmatrix}$

第二步，从相机坐标系变换到世界坐标系：

$\begin{bmatrix}X_c \\ Y_c\\ Z_c \end{bmatrix} = R \begin{bmatrix}X \\ Y\\ Z \end{bmatrix} + t$

将方程乘以$R^{-1}$，可以推导出：

$\begin{bmatrix}X \\ Y\\ Z \end{bmatrix} = \begin{bmatrix}X_c \\ Y_c \\ Z_c \end{bmatrix}R^{-1} - t R^{-1}= z\begin{bmatrix}x\\ y\\ 1 \end{bmatrix}R^{-1} - t R^{-1}$

代码

通过输入相机的内参，旋转向量，平移向量和像素坐标，可以通过以下函数求出对应的世界坐标点。
以下代码中需求注意要对平移向量取转置，将1x3矩阵变为3x1矩阵后，才能实现3x3矩阵和3x1矩阵的乘法运算。

void cameraToWorld(InputArray cameraMatrix, InputArray rV, InputArray tV, vector<Point2f> imgPoints, vector<Point3f> &worldPoints)
{
    Mat invK64, invK;
    invK64 = cameraMatrix.getMat().inv();
    invK64.convertTo(invK, CV_32F);
    Mat r, t, rMat;
    rV.getMat().convertTo(r, CV_32F);
    tV.getMat().convertTo(t, CV_32F);
    Rodrigues(r, rMat);

    //计算 invR * T
    Mat invR = rMat.inv();
    //cout << "invR\n" << invR << endl;
    //cout << "t\n" << t << t.t() << endl;
    Mat transPlaneToCam;
    if(t.size() == Size(1, 3)){
        transPlaneToCam = invR * t;//t.t();
    }
    else if(t.size() == Size(3, 1)){
        transPlaneToCam = invR * t.t();
    }
    else{
        return;
    }
    //cout << "transPlaneToCam\n" << transPlaneToCam << endl;

    int npoints = (int)imgPoints.size();
    //cout << "npoints\n" << npoints << endl;
    for (int j = 0; j < npoints; ++j){
        Mat coords(3, 1, CV_32F);
        Point3f pt;
        coords.at<float>(0, 0) = imgPoints[j].x;
        coords.at<float>(1, 0) = imgPoints[j].y;
        coords.at<float>(2, 0) = 1.0f;
        //[x,y,z] = invK * [u,v,1]
        Mat worldPtCam = invK * coords;
        //cout << "worldPtCam:" << worldPtCam << endl;
        //[x,y,1] * invR
        Mat worldPtPlane = invR * worldPtCam;
        //cout << "worldPtPlane:" << worldPtPlane << endl;
        //zc 
        float scale = transPlaneToCam.at<float>(2) / worldPtPlane.at<float>(2);
        //cout << "scale:" << scale << endl;
        Mat scale_worldPtPlane(3, 1, CV_32F);
        //scale_worldPtPlane.at<float>(0, 0) = worldPtPlane.at<float>(0, 0) * scale;
        //zc * [x,y,1] * invR
        scale_worldPtPlane = scale * worldPtPlane;
        //cout << "scale_worldPtPlane:" << scale_worldPtPlane << endl;
        //[X,Y,Z]=zc*[x,y,1]*invR - invR*T
        Mat worldPtPlaneReproject = scale_worldPtPlane - transPlaneToCam;
        //cout << "worldPtPlaneReproject:" << worldPtPlaneReproject << endl;
        pt.x = worldPtPlaneReproject.at<float>(0);
        pt.y = worldPtPlaneReproject.at<float>(1);
        //pt.z = worldPtPlaneReproject.at<float>(2);
        pt.z = 1.0f;
        worldPoints.push_back(pt);
    }
}

    def cameraToWorld(self, cameraMatrix, r, t, imgPoints):
        invK = np.asmatrix(cameraMatrix).I
        rMat = np.zeros((3, 3), dtype=np.float64)
        cv2.Rodrigues(r, rMat)
        #print('rMat=', rMat)
        #计算 invR * T
        invR =  np.asmatrix(rMat).I #3*3
        #print('invR=', invR)
        transPlaneToCam = np.dot(invR , np.asmatrix(t)) #3*3 dot 3*1 = 3*1
        #print('transPlaneToCam=', transPlaneToCam)
        worldpt = []   
        coords = np.zeros((3, 1), dtype=np.float64)

        for imgpt in imgPoints:
            coords[0][0] = imgpt[0][0]
            coords[1][0] = imgpt[0][1]
            coords[2][0] = 1.0
            worldPtCam = np.dot(invK , coords)  #3*3 dot 3*1 = 3*1
            #print('worldPtCam=', worldPtCam)
            #[x,y,1] * invR
            worldPtPlane = np.dot(invR , worldPtCam) #3*3 dot 3*1 = 3*1
            #print('worldPtPlane=', worldPtPlane)
            #zc 
            scale = transPlaneToCam[2][0] / worldPtPlane[2][0]
            #print("scale: ", scale)
            #zc * [x,y,1] * invR
            scale_worldPtPlane = np.multiply(scale , worldPtPlane)
            #print("scale_worldPtPlane: ", scale_worldPtPlane)
            #[X,Y,Z]=zc*[x,y,1]*invR - invR*T
            worldPtPlaneReproject = np.asmatrix(scale_worldPtPlane) - np.asmatrix(transPlaneToCam)  #3*1 dot 1*3 = 3*3
            #print("worldPtPlaneReproject: ", worldPtPlaneReproject)
            pt = np.zeros((3, 1), dtype=np.float64)
            pt[0][0] = worldPtPlaneReproject[0][0]
            pt[1][0] = worldPtPlaneReproject[1][0]
            pt[2][0] = 0
            worldpt.append(pt.T.tolist())
        #print('worldpt:',worldpt)
        return worldpt

验证

先使用projectPoints生成像素点：

Vec3f eulerAngles;//欧拉角

vector<Vec3d> translation_vectors;/* 每幅图像的平移向量 */

Mat rotationMatrix = eulerAnglesToRotationMatrix(eulerAngles);

*pR_matrix = rotationMatrix;

cvRodrigues2(pR_matrix, pnew_vec, 0);   //从旋转矩阵求旋转向量

Mat mat_tmp(pnew_vec->rows, pnew_vec->cols, pnew_vec->type, pnew_vec->data.fl);

cv::Mat distortion_coeffs1 = cv::Mat(1, 5, CV_32FC1, cv::Scalar::all(0)); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */

projectPoints(tempPointSet, mat_tmp, translation_vectors[i], intrinsic_matrix, distortion_coeffs1, image_points2);

使用以下欧拉角：

[0, 0, 0]//欧拉角度，表示平面和相机的角度
旋转向量：[0, 0, 0]

对应的平移向量，表示空间坐标原点相对在相平面原点偏移x=134mm，y=132mm，z=200mm。

原始:[134.0870803179094, 132.7580766544178, 200.3789038923399]

