U-net segmentation gives error for layers output size after starts to training

Question

Mahmut Uçar 2020-6-24

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/554161-u-net-segmentation-gives-error-for-layers-output-size-after-starts-to-training

评论： sugreev bal 2022-6-2

Trying to make segmentation for covid areas from CT images with using U-net. I had created mask image and added on original image in tiff format. createUnet funtion is used to create layers of network. I have resized the images to power of 2. Exact size is [512 512 4]. There will be 4 class. Code works fine until starts to training. In training part it starts for a while then gives the missmatch error. I am using Matlab R2019b.

Error using trainNetwork (line 170)

Invalid training data. The output size ([512 512 4]) of the last layer does not match the response size ([1 1 1]).

Error in Unet_segmen_guide_mismatch_error (line 39)

net = trainNetwork(imds,lgraph,options);

The code and createUnet() function are below. I hope you can help me to solve this problem...

Image_DataSet = imageDatastore('four_channel_CT', 'LabelSource','foldernames', 'FileExtensions', {'.tiff'});
DatasetPath=fullfile('E:\DEV\Covid\four_channel_CT');
imds=imageDatastore(DatasetPath, 'IncludeSubfolders', true,...
    'LabelSource','foldernames','fileextension',{'.tiff'});
labelDir = fullfile(DatasetPath,'testImages');
classNames = [ "Background","Lung","space","Covid",]; 
pixelLabelIds = 1:30;
inputTileSize = [512,512,4];
lgraph = createUnet();
% disp(lgraph.Layers)
initialLearningRate = 0.05;
maxEpochs = 150;
minibatchSize = 16;
l2reg = 0.0001;
options = trainingOptions('sgdm',...
    'InitialLearnRate',initialLearningRate, ...
    'Momentum',0.9,...
    'L2Regularization',l2reg,...
    'MaxEpochs',maxEpochs,...
    'MiniBatchSize',minibatchSize,...
    'LearnRateSchedule','piecewise',...    
    'Shuffle','every-epoch',...
    'GradientThresholdMethod','l2norm',...
    'GradientThreshold',0.05, ...
    'Plots','training-progress', ...
    'VerboseFrequency',20);
modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
net = trainNetwork(imds,lgraph,options);
save(['CovidUnet-' modelDateTime '-Epoch-' num2str(maxEpochs) '.mat'],'net','options');
*********************************************************************
*********************************************************************
function lgraph = createUnet()
% EDIT: modify these parameters for your application.
encoderDepth = 4;
initialEncoderNumChannels = 64;
inputTileSize = [512 512 4];
convFilterSize = [3 3];
inputNumChannels = 4;
numClasses = 4;
inputlayer = imageInputLayer(inputTileSize,'Name','ImageInputLayer');
[encoder, finalNumChannels] = iCreateEncoder(encoderDepth, convFilterSize, initialEncoderNumChannels, inputNumChannels);
firstConv = createAndInitializeConvLayer(convFilterSize, finalNumChannels, ...
    2*finalNumChannels, 'Bridge-Conv-1');
firstReLU = reluLayer('Name','Bridge-ReLU-1');
secondConv = createAndInitializeConvLayer(convFilterSize, 2*finalNumChannels, ...
    2*finalNumChannels, 'Bridge-Conv-2');
secondReLU = reluLayer('Name','Bridge-ReLU-2');
encoderDecoderBridge = [firstConv; firstReLU; secondConv; secondReLU];
dropOutLayer = dropoutLayer(0.5,'Name','Bridge-DropOut');
encoderDecoderBridge = [encoderDecoderBridge; dropOutLayer];
initialDecoderNumChannels = finalNumChannels;
upConvFilterSize = 2;
[decoder, finalDecoderNumChannels] = iCreateDecoder(encoderDepth, upConvFilterSize, convFilterSize, initialDecoderNumChannels);
layers = [inputlayer; encoder; encoderDecoderBridge; decoder];
finalConv = convolution2dLayer(1,numClasses,...
    'BiasL2Factor',0,...
    'Name','Final-ConvolutionLayer');
finalConv.Weights = randn(1,1,finalDecoderNumChannels,numClasses);
finalConv.Bias = zeros(1,1,numClasses);
smLayer = softmaxLayer('Name','Softmax-Layer');
pixelClassLayer = pixelClassificationLayer('Name','Segmentation-Layer');
layers = [layers; finalConv; smLayer; pixelClassLayer];
lgraph = layerGraph(layers);
for depth = 1:encoderDepth
   startLayer = sprintf('Encoder-Stage-%d-ReLU-2',depth); 
   endLayer = sprintf('Decoder-Stage-%d-DepthConcatenation/in2',encoderDepth-depth + 1);
   lgraph = connectLayers(lgraph,startLayer, endLayer);
end
end
%--------------------------------------------------------------------------
function [encoder, finalNumChannels] = iCreateEncoder(encoderDepth, convFilterSize, initialEncoderNumChannels, inputNumChannels)
encoder = [];
for stage = 1:encoderDepth
    % Double the layer number of channels at each stage of the encoder.
    encoderNumChannels = initialEncoderNumChannels * 2^(stage-1);
    
    if stage == 1
        firstConv = createAndInitializeConvLayer(convFilterSize, inputNumChannels, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-1']);
    else
        firstConv = createAndInitializeConvLayer(convFilterSize, encoderNumChannels/2, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-1']);
    end
    firstReLU = reluLayer('Name',['Encoder-Stage-' num2str(stage) '-ReLU-1']);
    
