commit cf19ef2020e17e1b23115a6ab9ac4db42ac441da Author: libr Date: Mon Oct 13 02:32:30 2025 +0800 first commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..496ee2c --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.DS_Store \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e72bfdd --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..d3c2a80 --- /dev/null +++ b/Makefile @@ -0,0 +1,6 @@ +pdf: + xelatex beamerJLU.tex +clean: + rm -f *.aux *.log *.nav *.out *.snm *.toc *.vrb + +all: clean pdf \ No newline at end of file diff --git a/assets/JLU_csw_background.png b/assets/JLU_csw_background.png new file mode 100644 index 0000000..9f3497e Binary files /dev/null and b/assets/JLU_csw_background.png differ diff --git a/assets/JLU_logo.png b/assets/JLU_logo.png new file mode 100644 index 0000000..ccdd5b9 Binary files /dev/null and b/assets/JLU_logo.png differ diff --git a/assets/JLU_logo_1.png b/assets/JLU_logo_1.png new file mode 100644 index 0000000..7599136 Binary files /dev/null and b/assets/JLU_logo_1.png differ diff --git a/assets/JLU_logo_2.png b/assets/JLU_logo_2.png new file mode 100644 index 0000000..8910b24 Binary files /dev/null and b/assets/JLU_logo_2.png differ diff --git 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100644 index 0000000..9f7a4f9 Binary files /dev/null and b/assets/oldlogo_RGB.png differ diff --git a/assets/oldlogo_RGB_negative.png b/assets/oldlogo_RGB_negative.png new file mode 100644 index 0000000..3d4053e Binary files /dev/null and b/assets/oldlogo_RGB_negative.png differ diff --git a/beamerJLU.tex b/beamerJLU.tex new file mode 100644 index 0000000..366caae --- /dev/null +++ b/beamerJLU.tex @@ -0,0 +1,256 @@ +\documentclass{beamer} +\usepackage{amsfonts,amsmath,oldgerm,mathrsfs,booktabs} +\usetheme{sintef} +\usepackage{xeCJK} +\usepackage{amsthm} +\usepackage{amssymb} +\usepackage{setspace} + +\newcommand{\testcolor}[1]{\colorbox{#1}{\textcolor{#1}{test}}~\texttt{#1}} + +\usefonttheme[onlymath]{serif} + +\titlebackground*{assets/JLU_math_background.png} + +\newcommand{\hrefcol}[2]{\textcolor{cyan}{\href{#1}{#2}}} +% \theoremstyle{definition} \newtheorem{definition}{Definition} + + +\theoremstyle{definition} \newtheorem{thm}{定理}[section] +\theoremstyle{definition} \newtheorem{lem}[thm]{引理} +\theoremstyle{definition} \newtheorem{prop}[thm]{命题} +\theoremstyle{definition} \newtheorem{coro}{推论}[thm] +\theoremstyle{definition} \newtheorem{df}[thm]{定义} + + + +\title{标题} +\subtitle{小标题} +\author{报告人} +\insti{xx大学} +\supervisor{xxx教授} +\date{xxxx年x月x日} +% \course{} +% \IDnumber{1234567} + + + + +\begin{document} +\maketitle + +\onehalfspacing +\setlength{\parindent}{2em} + +\allowdisplaybreaks[4] + +\begin{frame} + +This template is a based on \hrefcol{https://www.overleaf.com/latex/templates/xiang-gang-zhong-wen-da-xue-zhong-wen-mo-ban-cuhk-beamer-template/bpgghjpjkqxw}{beamer template adapted for Chinese University of Hong Kong (CUHK)} from \hrefcol{https://richardfury.github.io/}{Rui HU} which is based on \hrefcol{https://www.overleaf.com/latex/templates/sintef-presentation/jhbhdffczpnx}{SINTEF Presentation} from \hrefcol{mailto:federico.zenith@sintef.no}{Federico Zenith} and its derivation \hrefcol{https://github.com/TOB-KNPOB/Beamer-LaTeX-Themes}{Beamer-LaTeX-Themes} from Liu Qilong. 该模版基于\hrefcol{https://richardfury.github.io/}{Rui HU}所做的\hrefcol{https://www.overleaf.com/latex/templates/xiang-gang-zhong-wen-da-xue-zhong-wen-mo-ban-cuhk-beamer-template/bpgghjpjkqxw}{香港中文大学beamer模板}改编而成, 并源于\hrefcol{mailto:federico.zenith@sintef.no}{Federico Zenith}的\hrefcol{https://www.overleaf.com/latex/templates/sintef-presentation/jhbhdffczpnx}{SINTEF Presentation }模板以及Liu Qilong由它改编的模板\hrefcol{https://github.com/TOB-KNPOB/Beamer-LaTeX-Themes}{Beamer-LaTeX-Themes}. + +\vspace{\baselineskip} +This JLU style beamer template is adapted by \hrefcol{https://GEOR-TS.github.io/}{Junyan Ye}. 