/// <summary>
/// The example shows
/// - how to load a pretrained model and evaluate several nodes by combining their outputs
/// Note: The example uses the model 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.
/// The parameter 'modelFilePath' specifies the path to the model.
/// </summary>
void EvaluateCombinedOutputs(const wchar_t* modelFilePath, const DeviceDescriptor& device)
{
printf("\n===== Evaluate combined outputs =====\n");
// Load the model.
FunctionPtr modelFunc = Function::Load(modelFilePath, device);
// Get node of interest
std::wstring intermediateLayerName = L"final_avg_pooling";
FunctionPtr interLayerPrimitiveFunc = modelFunc->FindByName(intermediateLayerName);
Variable poolingOutput = interLayerPrimitiveFunc->Output();
// Create a function which combine outputs from the node "final_avg_polling" and the final layer of the model.
FunctionPtr evalFunc = Combine( { modelFunc->Output(), poolingOutput });
Variable inputVar = evalFunc->Arguments()[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.
std::vector<float> inputData(inputVar.Shape().TotalSize());
for (size_t i = 0; i < inputData.size(); ++i)
{
inputData[i] = static_cast<float>(i % 255);
}
// 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.
Variable modelOutput = evalFunc->Outputs()[0];
Variable interLayerOutput = evalFunc->Outputs()[1];
std::unordered_map<Variable, ValuePtr> outputDataMap = { { modelOutput, nullptr }, { interLayerOutput, nullptr } };
// Start evaluation on the device
evalFunc->Evaluate(inputDataMap, outputDataMap, device);
// Get evaluate result as dense outputs
for(auto & outputVariableValuePair : outputDataMap)
{
auto variable = outputVariableValuePair.first;
auto value = outputVariableValuePair.second;
std::vector<std::vector<float>> outputData;
value->CopyVariableValueTo(variable, outputData);
PrintOutput<float>(variable.Shape().TotalSize(), outputData);
}
}
/// <summary>
/// The example shows
/// - how to load model.
/// - how to prepare input data for a batch of samples.
/// - how to prepare input and output data map.
/// - how to evaluate a model.
/// - how to retrieve evaluation result and retrieve output data in dense format.
/// Note: The example uses the model 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.
/// The parameter 'modelFile' specifies the path to the model.
/// </summary>
void EvaluationBatchUsingDense(const wchar_t* modelFile, const DeviceDescriptor& device)
{
printf("\n===== Evaluate batch of samples using dense format.\n");
// The number of samples in the batch.
size_t sampleCount = 3;
// 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.
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.
Variable outputVar = modelFunc->Output();
// 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.
std::vector<float> inputData(inputVar.Shape().TotalSize() * sampleCount);
for (size_t i = 0; i < inputData.size(); ++i)
{
inputData[i] = static_cast<float>(i % 255);
}
// 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);
}
/// <summary>
/// The example shows
/// - how to load a pretrained model and evaluate an intermediate layer of its network.
/// Note: The example uses the model 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.
/// The parameter 'modelFilePath' specifies the path to the model.
/// </summary>
void EvaluateIntermediateLayer(const wchar_t* modelFilePath, const DeviceDescriptor& device)
{
printf("\n===== Evaluate intermediate layer =====\n");
// Load the model.
FunctionPtr rootFunc = Function::Load(modelFilePath, device);
std::wstring intermediateLayerName = L"final_avg_pooling";
FunctionPtr interLayerPrimitiveFunc = rootFunc->FindByName(intermediateLayerName);
// The Function returned by FindByName is a primitive function.
// For evaluation, it is required to create a composite function from the primitive function.
FunctionPtr modelFunc = AsComposite(interLayerPrimitiveFunc);
Variable outputVar = modelFunc->Output();
Variable inputVar = modelFunc->Arguments()[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.
std::vector<float> inputData(inputVar.Shape().TotalSize());
for (size_t i = 0; i < inputData.size(); ++i)
{
inputData[i] = static_cast<float>(i % 255);
}
// 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);
}
void RunEvaluationOnSingleSample(FunctionPtr evalInstance, const DeviceDescriptor& device)
{
// Get input variable. The model has only one single input.