生成空间坐标点：

        Size board_size = Size(11,8);
        Size square_size = Size(30, 30);
        vector<Point3f> tempPointSet;
        for (int j = 0; j<board_size.height; j++)
        {
            for (int i = 0; i<board_size.width; i++)
            {
                /* 假设定标板放在世界坐标系中z=0的平面上 */
                Point3f tempPoint;
                tempPoint.x = i*square_size.height;
                tempPoint.y = j*square_size.width;
                tempPoint.z = 0;
                tempPointSet.push_back(tempPoint);
            }
        }

经projectPoints计算后对应的像素空间点是：

projectPoints(tempPointSet, mat_tmp, translation_vectors[i], intrinsic_matrix, distortion_coeffs1, image_points2);
cout << "原始空间点:\n" << image_points2 << endl;
[1194.8174, 1074.1355;
 1285.1735, 1074.1355;
 1375.5295, 1074.1355;
 1465.8856, 1074.1355;
 1556.2417, 1074.1355;
 1646.5978, 1074.1355;
 1736.9539, 1074.1355;
 1827.3099, 1074.1355;
 1917.666, 1074.1355;
 2008.0221, 1074.1355;
 2098.3782, 1074.1355;
 1194.8174, 1164.5713;
 1285.1735, 1164.5713;
 1375.5295, 1164.5713;
 1465.8856, 1164.5713;
 1556.2417, 1164.5713;
 1646.5978, 1164.5713;
 1736.9539, 1164.5713;
 1827.3099, 1164.5713;
 1917.666, 1164.5713;
 2008.0221, 1164.5713;
 2098.3782, 1164.5713;
 1194.8174, 1255.0072;
 1285.1735, 1255.0072;
 1375.5295, 1255.0072;
 1465.8856, 1255.0072;
 1556.2417, 1255.0072;
 1646.5978, 1255.0072;
 1736.9539, 1255.0072;
 1827.3099, 1255.0072;
 1917.666, 1255.0072;
 2008.0221, 1255.0072;
 2098.3782, 1255.0072;
 1194.8174, 1345.443;
 1285.1735, 1345.443;
 1375.5295, 1345.443;
 1465.8856, 1345.443;
 1556.2417, 1345.443;
 1646.5978, 1345.443;
 1736.9539, 1345.443;
 1827.3099, 1345.443;
 1917.666, 1345.443;
 2008.0221, 1345.443;
 2098.3782, 1345.443;
 1194.8174, 1435.8789;
 1285.1735, 1435.8789;
 1375.5295, 1435.8789;
 1465.8856, 1435.8789;
 1556.2417, 1435.8789;
 1646.5978, 1435.8789;
 1736.9539, 1435.8789;
 1827.3099, 1435.8789;
 1917.666, 1435.8789;
 2008.0221, 1435.8789;
 2098.3782, 1435.8789;
 1194.8174, 1526.3147;
 1285.1735, 1526.3147;
 1375.5295, 1526.3147;
 1465.8856, 1526.3147;
 1556.2417, 1526.3147;
 1646.5978, 1526.3147;
 1736.9539, 1526.3147;
 1827.3099, 1526.3147;
 1917.666, 1526.3147;
 2008.0221, 1526.3147;
 2098.3782, 1526.3147;
 1194.8174, 1616.7506;
 1285.1735, 1616.7506;
 1375.5295, 1616.7506;
 1465.8856, 1616.7506;
 1556.2417, 1616.7506;
 1646.5978, 1616.7506;
 1736.9539, 1616.7506;
 1827.3099, 1616.7506;
 1917.666, 1616.7506;
 2008.0221, 1616.7506;
 2098.3782, 1616.7506;
 1194.8174, 1707.1864;
 1285.1735, 1707.1864;
 1375.5295, 1707.1864;
 1465.8856, 1707.1864;
 1556.2417, 1707.1864;
 1646.5978, 1707.1864;
 1736.9539, 1707.1864;
 1827.3099, 1707.1864;
 1917.666, 1707.1864;
 2008.0221, 1707.1864;
 2098.3782, 1707.1864]

经函数求出的空间坐标点是：

vector<Point3f> worldPoint;
cameraToWorld(intrinsic_matrix, mat_tmp, translation_vec_tmp, image_points2, worldPoint);
cout << "计算空间点:\n" << worldPoint << endl;
[0, 0, 1;
 30, 0, 1;
 60, 0, 1;
 90.000015, 0, 1;
 120.00002, 0, 1;
 149.99995, 0, 1;
 179.99998, 0, 1;
 209.99998, 0, 1;
 239.99998, 0, 1;
 270, 0, 1;
 300, 0, 1;
 0, 29.999985, 1;
 30, 29.999985, 1;
 60, 29.999985, 1;
 90.000015, 29.999985, 1;
 120.00002, 29.999985, 1;
 149.99995, 29.999985, 1;
 179.99998, 29.999985, 1;
 209.99998, 29.999985, 1;
 239.99998, 29.999985, 1;
 270, 29.999985, 1;
 300, 29.999985, 1;
 0, 60.000015, 1;
 30, 60.000015, 1;
 60, 60.000015, 1;
 90.000015, 60.000015, 1;
 120.00002, 60.000015, 1;
 149.99995, 60.000015, 1;
 179.99998, 60.000015, 1;
 209.99998, 60.000015, 1;
 239.99998, 60.000015, 1;
 270, 60.000015, 1;
 300, 60.000015, 1;
 0, 89.999969, 1;
 30, 89.999969, 1;
 60, 89.999969, 1;
 90.000015, 89.999969, 1;
 120.00002, 89.999969, 1;
 149.99995, 89.999969, 1;
 179.99998, 89.999969, 1;
 209.99998, 89.999969, 1;
 239.99998, 89.999969, 1;
 270, 89.999969, 1;
 300, 89.999969, 1;
 0, 120.00002, 1;
 30, 120.00002, 1;
 60, 120.00002, 1;
 90.000015, 120.00002, 1;
 120.00002, 120.00002, 1;
 149.99995, 120.00002, 1;
 179.99998, 120.00002, 1;
 209.99998, 120.00002, 1;
 239.99998, 120.00002, 1;
 270, 120.00002, 1;
 300, 120.00002, 1;
 0, 149.99998, 1;
 30, 149.99998, 1;
 60, 149.99998, 1;
 90.000015, 149.99998, 1;
 120.00002, 149.99998, 1;
 149.99995, 149.99998, 1;
 179.99998, 149.99998, 1;
 209.99998, 149.99998, 1;
 239.99998, 149.99998, 1;
 270, 149.99998, 1;
 300, 149.99998, 1;
 0, 179.99998, 1;
 30, 179.99998, 1;
 60, 179.99998, 1;
 90.000015, 179.99998, 1;
 120.00002, 179.99998, 1;
 149.99995, 179.99998, 1;
 179.99998, 179.99998, 1;
 209.99998, 179.99998, 1;
 239.99998, 179.99998, 1;
 270, 179.99998, 1;
 300, 179.99998, 1;
 0, 209.99998, 1;
 30, 209.99998, 1;
 60, 209.99998, 1;
 90.000015, 209.99998, 1;
 120.00002, 209.99998, 1;
 149.99995, 209.99998, 1;
 179.99998, 209.99998, 1;
 209.99998, 209.99998, 1;
 239.99998, 209.99998, 1;
 270, 209.99998, 1;
 300, 209.99998, 1]

