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pruning.tex
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pruning.tex
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\documentclass[oneside,final,12pt]{extreport}
\usepackage[breakable]{tcolorbox}
\tcbset{nobeforeafter} % prevents tcolorboxes being placing in paragraphs
\usepackage{float}
\floatplacement{figure}{H} % forces figures to be placed at the correct location
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
\usepackage{mathrsfs}
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
\definecolor{ansi-default-inverse-fg}{HTML}{FFFFFF}
\definecolor{ansi-default-inverse-bg}{HTML}{000000}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatibility definitions
\def\gt{>}
\def\lt{<}
\let\Oldtex\TeX
\let\Oldlatex\LaTeX
\renewcommand{\TeX}{\textrm{\Oldtex}}
\renewcommand{\LaTeX}{\textrm{\Oldlatex}}
% Document parameters
% Document title
\title{Pruning}
% Pygments definitions
\makeatletter
\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
\let\PY@ul=\relax \let\PY@tc=\relax%
\let\PY@bc=\relax \let\PY@ff=\relax}
\def\PY@tok#1{\csname PY@tok@#1\endcsname}
\def\PY@toks#1+{\ifx\relax#1\empty\else%
\PY@tok{#1}\expandafter\PY@toks\fi}
\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
\PY@it{\PY@bf{\PY@ff{#1}}}}}}}
\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
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\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
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% For linebreaks inside Verbatim environment from package fancyvrb.
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% Some characters . , ; ? ! / are not pygmentized.
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\catcode`\?\active
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\lccode`\~`\~
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% prompt
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% Slightly bigger margins than the latex defaults
%% my command
%%%%%%%%%%%%%%
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% Величина отступа
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% Сокращения
\newcommand{\urlTitle}{ $-$ URL: }
%%%%%%%%%%%%%%%
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% Отступ
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% Картинки
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figurename= Рисунок,
tablename= Таблица,
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{
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% Добавляем итератор \asbuk,
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% Меняем маркеры в перечислениях
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% Списки уровня 2
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% Перечисления
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% Удаляем отступы перед и после
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% Красная строка в начале главы
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% Убиваем перенос
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% Выравнивание по ширине
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%\usepackage[fontfamily=courier]{fancyvrb}
%\usepackage{verbatim}% configurable verbatim
% \makeatletter
% \def\verbatim@font{\normalfont\sffamily% select the font
% \let\do\do@noligs
% \verbatim@nolig@list}
%\makeatother
% Границы
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\setpapersize{A4}
% отступы
%\setmarginsrb
%{3cm} % левый
%{2cm} % верхний
%{1cm} % Правый
%{2cm} % Нижний
%{0pt}{0mm} % Высота - отступ верхнего колонтитула
%{0pt}{0mm} % Высота - отступ нижнего колонтитула
\setlength\hoffset{0cm}
\setlength\voffset{0cm}
\usepackage[top=2cm, bottom=2cm, left=3cm, right=2cm,
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% Настройка заглавиий
\addto\captionsrussian{% Replace "english" with the language you use
\renewcommand{\contentsname}% содержания
{\hfill\bfseries
СОДЕРЖАНИЕ
\hfill
}%
\renewcommand{\bibname}% списка источников
{\hfill\bfseries
СПИСОК ИСПОЛЬЗОВАННЫХ ИСТОЧНИКОВ
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}%
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[block]
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[\vspace{14pt}]% Отступ после
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% Конец настройка заглавий
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% Убрать отсупы в списке источников
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% ADD THE FOLLOWING COUPLE LINES INTO YOUR PREAMBLE
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%}
% Change indent
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% Добавить точки в оглавление
% Чтобы картинки вставляись
% куда надо
\usepackage{float}
% Для вычисления кол-ва страниц
\usepackage{lastpage}
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% Для вычисления кол-ва рисунков и таблиц
%%%
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\newcounter{totfigures}
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\providecommand\totfig{}
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\AtEndDocument{%
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\pretocmd{\chapter}{\addtocounter{totfigures}{\value{figure}}\setcounter{figure}{0}}{}{}
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%%%