Variable inputVar = evalInstance->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.
Variable outputVar = evalInstance->Output();
// 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.
std::vector<float> inputData(inputVar.Shape().TotalSize());
for (size_t i = 0; i < inputData.size(); ++i)
{
inputData[i] = static_cast<float>(i % 255);
}
// 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
evalInstance->Evaluate(inputDataMap, outputDataMap, device);
// Get evaluate result as dense output
ValuePtr outputVal = outputDataMap[outputVar];
std::vector<std::vector<float>> outputData;
outputVal->CopyVariableValueTo(outputVar, outputData);
}
static void PopulateNodeDef(const std::wstring& scope, const FunctionPtr& src, tensorflow::NodeDef& dst)
{
PopulateNodeDef(GetScopedName(scope, src), src->OpName(), src->Output().GetDataType(), src->Outputs(), dst);
}
void RunEvaluationClassifier(FunctionPtr evalFunc, const DeviceDescriptor& device)
{
const std::wstring inputNodeName = L"features";
Variable inputVar;
if (!GetInputVariableByName(evalFunc, inputNodeName, inputVar))
{
fprintf(stderr, "Input variable %S is not available.\n", inputNodeName.c_str());
throw("Input variable not found error.");
}
// Evaluate the network in several runs
size_t iterationCount = 4;
unsigned int randSeed = 2;
srand(randSeed);
size_t numSamples = 3;
std::vector<float> inputData(inputVar.Shape().TotalSize() * numSamples);
for (size_t t = 0; t < iterationCount; ++t)
{
for (size_t i = 0; i < inputData.size(); ++i)
{
inputData[i] = ((float)rand()) / RAND_MAX;
}
// Create input data shape. Adding sequence length and numSamples as axes.
// Todo: remove sequence length when only numSamples is supported.
// Todo: add convenience APIs to simplify data preparation here.
NDShape inputShape = inputVar.Shape().AppendShape({1, numSamples});
ValuePtr inputValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(inputShape, inputData, true));
// Define output.
ValuePtr outputValue;
auto outputVar = evalFunc->Output();
std::unordered_map<Variable, ValuePtr> outputs = {{outputVar, outputValue}};
// Evaluate the model
evalFunc->Forward({{inputVar, inputValue}}, outputs, device);
// Get output value
outputValue = outputs[outputVar];
// Todo: remove sequence length when only numSamples is supported.
// Todo: add convenience APIs to simplify retrieval of output results.
NDShape outputShape = outputVar.Shape().AppendShape({1, numSamples});
std::vector<float> outputData(outputShape.TotalSize());
NDArrayViewPtr cpuArrayOutput = MakeSharedObject<NDArrayView>(outputShape, outputData, false);
cpuArrayOutput->CopyFrom(*outputValue->Data());
assert(outputData.size() == outputVar.Shape()[0] * numSamples);
fprintf(stderr, "Evaluation result:\n");
size_t dataIndex = 0;
auto outputDim = outputVar.Shape()[0];
for (size_t i = 0; i < numSamples; i++)
{
fprintf(stderr, "Iteration:%lu, Sample %lu:\n", t, i);
fprintf(stderr, " ");
dataIndex = i * outputDim;
for (size_t j = 0; j < std::min((size_t)10, outputDim); j++)
{
fprintf(stderr, "%f ", outputData[dataIndex++]);
}
if (outputDim > 10)
{
fprintf(stderr, "...");
}
fprintf(stderr, "\n");
}
}
}
void TestReduceSum(size_t sampleRank, const DeviceDescriptor& device)
{
size_t numSequences = 7;
size_t maxAllowedSequenceLength = 11;
size_t maxDimSize = 23;
NDShape inputShape(sampleRank);
for (size_t i = 0; i < sampleRank; ++i)
inputShape[i] = (rand() % maxDimSize) + 1;
auto sequenceLengths = GenerateSequenceLengths(numSequences, maxAllowedSequenceLength);
auto sequences = GenerateSequences<float>(sequenceLengths, inputShape);
ValuePtr sequencesValue = Value::Create(inputShape, sequences, device, true);
// Test ReduceSum along a static axis
{
