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3d_gan_notes.tex
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3d_gan_notes.tex
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\documentclass[11pt]{article}
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% Basic figure setup, for now with no caption control since it's done
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% in the conversion process - todo).
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\@namedef{PY@tok@sc}{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\@namedef{PY@tok@dl}{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\@namedef{PY@tok@s2}{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\@namedef{PY@tok@sh}{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\@namedef{PY@tok@s1}{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\@namedef{PY@tok@mb}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@mf}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@mh}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@mi}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@il}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@mo}{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\@namedef{PY@tok@ch}{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
\@namedef{PY@tok@cm}{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
\@namedef{PY@tok@cpf}{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
\@namedef{PY@tok@c1}{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
\@namedef{PY@tok@cs}{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
\def\PYZsh{\char`\#}
\def\PYZpc{\char`\%}
\def\PYZdl{\char`\$}
\def\PYZhy{\char`\-}
\def\PYZsq{\char`\'}
\def\PYZdq{\char`\"}
\def\PYZti{\char`\~}
% for compatibility with earlier versions
\def\PYZat{@}
\def\PYZlb{[}
\def\PYZrb{]}
\makeatother
% For linebreaks inside Verbatim environment from package fancyvrb.
\makeatletter
\newbox\Wrappedcontinuationbox
\newbox\Wrappedvisiblespacebox
\newcommand*\Wrappedvisiblespace {\textcolor{red}{\textvisiblespace}}
\newcommand*\Wrappedcontinuationsymbol {\textcolor{red}{\llap{\tiny$\m@th\hookrightarrow$}}}
\newcommand*\Wrappedcontinuationindent {3ex }
\newcommand*\Wrappedafterbreak {\kern\Wrappedcontinuationindent\copy\Wrappedcontinuationbox}
% Take advantage of the already applied Pygments mark-up to insert
% potential linebreaks for TeX processing.
% {, <, #, %, $, ' and ": go to next line.
% _, }, ^, &, >, - and ~: stay at end of broken line.
% Use of \textquotesingle for straight quote.
\newcommand*\Wrappedbreaksatspecials {%
\def\PYGZus{\discretionary{\char`\_}{\Wrappedafterbreak}{\char`\_}}%
\def\PYGZob{\discretionary{}{\Wrappedafterbreak\char`\{}{\char`\{}}%
\def\PYGZcb{\discretionary{\char`\}}{\Wrappedafterbreak}{\char`\}}}%
\def\PYGZca{\discretionary{\char`\^}{\Wrappedafterbreak}{\char`\^}}%
\def\PYGZam{\discretionary{\char`\&}{\Wrappedafterbreak}{\char`\&}}%
\def\PYGZlt{\discretionary{}{\Wrappedafterbreak\char`\<}{\char`\<}}%
\def\PYGZgt{\discretionary{\char`\>}{\Wrappedafterbreak}{\char`\>}}%
\def\PYGZsh{\discretionary{}{\Wrappedafterbreak\char`\#}{\char`\#}}%
\def\PYGZpc{\discretionary{}{\Wrappedafterbreak\char`\%}{\char`\%}}%
\def\PYGZdl{\discretionary{}{\Wrappedafterbreak\char`\$}{\char`\$}}%
\def\PYGZhy{\discretionary{\char`\-}{\Wrappedafterbreak}{\char`\-}}%
\def\PYGZsq{\discretionary{}{\Wrappedafterbreak\textquotesingle}{\textquotesingle}}%
\def\PYGZdq{\discretionary{}{\Wrappedafterbreak\char`\"}{\char`\"}}%
\def\PYGZti{\discretionary{\char`\~}{\Wrappedafterbreak}{\char`\~}}%
}
% Some characters . , ; ? ! / are not pygmentized.
% This macro makes them "active" and they will insert potential linebreaks
\newcommand*\Wrappedbreaksatpunct {%
\lccode`\~`\.\lowercase{\def~}{\discretionary{\hbox{\char`\.}}{\Wrappedafterbreak}{\hbox{\char`\.}}}%
\lccode`\~`\,\lowercase{\def~}{\discretionary{\hbox{\char`\,}}{\Wrappedafterbreak}{\hbox{\char`\,}}}%
\lccode`\~`\;\lowercase{\def~}{\discretionary{\hbox{\char`\;}}{\Wrappedafterbreak}{\hbox{\char`\;}}}%
\lccode`\~`\:\lowercase{\def~}{\discretionary{\hbox{\char`\:}}{\Wrappedafterbreak}{\hbox{\char`\:}}}%
\lccode`\~`\?\lowercase{\def~}{\discretionary{\hbox{\char`\?}}{\Wrappedafterbreak}{\hbox{\char`\?}}}%
\lccode`\~`\!\lowercase{\def~}{\discretionary{\hbox{\char`\!}}{\Wrappedafterbreak}{\hbox{\char`\!}}}%
\lccode`\~`\/\lowercase{\def~}{\discretionary{\hbox{\char`\/}}{\Wrappedafterbreak}{\hbox{\char`\/}}}%
\catcode`\.\active
\catcode`\,\active
\catcode`\;\active
\catcode`\:\active
\catcode`\?\active
\catcode`\!\active
\catcode`\/\active
\lccode`\~`\~
}
\makeatother
\let\OriginalVerbatim=\Verbatim
\makeatletter
\renewcommand{\Verbatim}[1][1]{%
%\parskip\z@skip
\sbox\Wrappedcontinuationbox {\Wrappedcontinuationsymbol}%
\sbox\Wrappedvisiblespacebox {\FV@SetupFont\Wrappedvisiblespace}%
\def\FancyVerbFormatLine ##1{\hsize\linewidth
\vtop{\raggedright\hyphenpenalty\z@\exhyphenpenalty\z@
\doublehyphendemerits\z@\finalhyphendemerits\z@
\strut ##1\strut}%
}%
% If the linebreak is at a space, the latter will be displayed as visible
% space at end of first line, and a continuation symbol starts next line.