该JLU版的beamer模板由作者\hrefcol{https://GEOR-TS.github.io/}{Junyan Ye}改编而成. + +\vspace{\baselineskip} + +由于该模板改编的主要目的是为了本科毕业论文答辩, 实在找不到太合适的模板, 因此根据自己比较喜欢的模板\hrefcol{https://www.overleaf.com/latex/templates/xiang-gang-zhong-wen-da-xue-zhong-wen-mo-ban-cuhk-beamer-template/bpgghjpjkqxw}{beamer template adapted for Chinese University of Hong Kong (CUHK)}改编了一个, 并希望分享给所有需要答辩的JLUer. 该模板制作比较急, 因此许多基于原模板\hrefcol{https://www.overleaf.com/latex/templates/xiang-gang-zhong-wen-da-xue-zhong-wen-mo-ban-cuhk-beamer-template/bpgghjpjkqxw}{beamer template adapted for Chinese University of Hong Kong (CUHK)}的功能并未发掘和改编, 请大家见谅, 若想继续发掘有意思的功能(如chapter slide以及色块等)可参考\hrefcol{https://www.overleaf.com/latex/templates/xiang-gang-zhong-wen-da-xue-zhong-wen-mo-ban-cuhk-beamer-template/bpgghjpjkqxw}{beamer template adapted for Chinese University of Hong Kong (CUHK)}中的教程. + +接下来将简单介绍如何使用该beamer模板. + +% This template is released under \hrefcol{https://creativecommons.org/licenses/by-nc/4.0/legalcode}{Creative Commons CC BY 4.0} license + +\end{frame} + + +\section{如何制作幻灯片} + +\begin{frame}[fragile]{封面} + +在该文件的开头的以下代码进行修改得到想要的封面: +\begin{block}{封面代码} +\begin{verbatim} +\title{标题} +\subtitle{小标题} +\author{报告人} +\insti{xx大学} +\supervisor{xxx教授} +\date{xxxx年x月x日} +\end{verbatim} +\end{block} +\noindent 然后在\verb|\begin{document}|后加入代码\verb|\maketitle|来显示封面. + +若打算改变封面右下角的院徽/校徽, 可去设计工具用相同大小和颜色的画布加上想改成的院徽/校徽(我这里用的数学学院院徽), 然后在\verb|\begin{document}|前利用代码\verb|\titlebackground*|把图片的路径加进来即可. + +\end{frame} + +\begin{frame}[fragile]{制作第一张幻灯片} +\framesubtitle{非常容易} +我们的beamer模板是基于\texttt{sintef}主题制作的, 最简单的幻灯片制作如下即可(在\verb|\begin{document}|和\verb|\end{document}|之间): +\begin{block}{最简单的幻灯片制作} +\verb|\begin{frame}{标题}|\\ +\verb|\framesubtitle{小标题} %可加可不加, 不加则以所在section为小标题 | \\ +\verb|这是最简单的一张幻灯片制作| \\ +\verb|\end{frame}|\\ +\end{block} +\end{frame} + +\section{如何加入定理定义等模块} + +\begin{frame}[fragile]{定理} +中文定理模块可由以下代码生成 +\begin{block}{定理块} +\begin{verbatim} + \begin{thm} + 若$A=B$, $B=C$, 则 + \begin{equation} + A=C . + \end{equation} + \end{thm} +\end{verbatim} +\end{block} +\noindent 显示如下: +\begin{thm} + 若$A=B$, $B=C$, 则 + \begin{equation} + A=C . + \end{equation} +\end{thm} + +\end{frame} + +\begin{frame}[fragile]{定义} +中文定义模块可由以下代码生成 +\begin{block}{定义块} +\begin{verbatim} + \begin{df} + 若$C=A+B$, 则称$C$为$A$与$B$的和. + \end{df} +\end{verbatim} +\end{block} +\noindent 显示如下: +\begin{df} + 若$C=A+B$, 则称$C$为$A$与$B$的和. +\end{df} + +\end{frame} + +\begin{frame}[fragile]{引理} + 中文引理模块可由以下代码生成 +\begin{block}{引理块} +\begin{verbatim} + \begin{lem} + $A,B$可积. + \end{lem} +\end{verbatim} +\end{block} +\noindent 显示如下: +\begin{lem} + $A,B$可积. +\end{lem} + +\end{frame} + +\begin{frame}[fragile]{命题} + 中文命题模块可由以下代码生成 +\begin{block}{命题块} +\begin{verbatim} + \begin{prop} + $A$可积, 则$B$可积. + \end{prop} +\end{verbatim} +\end{block} +\noindent 显示如下: +\begin{prop} + $A$可积, 则$B$可积. +\end{prop} + +\end{frame} + +\begin{frame}[fragile]{推论} + 中文推论模块可由以下代码生成 +\begin{block}{推论块} +\begin{verbatim} + \begin{coro} + $A$可积, 则$B$不可积. + \end{coro} +\end{verbatim} +\end{block} +\noindent 显示如下: +\begin{coro} + $A$可积, 则$B$不可积. +\end{coro} + +\end{frame} + +\section{插图, 表格} + +\begin{frame}[fragile]{插图} +我们可以用以下代码进行插图 +\begin{block}{推论块} +\begin{verbatim} + \begin{figure}[H] + \centering %让图片居中 + \includegraphics[width=3cm,height=3cm]{assets/JLU_logo_1.png} + %设置图片格式和图片名（如果文档和图片不在一个文件夹下，需要给出图片路径） + \end{figure} +\end{verbatim} +\end{block} +\begin{figure}[H] + \centering %让图片居中 + \includegraphics[width=3cm,height=3cm]{assets/JLU_logo_1.png} +\end{figure} + +\end{frame} + +\begin{frame}[fragile]{表格} +我们可以用以下代码制表 +\begin{block}{表格块} +\begin{verbatim} +\begin{table}[H] \caption{标题} \resizebox{0.3\columnwidth}{!}{ \begin{tabular}{ccccccccc} +\toprule +第一列 & 第二列 & 第三列 & 第四列 \\ +\midrule +$1$ & $2$ & $3$ & $4$ \\ +\bottomrule +\end{tabular} } % \resizebox只是用于缩放表格, 视情况可删\end{table} +\end{verbatim} +\end{block} + +\begin{table}[H] + \caption{标题} + \resizebox{0.3\columnwidth}{!