% Режим релиза
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% Change intemize intend
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\setlist{itemindent= \mylength + \indentSpace ,leftmargin=0pt}
\begin{document}
\textbf{Приложение Б. Реализация алгоритмов отсечения}
\textbf{Глобальное прореживание нейронной сети (спарсификация)}
Ниже представлена возможная реализация оберток на полносвязанный линейный и
сверточный 2d слои.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{2}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{MaskedLinear}\PY{p}{(}\PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{in\PYZus{}features}\PY{p}{,} \PY{n}{out\PYZus{}features}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{MaskedLinear}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n}{in\PYZus{}features}\PY{p}{,} \PY{n}{out\PYZus{}features}\PY{p}{,} \PY{n}{bias}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{set\PYZus{}mask}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{mask}\PY{p}{)}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{tensor}\PY{p}{(}\PY{n}{mask}\PY{p}{,} \PY{n}{requires\PYZus{}grad}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{o}{.}\PY{n}{data} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{o}{.}\PY{n}{data}\PY{o}{*}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask}\PY{o}{.}\PY{n}{data}
\PY{k}{def} \PY{n+nf}{get\PYZus{}mask}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask}
\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}{F}\PY{o}{.}\PY{n}{linear}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bias}\PY{p}{)}
\PY{k}{class} \PY{n+nc}{MaskedConv2d}\PY{p}{(}\PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{in\PYZus{}channels}\PY{p}{,} \PY{n}{out\PYZus{}channels}\PY{p}{,} \PY{n}{kernel\PYZus{}size}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}
\PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{dilation}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{groups}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{MaskedConv2d}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n}{in\PYZus{}channels}\PY{p}{,} \PY{n}{out\PYZus{}channels}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{p}{,} \PY{n}{stride}\PY{p}{,} \PY{n}{padding}\PY{p}{,} \PY{n}{dilation}\PY{p}{,} \PY{n}{groups}\PY{p}{,} \PY{n}{bias}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{prune}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{mask}\PY{p}{)}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{tensor}\PY{p}{(}\PY{n}{mask}\PY{p}{,} \PY{n}{requires\PYZus{}grad}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{o}{.}\PY{n}{data} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{o}{.}\PY{n}{data}\PY{o}{*}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask}\PY{o}{.}\PY{n}{data}
\PY{k}{def} \PY{n+nf}{get\PYZus{}mask}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{mask}
\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}{F}\PY{o}{.}\PY{n}{conv2d}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{weight}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bias}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{stride}\PY{p}{,}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{padding}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{dilation}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{groups}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{weight\PYZus{}sparse}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{pruning\PYZus{}perc}\PY{p}{)}\PY{p}{:}
\PY{n}{all\PYZus{}weights} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{p} \PY{o+ow}{in} \PY{n}{model}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n}{p}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{p}{)}\PY{p}{)} \PY{o}{!=} \PY{l+m+mi}{1}\PY{p}{:}
\PY{n}{all\PYZus{}weights} \PY{o}{+}\PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{p}\PY{o}{.}\PY{n}{cpu}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{abs}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{numpy}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{flatten}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n}{threshold} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{percentile}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{array}\PY{p}{(}\PY{n}{all\PYZus{}weights}\PY{p}{)}\PY{p}{,} \PY{n}{pruning\PYZus{}perc}\PY{p}{)}
\PY{n}{masks} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{p} \PY{o+ow}{in} \PY{n}{model}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n}{p}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{size}\PY{p}{(}\PY{p}{)}\PY{p}{)} \PY{o}{!=} \PY{l+m+mi}{1}\PY{p}{:}
\PY{n}{pruned\PYZus{}inds} \PY{o}{=} \PY{n}{p}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{abs}\PY{p}{(}\PY{p}{)} \PY{o}{\PYZgt{}} \PY{n}{threshold}
\PY{n}{masks}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{pruned\PYZus{}inds}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{k}{return} \PY{n}{masks}
\end{Verbatim}
\end{tcolorbox}
Алгоритмы прунинга достаточно востребованы и поэтому в современных
фреймворках уже представлен набор инструментов позволяющий проводить
операцию прунинга. Ниже представлена архитектура нейронной сети
ResNet18, адаптированная под низкое разрешение и решающая задачу
классификации 10 классов.