% Stretch/shrink are however usually zero for typewriter font.
\def\FV@Space {%
\nobreak\hskip\z@ plus\fontdimen3\font minus\fontdimen4\font
\discretionary{\copy\Wrappedvisiblespacebox}{\Wrappedafterbreak}
{\kern\fontdimen2\font}%
}%
% Allow breaks at special characters using \PYG... macros.
\Wrappedbreaksatspecials
% Breaks at punctuation characters . , ; ? ! and / need catcode=\active
\OriginalVerbatim[#1,codes*=\Wrappedbreaksatpunct]%
}
\makeatother
% Exact colors from NB
\definecolor{incolor}{HTML}{303F9F}
\definecolor{outcolor}{HTML}{D84315}
\definecolor{cellborder}{HTML}{CFCFCF}
\definecolor{cellbackground}{HTML}{F7F7F7}
% prompt
\makeatletter
\newcommand{\boxspacing}{\kern\kvtcb@left@rule\kern\kvtcb@boxsep}
\makeatother
\newcommand{\prompt}[4]{
{\ttfamily\llap{{\color{#2}[#3]:\hspace{3pt}#4}}\vspace{-\baselineskip}}
}
% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
\hypersetup{
breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{1}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{import} \PY{n+nn}{torch}
\PY{k+kn}{import} \PY{n+nn}{torch}\PY{n+nn}{.}\PY{n+nn}{nn} \PY{k}{as} \PY{n+nn}{nn}
\PY{k+kn}{import} \PY{n+nn}{pytorch\PYZus{}lightning} \PY{k}{as} \PY{n+nn}{pl}
\PY{k+kn}{from} \PY{n+nn}{pytorch\PYZus{}lightning}\PY{n+nn}{.}\PY{n+nn}{loggers} \PY{k+kn}{import} \PY{n}{TensorBoardLogger}
\PY{k+kn}{from} \PY{n+nn}{argparse} \PY{k+kn}{import} \PY{n}{ArgumentParser}
\PY{k+kn}{from} \PY{n+nn}{torchsummary} \PY{k+kn}{import} \PY{n}{summary}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{2}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{data} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{modelnet10.npz}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{allow\PYZus{}pickle}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{train\PYZus{}voxel} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train\PYZus{}voxel}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{c+c1}{\PYZsh{} Training 3D voxel samples}
\PY{n}{test\PYZus{}voxel} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}voxel}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{c+c1}{\PYZsh{} Test 3D voxel samples}
\PY{n}{train\PYZus{}labels} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train\PYZus{}labels}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{c+c1}{\PYZsh{} Training labels (integers from 0 to 9)}
\PY{n}{test\PYZus{}labels} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}labels}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{c+c1}{\PYZsh{} Test labels (integers from 0 to 9)}
\PY{n}{class\PYZus{}map} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{class\PYZus{}map}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{c+c1}{\PYZsh{} Dictionary mapping the labels to their class names.}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{3}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{c+c1}{\PYZsh{} Helper functions}
\PY{c+c1}{\PYZsh{} Plot the voxels}
\PY{k}{def} \PY{n+nf}{plot\PYZus{}voxels}\PY{p}{(}\PY{n}{voxels}\PY{p}{,} \PY{n}{threshold}\PY{o}{=}\PY{l+m+mf}{0.5}\PY{p}{)}\PY{p}{:}
\PY{n}{n} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{voxels}\PY{p}{)}
\PY{n}{n\PYZus{}rows} \PY{o}{=} \PY{p}{(}\PY{n}{n} \PY{o}{+} \PY{l+m+mi}{1}\PY{p}{)} \PY{o}{/}\PY{o}{/} \PY{l+m+mi}{2} \PY{c+c1}{\PYZsh{} Calculate the number of rows needed for the given number of voxels}
\PY{n}{fig} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mi}{5} \PY{o}{*} \PY{n}{n\PYZus{}rows}\PY{p}{)}\PY{p}{)}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{voxel} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{voxels}\PY{p}{)}\PY{p}{:}
\PY{n}{binary\PYZus{}voxel} \PY{o}{=} \PY{n}{voxel} \PY{o}{\PYZgt{}} \PY{n}{threshold}
\PY{n}{ax} \PY{o}{=} \PY{n}{fig}\PY{o}{.}\PY{n}{add\PYZus{}subplot}\PY{p}{(}\PY{n}{n\PYZus{}rows}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{n}{idx} \PY{o}{+} \PY{l+m+mi}{1}\PY{p}{,} \PY{n}{projection}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{3d}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{ax}\PY{o}{.}\PY{n}{voxels}\PY{p}{(}\PY{n}{binary\PYZus{}voxel}\PY{p}{)}
\PY{n}{ax}\PY{o}{.}\PY{n}{set\PYZus{}title}\PY{p}{(}\PY{l+s+sa}{f}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Voxel }\PY{l+s+si}{\PYZob{}}\PY{n}{idx}\PY{+w}{ }\PY{o}{+}\PY{+w}{ }\PY{l+m+mi}{1}\PY{l+s+si}{\PYZcb{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{tight\PYZus{}layout}\PY{p}{(}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Count the number of correct predictions}
\PY{k}{def} \PY{n+nf}{count\PYZus{}correct\PYZus{}predictions}\PY{p}{(}
\PY{n}{predictions}\PY{p}{:} \PY{n}{torch}\PY{o}{.}\PY{n}{Tensor}\PY{p}{,} \PY{n}{threshold}\PY{p}{:} \PY{n+nb}{float}\PY{p}{,} \PY{n}{is\PYZus{}greater}\PY{o}{=}\PY{k+kc}{True}
\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n}{is\PYZus{}greater}\PY{p}{:}
\PY{k}{return} \PY{n}{torch}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{predictions} \PY{o}{\PYZgt{}} \PY{n}{threshold}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{k}{return} \PY{n}{torch}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{predictions} \PY{o}{\PYZlt{}} \PY{n}{threshold}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{4}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{Generator}\PY{p}{(}\PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Module}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{} Function from web: [https://github.com/black0017/3D\PYZhy{}GAN\PYZhy{}pytorch/blob/master/models/GAN3D.py]}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}
\PY{n+nb+bp}{self}\PY{p}{,}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{l+m+mi}{512}\PY{p}{,}
\PY{n}{out\PYZus{}dim}\PY{o}{=}\PY{l+m+mi}{64}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{noise\PYZus{}dim}\PY{o}{=}\PY{l+m+mi}{200}\PY{p}{,}