}{ + \begin{tabular}{ccccccccc} + \toprule + 第一列 & 第二列 & 第三列 & 第四列 \\ + \midrule + $1$ & $2$ & $3$ & $4$ \\ + \bottomrule + \end{tabular} + } +\end{table} + +\end{frame} + +\section{引用} + +\begin{frame}[fragile]{如何引用} + 直接用代码\verb|\cite{}|引用即可, 例如\cite{bagla2005cosmological}. + +\end{frame} + +\section{结尾} +\begin{frame}[fragile]{设置结尾页} +在\verb|\end{document}|前输入\verb|\backmatter|即可, 具体结尾页语句可在\texttt{beamerthemesintef.sty}文件对应处进行修改. + +\end{frame} + +\section{参考文献} + +\begin{frame}[allowframebreaks]{参考文献} +\tiny +\bibliographystyle{apalike} +\bibliography{bibliography} +\end{frame} + +\backmatter +\end{document} diff --git a/beamerthemesintef.sty b/beamerthemesintef.sty new file mode 100644 index 0000000..a5a99af --- /dev/null +++ b/beamerthemesintef.sty @@ -0,0 +1,345 @@ +% Template for Sapienza presentations with LaTeX beamer. +% +% Adapted by Andrea Gasparini, andrea@gasparini.cloud for +% Sapienza presentations. +% +% Original template by Federico Zenith, federico.zenith@sintef.no. +% Derived (through several iterations) from Håvard Berland's +% beamerthementnu class. +% +% +% This program is free software: you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation, either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program. If not, see . + +% TODO: remove top-right logo from title page + +\ProvidesPackage{beamerthemesintef}[2021/11/02] + +\RequirePackage{etoolbox} +\RequirePackage{tikz} +\RequirePackage{setspace} + +% Force 16:9 aspect ratio +\RequirePackage{geometry} +\geometry{paperwidth=20cm,paperheight=11cm} + +% Caladea is a Cambria clone, Carlito a Calibri clone +\RequirePackage{caladea,carlito} +\renewcommand{\familydefault}{\sfdefault} + +\RequirePackage{graphicx,sintefcolor} + +% No navigation symbols +\setbeamertemplate{navigation symbols}{} + +\newcommand{\@footlineauthor}{\insertauthor \enspace$\vert$\enspace} +\newcommand{\@footlinepayoff}{\@footlineauthor\inserttitle} +\newcommand{\footlinepayoff}[1]{\renewcommand{\@footlinepayoff}{#1}} +% Define footline content +\setbeamertemplate{footline}{% + \begin{beamercolorbox}[wd=\textwidth,ht=5mm,dp=3mm,rightskip=1cm,leftskip=1cm]{footline} + \insertframenumber/\inserttotalframenumber + \ifstrempty{\@footlinepayoff}{}{% + \usebeamerfont{footline}\hfill\@footlinepayoff + } + \end{beamercolorbox} +} + +% Set footline AND coordinate blocks with it +\newcommand{\footlinecolor}[1]{% if #1 is empty, makes footline transparent + \ifstrempty{#1}{% + \footlinepayoff{} + \setbeamercolor{footline}{fg=darkgray, bg=} + \setbeamercolor{block title}{fg=white,bg=maincolor} + \setbeamercolor{block body}{fg=white,bg=maincolor} + }{ + \footlinepayoff{\@footlineauthor\inserttitle} + \setbeamercolor{footline}{fg=white,bg=#1} + \setbeamercolor{block title}{fg=white,bg=#1} + \setbeamercolor{block body}{fg=white,bg=#1} + }% +} +\footlinecolor{} % Default: no footline + +\pgfdeclareimage[width=0.07\paperwidth]{logo}{assets/JLU_logo_negative_1.png} +%\pgfdeclareimage[width=0.07\paperwidth]{logo}{assets/logo_RGB} +\pgfdeclareimage[width=0.07\paperwidth]{whitelogo}{assets/JLU_logo_2.png} +%\pgfdeclareimage[width=0.07\paperwidth]{whitelogo}{assets/logo_RGB_negative} +\newcommand{\@logo}{logo} + +% Set colours +% NOTE! Beamer colors should be set in the preamble. It often works fine in the +% document itself, but NOT for normal text foreground. +% \themecolor should only be called from the preamble. +\newcommand{\themecolor}[1]{ + \ifstrequal{#1}{main}{% + \setbeamercolor{footline}{fg=white} + \setbeamercolor{normal text}{fg=white,bg=maincolor} + \setbeamercolor{structure}{fg=white} + \renewcommand{\@logo}{whitelogo} + \setbeamercolor{block title}{fg=maincolor,bg=sintefgrey} + \setbeamercolor{block body}{fg=darkgray,bg=JLUblockcolor} + % \setbeamercolor{block