| 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 |
| |
| |
| 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 |
|
|
