function [] = step_C_varpcanet(dirTest, dirUtilities, ext, numCoresFeatExtr, numCoresKnn, fidLogs, logS, savefile, plotFigures)
%--------------------------------------
%General parameters
stepPrint = 100;
%PCA Params
run('./params/paramsPCATuning.m');
%--------------------------------------
%Dir DBs
dbname_All = { ...
'ALL_IDB'
};
dbname_part_All = { ...
'ALL_IDB2'
};
dbname_ROI_All = { ...
'ROI_256'
};
%nets
net_name{1} = 'AlexNet';
net_name{2} = 'VGG16';
net_name{3} = 'VGG19';
net_name{4} = 'ResNet18';
net_name{5} = 'ResNet50';
net_name{6} = 'ResNet101';
net_name{7} = 'DenseNet201';
%--------------------------------------
colorS_init = 1;
colorS_tune = 1;
colorS_test = 1;
%--------------------------------------
% processDummyDirs();
%--------------------------------------
%Loop on dbs
for db = 1 : numel(dbname_All)
% for db = 2
%Close
close all
pause(0.2);
%DB selection
dbname = dbname_All{db};
dbnamePart = dbname_part_All{db};
ROI = dbname_ROI_All{db};
dirDB_wROI = [dirTest dbname '/' dbnamePart '/' ROI '/'];
%dirDB_noROI = [dirWorkspace dbname '/' dbnamePart '/' ];
%--------------------------------------
%loop on nets
for n = 1 : numel(net_name)
%switch net
switch n
case 1
net = alexnet;
layer = 'fc6';
conv_layer = 'conv5';
case 2
net = vgg16;
layer = 'fc6';
conv_layer = 'conv5_3';
case 3
net = vgg19;
layer = 'fc6';
conv_layer = 'conv5_4';
case 4
net = resnet18;
layer = 'fc1000';
conv_layer = 'res5b_relu';
case 5
net = resnet50;
layer = 'fc1000';
conv_layer = 'res5c_branch2c';
case 6
net = resnet101;
layer = 'fc1000';
conv_layer = 'res5c_branch2c';
case 7
net = densenet201;
layer = 'fc1000';
conv_layer = 'conv5_block32_2_conv';
end %switch
%--------------------------------------
%Folder creation
%RESULTS: dirs net
dirResults = ['./Results/' dbname '/' dbnamePart '/' net_name{n} '/'];
mkdir_pers(dirResults, savefile);
%RESULTS: log file
timeStampRaw = datestr(datetime);
timeStamp = strrep(timeStampRaw, ':', '-');
if savefile && logS
logFile = [dirResults dbname '_log_' timeStamp '.txt'];
fidLog = fopen(logFile, 'w');
fidLogs{2} = fidLog;
end %if savefile && log
%--------------------------------------
%Display
fprintf_pers(fidLogs, '\n');
fprintf_pers(fidLogs, '---------------\n');
fprintf_pers(fidLogs, 'ALL-Unsharpen\n');
fprintf_pers(fidLogs, [dbname '\n']);
fprintf_pers(fidLogs, [dbnamePart '\n']);
fprintf_pers(fidLogs, '---------------\n');
fprintf_pers(fidLogs, '\n');
%--------------------------------------
%display
fprintf_pers(fidLogs, '---------------\n');
fprintf_pers(fidLogs, ['Net: ' net_name{n} '\n']);
fprintf_pers(fidLogs, '---------------\n');
fprintf_pers(fidLogs, '\n')
%--------------------------------------
%DB processing
%Extract samples
files = dir([dirDB_wROI '*.' ext]);
%Compute labels
[problem, labels, numImagesAll] = computeLabels(dirDB_wROI, files);
%--------------------------------------
%Display
fprintf_pers(fidLogs, 'Extracting samples...\n');
fprintf_pers(fidLogs, ['\t' num2str(numImagesAll) ' images in total\n']);
fprintf_pers(fidLogs, '\n');
%--------------------------------------
%LOOP ON ITERATIONS
%Init
accuracy_knnAll = zeros(param.numIterations, 1);
cmc = cell(param.numIterations, 1);
%--------------------------------------
%Compute random fold indexes
%if outside iteration loop, random fold once (es. 10-fold)
