Error using trainNetwork (line 154) augmentedImageSource cannot form MiniBatches of data because input image sizes differ in 3rd dimension. Consider using 'ColorPreprocessing'

Question

Rida Shariff 2022-6-30

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1750955-error-using-trainnetwork-line-154-augmentedimagesource-cannot-form-minibatches-of-data-because-inp

回答： Sanyam 2022-7-13

i am using MATLAB r2018a and i have this problem.my coding is;

net = alexnet;
analyzeNetwork(net)
inputSize = net.Layers(1).InputSize
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels))
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
    softmaxLayer
    classificationLayer];
pixelRange = [-30 30];
imageAugmenter = imageDataAugmenter( ...
    'RandXReflection',true, ...
    'RandXTranslation',pixelRange, ...
    'RandYTranslation',pixelRange);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
    'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
options = trainingOptions('sgdm', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',6, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augimdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
netTransfer = trainNetwork(augimdsTrain,layers,options);
[YPred,scores] = classify(netTransfer,augimdsValidation);
idx = randperm(numel(imdsValidation.Files),4);
figure
for i = 1:4
    subplot(2,2,i)
    I = readimage(imdsValidation,idx(i));
    imshow(I)
    label = YPred(idx(i));
    title(string(label));
end
YValidation = imdsValidation.Labels;
accuracy = mean(YPred == YValidation)

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Answer 1

Sanyam 2022-7-13

0
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1750955-error-using-trainnetwork-line-154-augmentedimagesource-cannot-form-minibatches-of-data-because-inp#answer_1006585

It looks like your data set contains images of different color channels like grayscale, rgb etc. matlab does not allow 2 images of different dimensions in a mini-batch. One of the ways would be to convert all your images in grayscale, this can be done by defining augmented datastore like this:

augmentedImageDatastore(inputSize(1:2),yourData,'ColorPreprocessing','rgb2gray');

It converts all the rgb images to grayscale.

Hope that helps! Thanks!