cannot find the funcion "generateTargets"

Hello there,

I am trying to replicate the yolov3 example here. However, I cannot find a utility function, generateTargets.

which is called in modelGradients, another utility function. Please help.

I am using matlab r2021a

Thanks

% Generate target for predictions from the ground truth data.

[boxTarget, objectnessTarget, classTarget, objectMaskTarget, boxErrorScale] = generateTargets(gatheredPredictions,...

YTrain, inputImageSize, detector.AnchorBoxes, penaltyThreshold);

[MirPooya Salehi Moharer]

Dear xinyi shen

I'm facing the same problem. I cannot find a utility function, generateTargets. How did you solve your problem?

[xinyi shen]

Hi MirPooya,

Did you read Cris answer? typing the following command in your matlab,

openExample('deeplearning_shared/ObjectDetectionUsingYOLOV3DeepLearningExample')

You will find these file generated in the following folder

C:\Users\userName\Documents\MATLAB\Examples\R2021a\deeplearning_shared\ObjectDetectionUsingYOLOV3DeepLearningExample

[MirPooya Salehi Moharer]

Thank you. I managed to fix it. Thanks for your time.

Kind regads.

[Weiwei Luo]

I have exactly the same issue. I am using R2020B and R2021A. Open Example does not help. I still cannot find that function. I see it is called in line 216. It was mentined before line 198. But cannot find that function. Would you possible copy it here?

Answer 1

[Cris LaPierre]

You can open this example in MATLAB using the following code

openExample('deeplearning_shared/ObjectDetectionUsingYOLOV3DeepLearningExample')

On my computer, this corresponds to the following location:

C:\Users\userName\Documents\MATLAB\Examples\R2021a\deeplearning_shared\ObjectDetectionUsingYOLOV3DeepLearningExample

When I navigate to that folder, generateTargets is there. Note that this folder is not added to your path automatically. You will need to either make that folder your current folder, or add it to your path before the example can be run. When you use the command above to open the example, it automatically changes the current folder.

[xinyi shen]

Thanks!

I indeed did not find this example file indeed. But in another app, I found the functions.

Social Distancing Monitoring System

Answer 2

[Weiwei Luo]

I wrote the the MATLAB Support team and get the code.

function [boxDeltaTarget, objectnessTarget, classTarget, maskTarget, boxErrorScaleTarget] = generateTargets(YPredCellGathered, groundTruth,...
    inputImageSize, anchorBoxes, penaltyThreshold)
% originally at the back of the mlx file as utility function
% generateTargets creates target array for every prediction element
% x, y, width, height, confidence scores and class probabilities.

boxDeltaTarget = cell(size(YPredCellGathered,1),4);
objectnessTarget = cell(size(YPredCellGathered,1),1);
classTarget = cell(size(YPredCellGathered,1),1);
maskTarget = cell(size(YPredCellGathered,1),3);
boxErrorScaleTarget = cell(size(YPredCellGathered,1),1);

% Normalize the ground truth boxes w.r.t image input size.
gtScale = [inputImageSize(2) inputImageSize(1) inputImageSize(2) inputImageSize(1)];
groundTruth(:,1:4,:,:) = groundTruth(:,1:4,:,:)./gtScale;

anchorBoxesSet = cell2mat(anchorBoxes);

maskIdx = 1:size(anchorBoxesSet,1);
cellsz = cellfun(@size,anchorBoxes,'uni',false);
convMask = cellfun(@(v)v(1),cellsz);
anchorBoxMask = mat2cell(maskIdx,1,convMask)';

for numPred = 1:size(YPredCellGathered,1)
    
    % Select anchor boxes based on anchor box mask indices.
    anchors = anchorBoxes{numPred, :};
    
    bx = YPredCellGathered{numPred,2};
    by = YPredCellGathered{numPred,3};
    bw = YPredCellGathered{numPred,4};
    bh = YPredCellGathered{numPred,5};
    predClasses = YPredCellGathered{numPred,6};
    
    gridSize = size(bx);
    if numel(gridSize)== 3
        gridSize(4) = 1;
    end
    numClasses = size(predClasses,3)./size(anchors,1);
    
    % Initialize the required variables.
    mask = single(zeros(size(bx)));
    confMask = single(ones(size(bx)));
    classMask = single(zeros(size(predClasses)));
    tx = single(zeros(size(bx)));
    ty = single(zeros(size(by)));
    tw = single(zeros(size(bw)));
    th = single(zeros(size(bh)));
    tconf = single(zeros(size(bx)));
    tclass = single(zeros(size(predClasses)));
    boxErrorScale = single(ones(size(bx)));
    