body}{fg=darkgray,bg=sintefgrey} + }{% + \setbeamercolor{footline}{fg=darkgray} + \setbeamercolor{normal text}{fg=darkgray,bg=white} + \setbeamercolor{structure}{fg=maincolor} + \renewcommand{\@logo}{logo} + \setbeamercolor{block title}{fg=white,bg=maincolor} + \setbeamercolor{block body}{fg=darkgray,bg=JLUblockcolor} + %\setbeamercolor{block body}{fg=darkgray,bg=sintefgrey} + } +} +\themecolor{white} % Default: white theme + +\setbeamercolor{title}{fg=maincolor,bg=white} +\setbeamercolor{alerted text}{fg=sintefred} +\setbeamercolor{author}{fg=black} +\setbeamercolor{date}{fg=black} +\setbeamercolor{supervisor}{fg=maincolor} + +\setbeamerfont{author}{size=\small, series=\mdseries} +\setbeamerfont{supervisor}{size=\small, series=\mdseries} +\setbeamerfont{date}{size=\small} +\setbeamerfont{title}{series=\bfseries, size=\large} +\setbeamerfont{subtitle}{series=\mdseries,size=\small} +\setbeamerfont{frametitle}{series=\bfseries} +\setbeamerfont{framesubtitle}{series=\mdseries} +\setbeamerfont{footline}{size=\scriptsize} +\setbeamerfont{block title}{series=\bfseries} +%\setbeamerfont{block title}{series=\centering\bfseries} + +% Code to get prettier boxes +\setbeamertemplate{blocks}[rounded] + +% Bullets in several levels +\setbeamertemplate{itemize item}{\textbullet} +\setbeamertemplate{itemize subitem}{\textemdash} +\setbeamertemplate{itemize subsubitem}{\ensuremath{\circ}} + +\newenvironment{colorblock}[3][white]{% + \begingroup + \setbeamercolor{block title}{fg=#1,bg=#2} + \setbeamercolor{block body} {fg=#1,bg=#2} + \begin{block}{#3} + }{% + \end{block} + \endgroup +} + +% Put the logo in each slide's top left area +\setbeamertemplate{headline}{\hspace{0.05\textwidth}\pgfuseimage{\@logo}} + +% Define frame title and subtitle layout +\setbeamertemplate{frametitle}{% +% \vspace*{-4.0ex} + \vspace*{-4.50ex} + \begin{beamercolorbox}[leftskip=1.7cm]{frametitle}% + \usebeamerfont{frametitle}\insertframetitle\\ + \usebeamerfont{framesubtitle}\insertframesubtitle + \end{beamercolorbox} +} + +\def\@courseLabel{} +\def\@IDnumber{} +\def\@supervisor{} +\def\@insti{} + +\newcommand{\course}[1]{\def\@courseLabel{#1}} +\newcommand{\IDnumber}[1]{\def\@IDnumber{#1}} +\newcommand{\supervisor}[1]{\def\@supervisor{#1}} +\newcommand{\insti}[1]{\def\@insti{#1}} + +% Define the title page +\setbeamertemplate{title page}{% + \vskip2pt plus 1filll% + % Reposition the box in an inelegant way - but it works! + \onehalfspacing + \hspace{-15mm} + \vspace{28mm} + \begin{beamercolorbox}[wd=0.72\textwidth,sep=10pt,leftskip=11mm]{title}% + {\usebeamerfont{title}\inserttitle} + + {\usebeamerfont{subtitle}\insertsubtitle} + + {\usebeamerfont{subtitle}\@courseLabel} + + {\usebeamerfont{author}\usebeamercolor[fg]{author}\insertauthor \ifdefempty{\@IDnumber}{}{(\@IDnumber)}} + + \ifdefempty{\@supervisor}{}{\small{指导教师:} \usebeamerfont{supervisor}\usebeamercolor[fg]{supervisor}\@supervisor} + + \ifdefempty{\@insti}{}{ \usebeamerfont{author}\usebeamercolor[fg]{author}\@insti} + + {\usebeamerfont{date}\usebeamercolor[fg]{date}\insertdate} + \end{beamercolorbox} +} + +% Define slide splitting dimensions (e.g. title and chapter slides) +\newcommand{\TikzSplitSlide}[1]{% + \rule{0.56\paperwidth}{0pt}% + \begin{tikzpicture} + \clip (-0.1\paperwidth,-0.5\paperheight) -- + ( 0.5\paperwidth,-0.5\paperheight) -- + ( 0.5\paperwidth, 0.5\paperheight) -- + ( 0.1\paperwidth, 0.5\paperheight) -- cycle; + \node at (0.2\paperwidth,0) {% + \includegraphics[height=\paperheight]{#1}% + }; + \end{tikzpicture} +} + +\newbool{splittitle} +\newcommand{\@TitleBackground}{} +\newcommand{\titlebackground}{\@ifstar{\SplitBackground}{\FullBackground}} +\newcommand{\FullBackground}[1]{\renewcommand{\@TitleBackground}{#1}} +\newcommand{\SplitBackground}[1]{% + \booltrue{splittitle}%6 + \renewcommand{\@TitleBackground}{#1}% +} + +% Overwrite the maketitle command for the