【GiantPandaCV导语】本文主要介绍最最最基础的tikz命令和一些绘制CNN时需要的基础的LaTeX知识,希望能在尽可能短的时间内学会并实现使用tikz这个LaTeX工具包来绘制卷积神经网络示意图。
https://github.com/HarisIqbal88/PlotNeuralNet之前看到tikz可以画出这种图,感觉特别专业,所以萌发出了解一下tikz的想法。
1. overleaf平台
在电脑上安装过LaTeX都知道,LaTeX安装包巨大,并且安装速度缓慢,下载和安装的时间需要几乎一下午才能完成。庆幸的是有一个平台可以在线编译文档,那就是overleaf,如今overleaf也推出了中文版本网站:https://cn.overleaf.com/ 以下代码全部是在overleaf平台上编写运行得到的。
主页面 进入其中一个项目最左侧是项目文件列表,中间是代码编辑区,右侧是可视化区,十分方便,只要网络通常,就可以方便地得到结果。并且这个平台提供了好多模板,可以直接使用,太太太太太棒啦。
2. 快速入门tikz
快速熟悉还是要推荐《minimaltikz》这本电子书,可以直接访问http://cremeronline.com/LaTeX/minimaltikz.pdf获取或者在后台回复latex获取。
电子书封面这本书一共24页,算是尽量压缩了内容了,在这一节中将分析一下其中给的几个例子,用于快速入门:
所有tikz绘制图像的代码都应该在tikzpicture这个环境中使用。
\begin{tikzpicture}
...
\end{tikzpicture}
直角坐标系下:(,)的形式代表二维坐标系中的一个点,单位是cm。
极坐标系下:(:),代表极角,单位是度。
\coordinate可以对某个点进行重命名如:
\coordinate (s) at (0,1);
2.1 直线
那最基础的画几条线的实现是通过\draw完成:
\begin{tikzpicture}
\draw[help lines] (0,0) grid(3,3);
\coordinate (a) at (0,1);
\coordinate (b) at (3,3);
\coordinate (c) at (2,0);
\draw (a) -- (b) -- (c) --cycle;
\end{tikzpicture}
--符号代表两点之间的连线,可以连续链接多段。cycle代表让路径回到起点,生成闭合路径。
[图片上传失败...(image-42c02f-1600232503297)]
\draw还可以添加选项,比如让线变粗、变红、箭头等需求,都很简单。
\begin{tikzpicture}[scale=1]
\draw[help lines] (0,0) grid(5,5);
\draw (0,0) -- (1,2)--(3,0) --(5,5);
\draw [->] (0,0) -- (2,1);
\draw [<-] (2,3) -- (5,0);
\draw [|->] (0.5,3) -- (0,4);
\draw [<->] (0,6) -- (0,0) -- (6,0);
\end{tikzpicture}
不同的箭头
\begin{tikzpicture}
\draw[help lines] (0,0) grid(5,5);
\draw[thick] (0.5, 0.5) -- (3,3);
% [ultra thick, thick, thin, very thick]
\draw[line width=0.2cm] (1,0) -- (3,2);
\end{tikzpicture}
粗细控制
\begin{tikzpicture}
\draw[help lines] (0,0) grid(5,5);
\draw[ultra thick, dotted] (0,0) -- (2,3);
\draw[line width=0.2cm, dotted,red] (2,2) -- (4,0);
%[red, blue, green, cyan, magenta, yellow, black, gray, darkgray, lightgray, browbn, lime, olive, orange, pink, purple, teal, violet, white]
\end{tikzpicture}
颜色控制
2.2 曲线
画一些曲线就需要使用circle、rectangle、arc等进行约束。
\begin{tikzpicture}
\draw[help lines] (0,0) grid(5,5);
\draw[blue] (1,1) rectangle(3,3); % 正方形 需要左下角坐标和右上角坐标
\draw[red] (2,2) circle[radius=2]; %圆形 需要圆心坐标和半径
\draw[green] (1,0) arc [radius=1,start angle=180,end angle=360];
\draw[<->, rounded corners, thick, purple] (0,5) -- (0,0) -- (5,0);
\end{tikzpicture}
结果展示
\begin{tikzpicture}
\draw[help lines] (0,0) grid(6,3);
\draw[blue, thick] (0,0) to[out=90,in=180] (1,1) to[in=270,out=360] (2,2)
to[in=180,out=90] (3,3) to[in=90,out=360] (4,2) to[in=180,out=270] (5,1)
to[in=90, out=0] (6,0);
\end{tikzpicture}
这是练习画弧线的时候想练习的一个例子,结果如下
结果展示in代表进入的角度,out代表出来时候的角度,为了方便,笔者画了一个辅助图,对照代码方便理解。
