PlotNeuralNet-一款绘制神经网络的好工具
今天发现github上一款绘制神经网络的好工具，项目名称：PlotNeuralNet，clone下来试了一下，效果很好，目前主要支持的是卷积神经网络，卷积层、池化层、bottleneck、skip-connection、up-conv、Softmax等常规的层在代码中都有定义，还缺少RNN相关的可视化层展示，未来作者可能会补上。这里简单记录一下，分享给写论文插图不好绘制的朋友。
- 1.安装texlive
代码用Python编写，需要调用LaTex输出PDF格式文档，对于写论文的童鞋应该不陌生，需要说明的是，我尝试了一下，在Windows下运行输出会有很多报错，我也偷懒不想去仔细研究解决，在Ubuntu下试了一下很正常，这里我简单描述一下Ubuntu18.04下的texlive安装过程：
(1)下载texlive镜像
https://mirrors.tuna.tsinghua.edu.cn/CTAN/systems/texlive/Images/
(2)使用图形化安装界面，需要安装perl的tk组件
sudo apt-get install perl-tk
(3)加载镜像文件安装
sudo mount -o loop texlive.iso /mnt
cd /mnt
sudo ./install-tl -gui
(4)安装texlive-latex-extra
sudo apt-get install texlive-latex-extra这里只是为了使用这个绘图功能，写文章还是习惯Windows下使用TeXStudio调用TeXLive，对于需要在Linux干同样的事情，需要解决字体等问题，这里不赘述，需要的可以看Ubuntu下 TeX Live 2018 的安装与配置
2.绘图
(1)复制PlotNeuralNet
git clone https://github.com/HarisIqbal88/PlotNeuralNet下载下来后文件层次如图：
其中pycore中的tikzeng.py是核心，里面定义了绘图过程，可以绘制的层，如果想自定义新图层也可参考其中的代码添加，从代码中可以看出目前支持的绘图层有哪些，该代码主要完成Python代码向LaTeX的转换，层的定义在blocks.py代码中
import os
def to_head( projectpath ):
pathlayers = os.path.join( projectpath, 'layers/' )
return r"""
\documentclass[border=8pt, multi, tikz]{standalone}
\usepackage{import}
\subimport{"""+ pathlayers + r"""}{init}
\usetikzlibrary{positioning}
\usetikzlibrary{3d} %for including external image
"""
def to_cor():
return r"""
\def\ConvColor{rgb:yellow,5;red,2.5;white,5}
\def\ConvReluColor{rgb:yellow,5;red,5;white,5}
\def\PoolColor{rgb:red,1;black,0.3}
\def\UnpoolColor{rgb:blue,2;green,1;black,0.3}
\def\FcColor{rgb:blue,5;red,2.5;white,5}
\def\FcReluColor{rgb:blue,5;red,5;white,4}
\def\SoftmaxColor{rgb:magenta,5;black,7}
"""
def to_begin():
return r"""
\newcommand{\copymidarrow}{\tikz \draw[-Stealth,line width=0.8mm,draw={rgb:blue,4;red,1;green,1;black,3}] (-0.3,0) -- ++(0.3,0);}
\begin{document}
\begin{tikzpicture}
\tikzstyle{connection}=[ultra thick,every node/.style={sloped,allow upside down},draw=\edgecolor,opacity=0.7]
\tikzstyle{copyconnection}=[ultra thick,every node/.style={sloped,allow upside down},draw={rgb:blue,4;red,1;green,1;black,3},opacity=0.7]
"""
# layers definition
def to_input( pathfile, to='(-3,0,0)', width=8, height=8 ):
return r"""
\node[canvas is zy plane at x=0] (temp) at """+ to +""" {\includegraphics[width="""+ str(width)+"cm"+""",height="""+ str(height)+"cm"+"""]{"""+ pathfile +"""}};
"""
# Conv
def to_Conv( name, s_filer=256, n_filer=64, offset="(0,0,0)", to="(0,0,0)", width=1, height=40, depth=40, caption=" " ):
return r"""
\pic[shift={"""+ offset +"""}] at """+ to +"""
{Box={
name=""" + name +""",
caption="""+ caption +r""",
xlabel={{"""+ str(n_filer) +""", }},
zlabel="""+ str(s_filer) +""",
fill=\ConvColor,
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
# Conv,Conv,relu
# Bottleneck
def to_ConvConvRelu( name, s_filer=256, n_filer=(64,64), offset="(0,0,0)", to="(0,0,0)", width=(2,2), height=40, depth=40, caption=" " ):
return r"""
\pic[shift={ """+ offset +""" }] at """+ to +"""
{RightBandedBox={
name="""+ name +""",