\PY{n}{activation}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{sigmoid}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}
\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{Generator}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}channels} \PY{o}{=} \PY{n}{in\PYZus{}channels}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{=} \PY{n}{out\PYZus{}dim}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{out\PYZus{}dim} \PY{o}{/} \PY{l+m+mi}{16}\PY{p}{)}
\PY{n}{conv1\PYZus{}out\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}channels} \PY{o}{/} \PY{l+m+mf}{2.0}\PY{p}{)}
\PY{n}{conv2\PYZus{}out\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{conv1\PYZus{}out\PYZus{}channels} \PY{o}{/} \PY{l+m+mi}{2}\PY{p}{)}
\PY{n}{conv3\PYZus{}out\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{conv2\PYZus{}out\PYZus{}channels} \PY{o}{/} \PY{l+m+mi}{2}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{(}
\PY{n}{noise\PYZus{}dim}\PY{p}{,} \PY{n}{in\PYZus{}channels} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{ConvTranspose3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{in\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv1\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv1\PYZus{}out\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{ReLU}\PY{p}{(}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{ConvTranspose3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv1\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv2\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv2\PYZus{}out\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{ReLU}\PY{p}{(}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv3} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{ConvTranspose3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv2\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv3\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv3\PYZus{}out\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{ReLU}\PY{p}{(}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv4} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{ConvTranspose3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv3\PYZus{}out\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{out\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}
\PY{p}{)}
\PY{k}{if} \PY{n}{activation} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{sigmoid}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Sigmoid}\PY{p}{(}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Tanh}\PY{p}{(}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{project}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{x}\PY{p}{)}\PY{p}{:}
\PY{+w}{ }\PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{l+s+sd}{ projects and reshapes latent vector to starting volume}
\PY{l+s+sd}{ :param x: latent vector}
\PY{l+s+sd}{ :return: starting volume}
\PY{l+s+sd}{ \PYZdq{}\PYZdq{}\PYZdq{}}
\PY{k}{return} \PY{n}{x}\PY{o}{.}\PY{n}{view}\PY{p}{(}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}channels}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}dim}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{forward}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{x}\PY{p}{)}\PY{p}{:}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{project}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv3}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv4}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{5}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{Discriminator}\PY{p}{(}\PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Module}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{} Function code based on: [https://github.com/black0017/3D\PYZhy{}GAN\PYZhy{}pytorch/blob/master/models/GAN3D.py]}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{64}\PY{p}{,} \PY{n}{out\PYZus{}conv\PYZus{}channels}\PY{o}{=}\PY{l+m+mi}{512}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{Discriminator}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{n}{conv1\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{/} \PY{l+m+mi}{8}\PY{p}{)}
\PY{n}{conv2\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{/} \PY{l+m+mi}{4}\PY{p}{)}
\PY{n}{conv3\PYZus{}channels} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{/} \PY{l+m+mi}{2}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{=} \PY{n}{out\PYZus{}conv\PYZus{}channels}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{dim} \PY{o}{/} \PY{l+m+mi}{16}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Conv3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{in\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv1\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{4}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv1\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{LeakyReLU}\PY{p}{(}\PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Conv3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv1\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv2\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{4}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv2\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{LeakyReLU}\PY{p}{(}\PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv3} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Conv3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv2\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{conv3\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{4}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{conv3\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{LeakyReLU}\PY{p}{(}\PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv4} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Conv3d}\PY{p}{(}
\PY{n}{in\PYZus{}channels}\PY{o}{=}\PY{n}{conv3\PYZus{}channels}\PY{p}{,}