参考2.3 画函数曲线
\begin{tikzpicture}[xscale=6,yscale=6]
\draw[<->] (0,0.8) -- (0,0) -- (0.8,0);
\draw[green,thick,domain=0:0.5]
plot(\x, {0.025+\x*\x});
\draw[red, thick, domain=0:0.5]
plot(\x, {sqrt(\x)});
\draw[blue, thick, domain=0:0.5]
plot(\x, {abs(\x)});
\end{tikzpicture}
domain限制变量范围,然后可以画图,结果如下:
绘制函数曲线2.4 填充
\begin{tikzpicture}
\draw[fill=red,ultra thick] (0,0) rectangle(1,1);
\draw[fill=red,ultra thin, red] (2,0) rectangle(3,1);
\draw[fill] (5,0) circle[radius=1];
\draw [fill=orange] (9,0) rectangle (11,1);
\draw [fill=white] (9.25,0.25) rectangle (10,1.5);
\path [fill=gray] (0,-2) rectangle (1.5,-3);
\draw [fill=yellow] (2,-2) rectangle (3.5,-3);
\end{tikzpicture}
通过fill参数控制结果,效果如下:
填充结果2.6 添加文字
使用\node
\node [<options>] (<name>) at (<coordinate>) {<text>};
举个例子:
\begin{tikzpicture}[scale=2]
\draw [thick, <->] (0,1) -- (0,0) -- (1,0);
\draw[fill] (1,1) circle [radius=0.025];
\node [below right, red] at (.5,.75) {below right};
\node [above left, green] at (.5,.75) {above left};
\node [below left, purple] at (.5,.75) {below left};
\node [above right, magenta] at (.5,.75) {above right};
\end{tikzpicture}
[图片上传失败...(image-7617-1600232503297)]
其实CNN画图主要用的是画一条线的功能,下面来看如何画CNN。
3. 绘制一个CNN模块
对于一个初学者来说,https://github.com/HarisIqbal88/PlotNeuralNet 这个库虽然画的很好,但是难度曲线太高了,退而求其次,使用https://github.com/pprp/SimpleCVReproduction/tree/master/tikz_cnn 进行解析。
首先介绍一个LaTeX中用于封装的命令,\newcommand,当我们不希望写很长的命令,那就需要类似函数的一个方式,封装好固定的操作,根据传入参数完成执行。
\newcommand<命令>[<参数个数>][<首参数默认值>]{<具体的定义>}
举一个例子:
\newcommand\loves[2]{#1 喜欢 #2}
\loves{我}{你}
输出结果就是:我喜欢你
\newcommand{\networkLayer}[9]{
% Define the macro.
% 1st argument: Height and width of the layer rectangle slice.
% 2nd argument: Depth of the layer slice
% 3rd argument: X Offset --> use it to offset layers from previously drawn layers.
% 4th argument: Y Offset --> Use it when an output needs to be fed to multiple layers that are on the same X offset.
% 5th argument: Z Offset --> Use to offset layers from previous
% 6th argument: Options for filldraw.
% 7th argument: Text to be placed below this layer.
% 8th argument: Name of coordinates. When name = "start" this resets the offset counter
% 9th argument: list of nodes to connect to (previous layers)
% 全局变量
\xdef\totalOffset{\totalOffset}
\ifthenelse{\equal{#8} {start}}
{\FPset{totalOffset}{0}}
{}
\FPeval\currentOffset{0+(totalOffset)+(#3)}
\def\hw{#1} % Used to distinguish input resolution for current layer.
\def\b{0.02}
\def\c{#2} % Width of the cube to distinguish number of input channels for current layer.
\def\x{\currentOffset} % X offset for current layer.