caption="""+ caption +""",
xlabel={{ """+ str(n_filer[0]) +""", """+ str(n_filer[1]) +""" }},
zlabel="""+ str(s_filer) +""",
fill=\ConvColor,
bandfill=\ConvReluColor,
height="""+ str(height) +""",
width={ """+ str(width[0]) +""" , """+ str(width[1]) +""" },
depth="""+ str(depth) +"""
}
};
"""
# Pool
def to_Pool(name, offset="(0,0,0)", to="(0,0,0)", width=1, height=32, depth=32, opacity=0.5, caption=" "):
return r"""
\pic[shift={ """+ offset +""" }] at """+ to +"""
{Box={
name="""+name+""",
caption="""+ caption +r""",
fill=\PoolColor,
opacity="""+ str(opacity) +""",
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
# unpool4,
def to_UnPool(name, offset="(0,0,0)", to="(0,0,0)", width=1, height=32, depth=32, opacity=0.5, caption=" "):
return r"""
\pic[shift={ """+ offset +""" }] at """+ to +"""
{Box={
name="""+ name +r""",
caption="""+ caption +r""",
fill=\UnpoolColor,
opacity="""+ str(opacity) +""",
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
def to_ConvRes( name, s_filer=256, n_filer=64, offset="(0,0,0)", to="(0,0,0)", width=6, height=40, depth=40, opacity=0.2, caption=" " ):
return r"""
\pic[shift={ """+ offset +""" }] at """+ to +"""
{RightBandedBox={
name="""+ name + """,
caption="""+ caption + """,
xlabel={{ """+ str(n_filer) + """, }},
zlabel="""+ str(s_filer) +r""",
fill={rgb:white,1;black,3},
bandfill={rgb:white,1;black,2},
opacity="""+ str(opacity) +""",
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
# ConvSoftMax
def to_ConvSoftMax( name, s_filer=40, offset="(0,0,0)", to="(0,0,0)", width=1, height=40, depth=40, caption=" " ):
return r"""
\pic[shift={"""+ offset +"""}] at """+ to +"""
{Box={
name=""" + name +""",
caption="""+ caption +""",
zlabel="""+ str(s_filer) +""",
fill=\SoftmaxColor,
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
# SoftMax
def to_SoftMax( name, s_filer=10, offset="(0,0,0)", to="(0,0,0)", width=1.5, height=3, depth=25, opacity=0.8, caption=" " ):
return r"""
\pic[shift={"""+ offset +"""}] at """+ to +"""
{Box={
name=""" + name +""",
caption="""+ caption +""",
xlabel={{" ","dummy"}},
zlabel="""+ str(s_filer) +""",
fill=\SoftmaxColor,
opacity="""+ str(opacity) +""",
height="""+ str(height) +""",
width="""+ str(width) +""",
depth="""+ str(depth) +"""
}
};
"""
def to_connection( of, to):
return r"""
\draw [connection] ("""+of+"""-east) -- node {\midarrow} ("""+to+"""-west);
"""
def to_skip( of, to, pos=1.25):
return r"""
\path ("""+ of +"""-southeast) -- ("""+ of +"""-northeast) coordinate[pos="""+ str(pos) +"""] ("""+ of +"""-top) ;
\path ("""+ to +"""-south) -- ("""+ to +"""-north) coordinate[pos=1.25] ("""+ to +"""-top) ;
\draw [copyconnection] ("""+of+"""-northeast)
-- node {\copymidarrow}("""+of+"""-top)
-- node {\copymidarrow}("""+to+"""-top)
-- node {\copymidarrow} ("""+to+"""-north);
"""
def to_end():
return r"""
\end{tikzpicture}
\end{document}
"""
def to_generate( arch, pathname="file.tex" ):
with open(pathname, "w") as f:
for c in arch:
print(c)
f.write( c )
