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notebook.tex
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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\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{geometry} % Used to adjust the document margins
\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
% 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}
% 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 compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{dog\_app\_thomas\_Meng}
% 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@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
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\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
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\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
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\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{]}
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% Exact colors from NB
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% Prevent overflowing lines due to hard-to-break entities
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% Setup hyperref package
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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
\hypertarget{convolutional-neural-networks}{%
\section{Convolutional Neural
Networks}\label{convolutional-neural-networks}}
\hypertarget{project-write-an-algorithm-for-a-dog-identification-app}{%
\subsection{Project: Write an Algorithm for a Dog Identification
App}\label{project-write-an-algorithm-for-a-dog-identification-app}}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
In this notebook, some template code has already been provided for you,
and you will need to implement additional functionality to successfully
complete this project. You will not need to modify the included code
beyond what is requested. Sections that begin with
\textbf{`(IMPLEMENTATION)'} in the header indicate that the following
block of code will require additional functionality which you must
provide. Instructions will be provided for each section, and the
specifics of the implementation are marked in the code block with a
`TODO' statement. Please be sure to read the instructions carefully!
\begin{quote}
\textbf{Note}: Once you have completed all of the code implementations,
you need to finalize your work by exporting the Jupyter Notebook as an
HTML document. Before exporting the notebook to html, all of the code
cells need to have been run so that reviewers can see the final
implementation and output. You can then export the notebook by using the
menu above and navigating to \textbf{File -\textgreater{} Download as
-\textgreater{} HTML (.html)}. Include the finished document along with
this notebook as your submission.
\end{quote}
In addition to implementing code, there will be questions that you must
answer which relate to the project and your implementation. Each section
where you will answer a question is preceded by a \textbf{`Question X'}
header. Carefully read each question and provide thorough answers in the
following text boxes that begin with \textbf{`Answer:'}. Your project
submission will be evaluated based on your answers to each of the
questions and the implementation you provide.
\begin{quote}
\textbf{Note:} Code and Markdown cells can be executed using the
\textbf{Shift + Enter} keyboard shortcut. Markdown cells can be edited
by double-clicking the cell to enter edit mode.
\end{quote}
The rubric contains \emph{optional} ``Stand Out Suggestions'' for
enhancing the project beyond the minimum requirements. If you decide to
pursue the ``Stand Out Suggestions'', you should include the code in
this Jupyter notebook.
\#\# Step 0: Import Datasets
Make sure that you've downloaded the required human and dog datasets: *
Download the
\href{https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip}{dog
dataset}. Unzip the folder and place it in this project's home
directory, at the location \texttt{/dog\_images}.
\begin{itemize}
\tightlist
\item
Download the
\href{https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip}{human
dataset}. Unzip the folder and place it in the home directory, at
location \texttt{/lfw}.
\end{itemize}
\emph{Note: If you are using a Windows machine, you are encouraged to
use \href{http://www.7-zip.org/}{7zip} to extract the folder.}
In the code cell below, we save the file paths for both the human (LFW)
dataset and dog dataset in the numpy arrays \texttt{human\_files} and
\texttt{dog\_files}.
\hypertarget{author-declaration}{%
\subsubsection{Author declaration:}\label{author-declaration}}
Some of the code blocks used below are learned and originally from
Udacity course - Deep Learning.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}2}]:} \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{from} \PY{n+nn}{glob} \PY{k}{import} \PY{n}{glob}
\PY{c+c1}{\PYZsh{}\PYZsh{} load filenames for human and dog images from the Udacity server}
\PY{c+c1}{\PYZsh{}human\PYZus{}files = np.array(glob(\PYZdq{}/data/lfw/*/*\PYZdq{}))}
\PY{c+c1}{\PYZsh{}dog\PYZus{}files = np.array(glob(\PYZdq{}/data/dog\PYZus{}images/*/*/*\PYZdq{}))}
\PY{c+c1}{\PYZsh{}\PYZsh{} load filenames for human and dog images from local computer}
\PY{n}{human\PYZus{}files} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{array}\PY{p}{(}\PY{n}{glob}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{lfw/*/*}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)}
\PY{n}{dog\PYZus{}files} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{array}\PY{p}{(}\PY{n}{glob}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{dogImages/*/*/*}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} print number of images in each dataset}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{There are }\PY{l+s+si}{\PYZpc{}d}\PY{l+s+s1}{ total human images.}\PY{l+s+s1}{\PYZsq{}} \PY{o}{\PYZpc{}} \PY{n+nb}{len}\PY{p}{(}\PY{n}{human\PYZus{}files}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{There are }\PY{l+s+si}{\PYZpc{}d}\PY{l+s+s1}{ total dog images.}\PY{l+s+s1}{\PYZsq{}} \PY{o}{\PYZpc{}} \PY{n+nb}{len}\PY{p}{(}\PY{n}{dog\PYZus{}files}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
There are 13233 total human images.
There are 8351 total dog images.
\end{Verbatim}
\#\# Step 1: Detect Humans
In this section, we use OpenCV's implementation of
\href{http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html}{Haar
feature-based cascade classifiers} to detect human faces in images.
