![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1023410/image.png)
How to solve this error cause our origin pic is 512*512 but my GPU is not good enough so training is too slow and i wanna use 256*256 to let it train faster!
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imds1 = imageDatastore('E:\Picture\PDRR');
imds2 = imageDatastore ('E:\Picture\DRR');
augmenter = imageDataAugmenter( ...
'RandRotation',@()randi([0,1],1)*90, ...
'RandXReflection',true);
miniBatchSize = 4;
patchSize = [256 256];
patchds = randomPatchExtractionDatastore(imds1,imds2,patchSize, ...
'PatchesPerImage',8, ...
'DataAugmentation',augmenter);
patchds.MiniBatchSize = miniBatchSize;
lgraph=layerGraph();
tempLayers=[
imageInputLayer([256 256 1],"Name","ImageInputLayer","Normalization","rescale-zero-one")
convolution2dLayer([3 3],32,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution2dLayer([3 3],32,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-2")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
maxPooling2dLayer([2 2],"Name","Encoder-Stage-1-MaxPool","Stride",2 )
convolution2dLayer([3 3],64,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-2-ReLU-1")
convolution2dLayer([3 3],64,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
maxPooling2dLayer([2 2],"Name","Encoder-Stage-2-MaxPool","Stride",2)
convolution2dLayer([3 3],128,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-3-ReLU-1")
convolution2dLayer([3 3],128,"Name","Encoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-3-ReLU-2")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
dropoutLayer(0.5,"Name","Encoder-Stage-3-DropOut")
maxPooling2dLayer([2 2],"Name","Encoder-Stage-3-MaxPool","Stride",2)
convolution2dLayer([3 3],256,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Bridge-ReLU-1")
convolution2dLayer([3 3],256,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Bridge-ReLU-2")
dropoutLayer(0.5,"Name","Bridge-DropOut")
transposedConv2dLayer([2 2],128,"Name","Decoder-Stage-1-UpConv","Stride",2)
reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
convolution2dLayer([3 3],128,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-1-ReLU-1")
convolution2dLayer([3 3],128,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-1-ReLU-2")
transposedConv2dLayer([2 2],64 ,"Name","Decoder-Stage-2-UpConv","Stride",2)
reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
convolution2dLayer([3 3],64,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-2-ReLU-1")
convolution2dLayer([3 3],64,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-2-ReLU-2")
transposedConv2dLayer([2 2],32,"Name","Decoder-Stage-3-UpConv","Stride",2)
reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph=addLayers(lgraph,tempLayers);
tempLayers=[
depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
convolution2dLayer([3 3],32,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-3-ReLU-1")
convolution2dLayer([3 3],32,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Decoder-Stage-3-ReLU-2")
convolution2dLayer([2 2],1,"Name","Final-ConvolutionLayer","Padding","same","WeightsInitializer","he")
regressionLayer('Name','output')
];
lgraph=addLayers(lgraph,tempLayers);
clear tempLayers;
lgraph=connectLayers(lgraph,"Encoder-Stage-1-ReLU-2","Encoder-Stage-1-MaxPool");
lgraph=connectLayers(lgraph,"Encoder-Stage-1-ReLU-2","Decoder-Stage-3-DepthConcatenation/in2");
lgraph=connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Encoder-Stage-2-MaxPool");
lgraph=connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Decoder-Stage-2-DepthConcatenation/in2");
lgraph=connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Encoder-Stage-3-DropOut");
lgraph=connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Decoder-Stage-1-DepthConcatenation/in2");
lgraph=connectLayers(lgraph,"Decoder-Stage-1-UpReLU","Decoder-Stage-1-DepthConcatenation/in1");
lgraph=connectLayers(lgraph,"Decoder-Stage-2-UpReLU","Decoder-Stage-2-DepthConcatenation/in1");
lgraph=connectLayers(lgraph,"Decoder-Stage-3-UpReLU","Decoder-Stage-3-DepthConcatenation/in1");
figure
plot(lgraph)
maxEpochs =1;
epochIntervals = 1;
initLearningRate = 0.01;
learningRateFactor = 0.0001;
l2reg = 0.0001;
options = trainingOptions('sgdm', ...
'Momentum',0.9, ...
'InitialLearnRate',initLearningRate, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',10, ...
'LearnRateDropFactor',learningRateFactor, ...
'L2Regularization',l2reg, ...
'MaxEpochs',maxEpochs ,...
'MiniBatchSize',miniBatchSize, ...
'GradientThresholdMethod','l2norm', ...
'Plots','training-progress', ...
'GradientThreshold',0.01);
modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
net = trainNetwork(patchds,lgraph,options);
save(['trainedNet-' modelDateTime '-Epoch-' num2str(maxEpochs*epochIntervals) ...
'ScaleFactors-' num2str(234) '.mat'],'net','options');
pic = imread('E:\Picture\PDRR\00001.jpg');
out2 = predict(net,pic);
subplot(1,2,1)
imshow(pic)
subplot(1,2,2)
imshow(out2,[])
2 commentaires
Seth Furman
le 6 Juin 2022
Modifié(e) : Seth Furman
le 6 Juin 2022
I should mention that datestr is discouraged. Prefer datetime where possible.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1023410/image.png)
For example,
dt = datetime("now","Format","dd-MMM-yyyy-HH-mm-ss")
string(dt)
Joss Knight
le 11 Juin 2022
Is randomPatchExtractionDatastore what you actually want, or is it just your attempt to reduce the input size?
Réponses (1)
Dinesh
le 2 Mar 2023
Hi FangMing!
As per my understanding you want to resize the picture from 512*512 to 256 * 256 to increase the training speed.
You can use the function ‘imresize’ to resize the image, set the ‘scale’ parameter to 0.5.
You can refer the following MATLAB documentation for more details:
- https://www.mathworks.com/help/matlab/ref/imresize.html
- https://www.mathworks.com/help/deeplearning/ug/optimize-neural-network-training-speed-and-memory.html
Hope this helps !
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