Moved software and source code design to appendices

Additionally, the entire LaTeX setup was adapted for this use case.

Signed-off-by: Jim Martens <github@2martens.de>

appendix.tex

@@ -0,0 +1,45 @@
+\chapter{Software and Source Code Design}
+
+The source code of many published papers is either not available
+or seems like an afterthought: it is poorly documented, difficult
+to integrate into your own work, and often does not follow common
+software development best practices. Moreover, with Tensorflow,
+PyTorch, and Caffe there are at least three machine learning
+frameworks. Every research team seems to prefer another framework
+and sometimes even develops their own; this makes it difficult
+to combine the work of different authors.
+In addition to all this, most papers do not contain proper information
+regarding the implementation details, making it difficult to
+accurately replicate them if their source code is not available.
+
+Therefore, it was clear to me: I will release my source code and
+make it available as Python package on the PyPi package index.
+This makes it possible for other researchers to simply install
+a package and use the API to interact with my code. Additionally,
+the code has been designed to be future proof and work with
+the announced Tensorflow 2.0 by supporting eager mode.
+
+Furthermore, it is configurable, well documented, and conforms
+to the clean code guidelines: evolvability and extendability among
+others.
+%Unit tests are part of the code as well to identify common
+%issues early on, saving time in the process.
+% TODO: Unit tests (!)
+
+The code was designed to be modular: One module creates the command
+line interface (main.py), another implements the actions
+chosen in the CLI (cli.py), the MS COCO to SceneNet RGB-D mapping can
+be found in the definitions.py module,
+preparation of the data sets and retrieval of data is
+grouped in the data.py module, evaluation metrics have
+their separate module (evaluation.py), the configuration is
+accessed and handled by the config.py module, debug-only code
+can be found in debug.py, and the ssd.py module contains
+code to train the SSD and later predict with it. All
+code relating to the auto-encoder can be found in its own
+sub directory.
+
+Lastly, the SSD implementation from a third party repository
+has been modified to work inside a Python package architecture and
+with eager mode. It is stored as a Git submodule inside the package
+repository.

body.tex

@@ -366,9 +366,8 @@ be used to identify and reject these false positive cases.
 
 \label{chap:methods}
 
-This chapter explains the functionality of the Bayesian SSD, the
-decoding pipelines, and
-provides some information on the software and source code design.
+This chapter explains the functionality of the Bayesian SSD and the
+decoding pipelines.
 
 \section{Bayesian SSD for Model Uncertainty}
 
@@ -516,52 +515,6 @@ observation for removal.
 Per class confidence thresholding, non-maximum suppression, and
 top \(k\) selection happen like in vanilla SSD.
 
-\section{Software and Source Code Design}
-
-The source code of many published papers is either not available
-or seems like an afterthought: it is poorly documented, difficult
-to integrate into your own work, and often does not follow common
-software development best practices. Moreover, with Tensorflow,
-PyTorch, and Caffe there are at least three machine learning
-frameworks. Every research team seems to prefer another framework
-and sometimes even develops their own; this makes it difficult
-to combine the work of different authors.
-In addition to all this, most papers do not contain proper information
-regarding the implementation details, making it difficult to
-accurately replicate them if their source code is not available.
-
-Therefore, it was clear to me: I will release my source code and
-make it available as Python package on the PyPi package index.
-This makes it possible for other researchers to simply install
-a package and use the API to interact with my code. Additionally,
-the code has been designed to be future proof and work with
-the announced Tensorflow 2.0 by supporting eager mode.
-
-Furthermore, it is configurable, well documented, and conforms
-to the clean code guidelines: evolvability and extendability among
-others.
-%Unit tests are part of the code as well to identify common
-%issues early on, saving time in the process.
-% TODO: Unit tests (!)
-
-The code was designed to be modular: One module creates the command
-line interface (main.py), another implements the actions
-chosen in the CLI (cli.py), the MS COCO to SceneNet RGB-D mapping can
-be found in the definitions.py module,
-preparation of the data sets and retrieval of data is
-grouped in the data.py module, evaluation metrics have
-their separate module (evaluation.py), the configuration is
-accessed and handled by the config.py module, debug-only code
-can be found in debug.py, and the ssd.py module contains
-code to train the SSD and later predict with it. All
-code relating to the auto-encoder can be found in its own
-sub directory.
-
-Lastly, the SSD implementation from a third party repository
-has been modified to work inside a Python package architecture and
-with eager mode. It is stored as a Git submodule inside the package
-repository.
-
 \chapter{Experimental Setup and Results}
 
 \label{chap:experiments-results}

masterthesis.cls

@@ -3,14 +3,15 @@
 
 % Fallback, pass all unknown options to base class
 \DeclareOption*{
-    \PassOptionsToClass{\CurrentOption}{scrreprt}
+    \PassOptionsToClass{\CurrentOption}{scrbook}
 }
 
 % Process given options
 \ProcessOptions\relax
 
 % Load base class
-\LoadClass[a4paper]{scrreprt}
+\LoadClass[a4paper,twoside=false,%
+appendixprefix=true,numbers=noenddot]{scrbook}
 
 % do some stuff
 

masterthesis.sty

@@ -111,6 +111,7 @@
     \input{./private/definitions.tex}
 }{}
 
+\usepackage[titletoc]{appendix}
 
 \MakeOuterQuote{"}
 
@@ -175,10 +176,11 @@
     \tableofcontents
 }
 
-\newcommand{\finish}{%
-    %\clearpage
+\newcommand{\references}{%
     \printbibliography[heading=bibintoc]
+}
 
+\newcommand{\finish}{%
     %\clearpage
     \printglossary
 
@@ -189,10 +191,12 @@
     \else
         \clearpage
         \selectlanguage{ngerman}
+        \chapter*{Eidesstattliche Versicherung}
         \input{declaration.tex}
     \fi
     \if@library
         \clearpage
+        \chapter*{Erklärung zu Bibliothek}
         \input{library.tex}
     \else\fi
 }

thesis.tex

@@ -33,13 +33,31 @@
 % specify bib resource
 \addbibresource{ma.bib}
 
+\makeatletter
+\g@addto@macro\appendix{%
+\renewcommand*{\chapterformat}{%
+  {\appendixname\nobreakspace\thechapter\autodot\enskip}%
+}
+\renewcommand*{\chaptermarkformat}{%
+  {}%
+}
+}
+\makeatother
+
 % begin document
 \begin{document}
 % invoke start command(s) from masterthesis package
+\frontmatter
 \start
-
+\mainmatter
 \input{body.tex}
+\references
+\begin{appendices}
+    \appendix
+    \input{appendix.tex}
+\end{appendices}
 
 % invoke finish command(s) from masterthesis package
+\backmatter
 \finish
 \end{document}