OpenCV provides many pre-trained face detectors, stored as XML files on
\href{https://github.com/opencv/opencv/tree/master/data/haarcascades}{github}.
We have downloaded one of these detectors and stored it in the
\texttt{haarcascades} directory. In the next code cell, we demonstrate
how to use this detector to find human faces in a sample image.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}3}]:} \PY{k+kn}{import} \PY{n+nn}{cv2}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\PY{c+c1}{\PYZsh{} extract pre\PYZhy{}trained face detector}
\PY{n}{face\PYZus{}cascade} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{CascadeClassifier}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{haarcascades/haarcascade\PYZus{}frontalface\PYZus{}alt.xml}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{} load color (BGR) image}
\PY{n}{img} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{imread}\PY{p}{(}\PY{n}{human\PYZus{}files}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{} convert BGR image to grayscale}
\PY{n}{gray} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{cvtColor}\PY{p}{(}\PY{n}{img}\PY{p}{,} \PY{n}{cv2}\PY{o}{.}\PY{n}{COLOR\PYZus{}BGR2GRAY}\PY{p}{)}
\PY{c+c1}{\PYZsh{} find faces in image}
\PY{n}{faces} \PY{o}{=} \PY{n}{face\PYZus{}cascade}\PY{o}{.}\PY{n}{detectMultiScale}\PY{p}{(}\PY{n}{gray}\PY{p}{)}
\PY{c+c1}{\PYZsh{} print number of faces detected in the image}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Number of faces detected:}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{faces}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} get bounding box for each detected face}
\PY{k}{for} \PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{y}\PY{p}{,}\PY{n}{w}\PY{p}{,}\PY{n}{h}\PY{p}{)} \PY{o+ow}{in} \PY{n}{faces}\PY{p}{:}
\PY{c+c1}{\PYZsh{} add bounding box to color image}
\PY{n}{cv2}\PY{o}{.}\PY{n}{rectangle}\PY{p}{(}\PY{n}{img}\PY{p}{,}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{y}\PY{p}{)}\PY{p}{,}\PY{p}{(}\PY{n}{x}\PY{o}{+}\PY{n}{w}\PY{p}{,}\PY{n}{y}\PY{o}{+}\PY{n}{h}\PY{p}{)}\PY{p}{,}\PY{p}{(}\PY{l+m+mi}{255}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{)}
\PY{c+c1}{\PYZsh{} convert BGR image to RGB for plotting}
\PY{n}{cv\PYZus{}rgb} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{cvtColor}\PY{p}{(}\PY{n}{img}\PY{p}{,} \PY{n}{cv2}\PY{o}{.}\PY{n}{COLOR\PYZus{}BGR2RGB}\PY{p}{)}
\PY{c+c1}{\PYZsh{} display the image, along with bounding box}
\PY{n}{plt}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{cv\PYZus{}rgb}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Number of faces detected: 1
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_4_1.png}
\end{center}
{ \hspace*{\fill} \\}
Before using any of the face detectors, it is standard procedure to
convert the images to grayscale. The \texttt{detectMultiScale} function
executes the classifier stored in \texttt{face\_cascade} and takes the
grayscale image as a parameter.
In the above code, \texttt{faces} is a numpy array of detected faces,
where each row corresponds to a detected face. Each detected face is a
1D array with four entries that specifies the bounding box of the
detected face. The first two entries in the array (extracted in the
above code as \texttt{x} and \texttt{y}) specify the horizontal and
vertical positions of the top left corner of the bounding box. The last
two entries in the array (extracted here as \texttt{w} and \texttt{h})
specify the width and height of the box.
\hypertarget{write-a-human-face-detector}{%
\subsubsection{Write a Human Face
Detector}\label{write-a-human-face-detector}}
We can use this procedure to write a function that returns \texttt{True}
if a human face is detected in an image and \texttt{False} otherwise.
This function, aptly named \texttt{face\_detector}, takes a
string-valued file path to an image as input and appears in the code
block below.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{c+c1}{\PYZsh{} returns \PYZdq{}True\PYZdq{} if face is detected in image stored at img\PYZus{}path}
\PY{k}{def} \PY{n+nf}{face\PYZus{}detector}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{n}{img} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{imread}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}
\PY{n}{gray} \PY{o}{=} \PY{n}{cv2}\PY{o}{.}\PY{n}{cvtColor}\PY{p}{(}\PY{n}{img}\PY{p}{,} \PY{n}{cv2}\PY{o}{.}\PY{n}{COLOR\PYZus{}BGR2GRAY}\PY{p}{)}
\PY{n}{faces} \PY{o}{=} \PY{n}{face\PYZus{}cascade}\PY{o}{.}\PY{n}{detectMultiScale}\PY{p}{(}\PY{n}{gray}\PY{p}{)}
\PY{k}{return} \PY{n+nb}{len}\PY{p}{(}\PY{n}{faces}\PY{p}{)} \PY{o}{\PYZgt{}} \PY{l+m+mi}{0}
\end{Verbatim}
\hypertarget{implementation-assess-the-human-face-detector}{%
\subsubsection{(IMPLEMENTATION) Assess the Human Face
Detector}\label{implementation-assess-the-human-face-detector}}
\textbf{Question 1:} Use the code cell below to test the performance of
the \texttt{face\_detector} function.\\
- What percentage of the first 100 images in \texttt{human\_files} have
a detected human face?\\
- What percentage of the first 100 images in \texttt{dog\_files} have a
detected human face?