auto testReduceSum = [&sequences, &sequenceLengths, inputShape, sequencesValue, device, sampleRank](int reductionAxis, bool useNegativeAxisIndex)
{
size_t maxActualSequenceLength = sequencesValue->Shape()[inputShape.Rank()];
size_t numSequences = sequencesValue->Shape()[inputShape.Rank() + 1];
auto inputVar = InputVariable(inputShape, DataType::Float, L"input");
FunctionPtr reduceSumFunc;
bool reduceAll = (reductionAxis < 0);
if (reduceAll)
reduceSumFunc = ReduceSum(inputVar);
else
reduceSumFunc = ReduceSum(inputVar, Axis(useNegativeAxisIndex ? (reductionAxis - (int)sampleRank) : reductionAxis));
NDShape outputShape = reduceSumFunc->Output().Shape();
NDShape outputDataShape = outputShape;
if (!reduceAll)
outputDataShape = outputDataShape.AppendShape({ maxActualSequenceLength, numSequences });
std::vector<float> outputData(outputDataShape.TotalSize());
ValuePtr outputValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(outputDataShape, outputData, false), reduceAll ? nullptr : sequencesValue->Mask()->DeepClone());
std::unordered_map<Variable, ValuePtr> outputs = { { reduceSumFunc->Output(), outputValue } };
reduceSumFunc->Forward({ { inputVar, sequencesValue } }, outputs, device);
std::vector<size_t> inputShapeStrides = GetStrides(inputShape);
std::vector<size_t> outputShapeStrides = GetStrides(outputShape);
std::vector<float> expectedPerFrameTotals(outputShape.TotalSize() * maxActualSequenceLength * numSequences, 0.0f);
float expectedTotal = 0.0f;
for (size_t i = 0; i < numSequences; ++i)
{
size_t currentSequenceLength = sequenceLengths[i];
for (size_t j = 0; j < currentSequenceLength; ++j)
{
for (size_t k = 0; k < inputShape.TotalSize(); ++k)
{
auto inputIdx = UnflattenedShape(k, inputShapeStrides);
auto outputIdx = inputIdx;
if (!reduceAll)
outputIdx[reductionAxis] = 0;
else
outputIdx = {};
auto flatOutputIdx = FlattenedIndex(outputIdx, outputShapeStrides);
float value = sequences[i][(j * inputShape.TotalSize()) + k];
expectedPerFrameTotals[(((i * maxActualSequenceLength) + j) * outputShape.TotalSize()) + flatOutputIdx] += value;
expectedTotal += value;
}
}
}
if (reduceAll)
FloatingPointVectorCompare(outputData, std::vector<float>({ expectedTotal }), "testReduceSum: Forward prop results do not match expected results");
else
FloatingPointVectorCompare(outputData, expectedPerFrameTotals, "testReduceSum: Forward prop results do not match expected results");
};
// Reduce over all axes
testReduceSum(-1, false);
int reductionAxis = 0;
testReduceSum(reductionAxis, true);
if (reductionAxis < (inputShape.Rank() - 1))
reductionAxis++;
testReduceSum(reductionAxis, false);
if (reductionAxis < (inputShape.Rank() - 1))
reductionAxis++;
testReduceSum(reductionAxis, true);
}
// Test ReduceSum along a dynamic axis
{
auto testReduceSum = [&sequences, &sequenceLengths, inputShape, sequencesValue, device](const Axis& axis)
{
if (!axis.IsDynamicAxis())
RuntimeError("Called the dynamic axis ReduceSum test with a static axis");
size_t maxActualSequenceLength = sequencesValue->Shape()[inputShape.Rank()];
size_t numSequences = sequencesValue->Shape()[inputShape.Rank() + 1];
auto inputVar = InputVariable({ inputShape }, DataType::Float, L"input");
FunctionPtr reduceSumFunc = ReduceSum(inputVar, axis);
NDShape maskShape = { ((axis == Axis::DefaultBatchAxis()) ? maxActualSequenceLength : 1), ((axis == Axis::DefaultBatchAxis()) ? 1 : numSequences) };
NDShape outputShape = reduceSumFunc->Output().Shape();
auto outputDataShape = outputShape.AppendShape(maskShape);
std::vector<float> outputData(outputDataShape.TotalSize());
auto maskPtr = MakeSharedObject<NDMask>(maskShape, device);
ValuePtr outputValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(outputDataShape, outputData, false), maskPtr);
std::unordered_map<Variable, ValuePtr> outputs = { { reduceSumFunc->Output(), outputValue } };
