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Cannot train FasterRCNNObjectDetector on single GPU

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Angelo Dumitriu
Angelo Dumitriu 2018 年 10 月 14 日
Despite the training options specifying the execution environment to 'gpu', the training does not execute on GPU but on CPU. My drivers are updated and MATLAB is R2018a.
When I specity 'cpu', the Command Window log states "Training on single CPU", but when I set to 'gpu', nothing shows. This is the log with 'gpu' as ExecutionEnvironment.
Training a Faster R-CNN Object Detector for the following object classes:
* ROI
Step 1 of 4: Training a Region Proposal Network (RPN).
|========================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning |
| | | (hh:mm:ss) | Accuracy | RMSE | Rate |
|========================================================================================|
| 1 | 1 | 00:00:08 | 28.00% | 0.81 | 0.0005 |
| 1 | 50 | 00:01:04 | 64.00% | 0.88 | 0.0005 |
| 2 | 100 | 00:01:58 | 72.00% | 0.90 | 0.0005 |
| 3 | 150 | 00:02:52 | 80.00% | 0.85 | 0.0005 |
| 4 | 200 | 00:03:46 | 80.00% | 0.89 | 0.0001 |
| 5 | 250 | 00:04:40 | 76.00% | 0.87 | 0.0001 |
The code:
count = gpuDeviceCount;
gpu1 = gpuDevice(1);
inputLayer = imageInputLayer([32 32 3]);
filterSize = [3 3];
numFilters = 32;
middleLayers = [
convolution2dLayer(filterSize, numFilters, 'Padding', 1)
reluLayer()
convolution2dLayer(filterSize, numFilters, 'Padding', 1)
reluLayer()
maxPooling2dLayer(3, 'Stride',2)
];
finalLayers = [
fullyConnectedLayer(64)
reluLayer()
fullyConnectedLayer(width(trainingDatasetLight))
softmaxLayer()
classificationLayer()
];
layers = [
inputLayer
middleLayers
finalLayers
];
optionsStage1 = trainingOptions('sgdm', ...
'Momentum',0.7, ...
'MaxEpochs', 15, ...
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',3,...
'MiniBatchSize', 25, ...
'InitialLearnRate', 5e-4, ...
'CheckpointPath', tempdir, ...
'ExecutionEnvironment', 'gpu');
optionsStage2 = trainingOptions('sgdm', ...
'MaxEpochs', 10, ...
'MiniBatchSize', 50, ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',2, ...
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir, ...
'ExecutionEnvironment', 'gpu');
optionsStage3 = trainingOptions('sgdm', ...
'MaxEpochs', 10, ...
'MiniBatchSize', 50, ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',2, ...
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir, ...
'ExecutionEnvironment', 'gpu');
optionsStage4 = trainingOptions('sgdm', ...
'MaxEpochs', 10, ...
'MiniBatchSize', 50, ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',2, ...
'InitialLearnRate', 1e-3, ...
'CheckpointPath', tempdir, ...
'ExecutionEnvironment', 'gpu');
options = [
optionsStage1
optionsStage2
optionsStage3
optionsStage4
];
rng(0);
detector = trainFasterRCNNObjectDetector(trainingDatasetLight, layers, options, ...
'NegativeOverlapRange', [0 0.3], ...
'PositiveOverlapRange', [0.6 1], ...
'BoxPyramidScale', 1.2);
The GPU:
gpu1 =
CUDADevice with properties:
Name: 'GeForce 840M'
Index: 1
ComputeCapability: '5.0'
SupportsDouble: 1
DriverVersion: 10
ToolkitVersion: 9
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 4.2950e+09
AvailableMemory: 3.4248e+09
MultiprocessorCount: 3
ClockRateKHz: 1124000
ComputeMode: 'Default'
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceSelected: 1
Why won't it let me train on GPU?

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