Метод \texttt{calc\_weights} позволяет посчитать количество весов с
учетом сгенерированных для прунинга масок.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{3}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{class} \PY{n+nc}{BasicBlock}\PY{p}{(}\PY{n}{nn}\PY{o}{.}\PY{n}{Module}\PY{p}{)}\PY{p}{:}
\PY{n}{expansion} \PY{o}{=} \PY{l+m+mi}{1}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{in\PYZus{}planes}\PY{p}{,} \PY{n}{planes}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{super}\PY{p}{(}\PY{n}{BasicBlock}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{(}
\PY{n}{in\PYZus{}planes}\PY{p}{,} \PY{n}{planes}\PY{p}{,} \PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{3}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{n}{stride}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm2d}\PY{p}{(}\PY{n}{planes}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{(}\PY{n}{planes}\PY{p}{,} \PY{n}{planes}\PY{p}{,} \PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{3}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn2} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm2d}\PY{p}{(}\PY{n}{planes}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{shortcut} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}\PY{p}{)}
\PY{k}{if} \PY{n}{stride} \PY{o}{!=} \PY{l+m+mi}{1} \PY{o+ow}{or} \PY{n}{in\PYZus{}planes} \PY{o}{!=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{expansion}\PY{o}{*}\PY{n}{planes}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{shortcut} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}
\PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{(}\PY{n}{in\PYZus{}planes}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{expansion}\PY{o}{*}\PY{n}{planes}\PY{p}{,}
\PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{n}{stride}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}\PY{p}{,}
\PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm2d}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{expansion}\PY{o}{*}\PY{n}{planes}\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{n}{out} \PY{o}{=} \PY{n}{F}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn1}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn2}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv2}\PY{p}{(}\PY{n}{out}\PY{p}{)}\PY{p}{)}
\PY{n}{skip\PYZus{}branch} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{shortcut}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{out} \PY{o}{+}\PY{o}{=} \PY{n}{skip\PYZus{}branch}
\PY{n}{out} \PY{o}{=} \PY{n}{F}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{k}{return} \PY{n}{out}
\PY{k}{class} \PY{n+nc}{ResNet}\PY{p}{(}\PY{n}{nn}\PY{o}{.}\PY{n}{Module}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\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}{ResNet}\PY{p}{,} \PY{n+nb+bp}{self}\PY{p}{)}\PY{o}{.}\PY{n}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{p}{)}
\PY{n}{block} \PY{o}{=} \PY{n}{BasicBlock}
\PY{n}{num\PYZus{}blocks} \PY{o}{=} \PY{p}{[}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{]}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}planes} \PY{o}{=} \PY{l+m+mi}{64}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{,} \PY{l+m+mi}{64}\PY{p}{,} \PY{n}{kernel\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{3}\PY{p}{,}
\PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{bias}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn1} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{BatchNorm2d}\PY{p}{(}\PY{l+m+mi}{64}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer1} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{\PYZus{}make\PYZus{}layer}\PY{p}{(}\PY{n}{block}\PY{p}{,} \PY{l+m+mi}{64}\PY{p}{,} \PY{n}{num\PYZus{}blocks}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer2} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{\PYZus{}make\PYZus{}layer}\PY{p}{(}\PY{n}{block}\PY{p}{,} \PY{l+m+mi}{128}\PY{p}{,} \PY{n}{num\PYZus{}blocks}