Ideally, we would like 100\% of human images with a detected face and
0\% of dog images with a detected face. You will see that our algorithm
falls short of this goal, but still gives acceptable performance. We
extract the file paths for the first 100 images from each of the
datasets and store them in the numpy arrays \texttt{human\_files\_short}
and \texttt{dog\_files\_short}.
\textbf{Answer:} The accuracy of 100 human images in human face
detector: 98.00\%. The accuracy of 100 dog images in human face
detector: 17.00\%
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{k+kn}{from} \PY{n+nn}{tqdm} \PY{k}{import} \PY{n}{tqdm}
\PY{n}{human\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{human\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{dog\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{dog\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{c+c1}{\PYZsh{}\PYZhy{}\PYZsh{}\PYZhy{}\PYZsh{} Do NOT modify the code above this line. \PYZsh{}\PYZhy{}\PYZsh{}\PYZhy{}\PYZsh{}}
\PY{c+c1}{\PYZsh{}\PYZsh{} TODO: Test the performance of the face\PYZus{}detector algorithm }
\PY{c+c1}{\PYZsh{}\PYZsh{} on the images in human\PYZus{}files\PYZus{}short and dog\PYZus{}files\PYZus{}short.}
\PY{n}{human\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n}{dog\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{human\PYZus{}path}\PY{p}{,} \PY{n}{dog\PYZus{}path} \PY{o+ow}{in} \PY{n+nb}{zip}\PY{p}{(}\PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{,} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{k}{if}\PY{p}{(}\PY{n}{face\PYZus{}detector}\PY{p}{(}\PY{n}{human\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{human\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{human\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{k}{if}\PY{p}{(}\PY{n}{face\PYZus{}detector}\PY{p}{(}\PY{n}{dog\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{dog\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{dog\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the percentage of 100 human images in human face detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((human\PYZus{}counter/100)*100))
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the percentage of 100 dog images in human face detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((dog\PYZus{}counter/100)*100))
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
the percentage of 100 human images in human face detector: 98.00\%
the percentage of 100 dog images in human face detector: 17.00\%
\end{Verbatim}
We suggest the face detector from OpenCV as a potential way to detect
human images in your algorithm, but you are free to explore other
approaches, especially approaches that make use of deep learning :).
Please use the code cell below to design and test your own face
detection algorithm. If you decide to pursue this \emph{optional} task,
report performance on \texttt{human\_files\_short} and
\texttt{dog\_files\_short}.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}5}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} (Optional) }
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} TODO: Test performance of anotherface detection algorithm.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Feel free to use as many code cells as needed.}
\end{Verbatim}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\#\# Step 2: Detect Dogs
In this section, we use a
\href{http://pytorch.org/docs/master/torchvision/models.html}{pre-trained
model} to detect dogs in images.
\hypertarget{obtain-pre-trained-vgg-16-model}{%
\subsubsection{Obtain Pre-trained VGG-16
Model}\label{obtain-pre-trained-vgg-16-model}}
The code cell below downloads the VGG-16 model, along with weights that
have been trained on \href{http://www.image-net.org/}{ImageNet}, a very
large, very popular dataset used for image classification and other
vision tasks. ImageNet contains over 10 million URLs, each linking to an
image containing an object from one of
\href{https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a}{1000
categories}.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}6}]:} \PY{k+kn}{import} \PY{n+nn}{torch}
\PY{k+kn}{import} \PY{n+nn}{torchvision}\PY{n+nn}{.}\PY{n+nn}{models} \PY{k}{as} \PY{n+nn}{models}
\PY{c+c1}{\PYZsh{} define VGG16 model}
\PY{n}{VGG16} \PY{o}{=} \PY{n}{models}\PY{o}{.}\PY{n}{vgg16}\PY{p}{(}\PY{n}{pretrained}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{c+c1}{\PYZsh{} check if CUDA is available}
\PY{n}{use\PYZus{}cuda} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{cuda}\PY{o}{.}\PY{n}{is\PYZus{}available}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} move model to GPU if CUDA is available}
\PY{k}{if} \PY{n}{use\PYZus{}cuda}\PY{p}{:}
\PY{n}{VGG16} \PY{o}{=} \PY{n}{VGG16}\PY{o}{.}\PY{n}{cuda}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.torch/models/vgg16-397923af.pth
100\%|██████████| 553433881/553433881 [00:05<00:00, 97926926.02it/s]
\end{Verbatim}
Given an image, this pre-trained VGG-16 model returns a prediction
(derived from the 1000 possible categories in ImageNet) for the object
that is contained in the image.
\hypertarget{implementation-making-predictions-with-a-pre-trained-model}{%
\subsubsection{(IMPLEMENTATION) Making Predictions with a Pre-trained
Model}\label{implementation-making-predictions-with-a-pre-trained-model}}
In the next code cell, you will write a function that accepts a path to
an image (such as
\texttt{\textquotesingle{}dogImages/train/001.Affenpinscher/Affenpinscher\_00001.jpg\textquotesingle{}})
as input and returns the index corresponding to the ImageNet class that
is predicted by the pre-trained VGG-16 model. The output should always
be an integer between 0 and 999, inclusive.
