GPU arrays support only fundamental numeric or logical data types

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Ricky Hartono
Ricky Hartono on 15 Jan 2021
Commented: Joss Knight on 23 Jan 2021
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 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
imds = imageDatastore(trainingData.imageFilename);
blds = boxLabelDatastore(trainingData(:,2:end));
imds = imageDatastore(validationData.imageFilename);
blds = boxLabelDatastore(validationData(:,2:end));
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, ...
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)

Accepted Answer

Joss Knight
Joss Knight on 16 Jan 2021
It looks distinctly like one of your custom functions, preprocessData or augmentData, is doing something illegal with gpuArray objects. Try calling dbstop if all error so that MATLAB will break at the problematic line of code.
Joss Knight
Joss Knight on 23 Jan 2021
Here's an idea: wrap the code in your custom functions preprocessData and augmentData with a try...catch block. Then you can put a breakpoint inside the catch clause, allowing you to determine the original source of the error - assuming it was indeed your custom code.
% Put your original code here
catch ME
% Put a breakpoint here so you can inspect the error
getReport(ME) % This displays the error and call stack
Clearly we need to do better here with reporting the correct call stack so you can see the source of the error, but hopefully this should help.

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