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer3} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{\PYZus{}make\PYZus{}layer}\PY{p}{(}\PY{n}{block}\PY{p}{,} \PY{l+m+mi}{256}\PY{p}{,} \PY{n}{num\PYZus{}blocks}\PY{p}{[}\PY{l+m+mi}{2}\PY{p}{]}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer4} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{\PYZus{}make\PYZus{}layer}\PY{p}{(}\PY{n}{block}\PY{p}{,} \PY{l+m+mi}{512}\PY{p}{,} \PY{n}{num\PYZus{}blocks}\PY{p}{[}\PY{l+m+mi}{3}\PY{p}{]}\PY{p}{,} \PY{n}{stride}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{(}\PY{l+m+mi}{512}\PY{o}{*}\PY{n}{block}\PY{o}{.}\PY{n}{expansion}\PY{p}{,} \PY{n}{num\PYZus{}classes}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{\PYZus{}make\PYZus{}layer}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{block}\PY{p}{,} \PY{n}{planes}\PY{p}{,} \PY{n}{num\PYZus{}blocks}\PY{p}{,} \PY{n}{stride}\PY{p}{)}\PY{p}{:}
\PY{n}{strides} \PY{o}{=} \PY{p}{[}\PY{n}{stride}\PY{p}{]} \PY{o}{+} \PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{o}{*}\PY{p}{(}\PY{n}{num\PYZus{}blocks}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{layers} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{stride} \PY{o+ow}{in} \PY{n}{strides}\PY{p}{:}
\PY{n}{layers}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{block}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}planes}\PY{p}{,} \PY{n}{planes}\PY{p}{,} \PY{n}{stride}\PY{p}{)}\PY{p}{)}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{in\PYZus{}planes} \PY{o}{=} \PY{n}{planes} \PY{o}{*} \PY{n}{block}\PY{o}{.}\PY{n}{expansion}
\PY{k}{return} \PY{n}{nn}\PY{o}{.}\PY{n}{Sequential}\PY{p}{(}\PY{o}{*}\PY{n}{layers}\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}{out} \PY{o}{=} \PY{n}{F}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{bn1}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{conv1}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer1}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer2}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer3}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{layer4}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n}{F}\PY{o}{.}\PY{n}{adaptive\PYZus{}avg\PYZus{}pool2d}\PY{p}{(}\PY{n}{out}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{out} \PY{o}{=} \PY{n}{out}\PY{o}{.}\PY{n}{view}\PY{p}{(}\PY{n}{out}\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{n}{out} \PY{o}{=} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{linear}\PY{p}{(}\PY{n}{out}\PY{p}{)}
\PY{k}{return} \PY{n}{out}
\PY{k}{def} \PY{n+nf}{prune\PYZus{}unstructured}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{rate}\PY{p}{)}\PY{p}{:}
\PY{k}{for} \PY{n}{name}\PY{p}{,} \PY{n}{module} \PY{o+ow}{in} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{named\PYZus{}modules}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{)}\PY{p}{:}
\PY{n}{prune}\PY{o}{.}\PY{n}{l1\PYZus{}unstructured}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weight}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{amount}\PY{o}{=}\PY{n}{rate}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{prune\PYZus{}structured}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{rate}\PY{p}{)}\PY{p}{:}
\PY{k}{for} \PY{n}{name}\PY{p}{,} \PY{n}{module} \PY{o+ow}{in} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{named\PYZus{}modules}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{)}\PY{p}{:}