\PY{n}{out\PYZus{}channels}\PY{o}{=}\PY{n}{out\PYZus{}conv\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{4}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{bias}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm3d}\PY{p}{(}\PY{n}{out\PYZus{}conv\PYZus{}channels}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{LeakyReLU}\PY{p}{(}\PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{(}
\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim}\PY{p}{,} \PY{l+m+mi}{1}
\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{Sigmoid}\PY{p}{(}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{k}{def} \PY{n+nf}{get\PYZus{}representation}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{x}\PY{p}{)}\PY{p}{:}
\PY{n}{conv1} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{conv2} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2}\PY{p}{(}\PY{n}{conv1}\PY{p}{)}
\PY{n}{conv3} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv3}\PY{p}{(}\PY{n}{conv2}\PY{p}{)}
\PY{n}{conv4} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv4}\PY{p}{(}\PY{n}{conv3}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Create max pooling layers}
\PY{n}{maxpool2} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{MaxPool3d}\PY{p}{(}\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{8}\PY{p}{)}
\PY{n}{maxpool3} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{MaxPool3d}\PY{p}{(}\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{4}\PY{p}{)}
\PY{n}{maxpool4} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{MaxPool3d}\PY{p}{(}\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Apply max pooling}
\PY{n}{pooled\PYZus{}conv2} \PY{o}{=} \PY{n}{maxpool2}\PY{p}{(}\PY{n}{conv2}\PY{p}{)}
\PY{n}{pooled\PYZus{}conv3} \PY{o}{=} \PY{n}{maxpool3}\PY{p}{(}\PY{n}{conv3}\PY{p}{)}
\PY{n}{pooled\PYZus{}conv4} \PY{o}{=} \PY{n}{maxpool4}\PY{p}{(}\PY{n}{conv4}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Concatenate along channels}
\PY{n}{concatenated\PYZus{}features} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{cat}\PY{p}{(}
\PY{p}{[}\PY{n}{pooled\PYZus{}conv2}\PY{p}{,} \PY{n}{pooled\PYZus{}conv3}\PY{p}{,} \PY{n}{pooled\PYZus{}conv4}\PY{p}{]}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{1}
\PY{p}{)}
\PY{c+c1}{\PYZsh{} Flatten the concatenated features}
\PY{n}{features} \PY{o}{=} \PY{n}{concatenated\PYZus{}features}\PY{o}{.}\PY{n}{view}\PY{p}{(}\PY{n}{concatenated\PYZus{}features}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{,} \PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}
\PY{k}{return} \PY{n}{features}
\PY{k}{def} \PY{n+nf}{forward}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{x}\PY{p}{)}\PY{p}{:}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv3}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv4}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Flatten and apply linear + sigmoid}
\PY{n}{x} \PY{o}{=} \PY{n}{x}\PY{o}{.}\PY{n}{view}\PY{p}{(}
\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}conv\PYZus{}channels} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim} \PY{o}{*} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out\PYZus{}dim}
\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{out}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{k}{return} \PY{n}{x}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{6}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{c+c1}{\PYZsh{} Task A: Show 5 samples from the dataset}
\PY{n}{random\PYZus{}index} \PY{o}{=} \PY{p}{[}\PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{randint}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{train\PYZus{}voxel}\PY{p}{)}\PY{p}{)} \PY{k}{for} \PY{n}{\PYZus{}} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{5}\PY{p}{)}\PY{p}{]}
\PY{n}{plot\PYZus{}voxels}\PY{p}{(}\PY{n}{train\PYZus{}voxel}\PY{p}{[}\PY{n}{random\PYZus{}index}\PY{p}{]}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{3d_gan_notes_files/3d_gan_notes_5_0.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{7}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{GAN3D}\PY{p}{(}\PY{n}{pl}\PY{o}{.}\PY{n}{LightningModule}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{noise\PYZus{}dim}\PY{o}{=}\PY{l+m+mi}{200}\PY{p}{,} \PY{n}{g\PYZus{}lr}\PY{o}{=}\PY{l+m+mf}{0.0025}\PY{p}{,} \PY{n}{d\PYZus{}lr}\PY{o}{=}\PY{l+m+mf}{1e\PYZhy{}5}\PY{p}{,} \PY{n}{b1}\PY{o}{=}\PY{l+m+mf}{0.5}\PY{p}{,} \PY{n}{b2}\PY{o}{=}\PY{l+m+mf}{0.999}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{GAN3D}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Set up model with parameters}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{noise\PYZus{}dim} \PY{o}{=} \PY{n}{noise\PYZus{}dim}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator} \PY{o}{=} \PY{n}{Generator}\PY{p}{(}\PY{n}{noise\PYZus{}dim}\PY{o}{=}\PY{n}{noise\PYZus{}dim}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator} \PY{o}{=} \PY{n}{Discriminator}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{g\PYZus{}lr} \PY{o}{=} \PY{n}{g\PYZus{}lr}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{d\PYZus{}lr} \PY{o}{=} \PY{n}{d\PYZus{}lr}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b1} \PY{o}{=} \PY{n}{b1}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b2} \PY{o}{=} \PY{n}{b2}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{BCELoss}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{automatic\PYZus{}optimization} \PY{o}{=} \PY{k+kc}{False}