%[allIndexes, cvIndices] = computeAllIndexesFold(numImagesAll, labels, param);
%Loop
for r = 1 : param.numIterations
%--------------------------------------
%Display
fprintf_pers(fidLogs, ['Iteration N. ' num2str(r) '\n']);
%--------------------------------------
%File save info
fileSaveTest_iter = [dirResults '/results_iter_' num2str(r) '.mat'];
%--------------------------------------
%Compute random fold indexes
%--10-fold
%[indImagesTrain, indImagesTest, numImagesTrain, numImagesTest] = computeIndexesFold(cvIndices, r);
%--2-fold if inside iteration loop, random fold each iteration (repeated 2-fold)
[allIndexes, cvIndices] = computeAllIndexesFold(numImagesAll, labels, param);
[indImagesTrain, indImagesTest, numImagesTrain, numImagesTest] = computeIndexesFold(cvIndices, randi(2, 1));
%Corresponding labels
TrnLabels = labels(indImagesTrain);
TestLabels = labels(indImagesTest);
%--------------------------------------
%Display output number of images
fprintf_pers(fidLogs, ['\t' num2str(numImagesTrain) ' images are chosen for training\n']);
fprintf_pers(fidLogs, ['\t' num2str(numImagesTest) ' images are chosen for testing\n']);
%%%%%%%%%%%%%% TRAINING %%%%%%%%%%%%%
start_pool(numCoresFeatExtr);
%--------------------------------------
fprintf_pers(fidLogs, '\tTraining... \n')
%--------------------------------------
%Load images for training
fprintf_pers(fidLogs, '\t\tLoading images for training... \n')
[imagesCellTrain, filenameTrn, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_init, 100, dirUtilities, 0);
imagesCellTrain = adjustFormat(imagesCellTrain);
%norm
%[imagesCellTrain, dd1, dd2] = computeNorm(imagesCellTrain);
%find th_focus
fprintf_pers(fidLogs, '\tComputing th_focus init... \n')
%%%%%% QUI %%%%%%
th_focus_init = find_th_focus(imagesCellTrain, TrnLabels, [128 128], dirUtilities, fidLogs);
%th_focus_init = 7.3;
%puliamo
clear imagesCellTrain
%tune
%th_focus_start = round((th_focus_init - th_focus_init*percC/100)*10)/10;
%th_focus_end = round((th_focus_init + th_focus_init*percC/100)*10)/10;
th_focus_start = round((th_focus_init - 0.5)*10)/10;
th_focus_end = round((th_focus_init + 0.5)*10)/10;
%th_focus_start = th_focus_init;
%th_focus_end = th_focus_init;
%init
accuracy_knnALLFOCUS = [];
%--------------------------------------
%tuning th_focus
fprintf_pers(fidLogs, '\tTuning th_focus... \n')
%loop on th_focus
allfocuses = th_focus_start : 0.1 : th_focus_end;
%%%%%% QUI %%%%%%
for th_focus = allfocuses
%for th_focus = th_focus_start
%Display
fprintf_pers(fidLogs, '\n')
fprintf_pers(fidLogs, ['\t\tth_focus: ' num2str(th_focus) '\n']);
fprintf_pers(fidLogs, '\t\tLoading images... \n')
[imagesCellTrain, filenameTrn, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_tune, th_focus, dirUtilities, 0);
imagesCellTrain = adjustFormatForPCANet(imagesCellTrain, [128, 128]);
%--------------------------------------
%PCANet Training
%1 layer: PCA filters
[V, PCANet] = trainPCANet(imagesCellTrain, PCANet, fidLogs, param, numCoresFeatExtr);
%Feature extraction
fprintf_pers(fidLogs, '\t\tFeature extraction... \n')
[ftrain_all, numFeaturesTrain] = featExtrGaborAdapt(imagesCellTrain, V, PCANet, [], param, numImagesTrain, stepPrint);
%size
sizeTrain = size(ftrain_all, 2);
%--------------------------------------
%performance
fprintf_pers(fidLogs, '\t\tClassification - original... \n')