\PY{n}{prune}\PY{o}{.}\PY{n}{ln\PYZus{}structured}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weight}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{n}\PY{o}{=}\PY{l+m+mi}{2}\PY{p}{,} \PY{n}{amount}\PY{o}{=}\PY{n}{rate}\PY{p}{,} \PY{n}{dim}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{calc\PYZus{}weights}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{n}{result} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{name}\PY{p}{,} \PY{n}{module} \PY{o+ow}{in} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{named\PYZus{}modules}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Conv2d}\PY{p}{)} \PY{o+ow}{or} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{n}{torch}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{Linear}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{hasattr}\PY{p}{(}\PY{n}{module}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weight\PYZus{}mask}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{result} \PY{o}{+}\PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{torch}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{module}\PY{o}{.}\PY{n}{weight\PYZus{}mask}\PY{o}{.}\PY{n}{reshape}\PY{p}{(}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{n}{result} \PY{o}{+}\PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{module}\PY{o}{.}\PY{n}{weight}\PY{o}{.}\PY{n}{reshape}\PY{p}{(}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}
\PY{k}{return} \PY{n}{result}
\end{Verbatim}
\end{tcolorbox}
Далее реализуем простую функцию тренировки сети на тренировочных данных
и функцию для тестирования на валидационных данных.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{4}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{def} \PY{n+nf}{fit}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{train\PYZus{}loader}\PY{p}{,} \PY{n}{epoch\PYZus{}number}\PY{o}{=}\PY{l+m+mi}{5}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{cuda}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{optimizer} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{optim}\PY{o}{.}\PY{n}{Adam}\PY{p}{(}\PY{n}{model}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n}{error} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{CrossEntropyLoss}\PY{p}{(}\PY{p}{)}
\PY{n}{model}\PY{o}{.}\PY{n}{train}\PY{p}{(}\PY{p}{)}
\PY{k}{for} \PY{n}{epoch} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{epoch\PYZus{}number}\PY{p}{)}\PY{p}{:}
\PY{n}{correct} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{batch\PYZus{}idx}\PY{p}{,} \PY{p}{(}\PY{n}{X\PYZus{}batch}\PY{p}{,} \PY{n}{y\PYZus{}batch}\PY{p}{)} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{train\PYZus{}loader}\PY{p}{)}\PY{p}{:}
\PY{n}{var\PYZus{}X\PYZus{}batch} \PY{o}{=} \PY{n}{X\PYZus{}batch}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{var\PYZus{}y\PYZus{}batch} \PY{o}{=} \PY{n}{y\PYZus{}batch}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{optimizer}\PY{o}{.}\PY{n}{zero\PYZus{}grad}\PY{p}{(}\PY{p}{)}
\PY{n}{output} \PY{o}{=} \PY{n}{model}\PY{p}{(}\PY{n}{var\PYZus{}X\PYZus{}batch}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{n}{error}\PY{p}{(}\PY{n}{output}\PY{p}{,} \PY{n}{var\PYZus{}y\PYZus{}batch}\PY{p}{)}
\PY{n}{loss}\PY{o}{.}\PY{n}{backward}\PY{p}{(}\PY{p}{)}
\PY{n}{optimizer}\PY{o}{.}\PY{n}{step}\PY{p}{(}\PY{p}{)}
\PY{n}{predicted} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{max}\PY{p}{(}\PY{n}{output}\PY{o}{.}\PY{n}{data}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}
\PY{n}{correct} \PY{o}{+}\PY{o}{=} \PY{p}{(}\PY{n}{predicted} \PY{o}{==} \PY{n}{var\PYZus{}y\PYZus{}batch}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}
\PY{k}{if} \PY{n}{batch\PYZus{}idx} \PY{o}{\PYZpc{}} \PY{l+m+mi}{500} \PY{o}{==} \PY{l+m+mi}{0}\PY{p}{:}
\PY{k}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Epoch : \PYZob{}\PYZcb{} [\PYZob{}\PYZcb{}/\PYZob{}\PYZcb{} (\PYZob{}:.0f\PYZcb{}}\PY{l+s+s1}{\PYZpc{}}\PY{l+s+s1}{)]}\PY{l+s+se}{\PYZbs{}t}\PY{l+s+s1}{Loss: \PYZob{}:.6f\PYZcb{}}\PY{l+s+se}{\PYZbs{}t}\PY{l+s+s1}{ Accuracy:\PYZob{}:.3f\PYZcb{}}\PY{l+s+s1}{\PYZpc{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}
\PY{n}{epoch}\PY{p}{,} \PY{n}{batch\PYZus{}idx}\PY{o}{*}\PY{n+nb}{len}\PY{p}{(}\PY{n}{X\PYZus{}batch}\PY{p}{)}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{train\PYZus{}loader}\PY{o}{.}\PY{n}{dataset}\PY{p}{)}\PY{p}{,}
\PY{l+m+mf}{100.}\PY{o}{*}\PY{n}{batch\PYZus{}idx} \PY{o}{/} \PY{n+nb}{len}\PY{p}{(}\PY{n}{train\PYZus{}loader}\PY{p}{)}\PY{p}{,} \PY{n}{loss}\PY{o}{.}\PY{n}{data}\PY{p}{,}