Before writing the function, make sure that you take the time to learn
how to appropriately pre-process tensors for pre-trained models in the
\href{http://pytorch.org/docs/stable/torchvision/models.html}{PyTorch
documentation}.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}17}]:} \PY{k+kn}{from} \PY{n+nn}{PIL} \PY{k}{import} \PY{n}{Image}
\PY{k+kn}{import} \PY{n+nn}{torchvision}\PY{n+nn}{.}\PY{n+nn}{transforms} \PY{k}{as} \PY{n+nn}{transforms}
\PY{k+kn}{from} \PY{n+nn}{torchvision} \PY{k}{import} \PY{n}{datasets}
\PY{k+kn}{from} \PY{n+nn}{torch}\PY{n+nn}{.}\PY{n+nn}{autograd} \PY{k}{import} \PY{n}{Variable}
\PY{k}{def} \PY{n+nf}{VGG16\PYZus{}predict}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{l+s+sd}{ Use pre\PYZhy{}trained VGG\PYZhy{}16 model to obtain index corresponding to }
\PY{l+s+sd}{ predicted ImageNet class for image at specified path}
\PY{l+s+sd}{ }
\PY{l+s+sd}{ Args:}
\PY{l+s+sd}{ img\PYZus{}path: path to an image}
\PY{l+s+sd}{ }
\PY{l+s+sd}{ Returns:}
\PY{l+s+sd}{ Index corresponding to VGG\PYZhy{}16 model\PYZsq{}s prediction}
\PY{l+s+sd}{ \PYZsq{}\PYZsq{}\PYZsq{}}
\PY{c+c1}{\PYZsh{}\PYZsh{} TODO: Complete the function.}
\PY{c+c1}{\PYZsh{}\PYZsh{} Load and pre\PYZhy{}process an image from the given img\PYZus{}path}
\PY{c+c1}{\PYZsh{}\PYZsh{} Return the *index* of the predicted class for that image}
\PY{c+c1}{\PYZsh{}image = Image.open(img\PYZus{}path).convert(\PYZsq{}RGB\PYZsq{})}
\PY{c+c1}{\PYZsh{}normalize = transforms.Normalize(}
\PY{c+c1}{\PYZsh{}mean=[0.485, 0.456, 0.406],}
\PY{c+c1}{\PYZsh{}std=[0.229, 0.224, 0.225]}
\PY{c+c1}{\PYZsh{})}
\PY{c+c1}{\PYZsh{}data\PYZus{}transform = transforms.Compose([\PYZsh{}transforms.RandomResizedCrop(224), }
\PY{c+c1}{\PYZsh{}transforms.Resize((224,224)),}
\PY{c+c1}{\PYZsh{}transforms.ToTensor(),}
\PY{c+c1}{\PYZsh{}normalize}
\PY{c+c1}{\PYZsh{}])}
\PY{c+c1}{\PYZsh{}image = data\PYZus{}transform(image)[:3,:,:].unsqueeze(0)}
\PY{n}{normalize} \PY{o}{=} \PY{n}{transforms}\PY{o}{.}\PY{n}{Normalize}\PY{p}{(}
\PY{n}{mean}\PY{o}{=}\PY{p}{[}\PY{l+m+mf}{0.485}\PY{p}{,} \PY{l+m+mf}{0.456}\PY{p}{,} \PY{l+m+mf}{0.406}\PY{p}{]}\PY{p}{,}
\PY{n}{std}\PY{o}{=}\PY{p}{[}\PY{l+m+mf}{0.229}\PY{p}{,} \PY{l+m+mf}{0.224}\PY{p}{,} \PY{l+m+mf}{0.225}\PY{p}{]}\PY{p}{)}
\PY{n}{image\PYZus{}size} \PY{o}{=} \PY{l+m+mi}{224}
\PY{n}{loader} \PY{o}{=} \PY{n}{transforms}\PY{o}{.}\PY{n}{Compose}\PY{p}{(}\PY{p}{[}\PY{n}{transforms}\PY{o}{.}\PY{n}{Resize}\PY{p}{(}\PY{p}{(}\PY{n}{image\PYZus{}size}\PY{p}{,}\PY{n}{image\PYZus{}size}\PY{p}{)}\PY{p}{)}\PY{p}{,} \PY{n}{transforms}\PY{o}{.}\PY{n}{ToTensor}\PY{p}{(}\PY{p}{)}\PY{p}{,}
\PY{n}{normalize}\PY{p}{]}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{image\PYZus{}loader}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} load image, returns tensor}
\PY{n}{image} \PY{o}{=} \PY{n}{Image}\PY{o}{.}\PY{n}{open}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{loader}\PY{p}{(}\PY{n}{image}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{Variable}\PY{p}{(}\PY{n}{image}\PY{p}{,} \PY{n}{requires\PYZus{}grad}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{unsqueeze}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)} \PY{c+c1}{\PYZsh{}for vgg 16 or resnet\PYZhy{}50? }
\PY{k}{if} \PY{n}{use\PYZus{}cuda}\PY{p}{:}
\PY{k}{return} \PY{n}{image}\PY{o}{.}\PY{n}{cuda}\PY{p}{(}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{k}{return} \PY{n}{image}