可以对比按11*8格和30mm/格所生成空间坐标点结果，基本一致。

参考链接

摄像头和机械臂的手眼标定

一、背景

Calibration是机器人开发者永远的痛。虽然说方法说起来几十年前就有，但每一个要用摄像头的人都还是要经过一番痛苦的踩坑，没有轻轻松松拿来就效果好的包。其实人类不就是个手眼协调的先进“机器人”吗，O(∩_∩)O哈哈~

机器人视觉应用中，手眼标定是一个非常基础且关键的问题。简单来说手眼标定的目的就是获取机器人坐标系和相机坐标系的关系，最后将视觉识别的结果转移到机器人坐标系下。

手眼标定行业内分为两种形式，根据相机固定的地方不同，如果相机和机器人末端固定在一起，就称之为“眼在手”（eye in hand），如果相机固定在机器人外面的底座上，则称之为“眼在外”（eye to hand）。

二、手眼关系的数学描述

1. eye in hand，这种关系下，两次运动，机器人底座和标定板的关系始终不变。求解的量为相机和机器人末端坐标系的位姿关系。

2. eye to hand，这种关系下，两次运动，机器人末端和标定板的位姿关系始终不变。求解的量为相机和机器人底座坐标系之间的位姿关系。

手眼标定eye in hand 和eye to hand 的区别主要是机器人那边，一个是end相对于base，另一个是base相对于end。千万注意。

三、AX = XB问题的求解

旋转和平移分步法求解：

Y. Shiu, S. Ahmad Calibration of Wrist-Mounted Robotic Sensors by Solving Homogeneous Transform Equations of the Form AX = XB. In IEEE Transactions on Robotics and Automation, 5(1):16-29, 1989.

R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration. In IEEE Transactions on Robotics and Automation, 5(3):345-358, 1989.

迭代求解及相关资料可以看看相关网上的英文教程 Calibration and Registration Techniques for Robotics 其中也有一些AX= XB的matlab代码可以使用。

ROS 下也有相关的一些package可以利用

https://github.com/IFL-CAMP/easy_handeye

handeye - ROS Wiki

Visual Servoing Platform: Tutorial: Camera extrinsic calibration

HandEyeCalibration TUM这里也有很多手眼标定求解的参考链接

四、其他参考资料

3D 视觉之手眼标定邱强Flyqq 微信文章

手眼标定的两种方式_wzj5530的专栏-CSDN博客_手眼标定图不错

深入浅出地理解机器人手眼标定_二毛的博客-CSDN博客_机器人手眼标定部分halcon代码

eye-in-hand手眼标定系统_二毛的博客-CSDN博客 halcon代码

手眼标定之9点法_GoRunningSnail的博客-CSDN博客部分原理

UR5、Kinect2手眼标定总结_zhang970187013的博客-CSDN博客 UR5 与easy hand eye

一般用“两步法”求解基本方程，即先从基本方程上式求解出旋转部分，再代入求解出平移部分。

经典手眼标定算法之Tsai-Lenz的OpenCV实现_YunlinWang的博客-CSDN博客

============== Halcon 官方示例-手眼标定 ==================

五、Matlab下手眼标定解算

相机与机器人是eye-to-hand模式，机器人为加拿大Kinova 6轴机械臂，机器人pose为基座相对于末端的x，y，z，rx，ry，rz，rw, 单位为米。姿态使用单位四元数表示。

2017.08.29Kinova_pose_all_8_1.txt

0.4285232296,0.1977596461,-0.5595823047,0.4738016082,0.3209333657,-0.2268795901,-0.7880605703
0.2265712272,0.0261943502,-0.6661910850,0.5222343809,0.0461668455,-0.1315038186,-0.8413362107
0.1058885008,0.0527681997,-0.7419267756,0.5081638565,-0.0263721340,-0.1696570449,-0.8439730402
0.1127224767,-0.0420359377,-0.7413071786,0.5101706595,-0.0322252464,-0.1864402870,-0.8390038445
0.2592932751,-0.0329068529,-0.7162865014,0.5101641882,0.1265739325,-0.0153077050,-0.8505746380
-0.1724239544,0.0084144761,-0.6998332592,0.4989369998,-0.0973210400,-0.1244194561,-0.8521210503
-0.0534271258,0.0771779706,-0.6845820453,0.5195499916,0.0511217081,-0.1691595167,-0.8359661686
0.2598500029,0.0933230213,-0.6638257382,0.5673912325,0.1686973705,-0.1427337629,-0.7932436318

pattern pose为标定板相对于相机的x，y，z，rx，ry，rz，rw, 单位为米。姿态使用单位四元数表示。

2017.08.29Pattern_pose_all_8_1.txt

0.4277459616,-0.0267754900,0.0822384718,0.3555198802,-0.2313832954,-0.6738796808,-0.6049409568
0.4610891450,-0.0089776615,0.0394863697,-0.4168581229,0.4389318994,0.4953132086,0.6230833961
0.4159190432,0.0112235849,0.0112504715,-0.3559197420,0.4898053987,0.4542453721,0.6535081871
0.4143974465,0.0094036803,0.1073298638,-0.3438949365,0.4875942762,0.4507613906,0.6639294113
0.3473267442,-0.1866610227,0.0651366494,0.4966101920,-0.4291949558,-0.5361047392,-0.5308123169
0.3883165305,-0.2249779584,0.0093256602,-0.3595692001,0.5529429756,0.4090559739,0.6305848604
0.4309364801,-0.2354261800,-0.0925819097,-0.3831460128,0.4244707870,0.5043032275,0.6470718186
0.4362292324,-0.0778107597,-0.1007970399,0.4568889439,-0.3144470665,-0.5302104578,-0.6412896427

此Matlab文件调用数据进行离线解算。Calibration and Registration Techniques for Robotics 的这部分 Registering Two Sets of 6DoF Data with 1 Unknown，有code下载，下载好命名为shiu.m和tsai.m供下面程序调用就行。我这里贴出

function X = tsai_wechat(A,B)
% Calculates the least squares solution of
% AX = XB
% 
% A New Technique for Fully Autonomous 
% and Efficient 3D Robotics Hand/Eye Calibration
% Lenz Tsai
%
% Mili Shah
% July 2014
 