\PY{c+c1}{\PYZsh{} For logging}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator\PYZus{}losses} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator\PYZus{}losses} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{total\PYZus{}losses} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}correct} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}number\PYZus{}of\PYZus{}samples} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{def} \PY{n+nf}{forward}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{z}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator}\PY{p}{(}\PY{n}{z}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{adversarial\PYZus{}loss}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{y\PYZus{}hat}\PY{p}{,} \PY{n}{y}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss}\PY{p}{(}\PY{n}{y\PYZus{}hat}\PY{p}{,} \PY{n}{y}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{training\PYZus{}step}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{batch}\PY{p}{,} \PY{n}{batch\PYZus{}idx}\PY{p}{)}\PY{p}{:}
\PY{n}{imgs} \PY{o}{=} \PY{n}{batch}
\PY{n}{imgs} \PY{o}{=} \PY{n}{imgs}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{opt\PYZus{}g}\PY{p}{,} \PY{n}{opt\PYZus{}d} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{optimizers}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Adversarial ground truths}
\PY{n}{valid} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{ones}\PY{p}{(}\PY{n}{imgs}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{fake} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{zeros}\PY{p}{(}\PY{n}{imgs}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Generate a batch of images}
\PY{n}{z} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{randn}\PY{p}{(}\PY{n}{imgs}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{noise\PYZus{}dim}\PY{p}{)}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{generated\PYZus{}imgs} \PY{o}{=} \PY{n+nb+bp}{self}\PY{p}{(}\PY{n}{z}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Train Generator}
\PY{n}{discriminator\PYZus{}generated\PYZus{}imgs} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{p}{(}\PY{n}{generated\PYZus{}imgs}\PY{p}{)}
\PY{n}{g\PYZus{}loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{adversarial\PYZus{}loss}\PY{p}{(}\PY{n}{discriminator\PYZus{}generated\PYZus{}imgs}\PY{p}{,} \PY{n}{valid}\PY{p}{)}
\PY{n}{g\PYZus{}loss}\PY{o}{.}\PY{n}{backward}\PY{p}{(}\PY{p}{)}
\PY{n}{opt\PYZus{}g}\PY{o}{.}\PY{n}{step}\PY{p}{(}\PY{p}{)}
\PY{n}{opt\PYZus{}g}\PY{o}{.}\PY{n}{zero\PYZus{}grad}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Calculate discriminator accuracy}
\PY{n}{real\PYZus{}preds} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{p}{(}\PY{n}{imgs}\PY{p}{)}
\PY{n}{fake\PYZus{}preds} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{p}{(}\PY{n}{generated\PYZus{}imgs}\PY{o}{.}\PY{n}{detach}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n}{correct\PYZus{}real} \PY{o}{=} \PY{n}{count\PYZus{}correct\PYZus{}predictions}\PY{p}{(}\PY{n}{real\PYZus{}preds}\PY{p}{,} \PY{l+m+mf}{0.5}\PY{p}{,} \PY{n}{is\PYZus{}greater}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{correct\PYZus{}fake} \PY{o}{=} \PY{n}{count\PYZus{}correct\PYZus{}predictions}\PY{p}{(}\PY{n}{fake\PYZus{}preds}\PY{p}{,} \PY{l+m+mf}{0.5}\PY{p}{,} \PY{n}{is\PYZus{}greater}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
\PY{n}{total\PYZus{}correct} \PY{o}{=} \PY{n}{correct\PYZus{}real} \PY{o}{+} \PY{n}{correct\PYZus{}fake}
\PY{n}{accuracy} \PY{o}{=} \PY{n}{total\PYZus{}correct} \PY{o}{/} \PY{p}{(}\PY{l+m+mi}{2} \PY{o}{*} \PY{n}{imgs}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}correct} \PY{o}{+}\PY{o}{=} \PY{n}{correct\PYZus{}real} \PY{o}{+} \PY{n}{correct\PYZus{}fake}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}number\PYZus{}of\PYZus{}samples} \PY{o}{+}\PY{o}{=} \PY{n}{imgs}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Calculate discriminator loss}
\PY{n}{opt\PYZus{}d}\PY{o}{.}\PY{n}{zero\PYZus{}grad}\PY{p}{(}\PY{p}{)}
\PY{n}{real\PYZus{}loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{adversarial\PYZus{}loss}\PY{p}{(}\PY{n}{real\PYZus{}preds}\PY{p}{,} \PY{n}{valid}\PY{p}{)}
\PY{n}{fake\PYZus{}loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{adversarial\PYZus{}loss}\PY{p}{(}\PY{n}{fake\PYZus{}preds}\PY{p}{,} \PY{n}{fake}\PY{p}{)}
\PY{n}{d\PYZus{}loss} \PY{o}{=} \PY{p}{(}\PY{n}{real\PYZus{}loss} \PY{o}{+} \PY{n}{fake\PYZus{}loss}\PY{p}{)} \PY{o}{/} \PY{l+m+mi}{2}
\PY{c+c1}{\PYZsh{} Train Discriminator conditionally}
\PY{k}{if} \PY{o+ow}{not} \PY{n+nb}{hasattr}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{last\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} \PY{o+ow}{or} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{last\PYZus{}acc} \PY{o}{\PYZlt{}}\PY{o}{=} \PY{l+m+mf}{0.8}\PY{p}{:}
\PY{n}{d\PYZus{}loss}\PY{o}{.}\PY{n}{backward}\PY{p}{(}\PY{p}{)}
\PY{n}{opt\PYZus{}d}\PY{o}{.}\PY{n}{step}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Store current accuracy for next batch}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{last\PYZus{}acc} \PY{o}{=} \PY{n}{accuracy}
\PY{c+c1}{\PYZsh{} Log in tensorboard}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{g\PYZus{}loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{g\PYZus{}loss}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{d\PYZus{}loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{d\PYZus{}loss}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{p}{(}\PY{n}{g\PYZus{}loss} \PY{o}{+} \PY{n}{d\PYZus{}loss}\PY{p}{)} \PY{o}{/} \PY{l+m+mi}{2}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{loss}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator\PYZus{}losses}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{g\PYZus{}loss}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator\PYZus{}losses}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{d\PYZus{}loss}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{total\PYZus{}losses}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{loss}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{configure\PYZus{}optimizers}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{opt\PYZus{}g} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{optim}\PY{o}{.}\PY{n}{Adam}\PY{p}{(}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{lr}\PY{o}{=}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{g\PYZus{}lr}\PY{p}{,} \PY{n}{betas}\PY{o}{=}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b1}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b2}\PY{p}{)}