\PY{n}{image} \PY{o}{=} \PY{n}{image\PYZus{}loader}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}
\PY{n}{output} \PY{o}{=} \PY{n}{VGG16}\PY{p}{(}\PY{n}{image}\PY{p}{)}
\PY{k}{if} \PY{n}{use\PYZus{}cuda}\PY{p}{:}
\PY{n}{output} \PY{o}{=} \PY{n}{output}\PY{o}{.}\PY{n}{cuda}\PY{p}{(}\PY{p}{)}
\PY{n}{\PYZus{}}\PY{p}{,} \PY{n}{pred} \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{n}{index} \PY{o}{=} \PY{n}{pred}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} *** find the labels for ImageNet class, should be length 151 \PYZhy{} 268}
\PY{k}{return} \PY{n}{index} \PY{c+c1}{\PYZsh{} predicted class index}
\end{Verbatim}
\hypertarget{implementation-write-a-dog-detector}{%
\subsubsection{(IMPLEMENTATION) Write a Dog
Detector}\label{implementation-write-a-dog-detector}}
While looking at the
\href{https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a}{dictionary},
you will notice that the categories corresponding to dogs appear in an
uninterrupted sequence and correspond to dictionary keys 151-268,
inclusive, to include all categories from
\texttt{\textquotesingle{}Chihuahua\textquotesingle{}} to
\texttt{\textquotesingle{}Mexican\ hairless\textquotesingle{}}. Thus, in
order to check to see if an image is predicted to contain a dog by the
pre-trained VGG-16 model, we need only check if the pre-trained model
predicts an index between 151 and 268 (inclusive).
Use these ideas to complete the \texttt{dog\_detector} function below,
which returns \texttt{True} if a dog is detected in an image (and
\texttt{False} if not).
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}19}]:} \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{image path: }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{dog\PYZus{}files}\PY{p}{[}\PY{l+m+mi}{265}\PY{p}{]}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test:}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{VGG16\PYZus{}predict}\PY{p}{(}\PY{n}{dog\PYZus{}files}\PY{p}{[}\PY{l+m+mi}{100}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
image path: /data/dog\_images/train/024.Bichon\_frise/Bichon\_frise\_01714.jpg
test: 236
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}20}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} returns \PYZdq{}True\PYZdq{} if a dog is detected in the image stored at img\PYZus{}path}
\PY{k}{def} \PY{n+nf}{dog\PYZus{}detector}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{k}{if}\PY{p}{(}\PY{n}{VGG16\PYZus{}predict}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{151}\PY{p}{,}\PY{l+m+mi}{269}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{k+kc}{True}
\PY{k}{else}\PY{p}{:}
\PY{k}{return} \PY{k+kc}{False}
\PY{c+c1}{\PYZsh{} altervative: }
\PY{c+c1}{\PYZsh{} pred = VGG16\PYZus{}predict(img\PYZus{}path)}
\PY{c+c1}{\PYZsh{} return ((pred \PYZlt{}= 268) \PYZam{} (pred \PYZgt{}= 151)) }
\end{Verbatim}
\hypertarget{implementation-assess-the-dog-detector}{%
\subsubsection{(IMPLEMENTATION) Assess the Dog
Detector}\label{implementation-assess-the-dog-detector}}
\textbf{Question 2:} Use the code cell below to test the performance of
your \texttt{dog\_detector} function.\\
- What percentage of the images in \texttt{human\_files\_short} have a
detected dog?\\
- What percentage of the images in \texttt{dog\_files\_short} have a
detected dog?
\textbf{Answer:} 1) 100\% accuracy for human\_files\_short images in
dog\_detector.