errorStruct_original_temp = computeClassificationPerformance(numFeaturesTrain, sizeTrain, ftrain_all, TrnLabels, stepPrint, numCoresKnn, fidLogs, param);
%compute cmc
[cmc_original_temp, cmc_sum_original_temp] = computeCMC(errorStruct_original_temp.distMatrixTest, TrnLabels, ['cmc original temp iteration ' num2str(r)], 0);
%Error metrics
fprintf_pers(fidLogs, ['\t\tTraining accuracy (perc. of correctly classified samples, at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_original_temp.accuracy_knn*100));
fprintf_pers(fidLogs, ['\t\tAUC of CMC (at iteration n. ' num2str(r) '): %s\n'], num2str(cmc_sum_original_temp));
%assign
accuracy_knnALLFOCUS = [accuracy_knnALLFOCUS errorStruct_original_temp.accuracy_knn];
%accuracy_knnALLFOCUS = [accuracy_knnALLFOCUS cmc_sum_original_temp]; %maximize AUC of CMC
end %th_focus
%Puliamo
clear imagesCellTrain ftrain_all
%%%%%%%%%%%%%% APPLY BEST FOCUS %%%%%%%%%%%%%
fprintf_pers(fidLogs, '\n')
fprintf_pers(fidLogs, '\tApply best focus\n')
%best focusc
%[maxAcc, i_best_focus] = max(accuracy_knnALLFOCUS);
%i_best_focus = i_best_focus(1);
[sortA, isort] = sort(accuracy_knnALLFOCUS);
i_best_focus = isort(end); %highest th_focus that gives the best result
best_th_focus = allfocuses(i_best_focus);
fprintf_pers(fidLogs, ['\t\tBest focus: ' num2str(best_th_focus) ' \n']);
%training data
fprintf_pers(fidLogs, '\t\tLoading training images - original... \n')
[imagesCellTrain_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_test, 100, dirUtilities, 0);
imagesCellTrain_original = adjustFormat(imagesCellTrain_original);
%norm
%[imagesCellTrain_original, meanA_original, stdA_original] = computeNorm(imagesCellTrain_original);
fprintf_pers(fidLogs, '\t\tLoading training images - unsharpened... \n')
[imagesCellTrain_unsharp, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_test, best_th_focus, dirUtilities, 0);
imagesCellTrain_unsharp = adjustFormat(imagesCellTrain_unsharp);
%norm
%[imagesCellTrain_unsharp, meanA_unsharp, stdA_unsharp] = computeNorm(imagesCellTrain_unsharp);
%testing data
fprintf_pers(fidLogs, '\t\tLoading testing images - original... \n')
[imagesCellTest_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, 100, dirUtilities, 0);
[imagesCellTest_original, meanAll_test_original] = adjustFormat(imagesCellTest_original);
%imagesCellTest_original = applyNorm(imagesCellTest_original, meanA_original, stdA_original);
fprintf_pers(fidLogs, '\t\tLoading testing images - unsharpened... \n')
[imagesCellTest_unsharp, filenameTest, indexes_test_imUnsharpened] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, best_th_focus, dirUtilities, 0);
[imagesCellTest_unsharp, meanAll_test_unsharp] = adjustFormat(imagesCellTest_unsharp);
%imagesCellTest_unsharpened = applyNorm(imagesCellTest_unsharpened, meanA_unsharp, stdA_unsharp);
%%%%%%%%%%%%%% TESTING - PRE-TRAINED CNNs %%%%%%%%%%%%%
%pre-trained
fprintf_pers(fidLogs, '\t\tPretrained CNNs... \n')
%Feature extraction - ORIGINAL
fprintf_pers(fidLogs, '\t\t\tFeature extraction - original... \n')
ftrain_all_original = feature_extraction_cnn(imagesCellTrain_original, net, layer, colorS_test);
ftest_all_original = feature_extraction_cnn(imagesCellTest_original, net, layer, colorS_test);
%Feature extraction - UNSHARP
fprintf_pers(fidLogs, '\t\t\tFeature extraction - unsharp... \n')
