Error executing of the example code for training a custom Mask

Error executing of the example code for training a custom Mask R-CNN using cocodataset 2014

I followed the instructions in “Instance Segmentation Using Mask R-CNN Deep Learning” (ref[1]).

All the code worked perfectly until the last section “Train network” (ref[2]).

iteration = 1; 
    start = tic;
    
     % Create subplots for the learning rate and mini-batch loss
    fig = figure;
    [lossPlotter] = helper.configureTrainingProgressPlotter(fig);
    
    % Initialize verbose output
    helper.initializeVerboseOutput([]);
    
    % Custom training loop
    for epoch = 1:numEpochs
        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] = dlfeval(@networkGradients,X,gtBox,gtClass,gtMask,dlnet,params);
            dlnet.State = state;
            
            % Compute the learning rate for the current iteration
            learnRate = initialLearnRate/(1 + decay*iteration);
            
            if(~isempty(gradients) && ~isempty(loss))    
                [dlnet.Learnables,velocity] = sgdmupdate(dlnet.Learnables,gradients,velocity,learnRate,momentum);
            else
                continue;
            end
            
            helper.displayVerboseOutputEveryEpoch(start,learnRate,epoch,iteration,loss);
                
            % Plot loss/accuracy metric
            D = duration(0,0,toc(start),'Format','hh:mm:ss');
            addpoints(lossPlotter,numdetectMaskRCNN,Iteration,double(gather(extractdata(loss))))
            subplot(2,1,2)
            title(strcat("Epoch: ",num2str(epoch),", Elapsed: "+string(D)))
            drawnow
            
            iteration = iteration + 1;    
        end
    
    end
    net = dlnet;
    
    % Save the trained network
    modelDateTime = string(datetime('now','Format',"yyyy-MM-dd-HH-mm-ss"));
    save(strcat("trainedMaskRCNN-",modelDateTime,"-Epoch-",num2str(numEpochs),".mat"),'net');

First, there is no “numdetectMaskRCNN” predefined.

I simply deleted it and reexecuted the section. It then showes the following error:

Error using nnet.internal.cnn.dlnetwork/forward (line 239)

Layer ‘bn2a_branch2a’: Invalid input data. The value of ‘Variance’ is invalid. Expected input to be positive.

Error in nnet.internal.cnn.dlnetwork/CodegenOptimizationStrategy/propagateWithFallback (line 122)

[varargout{1:nargout}] = fcn(net, X, layerIndices, layerOutputIndices);

Error in nnet.internal.cnn.dlnetwork/CodegenOptimizationStrategy/forward (line 62)

[varargout{1:nargout}] = propagateWithFallback(strategy, functionSlot, @forward, net, X, layerIndices, layerOutputIndices);

Error in nnet.internal.cnn.dlnetwork/DefaultOptimizationStrategy/propagate (line 143)

[varargout{1:nargout}] = inferenceMethod(strategy.CodegenStrategyOriginal,…

Error in nnet.internal.cnn.dlnetwork/DefaultOptimizationStrategy/forward (line 77)

[varargout{1:nargout}] = propagate(strategy, net, X, …

Error in dlnetwork/forward (line 503)

[varargout{1:nargout}] = strategy.forward(net.PrivateNetwork, x, layerIndices, layerOutputIndices);

Error in networkGradients (line 21)

[YRPNRegDeltas, proposal, YRCNNClass, YRCNNReg, YRPNClass, YMask, state] = forward(…

Error in deep.internal.dlfeval (line 18)

[varargout{1:nout}] = fun(x{:});

Error in dlfeval (line 41)

[varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:});

I am wondering if there is anything I misunderstood so that the code doesn’t work for me.

It will be of great help if this could be figured out or fixed

ANSWER

Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

I figured out a solution to this issue from other resource.

The problem comes from the negative value returned by “state”. The original code is as below:

[gradients,loss,state] = dlfeval(@networkGradients,X,gtBox,gtClass,gtMask,dlnet,params);
dlnet.State = state;

Replace the last line (dlnet.State = state;) with the followings to ensure that all values assigned to “dlnet.State” are positive.

idx = dlnet.State.Parameter == "TrainedVariance";
boundAwayFromZero = @(X) max(X, eps('single'));
dlnet.State(idx,:) = dlupdate(boundAwayFromZero, dlnet.State(idx,:));

This will make the code work then.

But then I am now facing another problem. The training process takes so much time (days),

SEE COMPLETE ANSWER CLICK THE LINK

Error executing of the example code for training a custom Mask

Error executing of the example code for training a custom Mask