\begin{enumerate}
\def\labelenumi{\arabic{enumi})}
\setcounter{enumi}{1}
\tightlist
\item
1\% percent for human\_files\_short images in dog\_detector
\end{enumerate}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}21}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} TODO: Test the performance of the dog\PYZus{}detector function}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} on the images in human\PYZus{}files\PYZus{}short and dog\PYZus{}files\PYZus{}short.}
\PY{n}{human\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{human\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{dog\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{dog\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{human\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n}{dog\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{human\PYZus{}path}\PY{p}{,} \PY{n}{dog\PYZus{}path} \PY{o+ow}{in} \PY{n+nb}{zip}\PY{p}{(}\PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{,} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{k}{if}\PY{p}{(}\PY{n}{dog\PYZus{}detector}\PY{p}{(}\PY{n}{human\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{human\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{human\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{k}{if}\PY{p}{(}\PY{n}{dog\PYZus{}detector}\PY{p}{(}\PY{n}{dog\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{dog\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{dog\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the accuracy of 100 human images in dog detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((human\PYZus{}counter/100)*100))
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the accuracy of 100 dog images in dog detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((dog\PYZus{}counter/100)*100))
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
the accuracy of 100 human images in dog detector: 1.00\%
the accuracy of 100 dog images in dog detector: 100.00\%
\end{Verbatim}
\begin{itemize}
\tightlist
\item
the accuracy of 100 human images in dog detector alexnet is: 0.01
\item
the accuracy of 100 dog images in dog detector alexnet is: 0.95
\end{itemize}
We suggest VGG-16 as a potential network to detect dog images in your
algorithm, but you are free to explore other pre-trained networks (such
as
\href{http://pytorch.org/docs/master/torchvision/models.html\#inception-v3}{Inception-v3},
\href{http://pytorch.org/docs/master/torchvision/models.html\#id3}{ResNet-50},
etc). Please use the code cell below to test other pre-trained PyTorch
models. If you decide to pursue this \emph{optional} task, report
performance on \texttt{human\_files\_short} and
\texttt{dog\_files\_short}.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}134}]:} \PY{k+kn}{import} \PY{n+nn}{torch}
\PY{k+kn}{import} \PY{n+nn}{torchvision}\PY{n+nn}{.}\PY{n+nn}{models} \PY{k}{as} \PY{n+nn}{models}
\PY{c+c1}{\PYZsh{} define VGG16 model}
\PY{n}{net50} \PY{o}{=} \PY{n}{models}\PY{o}{.}\PY{n}{resnet50}\PY{p}{(}\PY{n}{pretrained}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{c+c1}{\PYZsh{}\PYZsh{} switch to evaluation mode}
\PY{n}{net50}\PY{o}{.}\PY{n}{eval}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{}\PYZsh{} use train to fine tuning}
\PY{c+c1}{\PYZsh{}model.train()}
\PY{c+c1}{\PYZsh{} check if CUDA is available}
\PY{n}{use\PYZus{}cuda} \PY{o}{=} \PY{n}{torch}\PY{o}{.}\PY{n}{cuda}\PY{o}{.}\PY{n}{is\PYZus{}available}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Freeze training for all \PYZdq{}features\PYZdq{} layers}
\PY{c+c1}{\PYZsh{}for param in net50.features.parameters():}
\PY{c+c1}{\PYZsh{} param.require\PYZus{}grad = False}
\PY{c+c1}{\PYZsh{} move model to GPU if CUDA is available}
\PY{k}{if} \PY{n}{use\PYZus{}cuda}\PY{p}{:}
\PY{n}{net50} \PY{o}{=} \PY{n}{net50}\PY{o}{.}\PY{n}{cuda}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}135}]:} \PY{k+kn}{from} \PY{n+nn}{PIL} \PY{k}{import} \PY{n}{Image}
\PY{k+kn}{import} \PY{n+nn}{torchvision}\PY{n+nn}{.}\PY{n+nn}{transforms} \PY{k}{as} \PY{n+nn}{transforms}
\PY{k+kn}{from} \PY{n+nn}{torchvision} \PY{k}{import} \PY{n}{datasets}
\PY{n}{Image}\PY{o}{.}\PY{n}{LOAD\PYZus{}TRUNCATED\PYZus{}IMAGES} \PY{o}{=} \PY{k+kc}{True}
\PY{k+kn}{from} \PY{n+nn}{torch}\PY{n+nn}{.}\PY{n+nn}{autograd} \PY{k}{import} \PY{n}{Variable}
\PY{k}{def} \PY{n+nf}{net50\PYZus{}predict}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{l+s+sd}{ Use pre\PYZhy{}trained VGG\PYZhy{}16 model to obtain index corresponding to }
\PY{l+s+sd}{ predicted ImageNet class for image at specified path}
\PY{l+s+sd}{ }
\PY{l+s+sd}{ Args:}
\PY{l+s+sd}{ img\PYZus{}path: path to an image}
\PY{l+s+sd}{ }
\PY{l+s+sd}{ Returns:}
\PY{l+s+sd}{ Index corresponding to resnet\PYZhy{}50 model\PYZsq{}s prediction}
\PY{l+s+sd}{ \PYZsq{}\PYZsq{}\PYZsq{}}