[m,n] = size(A); n = n/4;
S = zeros(3*n,3);
v = zeros(3*n,1);
%Calculate best rotation R
for i = 1:n
    A1 = logm(A(1:3,4*i-3:4*i-1)); 
    B1 = logm(B(1:3,4*i-3:4*i-1));
    a = [A1(3,2) A1(1,3) A1(2,1)]'; a = a/norm(a);
    b = [B1(3,2) B1(1,3) B1(2,1)]'; b = b/norm(b);
    S(3*i-2:3*i,:) = skew(a+b);
    v(3*i-2:3*i,:) = a-b;
end
x = S\v;
theta = 2*atan(norm(x));
x = x/norm(x);
R = (eye(3)*cos(theta) + sin(theta)*skew(x) + (1-cos(theta))*x*x')';
%Calculate best translation t
C = zeros(3*n,3);
d = zeros(3*n,1);
I = eye(3);
for i = 1:n
    C(3*i-2:3*i,:) = I - A(1:3,4*i-3:4*i-1);
    d(3*i-2:3*i,:) = A(1:3,4*i)-R*B(1:3,4*i);
end
t = C\d;
%Put everything together to form X
X = [R t;0 0 0 1];

Jaco_handeye_test_10.m 测试程序中用到了Peter Corke老师的机器人工具箱。我的Matlab版本R2013a，利用机器人工具箱的一些转换函数（四元数的构建，欧拉角转换等），它安装和基本使用参考这里：Matlab机器人工具箱_Learning by doing-CSDN博客_matlab机器人工具箱

clear;
close all;
clc;
 
% Robot pose in Quatenion xyzw
 
 JacoCartesianPose = importdata('E:\\Matlab_work\\handeye\\yake_handeye\\2017.08.29Kinova_pose_all_8_1.txt');
 
 [m,n] = size(JacoCartesianPose); % 8* 7
A = cell(1,m); % 1*8
 
for i = 1: m
    
    robotHtool_qua = Quaternion([ JacoCartesianPose(i, 7), JacoCartesianPose(i, 4), JacoCartesianPose(i, 5) , JacoCartesianPose(i, 6)]) ; 
    A{1,i}  = transl(JacoCartesianPose(i, 1), JacoCartesianPose(i, 2), JacoCartesianPose(i, 3)) *  robotHtool_qua.T;
   
end
 
% Pattern Pose(Homogeneous) stored in  cell B.
patternInCamPose = importdata('E:\\Matlab_work\\handeye\\yake_handeye\\2017.08.29Pattern_pose_all_8_1.txt');
 
[melem,nelem] = size(patternInCamPose); % 8*7
B=cell(1,melem);
for x=1: melem
    camHmarker_qua = Quaternion([ patternInCamPose(x, 7) , patternInCamPose(x, 4), patternInCamPose(x, 5) , patternInCamPose(x, 6)])    ;
    B{1,x} = transl(patternInCamPose(x, 1), patternInCamPose(x, 2), patternInCamPose(x, 3)) *  camHmarker_qua.T;
end
%
 
n2=m;
TA=cell(1,n2);
TB=cell(1,n2); 
 
%--------------------- 8 -----------------------------------
M1=[];
M2=[];
 for j=[1: m-1]% 1-7.
 
     TA{1, j} = inv(A{1, j}) * A{1, j+1};    
     M1=[M1 TA{1, j}];
     
     TB{1, j} = B{1, j} * inv(B{1, j+1});
     M2=[M2 TB{1, j}];
     
 end 
 
 % M1=[TA{1,1} TA{1,2} TA{1,3} TA{1,4} TA{1,5} TA{1,6} TA{1,7} ];
 % M2=[TB{1,1} TB{1,2} TB{1,3} TB{1,4} TB{1,5} TB{1,6} TB{1,7} ];
%--------------------- 8 -----------------------------------
 
C_Tsai=tsai(M1, M2)
T_Tsai =  (transl(C_Tsai))';
C_Tsai_rad = tr2rpy(C_Tsai);
C_Tsai_rpy_rx_ry_rz =rad2deg(C_Tsai_rad);
Q_Tsai_Qxyzw=Quaternion(C_Tsai);
fprintf('Tsai(rad) = \n%f, %f, %f, %f, %f, %f\n',T_Tsai(1,1), T_Tsai(1,2), T_Tsai(1,3), C_Tsai_rad(1,1), C_Tsai_rad(1,2), C_Tsai_rad(1,3));
fprintf('Tsai(deg) = \n%f, %f, %f, %f, %f, %f\n\n',T_Tsai(1,1), T_Tsai(1,2), T_Tsai(1,3), C_Tsai_rpy_rx_ry_rz(1,1), C_Tsai_rpy_rx_ry_rz(1,2), C_Tsai_rpy_rx_ry_rz(1,3));
fprintf('Tsai(Qxyzw) = \n %f, %f, %f, %f\n\n',  Q_Tsai_Qxyzw.v(1),  Q_Tsai_Qxyzw.v(2), Q_Tsai_Qxyzw.v(3), Q_Tsai_Qxyzw.s);

稍微解释一下，程序做的就是读入机器人和相机的两两姿态信息，转换为4x4 的齐次变换矩阵，送入tsai.m程序求解。

手眼标定eye in hand 和eye to hand 的区别主要是机器人那边，一个是end相对于base，另一个是base相对于end。千万注意。

====================平面九点标定法====================

当利用RGB相机或者是机器人只进行平面抓取（也即固定姿态抓取，机器人六自由度位置和姿态简化为只考虑平移，姿态人为给定并且固定，这时机器人可以移动到目标点上方），问题可以简化为平面RGB图像的目标像素点集A（x1，y1）与机器人在平面（X1，Y1）的点对关系。具体做法是相机识别像素点给到A，然后利用示教器查看机器人在基座标系下的坐标，当做B。

相机坐标和机器人坐标写成齐次的形式，投影矩阵X是一个3x3的矩阵我们需要6组对应点来求解最小配置解。利用奇异值分解SVD来求取。

D:\opencv_work\cubeSolver\cv_solver\ConsoleApplication1\CV_SVD.cpp

D:\Matlab_work\handeye\NinePoints_Calibration.m

//Solve equation:AX=b
 
#include <cv.h>
#include<opencv2/opencv.hpp>
using namespace std;
using namespace cv;
 
int main(int argc, char** argv)
{
	printf("\nSolve equation:AX=b\n\n");
 
	//Mat A = (Mat_<float>(6, 3) <<
	//480.8, 639.4, 1,
	//227.1, 317.5, 1,
	//292.4, 781.6, 1,
	//597.4, 1044.1, 1,
	//557.7, 491.6, 1,
	//717.8, 263.7, 1
	//		 );// 4x3
 
	//Mat B = (Mat_<float>(6, 3) <<
	//197170, 185349, 1,
	//195830, 186789, 1,
	//196174, 184591, 1,
	//197787, 183176, 1,
	//197575, 186133, 1,
	//198466, 187335, 1
	//		 );
 
	Mat A = (Mat_<float>(4, 3) << 
	2926.36, 2607.79, 1, 
	587.093, 2616.89, 1, 
	537.031, 250.311, 1, 
	1160.53, 1265.21, 1);// 4x3
 
	Mat B = (Mat_<float>(4, 3) << 
	320.389, 208.197, 1,
	247.77, 209.726, 1,
	242.809, 283.182, 1,
	263.152, 253.715, 1);
 
