CNTK Evaluate model has two inputs C++ - c++

Question

I have a project based on CNTK 2.3. I used the code from the integration tests to train MNIST classifier like this:
auto device = DeviceDescriptor::GPUDevice(0);
const size_t inputDim = sizeBlob * sizeBlob;
const size_t numOutputClasses = numberOfClasses;
const size_t hiddenLayerDim = 200;
auto input = InputVariable({ inputDim }, CNTK::DataType::Float, L"features");
auto scaledInput = ElementTimes(Constant::Scalar(0.00390625f, device), input);
auto classifierOutput = FullyConnectedDNNLayer(scaledInput, hiddenLayerDim, device, std::bind(Sigmoid, _1, L""));
auto outputTimesParam = Parameter(NDArrayView::RandomUniform<float>({ numOutputClasses, hiddenLayerDim }, -0.05, 0.05, 1, device));
auto outputBiasParam = Parameter(NDArrayView::RandomUniform<float>({ numOutputClasses }, -0.05, 0.05, 1, device));
classifierOutput = Plus(outputBiasParam, Times(outputTimesParam, classifierOutput), L"classifierOutput");
auto labels = InputVariable({ numOutputClasses }, CNTK::DataType::Float, L"labels");
auto trainingLoss = CNTK::CrossEntropyWithSoftmax(classifierOutput, labels, L"lossFunction");;
auto prediction = CNTK::ClassificationError(classifierOutput, labels, L"classificationError");
// Test save and reload of model
Variable classifierOutputVar = classifierOutput;
Variable trainingLossVar = trainingLoss;
Variable predictionVar = prediction;
auto combinedNet = Combine({ trainingLoss, prediction, classifierOutput }, L"MNISTClassifier");
//SaveAndReloadModel<float>(combinedNet, { &input, &labels, &trainingLossVar, &predictionVar, &classifierOutputVar }, device);
classifierOutput = classifierOutputVar;
trainingLoss = trainingLossVar;
prediction = predictionVar;
const size_t minibatchSize = 64;
const size_t numSamplesPerSweep = 60000;
const size_t numSweepsToTrainWith = 2;
const size_t numMinibatchesToTrain = (numSamplesPerSweep * numSweepsToTrainWith) / minibatchSize;
auto featureStreamName = L"features";
auto labelsStreamName = L"labels";
auto minibatchSource = TextFormatMinibatchSource(trainingSet, { { featureStreamName, inputDim },{ labelsStreamName, numOutputClasses } });
auto featureStreamInfo = minibatchSource->StreamInfo(featureStreamName);
auto labelStreamInfo = minibatchSource->StreamInfo(labelsStreamName);
LearningRateSchedule learningRatePerSample = TrainingParameterPerSampleSchedule<double>(0.003125);
auto trainer = CreateTrainer(classifierOutput, trainingLoss, prediction, { SGDLearner(classifierOutput->Parameters(), learningRatePerSample) });
size_t outputFrequencyInMinibatches = 20;
for (size_t i = 0; i < numMinibatchesToTrain; ++i)
{
auto minibatchData = minibatchSource->GetNextMinibatch(minibatchSize, device);
trainer->TrainMinibatch({ { input, minibatchData[featureStreamInfo] },{ labels, minibatchData[labelStreamInfo] } }, device);
PrintTrainingProgress(trainer, i, outputFrequencyInMinibatches);
size_t trainingCheckpointFrequency = 100;
if ((i % trainingCheckpointFrequency) == (trainingCheckpointFrequency - 1))
{
const wchar_t* ckpName = L"feedForward.net";
//trainer->SaveCheckpoint(ckpName);
//trainer->RestoreFromCheckpoint(ckpName);
}
}
combinedNet->Save(g_dnnFile);
That part works fine and I train the model then save to a model file. But when I try to evaluate a simple image to test the model it looks like something is wrong in the model.
// Load the model.
// The model is trained by <CNTK>/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py
// Please see README.md in <CNTK>/Examples/Image/Classification/ResNet about how to train the model.
FunctionPtr modelFunc = Function::Load(modelFile, device);
// Get input variable. The model has only one single input.
std::vector<Variable> inputs = modelFunc->Arguments();
Variable inputVar = modelFunc->Arguments()[0];
// The model has only one output.
// If the model has more than one output, use modelFunc->Outputs to get the list of output variables.
std::vector<Variable> outputs = modelFunc->Outputs();
Variable outputVar = outputs[0];
// Prepare input data.
// For evaluating an image, you first need to perform some image preprocessing to make sure that the input image has the correct size and layout
// that match the model inputs.
// Please note that the model used by this example expects the CHW image layout.
// inputVar.Shape[0] is image width, inputVar.Shape[1] is image height, and inputVar.Shape[2] is channels.
// For simplicity and avoiding external dependencies, we skip the preprocessing step here, and just use some artificially created data as input.
Mat image = imread(".....");
uint8_t* imagePtr = (uint8_t*)(image).data;
auto width = image.cols;
auto heigth = image.rows;
std::vector<float> inputData(inputVar.Shape().TotalSize());
for (size_t i = 0; i < inputData.size(); ++i)
{
auto curChVal = imagePtr[(i)];
inputData[i] = curChVal;
}
// Create input value and input data map
ValuePtr inputVal = Value::CreateBatch(inputVar.Shape(), inputData, device);
std::unordered_map<Variable, ValuePtr> inputDataMap = { { inputVar, inputVal } };
// Create output data map. Using null as Value to indicate using system allocated memory.
// Alternatively, create a Value object and add it to the data map.
std::unordered_map<Variable, ValuePtr> outputDataMap = { { outputVar, nullptr } };
// Start evaluation on the device
modelFunc->Evaluate(inputDataMap, outputDataMap, device);
// Get evaluate result as dense output
ValuePtr outputVal = outputDataMap[outputVar];
std::vector<std::vector<float>> outputData;
outputVal->CopyVariableValueTo(outputVar, outputData);
PrintOutput<float>(outputVar.Shape().TotalSize(), outputData);
I run the same code on c# and it works fine. What I found as a difference is that modelFunc->Arguments() should have one argument but it has two - it finds features and labels as two inputs but I need to have only feature as an input and it throws the following error:

Answer 1

Find input and output variables by name, instead of modelFunc->Arguments()[0].
Variable inputVar;
GetInputVariableByName(modelFunc, L"features", inputVar);
Variable outputVar;
GetOutputVaraiableByName(modelFunc, L"classifierOutput", outputVar);
GetInputVariableByName and GetOutputVaraiableByName() come from
https://github.com/Microsoft/CNTK/blob/v2.3.1/Tests/EndToEndTests/EvalClientTests/CNTKLibraryCPPEvalExamplesTest/EvalMultithreads.cpp#L316