\PY{c+c1}{\PYZsh{}\PYZsh{} TODO: Complete the function.}
\PY{c+c1}{\PYZsh{}\PYZsh{} Load and pre\PYZhy{}process an image from the given img\PYZus{}path}
\PY{c+c1}{\PYZsh{}\PYZsh{} Return the *index* of the predicted class for that image}
\PY{c+c1}{\PYZsh{}image = Image.open(img\PYZus{}path).convert(\PYZsq{}RGB\PYZsq{})}
\PY{c+c1}{\PYZsh{} normalize = transforms.Normalize(}
\PY{c+c1}{\PYZsh{} mean=[0.485, 0.456, 0.406],}
\PY{c+c1}{\PYZsh{} std=[0.229, 0.224, 0.225]}
\PY{c+c1}{\PYZsh{})}
\PY{c+c1}{\PYZsh{}data\PYZus{}transform = transforms.Compose([transforms.Resize(224,224),}
\PY{c+c1}{\PYZsh{}\PYZsh{}transforms.RandomResizedCrop(224), }
\PY{c+c1}{\PYZsh{}transforms.ToTensor(),}
\PY{c+c1}{\PYZsh{}normalize}
\PY{c+c1}{\PYZsh{}])}
\PY{c+c1}{\PYZsh{}output = data\PYZus{}transform(image)[:3,:,:].unsqueeze(0)}
\PY{c+c1}{\PYZsh{}output = net50(output)}
\PY{c+c1}{\PYZsh{}\PYZsh{} pre\PYZhy{}process image}
\PY{n}{normalize} \PY{o}{=} \PY{n}{transforms}\PY{o}{.}\PY{n}{Normalize}\PY{p}{(}
\PY{n}{mean}\PY{o}{=}\PY{p}{[}\PY{l+m+mf}{0.485}\PY{p}{,} \PY{l+m+mf}{0.456}\PY{p}{,} \PY{l+m+mf}{0.406}\PY{p}{]}\PY{p}{,}
\PY{n}{std}\PY{o}{=}\PY{p}{[}\PY{l+m+mf}{0.229}\PY{p}{,} \PY{l+m+mf}{0.224}\PY{p}{,} \PY{l+m+mf}{0.225}\PY{p}{]}\PY{p}{)}
\PY{n}{image\PYZus{}size} \PY{o}{=} \PY{l+m+mi}{224}
\PY{n}{loader} \PY{o}{=} \PY{n}{transforms}\PY{o}{.}\PY{n}{Compose}\PY{p}{(}\PY{p}{[}\PY{n}{transforms}\PY{o}{.}\PY{n}{Resize}\PY{p}{(}\PY{p}{(}\PY{n}{image\PYZus{}size}\PY{p}{,}\PY{n}{image\PYZus{}size}\PY{p}{)}\PY{p}{)}\PY{p}{,}
\PY{c+c1}{\PYZsh{}transforms.RandomResizedCrop(image\PYZus{}size),}
\PY{n}{transforms}\PY{o}{.}\PY{n}{ToTensor}\PY{p}{(}\PY{p}{)}\PY{p}{,}
\PY{n}{normalize}\PY{p}{]}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{image\PYZus{}loader}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} load image, returns tensor}
\PY{n}{image} \PY{o}{=} \PY{n}{Image}\PY{o}{.}\PY{n}{open}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{convert}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{RGB}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{loader}\PY{p}{(}\PY{n}{image}\PY{p}{)}\PY{o}{.}\PY{n}{float}\PY{p}{(}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{Variable}\PY{p}{(}\PY{n}{image}\PY{p}{,} \PY{n}{requires\PYZus{}grad}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
\PY{n}{image} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{unsqueeze}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)} \PY{c+c1}{\PYZsh{}for vgg 16 or resnet\PYZhy{}50? }
\PY{k}{if} \PY{n}{use\PYZus{}cuda}\PY{p}{:}
\PY{k}{return} \PY{n}{image}\PY{o}{.}\PY{n}{cuda}\PY{p}{(}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{k}{return} \PY{n}{image}
\PY{n}{image\PYZus{}tensor} \PY{o}{=} \PY{n}{image\PYZus{}loader}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}
\PY{c+c1}{\PYZsh{}print(image\PYZus{}tensor.shape), \PYZsh{}\PYZsh{} size should be (1,3,224,224)}
\PY{n}{output} \PY{o}{=} \PY{n}{net50}\PY{p}{(}\PY{n}{image\PYZus{}tensor}\PY{p}{)}
\PY{c+c1}{\PYZsh{}print(output.data.shape)}
\PY{n}{\PYZus{}}\PY{p}{,} \PY{n}{pred} \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{n}{index} \PY{o}{=} \PY{n}{pred}\PY{o}{.}\PY{n}{item}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} *** find the labels for ImageNet class, should be length 151 \PYZhy{} 268}
\PY{k}{return} \PY{n}{index} \PY{c+c1}{\PYZsh{} predicted class index}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}136}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} returns \PYZdq{}True\PYZdq{} if a dog is detected in the image stored at img\PYZus{}path}
\PY{k}{def} \PY{n+nf}{dog\PYZus{}detector\PYZus{}net50}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}\PY{p}{:}
\PY{n}{pred} \PY{o}{=} \PY{n}{net50\PYZus{}predict}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{)}
\PY{k}{return} \PY{p}{(}\PY{p}{(}\PY{n}{pred} \PY{o}{\PYZlt{}}\PY{o}{=} \PY{l+m+mi}{268}\PY{p}{)} \PY{o}{\PYZam{}} \PY{p}{(}\PY{n}{pred} \PY{o}{\PYZgt{}}\PY{o}{=} \PY{l+m+mi}{151}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{}if(net50\PYZus{}predict(img\PYZus{}path) in range(151,269)):}
\PY{c+c1}{\PYZsh{} return True}
\PY{c+c1}{\PYZsh{}else:}
\PY{c+c1}{\PYZsh{} return False}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{human\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{human\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{dog\PYZus{}files\PYZus{}short} \PY{o}{=} \PY{n}{dog\PYZus{}files}\PY{p}{[}\PY{p}{:}\PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{human\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n}{dog\PYZus{}counter} \PY{o}{=} \PY{l+m+mi}{0}
