cnn utilities

scripts/iterate_hws.sh

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+#!/bin/bash
+
+readonly base_dir="/home/imagine/cnn/scripts"
+readonly hws_dir="/home/imagine/cnn/hws"
+function do_something(){
+echo hola
+echo $1
+echo $2
+echo 
+#cd $hws_dir
+#cd $1
+#gedit $2.m
+cd $base_dir
+./run_mat.sh $1 $2
+}
+
+function main(){
+
+while read p; do
+do_something $p
+done < train_funcs.txt
+
+}
+
+main 

scripts/run_mat.sh

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+#!/bin/bash
+
+readonly matlab="/usr/local/MATLAB/R2014b/bin/matlab"
+readonly ml_flags="-nodesktop -noFigureWindows -nosplash"
+readonly t_limit="4h"
+readonly base_dir="/home/imagine/cnn/hws"
+readonly mega_log="/home/imagine/cnn/scripts/mega_log.txt"
+
+function main(){
+local folder=$1
+local script=$2
+
+cd $base_dir
+cd $folder
+
+rm -rf "textons-experiment"
+rm "train_log.txt"
+local command="$matlab $ml_flags -logfile train_log.txt -r $script"
+echo $command
+echo -e "\n\n $folder \n =========================\n$command \n\n ">> $mega_log
+
+timeout -s1 --foreground $t_limit $command  <<< "exit"
+#$command
+
+echo -e "\n -Time is up- \n" >> train_log.txt
+
+cat train_log.txt >> $mega_log
+
+}
+
+main $@

scripts/setup_dir.sh

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+#!/bin/bash
+
+function setup_dir(){
+
+local target_dir=$1
+readonly base_dir=/home/imagine/cnn/practical
+ln -s $base_dir/* $target_dir
+}
+
+function setup_all(){
+local all_dirs=$(ls -1 $1)
+for d in ${all_dirs}; do
+echo $1/$d
+setup_dir $1/$d
+done
+}
+
+function main(){
+if [ $# -ne 1 ]; then
+	echo Requires one argument
+else
+	if [ -d $1 ]; then
+		setup_all $1
+	else
+		echo $1 is not a directory
+	fi
+fi
+
+
+
+}
+main $@

scripts/test_all.m

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+for i = 1:length(dir_names)
+n=dir_names(i).name;
+try
+    s=test_net(n);
+catch
+    s=0;
+end
+course_scores{i}=struct('name',n,'score',s)
+end

scripts/test_net.m

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+function total_score=test_net(folder)
+%run('/home/imagine/cnn/practical/setup.m');
+test_db=load('home/imagine/cnn/scripts/test_textons');
+cd('/home/imagine/cnn/hws');
+cd(folder)
+cd textons-experiment
+epochs=dir('net-epoch-*.mat');
+net_t=load(epochs(end).name);
+net=net_t.net;
+if strcmp(net.layers{end}.type, 'softmaxloss')
+    net.layers(end)=[];
+end
+
+total_score=0;
+for i=1:length(test_db.patches)
+label=test_db.labels(1);
+p0=test_db.patches{i};
+p1=reshape(p0,128,128,1,[]);
+res=vl_simplenn(net,p1);
+resl=squeeze(res(end).x);
+score=zeros(size(p0,3),1);
+pred=score;
+for j=1:size(resl,2)
+    [score(j),pred(j)]=max(resl(:,j));
+end    
+round_score=sum(pred==label);
+total_score=total_score+ round_score;
+end