title page +\renewcommand{\maketitle}{ +\begingroup + \ifdefempty{\@TitleBackground}{}{% Skip if \@TitleBackground is empty + \setbeamertemplate{background}{% + \ifbool{splittitle}{% + \TikzSplitSlide{\@TitleBackground} + }{% Normal, full-screen background + \includegraphics[height=\paperheight]{\@TitleBackground} + } + } + } + \begin{frame}% + \titlepage% + \end{frame}% +\endgroup +} + +\makeatletter + \newenvironment{withoutheadline}{ + \setbeamertemplate{headline}[default] + \def\beamer@entrycode{\vspace*{-\headheight}} + }{} +\makeatother + +% Define chapter environment +\newenvironment{chapter}[3][]{% Args: image (optional), color, frame title + \begingroup + \themecolor{main} + \setbeamertemplate{footline}{} + \ifstrempty{#2}{ + \setbeamercolor{frametitle}{fg=white} + \setbeamercolor{normal text}{fg=white,bg=maincolor} + }{ + \setbeamercolor{frametitle}{fg=white} + \setbeamercolor{normal text}{fg=white,bg=#2} + } + \ifstrempty{#1}{}{\setbeamertemplate{background}{\TikzSplitSlide{#1}}} + \setbeamertemplate{frametitle}{% + \vspace*{8ex} + \begin{beamercolorbox}[wd=0.45\textwidth]{frametitle} + \usebeamerfont{frametitle}\insertframetitle\\ + \usebeamerfont{framesubtitle}\insertframesubtitle + \end{beamercolorbox} + } + \begin{frame}{#3} + \hspace*{0.05\textwidth}% + \minipage{0.35\textwidth}% + \usebeamercolor[fg]{normal text}% +}{% + \endminipage + \end{frame} + \endgroup +} + +% Define sidepic environment +\newenvironment{sidepic}[2]{% Args: image, frame title + \begingroup + \setbeamertemplate{background}{% + \hspace*{0.6\paperwidth}% + \includegraphics[height=\paperheight]{#1}% + } + \setbeamertemplate{frametitle}{% Same as normal, but with right skip + \vspace*{-3.5ex} + \begin{beamercolorbox}[leftskip=2cm,rightskip=0.4\textwidth]{frametitle}% + \usebeamerfont{frametitle}\insertframetitle\\ + \usebeamerfont{framesubtitle}\insertframesubtitle + \end{beamercolorbox} + } + \begin{frame}{#2} + \minipage{0.6\textwidth}% +}{% + \endminipage + \end{frame} + \endgroup +} + +% Define backmatter +\newcommand{\backmatter}[1][]{ +\begingroup + \themecolor{main} + \begin{frame}[c] + \centering + \begin{minipage}{\textwidth} + \usebeamercolor[fg]{normal text} + \centering +% \ifstrequal{#1}{notitle}{}{ +% \Huge \inserttitle +% \vspace{5mm} +% } + \Huge \textsl{\\谢谢大家!} + \end{minipage} + \end{frame} +\endgroup +} + +% Define sectioning and table of contents +\AtBeginSection[] +{ + \begingroup + \themecolor{main} + \begin{frame}{目录} +% \tableofcontents[currentsection] + \tableofcontents[sectionstyle=show/shaded,subsectionstyle=show/shaded/hide,subsubsectionstyle=show/shaded/hide] + \end{frame} + \endgroup +} + +\AtBeginSubsection[]{ +\begingroup + \themecolor{main} + \begin{frame}{目录} + \tableofcontents[sectionstyle=show/shaded,subsectionstyle=show/shaded/hide,subsubsectionstyle=show/shaded/hide] + \end{frame} + \endgroup +} + +\AtBeginSubsubsection[]{ +\begingroup + \themecolor{main} + \begin{frame}{目录} + \tableofcontents[sectionstyle=show/shaded,subsectionstyle=show/shaded/hide,subsubsectionstyle=show/shaded/hide] + \end{frame} + \endgroup +} + +% style of section presented in the table of contents +\setbeamertemplate{section in toc}{$\blacktriangleright$~\inserttocsection} + +% automate subtitle of each frame +\makeatletter + \pretocmd\beamer@checkframetitle{\framesubtitle{\thesection \, \secname}} +\makeatother + +% avoid numbering of frames that are breaked into multiply slides +\setbeamertemplate{frametitle continuation}{} \ No newline at end of file diff --git a/bibliography.bib b/bibliography.bib new file mode 100644 index 0000000..792f159 --- /dev/null +++ b/bibliography.bib @@ -0,0 +1,10 @@ +@article{bagla2005cosmological, + title={Cosmological N-body simulation: Techniques, scope and status}, + author={Bagla, Jasjeet Singh}, + journal={Current science}, + pages={1088--1100}, + year={2005}, + publisher={JSTOR} +} + + diff --git a/presentation_script.md b/presentation_script.md new file mode 100644 index 0000000..69642d9 --- /dev/null +++ b/presentation_script.md @@ -0,0 +1,593 @@ +# CIFAR-10图像分类模型横向测评 - 演讲稿 + +**演讲者**: 李博睿 (55230422) +**时长**: 约15-20分钟 +**场合**: 计算智能课程技术报告 + +--- + +## 【第1页 - 封面】 + +大家好！