reduceSumFunc->Forward({ { inputVar, sequencesValue } }, outputs, device);
std::vector<float> expectedTotals(outputDataShape.TotalSize(), 0.0f);
for (size_t i = 0; i < numSequences; ++i)
{
size_t currentSequenceLength = sequenceLengths[i];
for (size_t j = 0; j < currentSequenceLength; ++j)
{
for (size_t k = 0; k < inputShape.TotalSize(); ++k)
{
float value = sequences[i][(j * inputShape.TotalSize()) + k];
if (axis == Axis::DefaultBatchAxis())
expectedTotals[(j * inputShape.TotalSize()) + k] += value;
else
expectedTotals[(i * inputShape.TotalSize()) + k] += value;
}
}
}
FloatingPointVectorCompare(outputData, expectedTotals, "testReduceSum: Forward prop results do not match expected results");
};
testReduceSum(Axis::DefaultDynamicAxis());
}
}
Variable::Variable(const FunctionPtr& function)
: Variable(function->Output())
{
}
/// <summary>
/// The example shows
/// - how to prepare input data as sequence using sparse input.
/// The example uses the model trained by <CNTK>/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py
/// Please see README.md in <CNTK>/Examples/LanguageUnderstanding/ATIS about how to train the model.
/// The parameter 'modelFile' specifies the path to the model.
/// The vocabularyFile specifies the vacabulary file used by the ATIS model, e.g. <CNTK>/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl
/// The labelFile specifies the label file used by the ATIS model, e.g. <CNTK>/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl
/// </summary>
void EvaluationSingleSequenceUsingSparse(const wchar_t* modelFile, const wchar_t* vocabularyFile, const wchar_t* labelFile, const DeviceDescriptor& device)
{
printf("\n===== Evaluate single sequence using sparse input.\n");
// Load the model.
// The model is trained by <CNTK>/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py
// Please see README.md in <CNTK>/Examples/LanguageUnderstanding/ATIS about how to train the model.
FunctionPtr modelFunc = Function::Load(modelFile, device);
// Read word and slot index files.
std::unordered_map<std::string, size_t> vocabToIndex = BuildVocabIndex(vocabularyFile);
std::vector<std::string> indexToSlots = BuildSlotIndex(labelFile);
// Get input variable. The model has only one single input.
Variable inputVar = modelFunc->Arguments()[0];
size_t vocabSize = inputVar.Shape().TotalSize();
const char *inputSentence = "BOS i would like to find a flight from charlotte to las vegas that makes a stop in st. louis EOS";
std::vector<size_t> seqData;
std::vector<std::string> inputWords;
std::stringstream inputStream;
std::string word;
// build one-hot index for the input sequence.
inputStream.str(inputSentence);
while (inputStream >> word)
{
inputWords.push_back(word);
}
size_t seqLen = inputWords.size();
// For this example, only 1 non-zero value for each sample.
size_t numNonZeroValues = seqLen * 1;
std::vector<SparseIndexType> colStarts;
std::vector<SparseIndexType> rowIndices;
std::vector<float> nonZeroValues;
size_t count = 0;
for (; count < seqLen; count++)
{
// Get the index of the word
auto nonZeroValueIndex = static_cast<SparseIndexType>(vocabToIndex[inputWords[count]]);
// Add the sample to the sequence
nonZeroValues.push_back(1.0);
rowIndices.push_back(nonZeroValueIndex);
colStarts.push_back(static_cast<SparseIndexType>(count));
}
colStarts.push_back(static_cast<SparseIndexType>(numNonZeroValues));
// Create input value using one-hot vector and input data map
ValuePtr inputVal = Value::CreateSequence<float>(vocabSize, seqLen, colStarts.data(), rowIndices.data(), nonZeroValues.data(), numNonZeroValues, device);
std::unordered_map<Variable, ValuePtr> inputDataMap = { { inputVar, inputVal } };
// The model has only one output.