\PY{p}{)}
\PY{n}{opt\PYZus{}d} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{optim}\PY{o}{.}\PY{n}{Adam}\PY{p}{(}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{lr}\PY{o}{=}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{d\PYZus{}lr}\PY{p}{,} \PY{n}{betas}\PY{o}{=}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b1}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{b2}\PY{p}{)}
\PY{p}{)}
\PY{k}{return} \PY{p}{[}\PY{n}{opt\PYZus{}g}\PY{p}{,} \PY{n}{opt\PYZus{}d}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{on\PYZus{}train\PYZus{}epoch\PYZus{}end}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}
\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Total correct}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}
\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}correct}\PY{p}{)} \PY{o}{/} \PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}number\PYZus{}of\PYZus{}samples} \PY{o}{*} \PY{l+m+mi}{2}\PY{p}{)}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}correct} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{accumulated\PYZus{}number\PYZus{}of\PYZus{}samples} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{def} \PY{n+nf}{plot\PYZus{}losses}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mi}{10}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Generator Loss}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)} \PY{c+c1}{\PYZsh{} 2 row, 2 columns, 1st plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{generator\PYZus{}losses}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Generator Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{blue}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Generator Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Batch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Discriminator Loss}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{)} \PY{c+c1}{\PYZsh{} 2 row, 2 columns, 2nd plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator\PYZus{}losses}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Discriminator Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{red}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Discriminator Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Batch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Total Loss}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{3}\PY{p}{)} \PY{c+c1}{\PYZsh{} 2 row, 2 columns, 3rd plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{total\PYZus{}losses}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Total Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{green}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Total Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Batch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Display the plots}
\PY{n}{plt}\PY{o}{.}\PY{n}{tight\PYZus{}layout}\PY{p}{(}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{8}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{LinearClassifier}\PY{p}{(}\PY{n}{pl}\PY{o}{.}\PY{n}{LightningModule}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{lr}\PY{o}{=}\PY{l+m+mf}{1e\PYZhy{}3}\PY{p}{,} \PY{n}{discriminator}\PY{o}{=}\PY{k+kc}{None}\PY{p}{,} \PY{n}{num\PYZus{}classes}\PY{o}{=}\PY{l+m+mi}{10}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{LinearClassifier}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator} \PY{o}{=} \PY{n}{discriminator}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{lr} \PY{o}{=} \PY{n}{lr}
\PY{c+c1}{\PYZsh{} Instantiate linear layer, calculate input size from discriminator}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{(}
\PY{n}{discriminator}\PY{o}{.}\PY{n}{get\PYZus{}representation}\PY{p}{(}\PY{n}{torch}\PY{o}{.}\PY{n}{randn}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{,} \PY{l+m+mi}{64}\PY{p}{,} \PY{l+m+mi}{64}\PY{p}{,} \PY{l+m+mi}{64}\PY{p}{)}\PY{p}{)}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,}
\PY{n}{num\PYZus{}classes}\PY{p}{,}
\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{CrossEntropyLoss}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{automatic\PYZus{}optimization} \PY{o}{=} \PY{k+kc}{False}
\PY{c+c1}{\PYZsh{} For logging}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}loss} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}loss} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}training\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}validation\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{forward}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{x}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{training\PYZus{}step}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{batch}\PY{p}{,} \PY{n}{batch\PYZus{}idx}\PY{p}{)}\PY{p}{:}
\PY{n}{x}\PY{p}{,} \PY{n}{y} \PY{o}{=} \PY{n}{batch}
\PY{n}{x} \PY{o}{=} \PY{n}{x}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{y} \PY{o}{=} \PY{n}{y}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}\PY{o}{.}\PY{n}{long}\PY{p}{(}\PY{p}{)}
\PY{n}{opt} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{optimizers}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Get features from discriminator}
\PY{n}{features} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{o}{.}\PY{n}{get\PYZus{}representation}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{logits} \PY{o}{=} \PY{n+nb+bp}{self}\PY{p}{(}\PY{n}{features}\PY{o}{.}\PY{n}{detach}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{y}\PY{p}{)}