我是李博睿，学号55230422。 + +今天我要向大家汇报的题目是《CIFAR-10图像分类模型横向测评》。 + +在这次研究中，我对比了四种不同的深度学习模型在图像分类任务上的表现，包括经典的CNN、现代的ResNet-18，以及两种加入了注意力机制的ResNet变体。 + +那么，让我们开始今天的汇报。 + +--- + +## 【第2页 - 目录】 + +今天的汇报主要分为五个部分： + +首先，我会介绍**研究背景与动机**，说明为什么要做这个对比实验； + +第二部分，我会详细讲解**四个模型的架构**，包括它们的设计原理和关键特点； + +第三部分，我会**展示实验结果**，这是我们最关心的部分； + +第四部分，我会**深入分析这些结果**，解释为什么会出现这样的现象； + +最后，我会进行**反思**，指出实验中存在的问题，并提出改进方向。 + +--- + +## 【第3页 - 研究背景】 + +### CIFAR-10数据集 + +首先来看研究背景。 + +我选择的数据集是**CIFAR-10**，这是计算机视觉领域一个非常经典的基准数据集。 + +它包含**6万张32×32的彩色图像**，分为10个类别：飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。 + +其中5万张用于训练，1万张用于测试。 + +虽然图像分辨率不高，只有32×32像素，但正是因为这个"小"，CIFAR-10成为了快速验证和对比不同模型架构的理想选择。 + +### 研究意义 + +那么，做这个对比研究有什么意义呢？ + +**第一**，我想系统地评估不同深度学习架构的性能差异； + +**第二**，我特别关注注意力机制在图像分类中的作用——理论上它应该能提升性能，但实际效果如何？ + +**第三**，这个研究可以为实际应用中的模型选择提供参考依据。 + +--- + +## 【第4页 - 研究动机与目标】 + +### 核心研究问题 + +具体来说，我想回答四个核心问题： + +**第一个问题**：经典的CNN和现代的ResNet，它们的性能差异到底有多大？这个差距值得我们花10倍的参数量吗？ + +**第二个问题**：ResNet的核心创新——残差连接，它对训练深度网络的帮助是否真的如论文所说那么显著？ + +**第三个问题**：注意力机制近年来非常火热，CBAM和SE这两种注意力模块能否真正提升ResNet的性能？ + +**第四个问题**：性能提升和参数增加之间的权衡如何把握？我们是否应该一味追求更复杂的模型？ + +### 实验模型 + +为了回答这些问题，我选择了四个模型： + +**CNN**：一个3层的卷积神经网络，参数量约1.2M，代表经典的浅层架构； + +**ResNet-18**：18层的残差网络，参数量约11M，代表现代深度架构； + +**ResNet-18+CBAM**：在ResNet-18基础上加入了通道和空间双重注意力机制； + +**ResNet-18+SE**：在ResNet-18基础上加入了轻量级的通道注意力机制。 + +--- + +## 【第5页 - 模型1: CNN架构】 + +现在进入第二部分，模型架构讲解。 + +### CNN架构 + +首先看最简单的CNN模型。 + +它的结构非常直观：输入是3×32×32的RGB图像，经过三个卷积块，最后接两个全连接层，输出10个类别的概率。 + +**卷积层设计**：三层卷积分别将通道数从3增加到64、128、256。每层后面都接BatchNorm做归一化、ReLU做激活、MaxPool做降维。 + +**全连接层**：最后将特征图展平成4096维向量，通过一个512维的隐藏层，加了50%的Dropout防止过拟合，最终输出10维。 + +**特点**：这个模型的优点是**结构简单、参数少、训练快**。但缺点也很明显：**深度受限，表达能力有限**。如果我们简单地增加卷积层的数量，不仅难以训练，性能反而会下降，这就是所谓的"退化问题"。 + +--- + +## 【第6页 - 模型2: ResNet-18架构】 + +### 残差连接原理 + +ResNet正是为了解决这个"退化问题"而提出的。 + +它的核心思想是**残差学习**。看这个公式：y = F(x) + x + +传统网络学习的是从输入x到输出y的直接映射，而ResNet学习的是**残差**——也就是输入和输出的差值F(x)。 + +为什么这样设计？因为学习一个**"微小的变化"**，比学习一个**"完整的映射"**要容易得多。 + +### BasicBlock结构 + +ResNet-18由多个这样的BasicBlock堆叠而成。每个Block包含两个3×3卷积，关键是这条**红色的shortcut连接**——它直接把输入加到输出上。 + +整个网络分为4个Layer，每个Layer包含2个BasicBlock，总共18层。 + +### 核心优势 + +ResNet的核心优势就是**解决了梯度消失问题**。 + +因为有shortcut的存在，梯度可以直接从输出反向传播到输入，不会在深层网络中"消失"。这使得训练几百层的深度网络成为可能。 + +--- + +## 【第7页 - 模型3: ResNet-18+CBAM】 + +### CBAM注意力机制 + +第三个模型在ResNet-18基础上加入了CBAM——Convolutional Block Attention Module。 + +CBAM的核心理念是让模型学会两件事：**"关注什么"**和**"关注哪里"**。 + +### 通道注意力 + +**通道注意力**回答"关注什么"。 + +它通过全局平均池化和最大池化，把每个通道压缩成一个数值，然后用一个共享的MLP学习每个通道的重要性权重。 + +比如，对于识别"猫"这个任务，纹理特征的通道可能更重要，而对于识别"飞机"，形状特征的通道更重要。 + +### 空间注意力 + +**空间注意力**回答"关注哪里"。 + +它在特征图的每个空间位置生成一个权重，告诉模型应该关注图像的哪个区域。 + +比如识别猫的时候，应该更关注脸部和耳朵，而不是背景。 + +### 参数代价 + +CBAM的一个优点是**非常轻量**。它只增加了约0.2M参数，相对于ResNet-18的11M，仅增加了1.8%。 + +理论上，这么小的代价应该能带来可观的性能提升。但实际效果如何呢？我们等会看结果。 + +--- + +## 【第8页 - 模型4: ResNet-18+SE】 + +### SE注意力机制 + +第四个模型加入的是SE模块——Squeeze-and-Excitation。 + +SE比CBAM更简单，它只做通道注意力，不做空间注意力。 + +### 三步操作 + +SE的工作流程分为三步： + +**第一步，Squeeze（压缩）**：用全局平均池化把每个通道压缩成一个标量。这个操作捕获了整个通道的全局信息。 + +**第二步，Excitation（激励）**：通过两层全连接网络学习通道间的依赖关系。第一层降维，第二层升维，最后用Sigmoid激活得到0到1之间的权重。 + +**第三步，Scale（缩放）**：把学到的权重乘回原来的特征图，实现通道级的重标定。 + +### 效率优势 + +SE的参数更少，只增加约0.1M，相对ResNet-18仅增加0.9%。 + +所以，从参数效率角度看，SE比CBAM更轻量。 + +--- + +## 【第9页 - 实验结果展示】 + +好，现在我们进入最关键的部分——实验结果。 + +### 图表说明 + +大家看这张图，它包含三个子图： + +**左图**是**训练准确率曲线**。横轴是训练轮数（epoch），纵轴是准确率。我们可以看到四条不同颜色的曲线代表四个模型。 + +橙色的ResNet-18上升最快，最终达到约85%；绿色和红色的CBAM和SE在75-78%左右；蓝色的CNN最慢，只有约68%。 + +**中图**是**训练损失曲线**。同样是四条曲线，但这次是越低越好。ResNet-18（橙色）下降到约0.5，表现最好；其他模型在0.7-0.95之间。 + +**右图**是**测试准确率的柱状图**。这是最终的性能指标，也是我们最关心的。 + +ResNet-18最高，达到80.68%；ResNet-18+SE是74.06%；ResNet-18+CBAM是73.56%；CNN最低，71.95%。 + +红色虚线是平均值75.06%。 + +### 初步观察 + +从这张图，我们可以得出几个初步观察： + +**第一**，ResNet-18在所有指标上都表现最好； + +**第二**，令人意外的是，加了注意力机制的模型反而不如基础的ResNet-18； + +**第三**，所有模型的准确率都不算太高，最好的也只有80%多一点。 + +为什么会这样？我们接下来详细分析。 + +--- + +## 【第10页 - 测试准确率对比分析】 + +### 数据对比 + +首先看这个详细的对比表格。 + +**CNN**：1.2M参数，测试准确率71.95%，作为基线； + +**ResNet-18**：11M参数，80.68%，相比CNN提升了8.73个百分点； + +**ResNet-18+CBAM**：11.2M参数，73.56%，只比CNN高1.61个百分点； + +**ResNet-18+SE**：11.1M参数，74.06%，提升2.11个百分点。 + +平均准确率是75.06%。 + +### 关键发现 + +这里有三个关键发现： + +**第一个发现**：ResNet-18显著优于CNN。8.73个百分点的提升非常可观，证明了残差连接和深度网络的价值。 + +**第二个发现**，也是最意外的：**注意力机制的提升远不如预期**。 + +理论上，CBAM和SE应该进一步提升ResNet的性能，但实际上它们不仅没有提升，反而表现更差。CBAM甚至比基础的CNN都低！ + +**第三个发现**：所有模型的绝对准确率都不高。即使是最好的ResNet-18，也只有80.68%，离CIFAR-10的sota（95%+）还有很大差距。 + +### 原因分析 + +为什么会这样？我分析有几个可能的原因： + +**第一**，训练不充分。我只训练了50个epoch，从曲线看，准确率还在上升，损失还在下降，说明没有收敛。 + +**第二**，学习率可能设置不当。注意力机制可能需要不同的学习率策略。 + +**第三**，数据增强不足。标准的RandomCrop和RandomFlip可能不够。 + +**第四**，对于CIFAR-10这种32×32的小图像，空间注意力可能难以发挥作用。 + +--- + +## 【第11页 - 训练过程分析】 + +让我们更细致地分析训练过程。 + +### 训练准确率特征 + +左边这个block看训练准确率的特征： + +**CNN**收敛最慢，50个epoch后才到68%左右； + +**ResNet-18**收敛最快，而且最稳定，达到了85%； + +**加了注意力的两个模型**介于两者之间，75-78%。 + +但这里有个**异常现象**需要注意：ResNet-18的训练准确率是85%，但测试准确率只有80.68%，差了4个多百分点。这个gap比其他模型都大，说明**ResNet-18可能存在过拟合**。 + +### 训练损失特征 + +右边看训练损失： + +所有模型在初期都快速下降，这是正常的； + +ResNet-18下降到约0.5就稳定了； + +其他模型在0.7-0.95之间波动。 + +损失曲线都比较平滑，说明训练过程是稳定的。 + +### 收敛性分析 + +总体来说，**ResNet架构的梯度流动确实更好**，这从它更快的收敛速度就能看出来。 + +但是，快速收敛不等于最终性能一定最好，还要看是否过拟合。 + +--- + +## 【第12页 - 性能-复杂度权衡】 + +接下来我们从另一个角度看问题：性能和复杂度的权衡。 + +### 参数效率 + +我定义了一个简单的指标：**参数效率 = 准确率 / 参数量**。 + +这个指标越高，说明每个参数的"性价比"越高。 + +### 数据对比 + +看这个表格： + +CNN的效率是59.96，最高，因为它参数最少； + +ResNet-18是7.33； + +加了注意力的两个模型是6.57和6.67。 + +虽然CNN的效率最高，但它的绝对准确率太低了，只有72%，实用价值有限。 + +### 权衡建议 + +如果让我给出建议： + +**如果资源充足，追求最高准确率**，选ResNet-18。虽然它有11M参数，但80.68%的准确率在这四个模型中是最高的。 + +**如果资源非常受限**，比如要部署到嵌入式设备，那可能不得不选CNN，虽然准确率低一些。 + +**如果想要平衡**，ResNet-18+SE可能是个折中选择——它比基础ResNet只多了0.1M参数，准确率虽然低了一些，但参数效率还不错。 + +但说实话，在当前的训练设置下，这些结论都是初步的，需要更充分的训练来验证。 + +--- + +## 【第13页 - 实验中的问题】 + +现在进入第五部分：反思与改进。 + +作为一个严谨的研究，我必须承认，这次实验存在一些问题。 + +### 训练不充分 + +**最大的问题就是训练不充分**。 + +我只训练了50个epoch，这对于CIFAR-10来说远远不够。标准做法是训练200个epoch甚至更多。 + +从曲线可以看出，准确率还在稳步上升，损失还在下降，说明模型还有学习空间。 + +如果继续训练，我相信准确率还能进一步提升，可能会达到90%甚至更高。 + +### 注意力机制未发挥作用 + +**第二个问题是注意力机制没有发挥预期作用**。 + +CBAM和SE不仅没有提升性能，反而比基础ResNet表现更差。这绝对不正常。 + +我分析有几个可能的原因： + +**原因一**：训练不充分。注意力模块的参数需要更多的迭代才能学到有效的权重； + +**原因二**：学习率设置可能不合适。注意力模块可能需要不同的学习率或者warmup策略； + +**原因三**：初始化方法可能有问题。注意力权重的初始化很关键； + +**原因四**：CIFAR-10的图像太小了，只有32×32像素。对于这么小的图像，空间注意力可能没有太多发挥空间。如果换成ImageNet那种224×224的图像，效果可能会好很多。 + +### 测试准确率偏低 + +**第三个问题是整体准确率偏低**。 + +即使是最好的ResNet-18，80.68%的准确率也远低于文献中报告的95%+。 + +这再次说明我的训练策略需要大幅优化。 + +--- + +## 【第14页 - 改进方向】 + +既然发现了这些问题，那么改进方向就很明确了。 + +### 1. 训练策略优化 + +**第一组改进是关于训练策略的**： + +最重要的是**把epochs增加到200**，这是标准做法； + +使用**学习率调度**，比如MultiStepLR，在第60、120、160个epoch降低学习率； + +添加**学习率预热**，在前5个epoch让学习率逐步增加到设定值； + +使用**更强的数据增强**，比如RandomCrop、CutMix、MixUp等。这些技术已经被证明能显著提升性能。 + +### 2. 超参数调整 + +**第二组改进是超参数调整**： + +当前的初始学习率是0.1，可能偏大了，我会尝试降低到0.05； + +Batch Size 128是合理的，可以保持； + +但我会增加Weight Decay来加强正则化，防止过拟合。 + +### 3. 模型架构改进 + +**第三组改进是模型架构方面**： + +可以尝试更深的ResNet，比如ResNet-34或ResNet-50； + +调整CBAM的位置——也许不应该在每个Block后都加，而是只在关键位置加； + +尝试其他注意力机制，比如最近很火的ECA-Net； + +增加Dropout或者DropBlock来防止过拟合。 + +--- + +## 【第15页 - 未来工作展望】 + +### 短期改进 + +在**1-2周内**，我计划： + +**首先**，重新训练所有模型，把epochs增加到200； + +**其次**，优化学习率调度和数据增强策略； + +**第三**，详细记录训练过程中的每一个指标，包括训练loss、验证loss、学习率变化等； + +**最后**，绘制更完整的可视化，包括训练曲线、混淆矩阵、每个类别的准确率等。 + +### 中期探索 + +在**1个月的时间范围内**，我会： + +**第一**，实现更多注意力机制的变体，做更系统的对比； + +**第二**，尝试Transformer架构，比如Vision Transformer，看看它在CIFAR-10上的表现； + +**第三**，做详细的消融实验，分析每个组件的贡献； + +**第四**，把实验扩展到CIFAR-100或者其他数据集，验证结论的普适性。 + +### 长期目标 + +从长期来看，我对一些前沿方向很感兴趣： + +比如**神经架构搜索**，让算法自动找最优的网络结构； + +比如**知识蒸馏**，把大模型的知识迁移到小模型； + +比如**半监督学习**，利用未标注的数据提升性能。 + +--- + +## 【第16页 - 核心结论】 + +现在让我总结一下本次研究的核心结论。 + +### 主要发现 + +**第一个发现**：ResNet-18在测试集上表现最优，达到80.68%，显著优于CNN的71.95%。这验证了残差连接和深度网络的价值。 + +**第二个发现**：残差连接确实至关重要。它不仅让深层网络能够训练，而且收敛速度更快，性能更好。 + +**第三个发现**：在当前的训练设置下，注意力机制没有充分发挥作用。CBAM和SE的表现甚至不如基础ResNet。但这不代表注意力机制无效，更可能是训练策略需要优化。 + +**第四个发现**：50个epoch的训练远远不够。从曲线可以看出，模型还有很大的提升空间。 + +### 经验总结 + +从这次实验中，我总结了几条重要经验： + +**第一**，深度学习需要**充分的训练时间**。50个epoch可能在某些任务上够了，但对于从零开始训练的CIFAR-10，200个epoch才是起点。 + +**第二**，**超参数调优和数据增强至关重要**。同样的模型，不同的训练策略可能差距很大。 + +**第三**，注意力机制不是万能的，它需要**合适的训练策略**才能发挥作用。盲目加上去不一定有效。 + +**第四**，**实验设计要系统化、可重复**。要详细记录每个细节，这样才能从失败中学到东西。 + +### 下一步 + +基于这些发现，我的下一步计划是：**增加训练轮数到200，优化超参数，然后重新评估各模型的性能**。 + +我相信，经过这些改进，注意力机制应该能展现出它应有的价值。 + +--- + +## 【第17页 - 致谢页】 + +好，我的汇报到这里就结束了。 + +最后这一页列出了我的项目代码和相关文档： + +所有代码都在**run_all.py**这个脚本里，可以一键运行所有实验； + +实验结果就是刚才展示的**100.png**； + +详细的分析和讨论写在了**REPORT.md**这个报告文件里。 + +现在， + +# 谢谢大家的聆听！ + +# 欢迎提问！ + +--- + +## 【Q&A环节 - 可能的问题及回答】 + +### Q1: 为什么只训练50个epoch？ + +**A**: 这其实是我这次实验的一个失误。一开始我以为50个epoch足够了，但实际运行后发现，曲线还在上升，显然没有收敛。正常的CIFAR-10实验应该训练200个epoch以上。这也是为什么我在"反思与改进"部分专门指出了这个问题。下一步我会重新训练更多轮次。 + +### Q2: 为什么ResNet-18比加了注意力的模型还要好？ + +**A**: 这确实是个意外的结果。我分析有几个原因：首先，训练不充分，注意力模块的参数还没有学到有效的权重；其次，可能学习率或初始化设置不适合注意力机制；第三，CIFAR-10图像太小（32×32），空间注意力可能没有足够的发挥空间。在更大的图像上，比如ImageNet，CBAM通常能带来1-2%的提升。 + +### Q3: 你觉得哪个模型最实用？ + +**A**: 这要看应用场景。如果是在服务器端，GPU资源充足，我会选ResNet-18，因为它准确率最高。如果是在移动端或嵌入式设备上，资源受限，可能不得不选CNN或者更轻量的MobileNet。ResNet-18+SE是个折中选择——只增加0.1M参数，在优化训练后应该能有不错的性能。 + +### Q4: 下一步打算做什么改进？ + +**A**: 最紧迫的是重新训练，把epochs增加到200，并优化学习率调度和数据增强。我还想尝试更多的注意力机制变体，比如ECA-Net、Coordinate Attention等。长远来看，我对Transformer架构很感兴趣，想看看ViT在CIFAR-10上能达到什么水平。 + +### Q5: 为什么用SGD而不是Adam？ + +**A**: 这是个很好的问题。在图像分类任务中，SGD+Momentum通常比Adam有更好的泛化性能。虽然Adam收敛更快，但往往会陷入"尖锐的局部最优"，导致测试集表现不如训练集。SGD虽然慢，但能找到"平坦的局部最优"，泛化性更好。很多SOTA的图像分类工作都用SGD。当然，也有人尝试用AdamW（Adam的改进版），效果介于两者之间。 + +### Q6: 数据增强用了哪些方法？ + +**A**: 目前只用了最基础的RandomCrop和RandomHorizontalFlip。这显然不够。下一步我打算加入更强的增强方法，比如： +- CutMix: 把两张图像裁剪后混合 +- MixUp: 把两张图像按比例混合 +- AutoAugment: 自动搜索最优的增强策略 +- RandomErasing: 随机遮挡一部分区域 + +这些方法已经被证明能显著提升CIFAR-10的性能。 + +### Q7: 能达到95%以上的准确率吗？ + +**A**: 理论上可以。CIFAR-10的SOTA（State-of-the-art）准确率已经超过99%了。但要达到95%，需要： +1. 充分的训练（200+ epochs） +2. 更深的网络（ResNet-50/101）或更先进的架构（EfficientNet, Transformer） +3. 强大的数据增强 +4. 精心调优的超参数 +5. 可能还需要一些技巧，比如模型集成、知识蒸馏等 + +我目前的设置比较基础，所以80%是合理的。通过系统优化，90-95%是可以达到的目标。 + +### Q8: 这个项目花了多长时间？ + +**A**: 从代码实现到实验运行，大概花了一周时间。其中： +- 模型实现：2天 +- 训练脚本和可视化：2天 +- 运行实验：1天（50 epochs还是很快的） +- 分析结果和准备汇报：2天 + +如果重新训练200 epochs，可能还需要2-3天。 + +### Q9: 有遇到什么技术难点吗？ + +**A**: 主要有两个： +1. **CBAM的实现**：通道注意力和空间注意力怎么正确地集成到ResNet的BasicBlock中，我参考了原论文和一些开源实现才搞清楚。 +2. **可视化**：怎么把训练过程中的数据保存下来，然后生成漂亮的对比图，这花了不少时间调matplotlib的参数。 + +整体来说，得益于PyTorch强大的生态，大部分实现都比较顺利。 + +### Q10: 有什么想对后来者说的？ + +**A**: 几点建议： +1. **从小实验开始**：先在小数据集（如CIFAR-10）上验证想法，不要一上来就跑ImageNet。 +2. **重视基础**：把经典模型（CNN、ResNet）吃透，再去追新方法。 +3. **做好记录**：每次实验都要详细记录超参数、结果、遇到的问题。 +4. **勇于试错**：我的注意力机制实验"失败"了，但这本身就是一个重要发现。 +5. **多看论文**：理解为什么这样设计，而不只是调参。 + +最重要的是：**享受学习的过程**！深度学习既是科学也是艺术。 + +--- + +**【演讲结束】** + +再次感谢大家的聆听！如果还有问题，欢迎随时交流。 + +**谢谢！** diff --git a/sintefcolor.sty b/sintefcolor.sty new file mode 100644 index 0000000..7a47fa5 --- /dev/null +++ b/sintefcolor.sty @@ -0,0 +1,31 @@ +\NeedsTeXFormat{LaTeX2e} +\ProvidesPackage{sintefcolor}[2021/03/04] + +\RequirePackage{xcolor} + +% Main colour +%\definecolor{maincolor}{RGB/cmyk}{114,37,109/69,99,35,1} +\definecolor{maincolor}{RGB/cmyk}{24,66,153/84,57,0,40} +\definecolor{maincharcolor}{RGB/cmyk}{114,37,109/69,99,35,1} +% "Warm grey" +\definecolor{sintefgrey}{RGB/cmyk}{235,235,230/0,0,0,.1} +\colorlet{sintefgray}{sintefgrey} + +\definecolor{JLUblockcolor}{RGB/cmyk}{241,245,255/5,4,0,0} + +% Greens +\definecolorset{RGB/cmyk}{sintef}{}{lightgreen, 205,250,225/.23, 0,.20, 0;% + green, 20,185,120/.73, 0,.67, 0;% + darkgreen, 0, 70, 40/.93,.43,.92,.52} + +% Additional colours 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