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!

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,[])

[Seth Furman]

I should mention that datestr is discouraged. Prefer datetime where possible.

https://www.mathworks.com/help/matlab/ref/datetime.html

For example,

dt = datetime("now","Format","dd-MMM-yyyy-HH-mm-ss")

dt = datetime
   06-Jun-2022-16-07-40

string(dt)

ans = "06-Jun-2022-16-07-40"

[Joss Knight]

Is randomPatchExtractionDatastore what you actually want, or is it just your attempt to reduce the input size?

Answer 1

[Dinesh]

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:

1. https://www.mathworks.com/help/matlab/ref/imresize.html
2. https://www.mathworks.com/help/deeplearning/ug/optimize-neural-network-training-speed-and-memory.html

Hope this helps !