\def\y{#4} % Y offset for current layer.
\def\z{#5} % Z offset for current layer.
\def\inText{#7}
% Define references to points on the cube surfaces
\coordinate (#8_front) at (\x+\c , \z , \y);
\coordinate (#8_back) at (\x , \z , \y);
\coordinate (#8_top) at (\x+\c/2, \z+\hw/2, \y);
\coordinate (#8_bottom) at (\x+\c/2, \z-\hw/2, \y);
% Define cube coords
\coordinate (blr) at (\c+\x, -\hw/2+\z, -\hw/2+\y); %back lower right
\coordinate (bur) at (\c+\x, \hw/2+\z, -\hw/2+\y); %back upper right
\coordinate (bul) at (0 +\x, \hw/2+\z, -\hw/2+\y); %back upper left
\coordinate (fll) at (0 +\x, -\hw/2+\z, \hw/2+\y); %front lower left
\coordinate (flr) at (\c+\x, -\hw/2+\z, \hw/2+\y); %front lower right
\coordinate (fur) at (\c+\x, \hw/2+\z, \hw/2+\y); %front upper right
\coordinate (ful) at (0 +\x, \hw/2+\z, \hw/2+\y); %front upper left
% Draw connections from other points to the back of this node
\ifthenelse{\equal{#9} {}}
{} % 为空什么都不做
{ % 非空 开始画层与层之间的连线
\foreach \val in #9
% \val = start_front
\draw[line width=0.3mm] (\val)--(#8_back);
}
% Draw the layer body.
% back plane
\draw[line width=0.3mm](blr) -- (bur) -- (bul);
% front plane
\draw[line width=0.3mm](fll) -- (flr) node[midway,below] {\inText} -- (fur) -- (ful) -- (fll);
\draw[line width=0.3mm](blr) -- (flr);
\draw[line width=0.3mm](bur) -- (fur);
\draw[line width=0.3mm](bul) -- (ful);
% Recolor visible surfaces
% front plane
\filldraw[#6] ($(fll)+(\b,\b,0)$) -- ($(flr)+(-\b,\b,0)$) -- ($(fur)+(-\b,-\b,0)$) -- ($(ful)+(\b,-\b,0)$) -- ($(fll)+(\b,\b,0)$);
\filldraw[#6] ($(ful)+(\b,0,-\b)$) -- ($(fur)+(-\b,0,-\b)$) -- ($(bur)+(-\b,0,\b)$) -- ($(bul)+(\b,0,\b)$);
% Colored slice.
\ifthenelse {\equal{#6} {}}
{} % Do not draw colored slice if #6 is blank.
% Else, draw a colored slice.
{\filldraw[#6] ($(flr)+(0,\b,-\b)$) -- ($(blr)+(0,\b,\b)$) -- ($(bur)+(0,-\b,\b)$) -- ($(fur)+(0,-\b,-\b)$);}
\FPeval\totalOffset{0+(currentOffset)+\c}
\draw[ultra thick, red] (#8_back) circle[radius=0.02];
\node[left] at (#8_back) {back};
\draw[ultra thick, red] (#8_top) circle[radius=0.02];
\node[above] at (#8_top) {top};
\draw[ultra thick, red] (#8_bottom) circle[radius=0.02];
\node[below] at (#8_bottom) {bottom};
\draw[ultra thick, red] (#8_front) circle[radius=0.02];
\node[left] at (#8_front) {front};
}
假设以下命令调用,结果会是什么?
