Dimensions of arrays not consistent error with image segmentation network

Question

Jacob Heiss 2023-6-14

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1982634-dimensions-of-arrays-not-consistent-error-with-image-segmentation-network

回答： Jacob Heiss 2023-6-21

I am getting an error on line 80 of my code (when I try to train the network) where it says that the dimensions of the arrays being concatenated are not consistent. After looking into the problem a bit I double checked and all of my input images are 1024x1024 so I'm not sure where the problem is coming from.

clear all;
clc;
 
dataSetDir = 'C:\Users\jacob\Documents\School\Image Segmentation Training Data';
imageDir = fullfile(dataSetDir, 'Cores');
labelDir = fullfile(dataSetDir,'Labels Despeckeled');
imds = imageDatastore(imageDir);
classNames = ["background","core"];
labelIDs   = [255 0];
pxds = pixelLabelDatastore(labelDir,classNames,labelIDs);
 
% I = read(imds);
% C = read(pxds);
% 
% I = imresize(I,5);
% L = imresize(uint8(C{1}),5);
% imshowpair(I,L,'montage')
 
numFilters = 64;
filterSize = 3;
numClasses = 2;
 
inputSize = [1024,1024,3];
imgLayer = imageInputLayer(inputSize); 
 
conv = convolution2dLayer(filterSize,numFilters,'Padding',1);
relu = reluLayer();
poolSize = 2;
maxPoolDownsample2x = maxPooling2dLayer(poolSize,'Stride',2);
downsamplingLayers = [
   conv
   relu
   maxPoolDownsample2x
   conv
   relu
   maxPoolDownsample2x
   conv
   relu
   maxPoolDownsample2x
   ];
filterSize = 4;
transposedConvUpsample2x = transposedConv2dLayer(4,numFilters,'Stride',2,'Cropping',1);
upsamplingLayers = [
   transposedConvUpsample2x
   relu
   transposedConvUpsample2x
   relu
   transposedConvUpsample2x
   relu
   ];
conv1x1 = convolution2dLayer(1,numClasses);
finalLayers = [
   conv1x1
   softmaxLayer()
   pixelClassificationLayer()
   ];
layers = [
   imgLayer    
   downsamplingLayers
   upsamplingLayers
   finalLayers
   ];
opts = trainingOptions('sgdm', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',15, ...
    'MiniBatchSize',64, ...
    Plots="training-progress");
trainingData = combine(imds,pxds);
net = trainNetwork(trainingData,layers,opts);
testImage = imread('C:\Users\jacob\Documents\School\Image Segmentation Training Data\Cores\00.1nM Dox 231-001.png');
imshow(testImage)
C = semanticseg(testImage,net);
B = labeloverlay(testImage,C);
imshow(B)