\PY{k}{for} \PY{n}{human\PYZus{}path}\PY{p}{,} \PY{n}{dog\PYZus{}path} \PY{o+ow}{in} \PY{n+nb}{zip}\PY{p}{(}\PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{,} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{k}{if}\PY{p}{(}\PY{n}{dog\PYZus{}detector\PYZus{}net50}\PY{p}{(}\PY{n}{human\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{human\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{human\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{k}{if}\PY{p}{(}\PY{n}{dog\PYZus{}detector\PYZus{}net50}\PY{p}{(}\PY{n}{dog\PYZus{}files\PYZus{}short}\PY{p}{[}\PY{n}{dog\PYZus{}path}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{:}
\PY{n}{dog\PYZus{}counter} \PY{o}{+}\PY{o}{=} \PY{l+m+mi}{1}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the accuracy of 100 human images in resnet \PYZhy{} 50 dog detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((human\PYZus{}counter/100)*100))
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{the accuracy of 100 dog images in resnet \PYZhy{} 50 dog detector: }\PY{l+s+si}{\PYZpc{}.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}}\PY{k}{float}((dog\PYZus{}counter/100)*100))
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
the accuracy of 100 human images in resnet - 50 dog detector: 0.00\%
the accuracy of 100 dog images in resnet - 50 dog detector: 100.00\%
\end{Verbatim}
\begin{itemize}
\item
the accuracy of 100 human images in dog detector alexnet is: 1\%
\item
the accuracy of 100 dog images in dog detector alexnet is: 96\%
\item
the accuracy of 100 human images in dog detector resnet - 50 is: 0.00
\item
the accuracy of 100 dog images in dog detector resnet - 50 is: 100\%
\end{itemize}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\#\# Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
Now that we have functions for detecting humans and dogs in images, we
need a way to predict breed from images. In this step, you will create a
CNN that classifies dog breeds. You must create your CNN \emph{from
scratch} (so, you can't use transfer learning \emph{yet}!), and you must
attain a test accuracy of at least 10\%. In Step 4 of this notebook, you
will have the opportunity to use transfer learning to create a CNN that
attains greatly improved accuracy.
We mention that the task of assigning breed to dogs from images is
considered exceptionally challenging. To see why, consider that
\emph{even a human} would have trouble distinguishing between a Brittany
and a Welsh Springer Spaniel.
\begin{longtable}[]{@{}ll@{}}
\toprule
Brittany & Welsh Springer Spaniel\tabularnewline
\midrule
\endhead
&\tabularnewline
\bottomrule
\end{longtable}
It is not difficult to find other dog breed pairs with minimal
inter-class variation (for instance, Curly-Coated Retrievers and
American Water Spaniels).
\begin{longtable}[]{@{}ll@{}}
\toprule
Curly-Coated Retriever & American Water Spaniel\tabularnewline
\midrule
\endhead
&\tabularnewline
\bottomrule
\end{longtable}
Likewise, recall that labradors come in yellow, chocolate, and black.
Your vision-based algorithm will have to conquer this high intra-class
variation to determine how to classify all of these different shades as
the same breed.
\begin{longtable}[]{@{}ll@{}}
\toprule
Yellow Labrador & Chocolate Labrador\tabularnewline
\midrule
\endhead
&\tabularnewline
\bottomrule
\end{longtable}
We also mention that random chance presents an exceptionally low bar:
setting aside the fact that the classes are slightly imabalanced, a
random guess will provide a correct answer roughly 1 in 133 times, which
corresponds to an accuracy of less than 1\%.
Remember that the practice is far ahead of the theory in deep learning.
Experiment with many different architectures, and trust your intuition.
And, of course, have fun!
\hypertarget{implementation-specify-data-loaders-for-the-dog-dataset}{%
\subsubsection{(IMPLEMENTATION) Specify Data Loaders for the Dog
Dataset}\label{implementation-specify-data-loaders-for-the-dog-dataset}}
Use the code cell below to write three separate
\href{http://pytorch.org/docs/stable/data.html\#torch.utils.data.DataLoader}{data
loaders} for the training, validation, and test datasets of dog images
(located at \texttt{dog\_images/train}, \texttt{dog\_images/valid}, and
\texttt{dog\_images/test}, respectively). You may find
\href{http://pytorch.org/docs/stable/torchvision/datasets.html}{this
documentation on custom datasets} to be a useful resource. If you are
interested in augmenting your training and/or validation data, check out
the wide variety of
\href{http://pytorch.org/docs/stable/torchvision/transforms.html?highlight=transform}{transforms}!
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}40}]:} \PY{k+kn}{import} \PY{n+nn}{os}
\PY{k+kn}{import} \PY{n+nn}{torch}
\PY{k+kn}{from} \PY{n+nn}{torchvision} \PY{k}{import} \PY{n}{datasets}
\PY{k+kn}{import} \PY{n+nn}{torchvision}\PY{n+nn}{.}\PY{n+nn}{transforms} \PY{k}{as} \PY{n+nn}{transforms}
\PY{k+kn}{from} \PY{n+nn}{PIL} \PY{k}{import} \PY{n}{ImageFile}