 
	Mat X;
	cout << "A=" << endl << A << endl;
	cout << "B=" << endl << B << endl;
 
	solve(A, B, X, CV_SVD);
 
	cout << "X=" << endl << X << endl;
	Mat a1 = (Mat_<float>(1, 3) << 1864, 1273, 1);
	Mat b1 = a1*X;
	cout << "b1=" << endl << b1 << endl;
	cout << "真实值为：" << "283.265, 253.049, 1" << endl;
 
 
	getchar();
 
	return 0;
}

OpenCV: Operations on arrays

结果对比：左halcon C#（第三列为0,0,1，没做显示），右opencv c++，底下为Matlab结果，三者一致，算法检测通过。

=============

四轴平面机器人的手眼标定_Stones1025的博客-CSDN博客

这种方法利用点对，求仿摄变换矩阵

#include "pch.h"
#include <iostream>
 
#include <cv.h>
#include<opencv2/opencv.hpp>
 
using namespace cv;
 
 
int main(int argc, char** argv)
{
	printf("\nSolve equation:AX=b\n\n");
 
	Point2f srcTri[3];
	srcTri[0] = Point2f(480.8f, 639.4f);
	srcTri[1] = Point2f(227.1f, 317.5f);
	srcTri[2] = Point2f(292.4f, 781.6f);
 
	Point2f dstTri[3];
	dstTri[0] = Point2f(197170.0f, 185349.0f);
	dstTri[1] = Point2f(195830.0f, 186789.0f);
	dstTri[2] = Point2f(196174.0f, 184591.0f);
 
	Mat warp_mat = getAffineTransform(srcTri, dstTri);
	// 计算图像坐标
	std::cout << "img_corners：\n" << srcTri << std::endl;
	std::cout << "robot_corners：\n" << dstTri << std::endl;
	std::cout << warp_mat << std::endl;
 
	//
	//[5.284835627018051, -0.00236967559639317, 194630.5662011061;
	//0.4056177440315953, -4.793119669651512, 188218.6997054448]
 
	return 0;
}

================= Eye in hand 数据及Ground truth =========================

Marker in Camera 八组数据，单位：米及弧度，姿态用的是RotVector表示

0.11725,0.05736,0.32176,0.02860,-0.1078,3.06934
0.07959,0.06930,0.29829,0.66126,-0.2127,2.99042
0.14434,0.03227,0.40377,-0.9465,-0.1744,2.80668
0.11008,0.05605,0.35730,0.12422,-1.0665,2.87604
0.1131,0.0438171,0.299624,-0.058278,-0.514154,-3.06309
0.057039,0.109464,0.415275,0.414777,0.750109,-2.49495
0.13702,0.00520,0.38190,0.26431,-0.7554,2.26885
-0.03670,-0.04433,0.237292,0.807501,-0.27347,0.614594

Robot end-effector in Base 八组数据，单位：米及弧度，姿态用的是RotVector表示

0.400422,-0.0262847,0.365594,3.12734,-0.0857978,-0.0168582
0.423347,-0.16857,0.340184,-2.73844,0.089013,0.123284
0.42540,0.19543,0.29062,2.44351,-0.1777,-0.0722
0.58088,-0.0541,0.32633,-2.9303,0.06957,0.98985
0.2760,-0.033,0.3301,3.0813,0.0724,0.6077
0.54011,0.09071,0.34623,2.62279,0.75876,-0.6927
0.57732,-0.1346,0.37990,-2.6764,1.04868,0.82177
0.27844,-0.1371,0.28799,-0.8233,2.26319,0.36110

Ground truth：Camera in end-effector

Calibration Result:

-0.005816946773 -0.997849042178 -0.065295115862 0.053457653403

0.999355366046 -0.003487653357 -0.035730779845 -0.009463725064

0.035426197714 -0.065460868457 0.997226082298 -0.045113271057

0 0 0 1

RotVector(rad): -0.023440 -0.079412 1.57466

AngVector(rad): 1.576836 <-0.014865, -0.050362, 0.998620>

AngVector(deg): 90.346026 <-0.014865, -0.050362, 0.998620>

rx_ry_rz_rw: -0.010543 -0.079412 1.574660 0.704968

rx_ry_rz_rw_norm: -0.010543 -0.035718 0.708260 0.704968

参考链接

nvidia-smi GPU性能状态(Performance State)含义

我正在使用Nvidia GTX Titan X进行深度学习实验。
我正在使用nvidia-smi来监视GPU的运行状态，但是提供的工具的性能(性能)状态没有意义。

我已经查看了nvidia-smi手册，它表示以下内容：

Performance State
The current performance state for the GPU. States range from P0 (maximum performance) to P12 (minimum performance).

如果不在GPU上运行任何进程(空闲状态)，则GPU性能状态为p0。
但是，当运行一些计算繁重的过程时，状态变为p2。

我的问题是，为什么我的GPU闲置时处于P0状态，但是在执行繁重的计算任务时切换到P2？不应该相反吗？

另外，有没有办法使我的GPU始终在P0状态下运行(最高性能)？

令人困惑。

但是，nvidia-smi手册是正确的。

当一个或一组GPU处于空闲状态时，在计算机上运行nvidia-smi的过程通常会使其中一个GPU退出空闲状态。这是由于该工具正在收集的信息-需要唤醒其中一个GPU。

此唤醒过程最初会将GPU置于P0状态(最高性能状态)，但如果GPU空闲或不是特别忙碌，GPU驱动程序将监控该GPU，并最终开始降低性能状态以节省功耗。

另一方面，当GPU在工作负载下处于活动状态时，GPU驱动程序将根据其自身的启发式方法不断调整性能状态以提供最佳性能，同时使性能状态与实际工作负载相匹配。如果没有达到热或功率限制，则对于最活跃和最重的连续工作负载，性能状态应达到最高水平(P0)。

周期性很重但不连续的工作负载可能会导致GPU功耗状态在P0-P2级别附近波动。由于热(温度)或电源问题而"受限制"的GPU也可能会看到P状态降低。这种限制是显而易见的，并在nvidia-smi中单独报告，但是可能并非所有GPU类型都启用这种报告。

如果要在GPU上查看P0状态，我可以提供的最佳建议是运行短暂，繁重且连续的工作负载(例如，执行大型sgemm操作的工作)，然后在该工作负载期间监视GPU。在这种情况下应该可以看到P0状态。

如果您使用的是正在使用cuDNN库的机器学习应用程序(例如Caffe)，并且正在训练大型网络，则应该可以不时看到P0，因为cuDNN会执行类似于sgemm的操作通常情况下。

但是对于零星的工作负载，最常见的状态很有可能是P2。

要始终"强制" P0电源状态，可以尝试通过nvidia-smi工具尝试持久性模式和应用程序时钟。使用nvidia-smi --help或nvidia-smi的手册页了解选项。