\begin{tikzpicture}[scale=2]
\draw[help lines] (0,0) grid(2,2);
\draw[->,thick] (0,0,0) -- (0,0,2);
\draw[->,thick] (0,0,0) -- (0,2,0);
\draw[->,thick] (0,0,0) -- (2,0,0);
\draw[->,thick] (0,0,0) -- (2,2,0);
\draw[->,thick] (0,0,0) -- (1,2,0);
\draw[->,thick] (0,0,0) -- (0,2,2);
\draw[->,thick] (0,0,0) -- (2,0,2);
\draw[dotted,thick] (0,0,2) -- (0,2,2);
\draw[dotted,thick] (0,2,0) -- (0,2,2);
\draw[dotted,thick] (0,0,2) -- (2,0,2);
\draw[dotted,thick] (2,0,0) -- (2,0,2);
\draw[dotted,thick] (1,0,0) -- (1,0,2);
\draw[dotted,thick] (0,0,1) -- (2,0,1);
\draw[<->, thick] (0,2) -- (0,0) -- (2,0);
%HW -D - x- y- z - fill color - text - 坐标 - 链接
\networkLayer{1}{0.5}{0}{0}{0}{color=green!20}{conv1}{}{}
\end{tikzpicture}
显示结果如下:
可视化一个模块卷积神经网络的示意图实际上是一个个立方体构成的,立方体之间可能会有额外连线,代表特征融合;还可能需要题注,为这个特征图立方体进行命名;必须要有立方体的位置信息,长宽高;还需要颜色填充的功能;
综合以上需求,这个函数提供了9个参数分别是:
-
1 H&W 控制立方体右侧这一面的高度,默认为正方形。
-
2 Depth 控制深度
-
3 X 方向上的偏置
-
4 Y方向上的偏置
-
5 Z方向上的偏置
-
6 填充的颜色
-
7 Text展示的文本,放在最下侧
-
8 坐标名称,通过命名便于#9访问
-
9 通过名称指定连接位置,用于连接前方层的时候使用
[图片上传失败...(image-d86ab4-1600232503297)]
由于每绘制一个立方体,右侧立方体的X偏置就应该加上左侧立方体的Depth值,这部分代码这样处理的。
\FPset{totalOffset}{0} % 设置全局变量totaloffset
\xdef\totalOffset{\totalOffset}
\ifthenelse{\equal{#8} {start}}
% 如果#8坐标名称为start,那么将totaloffset归零
{\FPset{totalOffset}{0}}
{}% 否则什么都不做
\FPeval\currentOffset{0+(totalOffset)+(#3)}
% 计算当前offset也就是#3 X+totalOffset
赋值过程:
\def\hw{#1} % Used to distinguish input resolution for current layer.
\def\b{0.02}
\def\c{#2} % Width of the cube to distinguish number of input channels for current layer.
\def\x{\currentOffset} % X offset for current layer.
\def\y{#4} % Y offset for current layer.
\def\z{#5} % Z offset for current layer.
\def\inText{#7}
计算立方体表面坐标(将点可视化是额外添加的,为了便于理解)
[图片上传失败...(image-6afcda-1600232503297)]
% Define references to points on the cube surfaces
\coordinate (#8_front) at (\x+\c , \z , \y);
\coordinate (#8_back) at (\x , \z , \y);
\coordinate (#8_top) at (\x+\c/2, \z+\hw/2, \y);
\coordinate (#8_bottom) at (\x+\c/2, \z-\hw/2, \y);
计算7个顶点位置,被挡住的也可以计算,但是因为这里不打算绘制所以不计算。
7个顶点示意图% Define cube coords
\coordinate (blr) at (\c+\x, -\hw/2+\z, -\hw/2+\y); %back lower right
\coordinate (bur) at (\c+\x, \hw/2+\z, -\hw/2+\y); %back upper right
\coordinate (bul) at (0 +\x, \hw/2+\z, -\hw/2+\y); %back upper left
\coordinate (fll) at (0 +\x, -\hw/2+\z, \hw/2+\y); %front lower left
\coordinate (flr) at (\c+\x, -\hw/2+\z, \hw/2+\y); %front lower right
\coordinate (fur) at (\c+\x, \hw/2+\z, \hw/2+\y); %front upper right
\coordinate (ful) at (0 +\x, \hw/2+\z, \hw/2+\y); %front upper left
绘制立方块之间的连线:
% Draw connections from other points to the back of this node
\ifthenelse{\equal{#9} {}}
{} % 为空什么都不做
{ % 非空 开始画层与层之间的连线
\foreach \val in #9
% \val = start_front
\draw[line width=0.3mm] (\val)--(#8_back);
}
绘制立方体主体部分,也就是将7个点连接起来。
% back plane
\draw[line width=0.3mm](blr) -- (bur) -- (bul);
% front plane
\draw[line width=0.3mm](fll) -- (flr) node[midway,below] {\inText} -- (fur) -- (ful) -- (fll);
\draw[line width=0.3mm](blr) -- (flr);
\draw[line width=0.3mm](bur) -- (fur);
\draw[line width=0.3mm](bul) -- (ful);
填充颜色:
% front plane
\filldraw[#6] ($(fll)+(\b,\b,0)$) -- ($(flr)+(-\b,\b,0)$) -- ($(fur)+(-\b,-\b,0)$) -- ($(ful)+(\b,-\b,0)$) -- ($(fll)+(\b,\b,0)$);
\filldraw[#6] ($(ful)+(\b,0,-\b)$) -- ($(fur)+(-\b,0,-\b)$) -- ($(bur)+(-\b,0,\b)$) -- ($(bul)+(\b,0,\b)$);
% Colored slice.