代码中带了一些例子，绘制出来效果都不错，代码很简单，这里不做太多解释：
(2)一些示例
import sys
sys.path.append('../')
from pycore.tikzeng import *
# defined your arch
arch = [
to_head( '..' ),
to_cor(),
to_begin(),
to_Conv("conv1", 512, 64, offset="(0,0,0)", to="(0,0,0)", height=64, depth=64, width=2,caption="Conv1" ),
to_Pool("pool1", offset="(0,0,0)", to="(conv1-east)"),
to_Conv("conv2", 128, 64, offset="(1,0,0)", to="(pool1-east)", height=32, depth=32, width=2 ),
to_connection( "pool1", "conv2"),
to_Pool("pool2", offset="(0,0,0)", to="(conv2-east)", height=28, depth=28, width=1),
to_SoftMax("soft1", 10 ,"(3,0,0)", "(pool1-east)", caption="SOFT" ),
to_connection("pool2", "soft1"),
to_end()
]
def main():
namefile = str(sys.argv[0]).split('.')[0]
to_generate(arch, namefile + '.tex' )
if __name__ == '__main__':
main()将代码放置在pyexamples目录下，运行命令:
bash ../tikzmake.sh my_arch输出如图：
再如经典的Unet:
import sys
sys.path.append('../')
from pycore.tikzeng import *
from pycore.blocks import *
arch = [
to_head('..'),
to_cor(),
to_begin(),
#input
to_input( '../examples/fcn8s/cats.jpg' ),
#block-001
to_ConvConvRelu( name='ccr_b1', s_filer=500, n_filer=(64,64), offset="(0,0,0)", to="(0,0,0)", width=(2,2), height=40, depth=40 ),
to_Pool(name="pool_b1", offset="(0,0,0)", to="(ccr_b1-east)", width=1, height=32, depth=32, opacity=0.5),
*block_2ConvPool( name='b2', botton='pool_b1', top='pool_b2', s_filer=256, n_filer=128, offset="(1,0,0)", size=(32,32,3.5), opacity=0.5 ),
*block_2ConvPool( name='b3', botton='pool_b2', top='pool_b3', s_filer=128, n_filer=256, offset="(1,0,0)", size=(25,25,4.5), opacity=0.5 ),
*block_2ConvPool( name='b4', botton='pool_b3', top='pool_b4', s_filer=64, n_filer=512, offset="(1,0,0)", size=(16,16,5.5), opacity=0.5 ),
#Bottleneck
#block-005
to_ConvConvRelu( name='ccr_b5', s_filer=32, n_filer=(1024,1024), offset="(2,0,0)", to="(pool_b4-east)", width=(8,8), height=8, depth=8, caption="Bottleneck" ),
to_connection( "pool_b4", "ccr_b5"),
#Decoder
*block_Unconv( name="b6", botton="ccr_b5", top='end_b6', s_filer=64, n_filer=512, offset="(2.1,0,0)", size=(16,16,5.0), opacity=0.5 ),
to_skip( of='ccr_b4', to='ccr_res_b6', pos=1.25),
*block_Unconv( name="b7", botton="end_b6", top='end_b7', s_filer=128, n_filer=256, offset="(2.1,0,0)", size=(25,25,4.5), opacity=0.5 ),
to_skip( of='ccr_b3', to='ccr_res_b7', pos=1.25),
*block_Unconv( name="b8", botton="end_b7", top='end_b8', s_filer=256, n_filer=128, offset="(2.1,0,0)", size=(32,32,3.5), opacity=0.5 ),
to_skip( of='ccr_b2', to='ccr_res_b8', pos=1.25),
*block_Unconv( name="b9", botton="end_b8", top='end_b9', s_filer=512, n_filer=64, offset="(2.1,0,0)", size=(40,40,2.5), opacity=0.5 ),
to_skip( of='ccr_b1', to='ccr_res_b9', pos=1.25),
to_ConvSoftMax( name="soft1", s_filer=512, offset="(0.75,0,0)", to="(end_b9-east)", width=1, height=40, depth=40, caption="SOFT" ),
to_connection( "end_b9", "soft1"),
to_end()
]
def main():
namefile = str(sys.argv[0]).split('.')[0]
to_generate(arch, namefile + '.tex' )
if __name__ == '__main__':
main()