\PY{n+nb}{float}\PY{p}{(}\PY{n}{correct}\PY{o}{*}\PY{l+m+mi}{100}\PY{p}{)} \PY{o}{/} \PY{n+nb}{float}\PY{p}{(}\PY{n}{BATCH\PYZus{}SIZE}\PY{o}{*}\PY{p}{(}\PY{n}{batch\PYZus{}idx}\PY{o}{+}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{evaluate}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{loader}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{cuda}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{correct} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n}{model}\PY{o}{.}\PY{n}{eval}\PY{p}{(}\PY{p}{)}
\PY{k}{for} \PY{n}{test\PYZus{}imgs}\PY{p}{,} \PY{n}{test\PYZus{}labels} \PY{o+ow}{in} \PY{n}{loader}\PY{p}{:}
\PY{n}{test\PYZus{}imgs} \PY{o}{=} \PY{n}{test\PYZus{}imgs}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{test\PYZus{}labels} \PY{o}{=} \PY{n}{test\PYZus{}labels}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{output} \PY{o}{=} \PY{n}{model}\PY{p}{(}\PY{n}{test\PYZus{}imgs}\PY{p}{)}
\PY{n}{predicted} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{max}\PY{p}{(}\PY{n}{output}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}
\PY{n}{correct} \PY{o}{+}\PY{o}{=} \PY{p}{(}\PY{n}{predicted} \PY{o}{==} \PY{n}{test\PYZus{}labels}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}
\PY{k}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Test accuracy:\PYZob{}:.3f\PYZcb{}}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{ }\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(} \PY{n+nb}{float}\PY{p}{(}\PY{n}{correct}\PY{p}{)} \PY{o}{/} \PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{loader}\PY{p}{)}\PY{o}{*}\PY{n}{BATCH\PYZus{}SIZE}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
Изначальная сеть, ResNet18 имеет примерно 11.16 млн параметров.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{5}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{model} \PY{o}{=} \PY{n}{ResNet}\PY{p}{(}\PY{p}{)}
\PY{n}{model}\PY{o}{.}\PY{n}{calc\PYZus{}weights}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, boxrule=.5pt, size=fbox, pad at break*=1mm, opacityfill=0]
\prompt{Out}{outcolor}{5}{\hspace{3.5pt}}
\begin{Verbatim}[commandchars=\\\{\}]
11163200
\end{Verbatim}
\end{tcolorbox}
Далее подготовим данные для тренировки и валидации.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{6}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{BATCH\PYZus{}SIZE} \PY{o}{=} \PY{l+m+mi}{32}
\PY{n}{EPOCH} \PY{o}{=} \PY{l+m+mi}{10}
\PY{n}{DEVICE} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{cuda}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{SEED} \PY{o}{=} \PY{l+m+mi}{5}
\PY{n}{torch}\PY{o}{.}\PY{n}{manual\PYZus{}seed}\PY{p}{(}\PY{n}{SEED}\PY{p}{)}
\PY{n}{np}\PY{o}{.}\PY{n}{random}\PY{o}{.}\PY{n}{seed}\PY{p}{(}\PY{n}{SEED}\PY{p}{)}
\PY{n}{random}\PY{o}{.}\PY{n}{seed}\PY{p}{(}\PY{n}{SEED}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{ }{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{train\PYZus{}data} \PY{o}{=} \PY{n}{torchvision}\PY{o}{.}\PY{n}{datasets}\PY{o}{.}\PY{n}{FashionMNIST}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{train}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{,} \PY{n}{download}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{,} \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{p}{)}\PY{p}{)}
\PY{n}{test\PYZus{}data} \PY{o}{=} \PY{n}{torchvision}\PY{o}{.}\PY{n}{datasets}\PY{o}{.}\PY{n}{FashionMNIST}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{train}\PY{o}{=}\PY{n+nb+bp}{False}\PY{p}{,} \PY{n}{download}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{,} \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{p}{)}\PY{p}{)}
\PY{n}{train\PYZus{}loader} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{utils}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{DataLoader}\PY{p}{(}\PY{n}{train\PYZus{}data}\PY{p}{,} \PY{n}{batch\PYZus{}size} \PY{o}{=} \PY{n}{BATCH\PYZus{}SIZE}\PY{p}{,} \PY{n}{shuffle} \PY{o}{=} \PY{n+nb+bp}{True}\PY{p}{)}