ftrain_all_unsharp = feature_extraction_cnn(imagesCellTrain_unsharp, net, layer, colorS_test);
ftest_all_unsharp = feature_extraction_cnn(imagesCellTest_unsharp, net, layer, colorS_test);
%size
numFeatures = size(ftest_all_unsharp, 1);
sizeTest = size(ftest_all_unsharp, 2);
%--------------------------------------
%Classification performance
%Original
%fprintf_pers(fidLogs, '\t\t\tClassification - original... \n')
%errorStruct_pretrained_original(r) = computeClassificationPerformance(numFeatures, sizeTest, ftest_all_original, TestLabels, stepPrint, numCoresKnn, fidLogs, param);
errorStruct_pretrained_original(r) = computeClassificationPerformanceTrainTest(numFeatures, sizeTest, ftrain_all_original, ftest_all_original, TrnLabels, TestLabels, stepPrint, numCoresKnn, fidLogs, param);
%Unsharp
%fprintf_pers(fidLogs, '\t\t\tClassification - unsharp... \n')
%errorStruct_pretrained_unsharp(r) = computeClassificationPerformance(numFeatures, sizeTest, ftest_all_unsharp, TestLabels, stepPrint, numCoresKnn, fidLogs, param);
errorStruct_pretrained_unsharp(r) = computeClassificationPerformanceTrainTest(numFeatures, sizeTest, ftrain_all_unsharp, ftest_all_unsharp, TrnLabels, TestLabels, stepPrint, numCoresKnn, fidLogs, param);
%puliamo
clear ftest_all_original ftest_all_unsharp
%compute cmc
[cmc1, cmc_sum1] = computeCMC_trainTest(errorStruct_pretrained_original(r).distMatrixTest, TestLabels, ['cmc original iteration ' num2str(r)], plotFigures);
[cmc2, cmc_sum2] = computeCMC_trainTest(errorStruct_pretrained_unsharp(r).distMatrixTest, TestLabels, ['cmc unsharp iteration ' num2str(r)], plotFigures);
cmc_original{r} = cmc1;
cmc_sum_original{r} = cmc_sum1;
cmc_unsharp{r} = cmc2;
cmc_sum_unsharp{r} = cmc_sum2;
errorStruct_pretrained_original(r).rank5 = cmc1(5);
errorStruct_pretrained_unsharp(r).rank5 = cmc2(5);
%Display
fprintf_pers(fidLogs, '\n')
fprintf_pers(fidLogs, ['\tPretrained - Accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_pretrained_original(r).accuracy_knn*100));
fprintf_pers(fidLogs, ['\tPretrained - Accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_pretrained_unsharp(r).accuracy_knn*100));
fprintf_pers(fidLogs, ['\tPretrained - Rank 5 accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(cmc1(5)*100));
fprintf_pers(fidLogs, ['\tPretrained - Rank 5 accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(cmc2(5)*100));
pause(0.1)
%%%%%%%%%%%%%% TESTING - FINE TUNING CNNs %%%%%%%%%%%%%
%pre-trained
fprintf_pers(fidLogs, '\n')
fprintf_pers(fidLogs, '\tFine tuning CNNs... \n')
%fprintf_pers(fidLogs, '\t\t\tTraining... \n')
pixelRange = [-30 30];
rotRange = [-180 180];
imageAugmenter = imageDataAugmenter( ...
'RandXReflection', true, ...
'RandYReflection', true, ...
'RandRotation', rotRange);
%'RandXTranslation', pixelRange, ...
%'RandYTranslation', pixelRange ...
numClasses = numel(unique(labels));
inputSize = net.Layers(1).InputSize;
% layersTransfer = net.Layers(1:end-3);
% layers = [
% layersTransfer
% fullyConnectedLayer(numClasses, 'WeightLearnRateFactor', 20, 'BiasLearnRateFactor', 20)
% softmaxLayer
% classificationLayer];
%change last layers
lgraph = replaceLayers(net, numClasses);
%options
options = trainingOptions('sgdm', ...
'MiniBatchSize', 20, ... %128 20
'MaxEpochs', 100, ...
'InitialLearnRate', 1e-4, ...
'Shuffle', 'every-epoch', ... 'never'
'ValidationFrequency', 3, ...
'Verbose', false, ...
