How to change architecture of conditional GAN to generate 224x224x3 images?

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Alok
Alok 2022 年 8 月 4 日
編集済み: Alok 2022 年 8 月 6 日
This example is for image size 64x64x3. I am wondering what changes should be done in layersGenerator and layersDiscriminator to generate 224x224x3 images.
This is my code:
inputSize = [224 224 3] or [256 256 3];
Note if Factor=2 (below) then I get image size 128x128x3. If Factor=4 then generated image size if 256x256x3. However, during the loop, it gives an error that trainedVariance is negative.
inputSize = [64 64 3];
Factor = 4; %if Factor =2 then 128x128x3 image size is generated;
inputSize = Factor*inputSize(1:2);
numClasses = 2;
augimds = augmentedImageDatastore(inputSize(1:2),XTrain,YTrain);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),XValidation,YValidation);
numLatentInputs = 100;%100
embeddingDimension = 50;
numFilters = Factor*64;%224;
filterSize = 5;
projectionSize = Factor*[4 4 1024];
layersGenerator = [
featureInputLayer(numLatentInputs)
fullyConnectedLayer(prod(projectionSize))
functionLayer(@(X) feature2image(X,projectionSize),Formattable=true)
concatenationLayer(3,2,Name="cat");
transposedConv2dLayer(filterSize,4*numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,2*numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,3,Stride=2,Cropping="same")
tanhLayer];
lgraphGenerator = layerGraph(layersGenerator);
layers = [
featureInputLayer(1)
embeddingLayer(embeddingDimension,numClasses)
fullyConnectedLayer(prod(projectionSize(1:2)))
functionLayer(@(X) feature2image(X,[projectionSize(1:2) 1]),Formattable=true,Name="emb_reshape")];
lgraphGenerator = addLayers(lgraphGenerator,layers);
lgraphGenerator = connectLayers(lgraphGenerator,"emb_reshape","cat/in2");
netG = dlnetwork(lgraphGenerator);
dropoutProb = 0.75;
%numFilters = 64;
scale = 0.2;
filterSize = 5;
layersDiscriminator = [
imageInputLayer(inputSize,Normalization="none")
dropoutLayer(dropoutProb)
concatenationLayer(3,2,Name="cat")
convolution2dLayer(filterSize,numFilters,Stride=2,Padding="same")
leakyReluLayer(scale)
convolution2dLayer(filterSize,2*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
convolution2dLayer(filterSize,4*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
convolution2dLayer(filterSize,8*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
convolution2dLayer(Factor*4,1)];
lgraphDiscriminator = layerGraph(layersDiscriminator);
layers = [
featureInputLayer(1)
embeddingLayer(embeddingDimension,numClasses)
fullyConnectedLayer(prod(inputSize(1:2)))
functionLayer(@(X) feature2image(X,[inputSize(1:2) 1]),Formattable=true,Name="emb_reshape")];
lgraphDiscriminator = addLayers(lgraphDiscriminator,layers);
lgraphDiscriminator = connectLayers(lgraphDiscriminator,"emb_reshape","cat/in2");
netD = dlnetwork(lgraphDiscriminator);
However, the above code gives an error at
[~,~,gradientsG,gradientsD,stateG,scoreG,scoreD] = ...
dlfeval(@modelLoss2,netG,netD,X,T,Z,flipFactor);
The size of generated image at
[XGenerated,stageG] = forward(netG,Z,T);
is 256x256x3. However, an error comes stating that trainedVariance is not positive
Could you assist me which transposedConv2dLayer to change to adjust the size to 224x224x3 or 256x256x3?
Thanks for your help

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