\PY{n}{preds} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{argmax}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{acc} \PY{o}{=} \PY{p}{(}\PY{n}{preds} \PY{o}{==} \PY{n}{y}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}
\PY{n}{loss}\PY{o}{.}\PY{n}{backward}\PY{p}{(}\PY{p}{)}
\PY{n}{opt}\PY{o}{.}\PY{n}{step}\PY{p}{(}\PY{p}{)}
\PY{n}{opt}\PY{o}{.}\PY{n}{zero\PYZus{}grad}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Logging}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train\PYZus{}loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{loss}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{acc}\PY{p}{,} \PY{n}{on\PYZus{}epoch}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}loss}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{loss}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}training\PYZus{}acc}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{acc}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{k}{return} \PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:} \PY{n}{acc}\PY{p}{\PYZcb{}}
\PY{k}{def} \PY{n+nf}{validation\PYZus{}step}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{batch}\PY{p}{,} \PY{n}{batch\PYZus{}idx}\PY{p}{)}\PY{p}{:}
\PY{n}{x}\PY{p}{,} \PY{n}{y} \PY{o}{=} \PY{n}{batch}
\PY{n}{x} \PY{o}{=} \PY{n}{x}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{y} \PY{o}{=} \PY{n}{y}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}\PY{o}{.}\PY{n}{long}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Get features from discriminator}
\PY{n}{features} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{o}{.}\PY{n}{get\PYZus{}representation}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{logits} \PY{o}{=} \PY{n+nb+bp}{self}\PY{p}{(}\PY{n}{features}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{y}\PY{p}{)}
\PY{n}{preds} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{argmax}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{acc} \PY{o}{=} \PY{p}{(}\PY{n}{preds} \PY{o}{==} \PY{n}{y}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Logging}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{val\PYZus{}loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{loss}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{val\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{acc}\PY{p}{,} \PY{n}{on\PYZus{}epoch}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}loss}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{loss}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}validation\PYZus{}acc}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{acc}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{k}{return} \PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{val\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:} \PY{n}{acc}\PY{p}{\PYZcb{}}
\PY{k}{def} \PY{n+nf}{test\PYZus{}step}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{batch}\PY{p}{,} \PY{n}{batch\PYZus{}idx}\PY{p}{)}\PY{p}{:}
\PY{n}{x}\PY{p}{,} \PY{n}{y} \PY{o}{=} \PY{n}{batch}
\PY{n}{x} \PY{o}{=} \PY{n}{x}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}
\PY{n}{y} \PY{o}{=} \PY{n}{y}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{device}\PY{p}{)}\PY{o}{.}\PY{n}{long}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Get features from discriminator}
\PY{n}{features} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{discriminator}\PY{o}{.}\PY{n}{get\PYZus{}representation}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{logits} \PY{o}{=} \PY{n+nb+bp}{self}\PY{p}{(}\PY{n}{features}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{loss}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{y}\PY{p}{)}
\PY{n}{preds} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{argmax}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{acc} \PY{o}{=} \PY{p}{(}\PY{n}{preds} \PY{o}{==} \PY{n}{y}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Logging}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{loss}\PY{p}{,} \PY{n}{prog\PYZus{}bar}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{acc}\PY{p}{,} \PY{n}{prog\PYZus{}bar}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{on\PYZus{}train\PYZus{}epoch\PYZus{}end}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{avg\PYZus{}acc} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}training\PYZus{}acc}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{avg\PYZus{}train\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{avg\PYZus{}acc}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}acc}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{avg\PYZus{}acc}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Reset temp accuracy}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}training\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{on\PYZus{}validation\PYZus{}epoch\PYZus{}end}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{avg\PYZus{}acc} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}validation\PYZus{}acc}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{log}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{avg\PYZus{}val\PYZus{}acc}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{avg\PYZus{}acc}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}acc}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{avg\PYZus{}acc}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Reset temp accuracy}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{temp\PYZus{}validation\PYZus{}acc} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{def} \PY{n+nf}{configure\PYZus{}optimizers}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n}{torch}\PY{o}{.}\PY{n}{optim}\PY{o}{.