尽管我认为这通常不适用于Tesla GPU，但除非特别设置更高的应用时钟，否则某些NVIDIA GPU可能会在计算负载下将自身限制为P2功耗状态。使用nvidia-smi -a命令查看可用于GPU的当前应用程序时钟，默认应用程序时钟和最大时钟。 (某些GPU(包括较旧的GPU)可能会在其中某些字段中显示N / A。这通常表明应用程序时钟无法通过nvidia-smi进行修改。)如果在计算负载期间卡似乎以P2状态运行，则可能通过将应用程序时钟增加到最大可用时钟(即最大时钟)，可以将其增加到P0状态。使用nvidia-smi --help了解如何格式化命令以更改GPU上的应用程序时钟。修改应用程序时钟或启用可修改的应用程序时钟可能需要root / admin特权。设置GPU持久模式也可能是理想的或必要的。这将防止驱动程序在GPU活动期间"卸载"，这可能导致驱动程序重新加载时重置应用程序时钟。

对于这种情况下受影响的卡，此默认行为是在计算负载下限制为P2，这是由GPU驱动程序设计的。

参考链接

ubuntu 21.10(GeForce GTX 3060 12GB)编译StyleGAN3

安装驱动：

# 清理全部的其他版本的nvidia驱动
$ sudo apt-get purge nvidia-*

$ sudo reboot

# nvidia-smi
$ sudo apt install nvidia-utils-470

# 驱动
$ sudo apt install nvidia-driver-470

# cuda 11.3 
$ sudo apt install nvidia-cuda-toolkit

$ sudo apt-get update

# 部分驱动可能会更新，需要执行更新，否则可能依旧不正常
$ sudo apt-get dist-upgrade

$ sudo apt-get autoremove

# 重启，否则部分驱动可能工作不正常
$ sudo reboot

在 Anaconda 上建立独立的编译环境，然后执行编译：

# wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh
# 国内镜像下载
$ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.11-Linux-x86_64.sh

$ bash Anaconda3-*-Linux-x86_64.sh

# 更新到最新版本
$ conda update -n base -c defaults conda

参考 Anaconda conda切换为国内源加速下载。

编译配置StyleGAN3

$ sudo apt-get install git

$ git clone git@github.com:NVlabs/stylegan3.git

$ cd stylegan3

$ conda env create -f environment.yml

$ conda activate stylegan3

$ pip install psutil

# cudnn加速
$ conda install cudnn

# 目前测试 RTX 3060 12GB的情况下，batch建议是2，更高会报告OOM
# 并且当batch低于4的时候，需要同时指定 --mbstd-group=2
$ python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/metfaces-1024x1024.zip --gpus=1 --batch=2 --mbstd-group=2 --gamma=8.2 --mirror=1 --metrics=none

如果报错：

Constructing networks...
Setting up PyTorch plugin "bias_act_plugin"... Failed!
Traceback (most recent call last):
  File "~/source/stylegan3/train.py", line 286, in <module>
    main() # pylint: disable=no-value-for-parameter
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1128, in __call__
    return self.main(*args, **kwargs)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1053, in main
    rv = self.invoke(ctx)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1395, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 754, in invoke
    return __callback(*args, **kwargs)
  File "~/source/stylegan3/train.py", line 281, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "~/source/stylegan3/train.py", line 96, in launch_training
    subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
  File "~/source/stylegan3/train.py", line 47, in subprocess_fn
    training_loop.training_loop(rank=rank, **c)
  File "~/source/stylegan3/training/training_loop.py", line 168, in training_loop
    img = misc.print_module_summary(G, [z, c])
  File "~/source/stylegan3/torch_utils/misc.py", line 216, in print_module_summary
    outputs = module(*inputs)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1071, in _call_impl
    result = forward_call(*input, **kwargs)
  File "~/source/stylegan3/training/networks_stylegan3.py", line 511, in forward
    ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1071, in _call_impl
    result = forward_call(*input, **kwargs)
  File "~/source/stylegan3/training/networks_stylegan3.py", line 151, in forward
    x = getattr(self, f'fc{idx}')(x)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1071, in _call_impl
    result = forward_call(*input, **kwargs)
  File "~/source/stylegan3/training/networks_stylegan3.py", line 100, in forward
    x = bias_act.bias_act(x, b, act=self.activation)
  File "~/source/stylegan3/torch_utils/ops/bias_act.py", line 84, in bias_act
    if impl == 'cuda' and x.device.type == 'cuda' and _init():
  File "~/source/stylegan3/torch_utils/ops/bias_act.py", line 41, in _init
    _plugin = custom_ops.get_plugin(
  File "~/source/stylegan3/torch_utils/custom_ops.py", line 136, in get_plugin
    torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1080, in load
    return _jit_compile(
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1318, in _jit_compile
    return _import_module_from_library(name, build_directory, is_python_module)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 1701, in _import_module_from_library
    module = importlib.util.module_from_spec(spec)
  File "<frozen importlib._bootstrap>", line 565, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 1173, in create_module
  File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
ImportError: ~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/lib/../../../../libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by ~/.cache/torch_extensions/bias_act_plugin/3cb576a0039689487cfba59279dd6d46-nvidia-geforce-rtx-3060/bias_act_plugin.so)

上述报错产生的原因是在 Anaconda 下载的包，在进行编译的时候，使用了高版本的 libstdc++.so。而运行时却使用了Anaconda 环境里低版本的 libstdc++.so 导致报错。

了解了原因，解决方法就比较简单了，可以手工升级 Anaconda 环境下的 libstdc++.so 动态库。

如下：

$ conda activate stylegan3

$ conda install cmake

$ conda install make

# 关键升级命令,更新当前项目里面的 libstdc++.so
$ conda install -c conda-forge libstdcxx-ng

# 删除上次失败时候的编译缓存
$ rm -rf ~/.cache

# 目前测试 RTX 3060 12GB的情况下，batch建议是2，更高会报告OOM
# 当batch=4的时候会在第11天的时候报告OOM
# 并且当batch低于4的时候，需要同时指定 --mbstd-group=2
$ python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/metfaces-1024x1024.zip --gpus=1 --batch=2 --mbstd-group=2 --gamma=8.2 --mirror=1 --metrics=none

