MATLAB: GPU arrays support only fundamental numeric or logical data types

I tried to use Faster R-CNN with the pretrained model of ResNet18 where I froze some of the layers to detect 5 different objects inside image. I've successfully train the model using only the training data (in table). However when I include the validation data, the MATLAB asked me to change the validation and training data to be in datastore format (basically I followed the steps here https://www.mathworks.com/help/vision/ug/object-detection-using-faster-r-cnn-deep-learning.html). I've tried to train the model, the training can go on, but there is a pop-up warning for every iteration(?) saying the GPU arrays support only fundamental numeric or logical data types. Also, the training plot for loss function can't be shown.

load data.mat
lgraph=resnet18_12_freeze
%trainingData
imds = imageDatastore(trainingData.imageFilename);
blds = boxLabelDatastore(trainingData(:,2:end));
trainingData=combine(imds,blds);
%validationData
imds = imageDatastore(validationData.imageFilename);
blds = boxLabelDatastore(validationData(:,2:end));
validationData=combine(imds,blds);
inputSize = [224 224 3];
trainingData = transform(trainingData, @(data)preprocessData(data,inputSize));
augmentedTrainingData = transform(trainingData,@augmentData);
augmentedData = cell(4,1);
trainingData = transform(augmentedTrainingData,@(data)preprocessData(data,inputSize));
validationData = transform(validationData,@(data)preprocessData(data,inputSize));
options = trainingOptions('sgdm', ...
'MiniBatchSize', 2, ...
'InitialLearnRate', 1e-3, ...
'MaxEpochs', 50, ...
'VerboseFrequency', 50, ...
'ValidationData', validationData, ...
'ValidationFrequency',50, ...
'ExecutionEnvironment', 'gpu', ...
'CheckpointPath', tempdir, ...
'Plots','training-progress');
  
  
      
detector = trainFasterRCNNObjectDetector(trainingData, lgraph, options, ...
        'NegativeOverlapRange',[0.1 0.5], ...
        'PositiveOverlapRange',[0.5 1]);

Warning message

Warning: GPU arrays support only fundamental numeric or logical data types. 
> In nnet.internal.cnn.util/TrainingPlotReporter/cleanUpAfterPlotError (line 109)
In nnet.internal.cnn.util/TrainingPlotReporter/reportIteration (line 58)
In nnet.internal.cnn.util/VectorReporter/computeAndReport (line 64)
In nnet.internal.cnn.util/VectorReporter/reportIteration (line 20)
In nnet.internal.cnn/Trainer/train (line 142)
In vision.internal.cnn.trainNetwork (line 102)
In trainFasterRCNNObjectDetector>iTrainEndToEnd (line 901)
In trainFasterRCNNObjectDetector (line 428)
In trainFasterRCNN (line 35)

Best Answer