\PY{n}{test\PYZus{}loader} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{utils}\PY{o}{.}\PY{n}{data}\PY{o}{.}\PY{n}{DataLoader}\PY{p}{(}\PY{n}{test\PYZus{}data}\PY{p}{,} \PY{n}{batch\PYZus{}size} \PY{o}{=} \PY{n}{BATCH\PYZus{}SIZE}\PY{p}{,} \PY{n}{shuffle} \PY{o}{=} \PY{n+nb+bp}{True}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
Первоначально обучим нашу нейронную сеть на данных перед применением алгоритма прунинга
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{8}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{model}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{DEVICE}\PY{p}{)}
\PY{n}{fit}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{train\PYZus{}loader}\PY{p}{,} \PY{n}{epoch\PYZus{}number}\PY{o}{=}\PY{n}{EPOCH}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{n}{DEVICE}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{Verbatim}[commandchars=\\\{\}]
Epoch : 0 [0/60000] Loss: 2.363 Accuracy:9.375\%
Epoch : 0 [16000/60000] Loss: 0.40 Accuracy:75.680\%
Epoch : 0 [32000/60000] Loss: 0.433 Accuracy:80.045\%
Epoch : 0 [48000/60000] Loss: 0.345 Accuracy:82.491\%
Epoch : 1 [0/60000] Loss: 0.120 Accuracy:96.875\%
Epoch : 1 [16000/60000] Loss: 0.383 Accuracy:89.590\%
Epoch : 1 [32000/60000] Loss: 0.0725 Accuracy:89.623\%
Epoch : 1 [48000/60000] Loss: 0.173 Accuracy:89.740\%
Epoch : 2 [0/60000] Loss: 0.269 Accuracy:90.625\%
Epoch : 2 [16000/60000] Loss: 0.142 Accuracy:91.442\%
Epoch : 2 [32000/60000] Loss: 0.214 Accuracy:91.296\%
Epoch : 2 [48000/60000] Loss: 0.088 Accuracy:91.383\%
Epoch : 3 [0/60000] Loss: 0.154 Accuracy:93.750\%
Epoch : 3 [16000/60000] Loss: 0.042 Accuracy:92.633\%
Epoch : 3 [32000/60000] Loss: 0.285 Accuracy:92.576\%
Epoch : 3 [48000/60000] Loss: 0.324 Accuracy:92.634\%
Epoch : 4 [0/60000] Loss: 0.119 Accuracy:93.750\%
Epoch : 4 [16000/60000] Loss: 0.071 Accuracy:93.787\%
Epoch : 4 [32000/60000] Loss: 0.251 Accuracy:93.697\%
Epoch : 4 [48000/60000] Loss: 0.168 Accuracy:93.606\%
Epoch : 5 [0/60000] Loss: 0.274 Accuracy:87.500\%
Epoch : 5 [16000/60000] Loss: 0.111 Accuracy:94.785\%
Epoch : 5 [32000/60000] Loss: 0.057 Accuracy:94.621\%
Epoch : 5 [48000/60000] Loss: 0.342 Accuracy:94.447\%
Epoch : 6 [0/60000] Loss: 0.077 Accuracy:96.875\%
Epoch : 6 [16000/60000] Loss: 0.084 Accuracy:95.677\%
Epoch : 6 [32000/60000] Loss: 0.412 Accuracy:95.370\%
Epoch : 6 [48000/60000] Loss: 0.077 Accuracy:95.293\%
Epoch : 7 [0/60000] Loss: 0.0219 Accuracy:100.000\%
Epoch : 7 [16000/60000] Loss: 0.191 Accuracy:96.158\%
Epoch : 7 [32000/60000] Loss: 0.084 Accuracy:96.182\%
Epoch : 7 [48000/60000] Loss: 0.099 Accuracy:96.128\%
Epoch : 8 [0/60000] Loss: 0.051 Accuracy:100.000\%
Epoch : 8 [16000/60000] Loss: 0.0085 Accuracy:97.343\%
Epoch : 8 [32000/60000] Loss: 0.02 Accuracy:96.966\%
Epoch : 8 [48000/60000] Loss: 0.089 Accuracy:96.877\%
Epoch : 9 [0/60000] Loss: 0.041 Accuracy:100.000\%
Epoch : 9 [16000/60000] Loss: 0.004 Accuracy:97.967\%
Epoch : 9 [32000/60000] Loss: 0.012 Accuracy:97.855\%
Epoch : 9 [48000/60000] Loss: 0.163 Accuracy:97.712\%
\end{Verbatim}
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{9}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{evaluate}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{test\PYZus{}loader}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{n}{DEVICE}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{Verbatim}[commandchars=\\\{\}]
Test accuracy:0.932\%
\end{Verbatim}
Далее попробуем применить спарсификацию на полученной модели напрямую.
Посмотрим на просадку в качестве
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{10}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{pruned\PYZus{}model} \PY{o}{=} \PY{n}{copy}\PY{o}{.}\PY{n}{deepcopy}\PY{p}{(}\PY{n}{model}\PY{p}{)}
\PY{n}{pruned\PYZus{}model}\PY{o}{.}\PY{n}{prune\PYZus{}unstructured}\PY{p}{(}\PY{l+m+mf}{0.5}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
Посмотрим сколько теперь параметров имеет наша сеть. Видно, что,
действительно мы смогли убрать 50\% весов.
