Roberto43 asked . 2021-06-22

3-D Brain Tumor Segmentation ERROR

Hi all,

I'm getting the following error running the 3-D Brain Tumor Segmentation example

(https://www.mathworks.com/help/images/segment-3d-brain-tumor-using-deep-learning.html)

Error using trainNetwork (line 165)

Incorrect loss type returned by 'forwardLoss' in the output layer. Expected to be 'single', but instead was 'gpuArray'.

The output layer is dicePixelClassification3dLayer. It seems like there's some kind of error in the definition of that custom layer. Converting to single the outputs of the layer methods didn't help, same error.

If it's helpful I'm using r2019a on windows 10 on a i9 9900k + 1080ti.

u-net , programming , matlab , deep learning

Expert Answer

Prashant Kumar answered . 2024-12-21 17:19:08

Hi,

The issue exposed here is related to low GPU memory. That being said, the following error should not be thrown. I have heard that this issue is known, and the concerned parties may be investigating further.

'Conversion to single from gpuArray is not possible'

A possible Workaround:

The attached patch will fix the error mentioned above, in the sense that the above error message will not be thrown if the training process errors out due to low GPU memory. Follow the steps below to apply the patch.

1) Save the attached zip file to your $MATLABROOT folder. The $MATLABROOT folder can be determined by running the ‘matlabroot’ command.

2) Using MATLAB, navigate to your $MATLABROOT folder by executing the command:

cd(matlabroot) in the MATLAB Command Window.

3) Execute the following command to unzip the file:

unzip('crop2dLayerPatch_18a_04_03_2018.zip')

You might need to start MATLAB as an administrator for this step.

4) Exit MATLAB and restart.

5) Execute the following command:

rehash toolboxcache

The GPU was running out of memory because the mini batch size is large and use up a lot of GPU memory. This value can be lowered by setting the "MiniBatchSize" parameter in the options that is passed to the "trainNetwork" function. I suggest you to start with 2 and if the problem doesn’t occur then you can increase the size in powers of 2.

The above should remove the error that is coming in trainNetwork.

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