目前测试发现，当batch=4的时候会在第11天的时候报告OOM，如下：

tick 444   kimg 1776.0   time 11d 17h 14m  sec/tick 2292.6  sec/kimg 573.16  maintenance 0.2    cpumem 5.40   gpumem 7.69   reserved 10.03  augment 0.344
Traceback (most recent call last):
  File "~/source/stylegan3/train.py", line 286, in <module>
    main() # pylint: disable=no-value-for-parameter
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1128, in __call__
    return self.main(*args, **kwargs)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1053, in main
    rv = self.invoke(ctx)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1395, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 754, in invoke
    return __callback(*args, **kwargs)
  File "~/source/stylegan3/train.py", line 281, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "~/source/stylegan3/train.py", line 96, in launch_training
    subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
  File "~/source/stylegan3/train.py", line 47, in subprocess_fn
    training_loop.training_loop(rank=rank, **c)
  File "~/source/stylegan3/training/training_loop.py", line 278, in training_loop
    loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
  File "~/source/stylegan3/training/loss.py", line 81, in accumulate_gradients
    loss_Gmain.mean().mul(gain).backward()
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/_tensor.py", line 255, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/autograd/__init__.py", line 147, in backward
    Variable._execution_engine.run_backward(
  File "~/anaconda3/envs/stylegan3/lib/python3.9/site-packages/torch/autograd/function.py", line 87, in apply
    return self._forward_cls.backward(self, *args)  # type: ignore[attr-defined]
  File "~/source/stylegan3/torch_utils/ops/grid_sample_gradfix.py", line 50, in backward
    grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
  File "~/source/stylegan3/torch_utils/ops/grid_sample_gradfix.py", line 59, in forward
    grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
RuntimeError: CUDA out of memory. Tried to allocate 1.39 GiB (GPU 0; 11.76 GiB total capacity; 7.06 GiB already allocated; 443.88 MiB free; 10.02 GiB reserved in total by PyTorch)

参考链接

ImageNet(2010-2017)图像识别数据集

ImageNet 数据集是目前世界上图像识别最大的数据库，根据 WordNet 层次结构（目前仅限物体）组织，主要用于机器视觉领域的图像分类和目标检测。其中层次结构的每个节点由数百和数千个图像描绘，每个节点平均有超过 500 个图像，有大约 1500 万张图片，2.2 万类。 ImageNet 数据集于 2009 年由斯坦福大学的李飞飞等人在视觉科学学会（VSS）首次发布，而后自 2010 年起一年一度的 ImageNet 大规模视觉识别挑战（ILSVRC）挑战赛不断完善 ImageNet 数据集。

ImageNet.torrent 需要占用磁盘空间 860.55 GB

参考链接

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用Pytorch Pix2PixGAN(CUDA-10.1)

1. 参照 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 建立 `pytorch 1.0.1` 的编译环境，并解决编译时遇到的问题。

2. 依旧是推荐在 Anaconda 上建立独立的编译环境，然后执行编译：

$ sudo apt-get install git

# conda remove -n Pix2Pix --all

$ conda create -n Pix2Pix -y python=3.6.8 pip

$ source activate Pix2Pix

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败 
$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行
# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py
$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

# 如果不需要使用cuda的话，这里还要加上一句：export NO_CUDA=1

$ python setup.py clean

# 卸载以前安装的pytorch
$ conda uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability” 
# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常
# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ python setup.py install

# 对于开发者模式，可以使用
# python setup.py build develop

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常
$ cd ..

# 退出环境 
$ conda deactivate

编译出错信息，参考 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 里面的介绍解决。

3. 编译安装 TorchVision

$ sudo apt-get install git

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境 
$ conda deactivate

4. 检出 CycleGAN and pix2pix in PyTorch 的代码，并安装依赖

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

# 也可本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/12/pytorch-CycleGAN-and-pix2pix.zip

$ cd pytorch-CycleGAN-and-pix2pix

# 下载人脸替换部分的数据集
$ bash datasets/download_pix2pix_dataset.sh facades

# 也可本站下载然后自己参照脚本解压缩到指定目录 https://www.mobibrw.com/wp-content/uploads/2019/12/facades.tar.gz

# 安装依赖
$ pip install pillow==6.2.1
$ pip install dominate==2.4.0
$ pip install visdom==0.1.8.9

# 修正错误 models/networks.py
# TypeError: cuda() got an unexpected keyword argument 'device_id'
$ sed -i "s/netG\.cuda(device_id=gpu_ids\[0\])/netG.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/netD\.cuda(device_id=gpu_ids\[0\])/netD.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/network\.cuda(device_id=gpu_ids\[0\])/network.cuda(gpu_ids[0])/g" models/base_model.py

# 开启WEB服务,主要是第一次运行需要下载部分辅助软件包，
# 训练之前需要执行，否则下面训练的时候会报错
$ python -m visdom.server & 

# 等待屏幕上出现 “You can navigate to http://localhost:8097” 代表服务启动成功

# 执行训练
$ bash scripts/train_pix2pix.sh

执行训练的时候，如果出现如下错误：

Traceback (most recent call last):
  File "train.py", line 47, in <module>
    errors = model.get_current_errors()
  File "~/pytorch-CycleGAN-and-pix2pix/models/pix2pix_model.py", line 122, in get_current_errors
    return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

这个原因是由于 PyTorch 版本差异造成的,（作者在 `Pytorch 0.4.1` 版本上测试，我们在 `Pytorch 1.0.1` 版本上测试），执行如下命令修复：

#loss_G_GAN.data[0] 替换为 loss_G_GAN.item()

$ sed -i "s/self\.loss_G_GAN\.data\[0]/self.loss_G_GAN.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_G_L1\.data\[0]/self.loss_G_L1.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_real\.data\[0]/self.loss_D_real.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_fake\.data\[0]/self.loss_D_fake.item()/g" models/pix2pix_model.py

5. 测试训练结果

$ bash scripts/test_pix2pix.sh

# 观察结果需要打开 ./results/facades_pix2pix/test_latest/index.html

参考链接

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用MaskTextSpotter(CUDA-10.1)进行训练

参考在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1) 建立能运行的测试环境。

由于测试集使用的是 icdar2013 ,因此，务必保证已经可以在 icdar2013 数据集中进行测试。

接下来就是进行数据训练：

1. 修改训练脚本，默认情况下，训练脚本中使用了 8 张卡进行训练，我们只有一张卡，因此要调整训练参数

$ cd MaskTextSpotter

$ export ROOT_PATH=`pwd`

$ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh

2. 下载训练集 MaskTextSpotter 默认使用的是 SynthText 数据集进行训练，需要先下载这个数据集，大约 40GB

$ mkdir datasets

$ cd datasets

$ sudo apt-get install aria2

$ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783'

# 也可下载种子文件 wget https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip

3. 解压缩 SynthText 数据集到指定目录

$ mkdir synthtext

$ unzip SynthText/data/SynthText.zip -d synthtext

# 目录改名
$ mv synthtext/SynthText synthtext/train_images

4. 下载转换后的 SynthText 数据集索引文件，上面解压缩出来的索引是 .mat 扩展名的文件，我们需要转换成 MaskTextSpotter 需要的数据索引文件，作者提供了一份已经转换好的文件，我们直接下载并使用这个文件即可,这个文件大概要 1.6GB 的样子。

$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa

# 解压缩到指定目录
$ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext

# 目录改名
$ mv synthtext/SynthText_GT_E2E synthtext/train_gts

5. 生成训练文件 train_list.txt

import os

path = 'train_images'

train_list = 'train_list.txt'

tf = open(train_list, 'w')

for root, dirs, files in os.walk(path):
    files = [f for f in files if not f[0] == '.']
    dirs[:] = [d for d in dirs if not d[0] == '.']
    # use files and dirs
    for file_name in files:
        fn = os.path.join(root, file_name)
        fn = fn.replace('./', '')
        fn = fn.replace(path + '/', '')
        ext = os.path.splitext(fn)[1]
        if '.jpg' == ext :  
            tf.write(fn + '\n')

tf.close()