'Plots', 'none'); % 'training-progress'
fprintf_pers(fidLogs, '\t\tTraining original... \n')
%netTransfer_original = fineTuneCNN(imagesCellTrain_original, TrnLabels, './dummy_train_original/', inputSize, imageAugmenter, layers, options);
netTransfer_original = fineTuneCNN(imagesCellTrain_original, TrnLabels, './dummy_train_original/', inputSize, imageAugmenter, lgraph, options);
fprintf_pers(fidLogs, '\t\tTraining unsharp... \n')
%netTransfer_unsharp = fineTuneCNN(imagesCellTrain_unsharp, TrnLabels, './dummy_train_unsharp/', inputSize, imageAugmenter, layers, options);
netTransfer_unsharp = fineTuneCNN(imagesCellTrain_unsharp, TrnLabels, './dummy_train_unsharp/', inputSize, imageAugmenter, lgraph, options);
%
%fprintf_pers(fidLogs, '\t\t\tTesting... \n');
%cm
fprintf_pers(fidLogs, '\t\tTesting original... \n')
errorStruct_finetune_original(r) = computeClassPerformanceFineTuneCNN(imagesCellTest_original, TestLabels, './dummy_test_original/', inputSize, netTransfer_original, fidLogs);
fprintf_pers(fidLogs, '\t\tTesting unsharp... \n')
errorStruct_finetune_unsharp(r) = computeClassPerformanceFineTuneCNN(imagesCellTest_unsharp, TestLabels, './dummy_test_unsharp/', inputSize, netTransfer_unsharp, fidLogs);
%Display
fprintf_pers(fidLogs, ['\tFine tuning - Accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_finetune_original(r).accuracy_knn*100));
fprintf_pers(fidLogs, ['\tFine tuning - Accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_finetune_unsharp(r).accuracy_knn*100));
%--------------------------------------
%Save
if savefile
save(fileSaveTest_iter, 'errorStruct_pretrained_original', 'errorStruct_pretrained_unsharp', 'errorStruct_finetune_original', 'errorStruct_finetune_unsharp', 'cmc_original', 'cmc_unsharp');
end %if savefile
%--------------------------------------
%Display progress
fprintf_pers(fidLogs, '\n');
%GRAD-CAM
fprintf_pers(fidLogs, 'Grad-CAM\n');
fprintf_pers(fidLogs, '\n');
dirGcam = [dirResults 'gcam_iter_' num2str(r) '/'];
mkdir_pers(dirGcam, savefile);
[imagesCellTest_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, 100, dirUtilities, 0);
[imagesCellTest_unsharp, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, best_th_focus, dirUtilities, 0);
computeGradCam(imagesCellTest_original, imagesCellTest_unsharp, meanAll_test_original, meanAll_test_unsharp, indexes_test_imUnsharpened, ...
netTransfer_original, netTransfer_unsharp, conv_layer, inputSize, filenameTest, TestLabels, dirGcam);
%computeGradCam2(imagesCellTest_original, imagesCellTest_unsharp, meanAll_test_original, meanAll_test_unsharp, indexes_test_imUnsharpened, ...
%netTransfer_original, netTransfer_unsharp, conv_layer, inputSize, filenameTest, TestLabels, dirGcam);
%Puliamo
clear imagesCellTrain_original imagesCellTrain_unsharpenend imagesCellTest_original imagesCellTest_unsharpened netTransfer_original netTransfer_unsharp layers
end %for r = 1 : param.numIterations
close all
pause(0.1)
%display
fprintf_pers(fidLogs, '\n');
%--------------------------------------
%Average classification performance
%PRETRAINED
%Error metrics
fprintf_pers(fidLogs, '\n');
%original
fprintf_pers(fidLogs, 'Pretrained - Original\n')
stampaErrors(errorStruct_pretrained_original, fidLogs);
fprintf_pers(fidLogs, '\tRank 5 accuracy (mean; std): %s%%; %s%% \n', num2str(mean([errorStruct_pretrained_original.rank5])*100), num2str(std([errorStruct_pretrained_original.rank5])*100));
%unsharp
fprintf_pers(fidLogs, 'Pretrained - Unsharp\n')
stampaErrors(errorStruct_pretrained_unsharp, fidLogs);
fprintf_pers(fidLogs, '\tRank 5 accuracy (mean; std): %s%%; %s%% \n', num2str(mean([errorStruct_pretrained_unsharp.rank5])*100), num2str(std([errorStruct_pretrained_unsharp.rank5])*100));
%FINE TUNING
%Error metrics
fprintf_pers(fidLogs, '\n');
%original
fprintf_pers(fidLogs, 'Fine tuning - Original\n')
stampaErrors(errorStruct_finetune_original, fidLogs);
%unsharp
fprintf_pers(fidLogs, 'Fine tuning - Unsharp\n')
stampaErrors(errorStruct_finetune_unsharp, fidLogs);
%--------------------------------------
%Average CMC
%original
stampaAvgCMC(cmc_original, 'original', dirResults, savefile, plotFigures);
%unsharp
stampaAvgCMC(cmc_unsharp, 'unsharp', dirResults, savefile, plotFigures);
%--------------------------------------
%Display progress
fprintf_pers(fidLogs, '\n');
%--------------------------------------
%Close file log
if savefile && logS
fclose(fidLog);
end %if savefile && log
% delete(gcp('nocreate'));
fclose('all');
%close
close all
pause(0.1)
end %for n
end %for db
Datasets
Models
