mirror of https://github.com/2martens/uni.git
[Masterproj] Added sample neural network drawn with tikz
Signed-off-by: Jim Martens <github@2martens.de>
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\begin{figure}
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\DisableQuotes
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\centering
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\begin{tikzpicture}[
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every node/.style={
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outer sep=0,
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inner sep=0,
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node distance=0.2cm
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},
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layerconv/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("ReLU + Pool") - 2em},
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% The border:
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% The filling:
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fill=conv,
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font=\sffamily,
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rotate=90,
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},
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layerrelu/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("ReLU + Pool") - 2em},
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% The filling:
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fill=relu,
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font=\sffamily,
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rotate=90,
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},
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layerfc/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("ReLU + Pool") - 2em},
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% The filling:
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fill=fc,
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font=\sffamily,
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rotate=90,
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},
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layerfc2/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("FC 3D Box") - 1.7em},
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% The filling:
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fill=fc,
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font=\sffamily,
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rotate=90,
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},
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layerfclarge/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("Concatenation") + 1.5em},
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% The filling:
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fill=fc,
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font=\sffamily,
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rotate=90,
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},
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layersoftmax/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("ReLU + Pool") - 2em},
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% The filling:
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fill=softmax,
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font=\sffamily,
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rotate=90,
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},
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layersmooth/.style={
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% The shape:
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rectangle,
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% The size:
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minimum height=3mm,
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minimum width={width("FC 3D Box") - 1.7em},
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% The filling:
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fill=softmax,
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font=\sffamily,
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rotate=90,
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},
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layergray/.style={
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% The shape:
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rectangle,
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% The size:
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minimum width={width("2D VGG on ImageNet") - 3em},
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minimum height={width("FC 3D Box") - 1.7em},
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% The filling:
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fill=gray2,
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font=\sffamily,
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anchor=south west,
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}]
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\node (bedB) at (0,0) {\includegraphics[scale=0.12]{upperbed}};
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\node (bedT) [above=of bedB] {\includegraphics[scale=0.15]{lowerbed}};
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\node (conv1) [right=0.35 of bedT] {\begin{tikzpicture} \node [layerconv] {\tiny Conv 1}; \end{tikzpicture}};
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\node (relu1) [right= of conv1] {\begin{tikzpicture} \node [layerrelu] {\tiny ReLU + Pool}; \end{tikzpicture}};
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\node (conv2) [right= of relu1] {\begin{tikzpicture} \node [layerconv] {\tiny Conv 2}; \end{tikzpicture}};
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\node (relu2) [right= of conv2] {\begin{tikzpicture} \node [layerrelu] {\tiny ReLU + Pool}; \end{tikzpicture}};
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\node (conv3) [right= of relu2] {\begin{tikzpicture} \node [layerconv] {\tiny Conv 3}; \end{tikzpicture}};
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\node (relu3) [right= of conv3] {\begin{tikzpicture} \node [layerrelu] {\tiny ReLU}; \end{tikzpicture}};
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\node (vgg) [below right=-0.01 and 0.4 of bedB,layergray] {\scriptsize 2D VGG on ImageNet};
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\node (fc1) [right= of vgg] {\begin{tikzpicture} \node [layerfc2] {\tiny FC 1}; \end{tikzpicture}};
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\node (fc2) [right= of relu3] {\begin{tikzpicture} \node [layerfc] {\tiny FC 2}; \end{tikzpicture}};
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\node (concat) [above right=0.25 and 0.3 of fc1.north east,anchor=west] {\begin{tikzpicture} \node [layerfclarge] {\tiny Concatenation}; \end{tikzpicture}};
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\node (fc3) [right=0.3 of concat] {\begin{tikzpicture} \node [layerfclarge] {\tiny FC 3}; \end{tikzpicture}};
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\node (fcclass) [right=0.3 of fc3.north east,anchor=north west] {\begin{tikzpicture} \node [layerfc] {\tiny FC Class}; \end{tikzpicture}};
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\node (fc3dbox) [right=0.3 of fc3.south east,anchor=south west] {\begin{tikzpicture} \node [layerfc2] {\tiny FC 3D Box}; \end{tikzpicture}};
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\node (softmax) [right=of fcclass] {\begin{tikzpicture} \node [layersoftmax] {\tiny Softmax}; \end{tikzpicture}};
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\node (smooth) [right=of fc3dbox] {\begin{tikzpicture} \node [layersmooth] {\tiny L1 Smooth}; \end{tikzpicture}};
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\draw (vgg.east) edge (fc1.west)
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(conv1) edge (relu1)
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(relu1) edge (conv2)
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(conv2) edge (relu2)
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(relu2) edge (conv3)
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(conv3) edge (relu3)
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(relu3) edge (fc2)
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;
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\draw[->] (bedB) edge (bedB -| vgg.west)
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(bedT) edge (conv1)
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(fc1) edge (fc1 -| concat.west)
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(fc2) edge (fc2 -| concat.west)
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(concat) edge (fc3)
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(fcclass) edge (softmax)
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(fc3dbox) edge (smooth)
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(fc3.east |- fcclass) edge (fcclass)
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(fc3.east |- fc3dbox) edge (fc3dbox)
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;
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\end{tikzpicture}
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\EnableQuotes
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\caption{\textbf{Joint Object Recognition Network:} For each 3D region proposal,
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the 3D volume from depth is fed to a 3D ConvNet and the 2D projection of the
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3D proposal is fed to a 2D ConvNet. Jointly they learn the object category
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and 3D box regression.}
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\label{fig:system}
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\end{figure}
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