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nmb_of_labels=1000;
nmb_of_labs_per_module=25;
nmb_of_modules=(nmb_of_labels/nmb_of_labs_per_module);
relative_lab_seq=1:nmb_of_labs_per_module;
nmb_of_subsets=2;
patch=0;
parfor module=1:nmb_of_modules
m_label_ids=[];
m_labels=[];
m_data=[];
m_label_table=[relative_lab_seq;relative_lab_seq+(module-1)*nmb_of_labs_per_module];
for imgnt1kdataset=1:10
% change the path to the folder containing the ImageNet-1k mat
% files
reportname1 = sprintf('/work/mathbiology/lheath2/data/imagenet1k/mat/train_data_batch_%d.mat', imgnt1kdataset);
temp_lpad=load(reportname1,'data','labels') %
data=temp_lpad.data;
labels=temp_lpad.labels;
pos_seq=1:length(labels);
for labs=relative_lab_seq
idx=(labels==(labs+(module-1)*nmb_of_labs_per_module));
aa=pos_seq(idx);
bb=[0*aa+imgnt1kdataset;aa;labels(idx)];
m_label_ids=[m_label_ids, bb];
m_labels=[m_labels,0*aa+labs];
m_data=[m_data;data(idx,:)];
end
end
nmb_dt=length(m_labels);
set_lng=fix(nmb_dt/nmb_of_subsets);
for subset=1:nmb_of_subsets
if subset<nmb_of_subsets
set=(1:set_lng)+(subset-1)*set_lng;
else
set=(1+(subset-1)*set_lng):nmb_dt;
end
data=m_data(set,:);
labels=m_labels(:,set);
label_ids=m_label_ids(:,set);
label_table=m_label_table;
out=fun_save_modularized_data(patch, module, subset,nmb_of_labs_per_module,data,labels,label_ids,label_table)
end
module
end
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