Semantic Segmentation Output & Response size

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Monika Zeilhofer
Monika Zeilhofer 2020-7-28
回答： Divya Gaddipati 2020-8-10
Hello,
when training a pretrained model (resnet18) for image segmentation, I always get the following error:
Error using trainNetwork (line 170)
Invalid training data. The output size ([250 1000 2]) of the last layer does not match the response size
([250 1000 1]).
Error in SemanticSegmentation (line 45)
[net, info] = trainNetwork(pximds,lgraph,options);
Already checke the network with the deep network designer and it says no errors.
Here you can see my code:
imds=imageDatastore('\\tuwzs9a-vm-02\homes\ga54saw\windows\dokumente\QuaBu\Geteilte Bilder\test')
classNames = ["noKnot", "Knot"];
pixelLabelID = [0; 1];
pxds = pixelLabelDatastore('\\tuwzs9a-vm-02\homes\ga54saw\windows\dokumente\QuaBu',classNames,pixelLabelID);
pximds = pixelLabelImageDatastore(imds,pxds);
[imdsTrain, imdsVal, imdsTest, pxdsTrain, pxdsVal, pxdsTest] = partitionData(imds,pxds);
% Specify the network image size. This is typically the same as the traing image sizes.
imageSize = [250 1000 3];
% Specify the number of classes.
numClasses = 2;
% Create DeepLab v3+.
lgraph = deeplabv3plusLayers(imageSize, numClasses, "resnet18");
%lgraph=segnetLayers(imageSize,numClasses,2);
tbl = countEachLabel(pxds);
imageFreq = tbl.PixelCount ./ tbl.ImagePixelCount;
classWeights = median(imageFreq) ./ imageFreq;
pxLayer = pixelClassificationLayer('Name','labels','Classes',tbl.Name,'ClassWeights',classWeights);
lgraph = replaceLayer(lgraph,"classification",pxLayer);
% Define validation data.
pximdsVal = pixelLabelImageDatastore(imdsVal,pxdsVal);
% Define training options.
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',10,...
'LearnRateDropFactor',0.3,...
'Momentum',0.9, ...
'InitialLearnRate',1e-3, ...
'L2Regularization',0.005, ...
'ValidationData',pximdsVal,...
'MaxEpochs',30, ...
'MiniBatchSize',2, ...
'Shuffle','every-epoch', ...
'CheckpointPath', tempdir, ...
'VerboseFrequency',2,...
'Plots','training-progress',...
'ValidationPatience', 4);
%Start Training
pximds = pixelLabelImageDatastore(imdsTrain,pxdsTrain);
net = trainNetwork(pximds,lgraph,options);
%Test
I = readimage(imdsTest,9);
C = semanticseg(I, net);
B = labeloverlay(I,C,'Colormap',cmap,'Transparency',0.4);
imshow(B)
pixelLabelColorbar(cmap, classes);
Would be really happy to get some help to solve the problem.
Thank you very much.

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回答（1 个）

Divya Gaddipati
Divya Gaddipati 2020-8-10
As it is evident from the error message, the size at the output layer should be same as the ground truth image size. Try changing the output layer size to [250 1000 1].
Here's a link to Semantic Segmentation example in MATLAB:
https://www.mathworks.com/help/vision/examples/semantic-segmentation-using-deep-learning.html

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