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{11}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{pruned\PYZus{}model}\PY{o}{.}\PY{n}{calc\PYZus{}weights}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{tcolorbox}[breakable, boxrule=.5pt, size=fbox, pad at break*=1mm, opacityfill=0]
\prompt{Out}{outcolor}{11}{\hspace{3.5pt}}
\begin{Verbatim}[commandchars=\\\{\}]
5584160
\end{Verbatim}
\end{tcolorbox}
Измерим качество модели после такого алгоритма спарсификации
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{12}{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{n}{evaluate}\PY{p}{(}\PY{n}{pruned\PYZus{}model}\PY{p}{,} \PY{n}{test\PYZus{}loader}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{n}{DEVICE}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}
\begin{Verbatim}[commandchars=\\\{\}]
Test accuracy:0.909\%
\end{Verbatim}
Точность модели просела на 2.3\%.
\textbf{Итеративное прореживание}
Идин из способов сжатия нейронных сетей - итеративное прореживание
(Incremental Magnitude Pruning). Здесь используется более хитрый подход. Будем идти с шагом, каждый раз будем отключать внутри сети несколько десятков процентов связей.
Мы составим расписание для сети в виде списка и напишем более умную
функцию тренировки
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{ }{\hspace{4pt}}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k}{def} \PY{n+nf}{smart\PYZus{}prune\PYZus{}shed}\PY{p}{(}\PY{n}{model}\PY{p}{,} \PY{n}{train\PYZus{}loader}\PY{p}{,} \PY{n}{schedule}\PY{p}{,} \PY{n}{device}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{cpu}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{optimizer} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{optim}\PY{o}{.}\PY{n}{Adam}\PY{p}{(}\PY{n}{model}\PY{o}{.}\PY{n}{parameters}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{n}{error} \PY{o}{=} \PY{n}{nn}\PY{o}{.}\PY{n}{CrossEntropyLoss}\PY{p}{(}\PY{p}{)}
\PY{n}{model}\PY{o}{.}\PY{n}{train}\PY{p}{(}\PY{p}{)}
\PY{k}{for} \PY{n}{rate}\PY{p}{,} \PY{n}{epochs} \PY{o+ow}{in} \PY{n}{schedule}\PY{p}{:} \PY{c+c1}{\PYZsh{} Идем шагами, согласно тому расписанию, которое передали в функцию}
\PY{n}{t} \PY{o}{=} \PY{n}{rate}\PY{o}{/}\PY{l+m+mi}{100} \PY{c+c1}{\PYZsh{} Считаем очередное пороговое значение}
\PY{n}{model}\PY{o}{.}\PY{n}{prune\PYZus{}unstructured}\PY{p}{(}\PY{n}{t}\PY{p}{)} \PY{c+c1}{\PYZsh{} Отключаем слабые связи}
\PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{epochs}\PY{p}{)}\PY{p}{:}
\PY{n}{correct} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{batch\PYZus{}idx}\PY{p}{,} \PY{p}{(}\PY{n}{X\PYZus{}batch}\PY{p}{,} \PY{n}{y\PYZus{}batch}\PY{p}{)} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{train\PYZus{}loader}\PY{p}{)}\PY{p}{:} \PY{c+c1}{\PYZsh{} Далее дообучаем модель как обычно в течение указанного количества эпох}
\PY{n}{var\PYZus{}X\PYZus{}batch} \PY{o}{=} \PY{n}{X\PYZus{}batch}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{var\PYZus{}y\PYZus{}batch} \PY{o}{=} \PY{n}{y\PYZus{}batch}\PY{o}{.}\PY{n}{to}\PY{p}{(}\PY{n}{device}\PY{p}{)}
\PY{n}{optimizer}\PY{o}{.}\PY{n}{zero\PYZus{}grad}\PY{p}{(}\PY{p}{)}
\PY{n}{output} \PY{o}{=} \PY{n}{model}\PY{p}{(}\PY{n}{var\PYZus{}X\PYZus{}batch}\PY{p}{)}
\PY{n}{loss} \PY{o}{=} \PY{n}{error}\PY{p}{(}\PY{n}{output}\PY{p}{,} \PY{n}{var\PYZus{}y\PYZus{}batch}\PY{p}{)}