    % Get the IOU of predictions with groundtruth.
    iou = getMaxIOUPredictedWithGroundTruth(bx,by,bw,bh,groundTruth);
    
    % Donot penalize the predictions which has iou greater than penalty
    % threshold.
    confMask(iou > penaltyThreshold) = 0;
    
    for batch = 1:gridSize(4)
        truthBatch = groundTruth(:,1:5,:,batch);
        truthBatch = truthBatch(all(truthBatch,2),:);
        
        % Get boxes with center as 0.
        gtPred = [0-truthBatch(:,3)/2,0-truthBatch(:,4)/2,truthBatch(:,3),truthBatch(:,4)];
        anchorPrior = [0-anchorBoxesSet(:,2)/(2*inputImageSize(2)),0-anchorBoxesSet(:,1)/(2*inputImageSize(1)),anchorBoxesSet(:,2)/inputImageSize(2),anchorBoxesSet(:,1)/inputImageSize(1)];
        
        % Get the iou of best matching anchor box.
        overLap = bboxOverlapRatio(gtPred,anchorPrior);
        [~,bestAnchorIdx] = max(overLap,[],2);
        
        % Select gt that are within the mask.
        index = ismember(bestAnchorIdx,anchorBoxMask{numPred});
        truthBatch = truthBatch(index,:);
        bestAnchorIdx = bestAnchorIdx(index,:);
        bestAnchorIdx = bestAnchorIdx - anchorBoxMask{numPred}(1,1) + 1;
        
        if ~isempty(truthBatch)
            % Convert top left position of ground-truth to centre coordinates.
            truthBatch = [truthBatch(:,1)+truthBatch(:,3)./2,truthBatch(:,2)+truthBatch(:,4)./2,truthBatch(:,3),truthBatch(:,4),truthBatch(:,5)];
            
            errorScale = 2 - truthBatch(:,3).*truthBatch(:,4);
            truthBatch = [truthBatch(:,1)*gridSize(2),truthBatch(:,2)*gridSize(1),truthBatch(:,3)*inputImageSize(2),truthBatch(:,4)*inputImageSize(1),truthBatch(:,5)];
            for t = 1:size(truthBatch,1)
                
                % Get the position of ground-truth box in the grid.
                colIdx = ceil(truthBatch(t,1));
                colIdx(colIdx<1) = 1;
                colIdx(colIdx>gridSize(2)) = gridSize(2);
                rowIdx = ceil(truthBatch(t,2));
                rowIdx(rowIdx<1) = 1;
                rowIdx(rowIdx>gridSize(1)) = gridSize(1);
                pos = [rowIdx,colIdx];
                anchorIdx = bestAnchorIdx(t,1);
                
                mask(pos(1,1),pos(1,2),anchorIdx,batch) = 1;
                confMask(pos(1,1),pos(1,2),anchorIdx,batch) = 1;
                
                % Calculate the shift in ground-truth boxes.
                tShiftX = truthBatch(t,1)-pos(1,2)+1;
                tShiftY = truthBatch(t,2)-pos(1,1)+1;
                tShiftW = log(truthBatch(t,3)/anchors(anchorIdx,2));
                tShiftH = log(truthBatch(t,4)/anchors(anchorIdx,1));
                
                % Update the target box.
                tx(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftX;
                ty(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftY;
                tw(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftW;
                th(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftH;
                boxErrorScale(pos(1,1),pos(1,2),anchorIdx,batch) = errorScale(t);
                tconf(rowIdx,colIdx,anchorIdx,batch) = 1;
                classIdx = (numClasses*(anchorIdx-1))+truthBatch(t,5);
                tclass(rowIdx,colIdx,classIdx,batch) = 1;
                classMask(rowIdx,colIdx,(numClasses*(anchorIdx-1))+(1:numClasses),batch) = 1;
            end
        end
    end
    boxDeltaTarget(numPred,:) = [{tx} {ty} {tw} {th}];
    objectnessTarget{numPred,1} = tconf;
    classTarget{numPred,1} = tclass;
    maskTarget(numPred,:) = [{mask} {confMask} {classMask}];
    boxErrorScaleTarget{numPred,:} = boxErrorScale;
end
end

[arindam mondal]

I cannot find the function 'getMaxIOUPredictedWithGroundTruth'. please help