}\PY{n}{Adam}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{lr}\PY{o}{=}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{lr}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{plot\PYZus{}losses}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mi}{10}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Training Loss}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)} \PY{c+c1}{\PYZsh{} 1 row, 2 columns, 1st plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}loss}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Training Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{blue}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Training Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Batch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Validation Loss}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{)} \PY{c+c1}{\PYZsh{} 1 row, 2 columns, 2nd plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}loss}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Validation Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{red}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Validation Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Batch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loss}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Training Accuracy}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{3}\PY{p}{)} \PY{c+c1}{\PYZsh{} 1 row, 2 columns, 3rd plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{training\PYZus{}acc}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Training Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{green}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Training Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Epoch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Plot Validation Accuracy}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)} \PY{c+c1}{\PYZsh{} 1 row, 2 columns, 4th plot}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{validation\PYZus{}acc}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Validation Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{orange}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Validation Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Epoch}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Accuracy}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{9}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{c+c1}{\PYZsh{} Custom dataset}
\PY{k+kn}{from} \PY{n+nn}{torchvision} \PY{k+kn}{import} \PY{n}{transforms}
\PY{k+kn}{from} \PY{n+nn}{torch}\PY{n+nn}{.}\PY{n+nn}{utils}\PY{n+nn}{.}\PY{n+nn}{data} \PY{k+kn}{import} \PY{n}{Dataset}\PY{p}{,} \PY{n}{DataLoader}
\PY{c+c1}{\PYZsh{} Transformations to be applied on the data}
\PY{n}{transform} \PY{o}{=} \PY{n}{transforms}\PY{o}{.}\PY{n}{Compose}\PY{p}{(}
\PY{p}{[}
\PY{n}{transforms}\PY{o}{.}\PY{n}{ToTensor}\PY{p}{(}\PY{p}{)}\PY{p}{,}
\PY{n}{transforms}\PY{o}{.}\PY{n}{Lambda}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{x}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{,} \PY{c+c1}{\PYZsh{} Convert tensor to float type}
\PY{n}{transforms}\PY{o}{.}\PY{n}{Lambda}\PY{p}{(}
\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{x}\PY{o}{.}\PY{n}{unsqueeze}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)} \PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{shape}\PY{p}{)} \PY{o}{==} \PY{l+m+mi}{3} \PY{k}{else} \PY{n}{x}
\PY{p}{)}\PY{p}{,} \PY{c+c1}{\PYZsh{} Add channel dimension}
\PY{p}{]}
\PY{p}{)}
\PY{c+c1}{\PYZsh{} Create datasets}
\PY{k}{class} \PY{n+nc}{VoxelDataset}\PY{p}{(}\PY{n}{Dataset}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{data}\PY{p}{,} \PY{n}{label}\PY{p}{,} \PY{n}{if\PYZus{}label}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,} \PY{n}{transform}\PY{o}{=}\PY{k+kc}{None}\PY{p}{,} \PY{n}{desired\PYZus{}label}\PY{o}{=}\PY{k+kc}{None}\PY{p}{)}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{if\PYZus{}label} \PY{o}{=} \PY{n}{if\PYZus{}label}
\PY{k}{if} \PY{n}{desired\PYZus{}label} \PY{o+ow}{is} \PY{o+ow}{not} \PY{k+kc}{None}\PY{p}{:}
\PY{n}{indices} \PY{o}{=} \PY{p}{[}\PY{n}{i} \PY{k}{for} \PY{n}{i}\PY{p}{,} \PY{n}{l} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{label}\PY{p}{)} \PY{k}{if} \PY{n}{l} \PY{o}{==} \PY{n}{desired\PYZus{}label}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{data} \PY{o}{=} \PY{p}{[}\PY{n}{data}\PY{p}{[}\PY{n}{i}\PY{p}{]} \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n}{indices}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{label} \PY{o}{=} \PY{p}{[}\PY{n}{label}\PY{p}{[}\PY{n}{i}\PY{p}{]} \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n}{indices}\PY{p}{]}
\PY{k}{else}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{data} \PY{o}{=} \PY{n}{data}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{label} \PY{o}{=} \PY{n}{label}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{transform} \PY{o}{=} \PY{n}{transform}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}len\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb}{len}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{data}\PY{p}{)}
\PY{k}{def} \PY{n+nf+fm}{\PYZus{}\PYZus{}getitem\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{index}\PY{p}{)}\PY{p}{:}
\PY{n}{voxel} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{data}\PY{p}{[}\PY{n}{index}\PY{p}{]}
\PY{k}{if} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{transform}\PY{p}{:}
\PY{n}{voxel} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{transform}\PY{p}{(}\PY{n}{voxel}\PY{p}{)}
\PY{k}{if} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{if\PYZus{}label}\PY{p}{:}
\PY{k}{return} \PY{n}{voxel}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{label}\PY{p}{[}\PY{n}{index}\PY{p}{]}
\PY{k}{return} \PY{n}{voxel}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{10}{\boxspacing}