    secondConv = createAndInitializeConvLayer(convFilterSize, encoderNumChannels, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-2']);
    secondReLU = reluLayer('Name',['Encoder-Stage-' num2str(stage) '-ReLU-2']);
    
    encoder = [encoder;firstConv; firstReLU; secondConv; secondReLU];
    
    if stage == encoderDepth
        dropOutLayer = dropoutLayer(0.5,'Name',['Encoder-Stage-' num2str(stage) '-DropOut']);
        encoder = [encoder; dropOutLayer];
    end
    
    maxPoolLayer = maxPooling2dLayer(2, 'Stride', 2, 'Name',['Encoder-Stage-' num2str(stage) '-MaxPool']);
    
    encoder = [encoder; maxPoolLayer];
end
finalNumChannels = encoderNumChannels;
end
%--------------------------------------------------------------------------
function [decoder, finalDecoderNumChannels] = iCreateDecoder(encoderDepth, upConvFilterSize, convFilterSize, initialDecoderNumChannels)
decoder = [];
for stage = 1:encoderDepth    
    % Half the layer number of channels at each stage of the decoder.
    decoderNumChannels = initialDecoderNumChannels / 2^(stage-1);
    
    upConv = createAndInitializeUpConvLayer(upConvFilterSize, 2*decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-UpConv']);
    upReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-UpReLU']);
    
    % Input feature channels are concatenated with deconvolved features within the decoder.
    depthConcatLayer = depthConcatenationLayer(2, 'Name', ['Decoder-Stage-' num2str(stage) '-DepthConcatenation']);
    
    firstConv = createAndInitializeConvLayer(convFilterSize, 2*decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-Conv-1']);
    firstReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-ReLU-1']);
    
    secondConv = createAndInitializeConvLayer(convFilterSize, decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-Conv-2']);
    secondReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-ReLU-2']);
    
    decoder = [decoder; upConv; upReLU; depthConcatLayer; firstConv; firstReLU; secondConv; secondReLU];
end
finalDecoderNumChannels = decoderNumChannels;
end
%--------------------------------------------------------------------------
function convLayer = createAndInitializeConvLayer(convFilterSize, inputNumChannels, outputNumChannels, layerName)
convLayer = convolution2dLayer(convFilterSize,outputNumChannels,...
    'Padding', 'same',...
    'BiasL2Factor',0,...
    'Name',layerName);
% He initialization is used
convLayer.Weights = sqrt(2/((convFilterSize(1)*convFilterSize(2))*inputNumChannels)) ...
    * randn(convFilterSize(1),convFilterSize(2), inputNumChannels, outputNumChannels);
convLayer.Bias = zeros(1,1,outputNumChannels);
convLayer.BiasLearnRateFactor = 2;
end
%--------------------------------------------------------------------------
function upConvLayer = createAndInitializeUpConvLayer(UpconvFilterSize, inputNumChannels, outputNumChannels, layerName)
upConvLayer = transposedConv2dLayer(UpconvFilterSize, outputNumChannels,...
    'Stride',2,...
    'BiasL2Factor',0,...
    'Name',layerName);
% The transposed conv filter size is a scalar
upConvLayer.Weights = sqrt(2/((UpconvFilterSize^2)*inputNumChannels)) ...
    * randn(UpconvFilterSize,UpconvFilterSize,outputNumChannels,inputNumChannels);
upConvLayer.Bias = zeros(1,1,outputNumChannels);
upConvLayer.BiasLearnRateFactor = 2;
end

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Divya Gaddipati 2020-7-23

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/554161-u-net-segmentation-gives-error-for-layers-output-size-after-starts-to-training#answer_469851

It seems like your last conv layer is of size [512, 512, 4], whereas it should have been [512,512,1].

You can use analyzeNetwork on your created network (lgraph) to understand what the output sizes are each layer.

Refer to the examples in the following link for more information on using pixelClassificationLayer

https://www.mathworks.com/help/vision/ref/nnet.cnn.layer.pixelclassificationlayer.html

Here's a link to the documentation of analyzeNetwork

https://www.mathworks.com/help/deeplearning/ref/analyzenetwork.html

2 个评论
显示无隐藏无

Mahmut Uçar 2020-7-24

i had solved the problem with using different u-net function, but i will search that it seems logical. I was confused about [1 1 1] but its very logical to means [512 512 1]. Thank you

Manjunath R V 2021-5-17

Hello Mahmut if u r code is working fine means please share this with me

请先登录，再进行评论。

Answer 2

bochra jaafar 2021-11-9

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/554161-u-net-segmentation-gives-error-for-layers-output-size-after-starts-to-training#answer_827074

Hi Mahmut I have the same prolem . Can you please tell me how you solved it .

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

sugreev bal 2022-6-2

hello mahmut does the code applicable for 1d signal

请先登录，再进行评论。

U-net segmentation gives error for layers output size after starts to training

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

采纳的回答

2 个评论
显示无隐藏无

更多回答（1 个）

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

另请参阅

类别

标签

Community Treasure Hunt

U-net segmentation gives error for layers output size after starts to training

0 个评论 显示 -2更早的评论隐藏 -2更早的评论

采纳的回答

2 个评论 显示 无隐藏 无

更多回答（1 个）

1 个评论 显示 -1更早的评论隐藏 -1更早的评论

另请参阅

类别

标签

Community Treasure Hunt

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

2 个评论
显示无隐藏无

1 个评论
显示 -1更早的评论隐藏 -1更早的评论