\ifthenelse {\equal{#6} {}}
{} % Do not draw colored slice if #6 is blank.
% Else, draw a colored slice.
{\filldraw[#6] ($(flr)+(0,\b,-\b)$) -- ($(blr)+(0,\b,\b)$) -- ($(bur)+(0,-\b,\b)$) -- ($(fur)+(0,-\b,-\b)$);}
一个卷积神经网络结构图
上边的图是通过以下代码生成的:
\begin{tikzpicture}
% INPUT
\networkLayer{3.0}{0.03}{0.0}{0.0}{0.0}{color=gray!80}{}{start}{}
% ENCODER
\networkLayer{3.0}{0.1}{0.5}{0.0}{0.0}{color=white}{conv}{}{} % S1
\networkLayer{3.0}{0.1}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{2.5}{0.2}{0.1}{0.0}{0.0}{color=white}{conv}{}{} % S1
\networkLayer{2.5}{0.2}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{2.0}{0.4}{0.1}{0.0}{0.0}{color=white}{conv}{}{} % S1
\networkLayer{2.0}{0.4}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{1.5}{0.8}{0.1}{0.0}{0.0}{color=white}{conv}{}{} % S1
\networkLayer{1.5}{0.8}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{1.0}{1.5}{0.1}{0.0}{0.0}{color=white}{conv}{}{} % S1
\networkLayer{1.0}{1.5}{0.1}{0.0}{0.0}{color=white}{}{mid}{} % S2
\networkLayer{1.0}{0.5}{1.5}{0.0}{-1.5}{color=green!50}{}{bot}{{mid_front}}
\networkLayer{1.0}{0.5}{-0.5}{0.0}{1.5}{color=green!50}{}{top}{{mid_front}}
\networkLayer{1.0}{0.5}{1.5}{0.0}{0.0}{color=blue!50}{sum}{}{{bot_front,top_front}}
% DECODER
\networkLayer{1.0}{1.5}{0.1}{0.0}{0.0}{color=white}{deconv}{}{} % S1
\networkLayer{1.0}{1.5}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{1.5}{0.8}{0.1}{0.0}{0.0}{color=white}{deconv}{}{} % S1
\networkLayer{1.5}{0.8}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{2.0}{0.4}{0.1}{0.0}{0.0}{color=white}{}{}{} % S1
\networkLayer{2.0}{0.4}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{2.5}{0.2}{0.1}{0.0}{0.0}{color=white}{}{}{} % S1
\networkLayer{2.5}{0.2}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
\networkLayer{3.0}{0.1}{0.1}{0.0}{0.0}{color=white}{}{}{} % S1
\networkLayer{3.0}{0.1}{0.1}{0.0}{0.0}{color=white}{}{}{} % S2
% OUTPUT
\networkLayer{3.0}{0.05}{0.9}{0.0}{0.0}{color=red!40}{}{}{} % Pixelwise segmentation with classes.
\end{tikzpicture}
需要注意的是#8和#9命令,mid_front代表的是链接#8=mid的front部分,front也可以被top、back、bottom取代。
[图片上传失败...(image-320d13-1600232503297)]
4. 资源推荐
https://cn.overleaf.com/project/5e8c38c31cccb20001a4998d
https://cn.overleaf.com/project/5f50b21ae802b6000155ec4f
https://github.com/HarisIqbal88/PlotNeuralNet
https://github.com/pprp/SimpleCVReproduction/tree/master/tikz_cnn
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