执行脚本，生成文件

$ cd synthtext

$ python gen_train.py

执行测试

$ cd $ROOT_PATH

# 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files”
# 参数设置为 0 代表从主进程加载图片资源
$ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml

# 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足
# 解决 “RuntimeError: CUDA out of memory.”
$ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml

# 修正错误 “AttributeError: module 'torch' has no attribute 'bool'”
# 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本
# 需要修改为torch.uint8
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py


# 修改SOLVER设置上的GPU相关参数
# https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
# 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行
$ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml

# 4GB 显存设置为 8 ，8GB显存可以设置为64/128 
$ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml

# 目前在RTX 2070 Super 8GB显存版本上测试来看，使用
# “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl” 
# 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128 

# 进入运行环境
$ source activate MaskTextSpotter

$ bash train.sh

注意，我们在 configs/pretrain.yaml 加载的权重文件是 "WEIGHT: "./outputs/finetune/model_finetune.pth" ,这个权重文件是从 SynthText 训练得来的，那么这个"model_finetune.pth"是怎么生成的呢？

作者没有详细介绍，我们从 masktextspotter.caffe2 项目的配置文件中可以知道，这个文件其实是从 " WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl" 开始生成的。这个文件也可以从本站下载 R-50.pkl

R-50.pkl: converted copy of MSRA’s original ResNet-50 model

具体配置文件内容参考如下：

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
  NUM_CLASSES: 2
  FASTER_RCNN: True
  MASK_ON: True
  NAME: shrink++
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.005   #synth
  GAMMA: 0.1
  MAX_ITER: 200000
  STEPS: [0, 120000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
  USE_DEFORMABLE: False
  RPN_ASPECT_RATIOS: (0.5, 1, 2)
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
  ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
  RESOLUTION: 28  # (output mask resolution) default 14
  RESOLUTION_H: 32
  RESOLUTION_W: 128
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14  # default 7
  ROI_XFORM_RESOLUTION_H: 16
  ROI_XFORM_RESOLUTION_W: 64
  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default GaussianFill
  IS_E2E: True
  MASK_BATCH_SIZE_PER_IM: 16
  WEIGHT_LOSS_MASK: 1.0
  WEIGHT_LOSS_CHAR_BOX: 1.0
  WEIGHT_WH: True  ## default is false 

TRAIN:
  BATCH_SIZE_PER_IM: 512
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
  AUTO_RESUME: True
  SNAPSHOT_ITERS: 10000

  ##################### pre-train on synth ##########################
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
  DATASETS: ('synth_train', )
  SCALES: (800,)
  MAX_SIZE: 1333
  IMS_PER_BATCH: 2
  ASPECT_GROUPING: True
  MIX_TRAIN: False
  USE_CHARANNS: [True]

  ######################  Fine tune   #################################
  # MIX_TRAIN: True
  # WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl
  # DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train')
  # USE_CHARANNS: [False, True, True, True, False]
  # # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly
  # # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125]
  # MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6]
  # SCALES: (600, 800, 1000)
  # MAX_SIZE: 1333
  # # # SCALES: (800,)
  # # # MAX_SIZE: 1333
  # IMS_PER_BATCH: 1
  # ASPECT_GROUPING: False

IMAGE:
  aug: False
  saturation_prob: 0.5
  saturation_lower: 0.5
  saturation_upper: 1.5
  hue_prob: 0.5
  hue_delta: 18
  lighting_noise_prob: 0.5
  contrast_prob: 0.5
  contrast_lower: 0.5
  contrast_upper: 1.5
  brightness_prob: 0.5
  brightness_delta: 32
  rotate_prob: 0.5
  rotate_delta: 15
TEST:
  OUTPUT_POLYGON: False # only set to True for totaltext
  WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl
  DATASETS: ('icdar2015_test',)
  SCALES: (1000,)
  MAX_SIZE: 3333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
  VIS: False
  SCORE_THRESH: 0.2
  BBOX_AUG:
    ENABLED: False
    SCORE_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    COORD_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    H_FLIP: False
    SCALES: (800,)
    MAX_SIZE: 2000
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH_LO: 2500   # 50^2
    AREA_TH_HI: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  MASK_AUG:
    ENABLED: False
    HEUR: SOFT_AVG
    H_FLIP: False
    SCALES: (1600,)
    MAX_SIZE: 3333
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  BBOX_VOTE:
    ENABLED: True
    VOTE_TH: 0.9
  SOFT_NMS:
    ENABLED: False
OUTPUT_DIR: .

其实我们直接删除或者注释掉权重文件加载部分也是可以的。只是，如果想要复现原作者的测试成果的话，我们最好使用相同的配置信息。

对于 4GB 显存的机器来说，由于显存非常有限，导致非常可能在运行的途中出现 "RuntimeError: CUDA out of memory." ,目前测试来看，继续执行命令即可。

训练结果存储在 outputs/pretrain 目录下，训练结果会在训练到一定阶段之后，存储到这个目录下。

如果出现类似如下错误，请适当减少学习速率 BASE_LR

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [33,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [36,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [37,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [40,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [42,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [43,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [44,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [45,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [46,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [47,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [48,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [49,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [50,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [51,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [52,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [53,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [54,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [55,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [56,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [60,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [61,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [62,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [63,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [64,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [65,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [66,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [67,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [68,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [75,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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Traceback (most recent call last):
  File "tools/train_net.py", line 173, in <module>
    main()
  File "tools/train_net.py", line 166, in main
    model = train(cfg, args.local_rank, args.distributed)
  File "tools/train_net.py", line 76, in train
    cfg,
  File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train
    loss_dict = model(images, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
    proposals, proposal_losses = self.rpn(images, features, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward
    return self._forward_train(anchors, objectness, rpn_box_regression, targets)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train
    anchors, objectness, rpn_box_regression, targets
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward
    sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map
    boxlist = remove_small_boxes(boxlist, self.min_size)
  File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes
    (ws >= min_size) & (hs >= min_size)
RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered

参考链接

PyTorch运行时提示'ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.'

在测试编译FOTS 的时候，出现如下错误：

(FOTS) $~/Source/FOTS.PyTorch$ bash build.sh 
Compiling crop_and_resize kernels by nvcc...
Traceback (most recent call last):
  File "build.py", line 3, in <module>
    from torch.utils.ffi import create_extension
  File "~/.conda/envs/FOTS/lib/python2.7/site-packages/torch/utils/ffi/__init__.py", line 1, in <module>
    raise ImportError("torch.utils.ffi is deprecated. Please use cpp extensions instead.")
ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.

最省事的情况是降级`pytorch`版本到`0.4`，强烈建议使用`Anaconda`创建独立的`Python`开发环境，然后在干净的环境中运行。

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