i have a problem with augmentedimagedatastore how can i solve it?

matlabroot = 'C:\Users\user a\Desktop\Hind\Image C\data101';
DatasetPath = fullfile(matlabroot);
Data = imageDatastore(DatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
CountLabel = Data.countEachLabel;
auds = augmentedImageDatastore([224 224],Data);
trainData1 = auds;
[trainData] = auds;
netWidth = 16;
layers = [
    imageInputLayer([224 224 3],'Name','input') 
    convolution2dLayer(3,netWidth,'Padding','same','Name','convInp')
    batchNormalizationLayer('Name','BNInp')
    reluLayer('Name','reluInp')
   
    convolutionalUnit(netWidth,1,'S1U1')
    additionLayer(2,'Name','add11')
    reluLayer('Name','relu11')
    convolutionalUnit(netWidth,1,'S1U2')
    additionLayer(2,'Name','add12')
    reluLayer('Name','relu12')
    
    convolutionalUnit(2*netWidth,2,'S2U1')
    additionLayer(2,'Name','add21')
    reluLayer('Name','relu21')
    convolutionalUnit(2*netWidth,1,'S2U2')
    additionLayer(2,'Name','add22')
    reluLayer('Name','relu22')
    
    convolutionalUnit(4*netWidth,2,'S3U1')
    additionLayer(2,'Name','add31')
    reluLayer('Name','relu31')
    convolutionalUnit(4*netWidth,1,'S3U2')
    additionLayer(2,'Name','add32')
    reluLayer('Name','relu32')
    
    averagePooling2dLayer(8,'Name','globalPool')
    fullyConnectedLayer(2,'Name','fcFinal')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classoutput')
    ];

lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'reluInp','add11/in2');
lgraph = connectLayers(lgraph,'relu11','add12/in2');

skip1 = [
    convolution2dLayer(1,2*netWidth,'Stride',2,'Name','skipConv1')
    batchNormalizationLayer('Name','skipBN1')];

lgraph = addLayers(lgraph,skip1);
lgraph = connectLayers(lgraph,'relu12','skipConv1');
lgraph = connectLayers(lgraph,'skipBN1','add21/in2');
lgraph = connectLayers(lgraph,'relu21','add22/in2');

skip2 = [
    convolution2dLayer(1,4*netWidth,'Stride',2,'Name','skipConv2')
    
    batchNormalizationLayer('Name','skipBN2')];

lgraph = addLayers(lgraph,skip2);
lgraph = connectLayers(lgraph,'relu22','skipConv2');
lgraph = connectLayers(lgraph,'skipBN2','add31/in2');
lgraph = connectLayers(lgraph,'relu31','add32/in2');

options = trainingOptions('sgdm', 'MiniBatchSize',128,'MaxEpochs',10,'InitialLearnRate',1e-4,'ExecutionEnvironment','parallel');

[trainedNet1,traininfo] = trainNetwork(trainData,lgraph,options);

And i get this error:

Error in algo (line 105)

[trainedNet1,traininfo] = trainNetwork(trainData,lgraph,options);

Caused by:

Error using nnet.internal.cnn.DistributedDispatcher/computeInParallel (line 193)

Error detected on worker 2.

Error using augmentedImageDatastore/applyAugmentationPipeline (line 401)

augmentedImageDatastore cannot form MiniBatches of data because input image sizes differ in 3rd dimension. Consider

using 'ColorPreprocessing' option to ensure all augmented images have same number of channels.

Answer 1

[Sanyam]

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!