When I was running the mask RCNN code, I was prompted that I was out of memory, but I changed a good computer. The same problem still exists. Can anyone solve it

doTraining = true;

if doTraining

iteration = 1;

start = tic;

% Create subplots for the learning rate and mini-batch loss

fig = figure;

[lossPlotter, learningratePlotter] = helper.configureTrainingProgressPlotter(fig);

% Initialize verbose output

helper.initializeVerboseOutput([]);

% Custom training loop

for epoch = 1:maxEpochs

reset(mbqTrain)

shuffle(mbqTrain)

while hasdata(mbqTrain)

% Get next batch from minibatchqueue

[X,gtBox,gtClass,gtMask] = next(mbqTrain);

% Evaluate the model gradients and loss using dlfeval

[gradients,loss,state,learnables] = dlfeval(@networkGradients,X,gtBox,gtClass,gtMask,net,params);

idx = dlnet.State.Parameter == "TrainedVariance";

boundAwayFromZero = @(X) max(X, eps('single'));

dlnet.State(idx,:) = dlupdate(boundAwayFromZero, dlnet.State(idx,:));

% Compute the learning rate for the current iteration

learnRate = initialLearnRate/(1 + decay*(epoch-1));

if(~isempty(gradients) && ~isempty(loss))

[net.AllLearnables,velocity] = sgdmupdate(learnables,gradients,velocity,learnRate,momentum);

else

continue;

end

% Plot loss/accuracy metric every 10 iterations

if(mod(iteration,10)==0)

helper.displayVerboseOutputEveryEpoch(start,learnRate,epoch,iteration,loss);

D = duration(0,0,toc(start),'Format','hh:mm:ss');

addpoints(learningratePlotter,iteration,learnRate)

addpoints(lossPlotter,iteration,double(gather(extractdata(loss))))

subplot(2,1,2)

title(strcat("Epoch: ",num2str(epoch),", Elapsed: "+string(D)))

drawnow

end

iteration = iteration + 1;

end

end

% Save the trained network

modelDateTime = string(datetime('now','Format',"yyyy-MM-dd-HH-mm-ss"));

save(strcat("trainedMaskRCNN-",modelDateTime,"-Epoch-",num2str(epoch),".mat"),'net');

end