// If the model has more than one output, use modelFunc->Outputs to get the list of output variables.
Variable outputVar = modelFunc->Output();
// 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);
// output the result
size_t outputSampleSize = outputVar.Shape().TotalSize();
if (outputData.size() != 1)
{
throw("Only one sequence of slots is expected as output.");
}
std::vector<float> slotSeq = outputData[0];
if (slotSeq.size() % outputSampleSize != 0)
{
throw("The number of elements in the slot sequence is not a multiple of sample size");
}
size_t numOfSlotsInOutput = slotSeq.size() / outputSampleSize;
if (inputWords.size() != numOfSlotsInOutput)
{
throw("The number of input words and the number of output slots do not match");
}
for (size_t i = 0; i < numOfSlotsInOutput; i++)
{
float max = slotSeq[i * outputSampleSize];
size_t maxIndex = 0;
for (size_t j = 1; j < outputSampleSize; j++)
{
if (slotSeq[i * outputSampleSize + j] > max)
{
max = slotSeq[i * outputSampleSize + j];
maxIndex = j;
}
}
printf(" %10s ---- %s\n", inputWords[i].c_str(), indexToSlots[maxIndex].c_str());
}
printf("\n");
}
/// <summary>
/// The example shows
/// - how to load model.
/// - how to prepare input data as batch of sequences with variable length.
/// how to prepare data using one-hot vector format.
/// - how to prepare input and output data map.
/// - how to evaluate a model.
/// The example uses the model trained by <CNTK>/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py
/// Please see README.md in <CNTK>/Examples/LanguageUnderstanding/ATIS about how to train the model.
/// The parameter 'modelFile' specifies the path to the model.
/// The vocabularyFile specifies the vacabulary file used by the ATIS model, e.g. <CNTK>/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl
/// The labelFile specifies the label file used by the ATIS model, e.g. <CNTK>/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl
/// </summary>
void EvaluationBatchOfSequencesUsingOneHot(const wchar_t* modelFile, const wchar_t* vocabularyFile, const wchar_t* labelFile, const DeviceDescriptor& device)
{
printf("\n===== Evaluate batch of sequences with variable length using one-hot vector.\n");
// Load the model.
// The model is trained by <CNTK>/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py
// Please see README.md in <CNTK>/Examples/LanguageUnderstanding/ATIS about how to train the model.
FunctionPtr modelFunc = Function::Load(modelFile, device);
// Read word and slot index files.
std::unordered_map<std::string, size_t> vocabToIndex = BuildVocabIndex(vocabularyFile);
std::vector<std::string> indexToSlots = BuildSlotIndex(labelFile);
// Get input variable. The model has only one single input.
Variable inputVar = modelFunc->Arguments()[0];
size_t vocabSize = inputVar.Shape().TotalSize();
std::vector<const char *> inputSentences = {
"BOS i would like to find a flight from charlotte to las vegas that makes a stop in st. louis EOS",
"BOS flights from new york to seattle EOS"
};
// Prepare input data.
std::vector<std::vector<std::string>> inputWordsList(inputSentences.size());
// Each sample is represented by an index to the one-hot vector, so the index of the non-zero value of each sample is saved in the inner list.
// The outer list represents sequences contained in the batch.
std::vector<std::vector<size_t>> inputBatch;
// SeqStartFlagBatch is used to indicate whether this sequence is a new sequence (true) or concatenating the previous sequence (false).
std::vector<bool> seqStartFlagBatch;
std::string word;
size_t index;
for (size_t seqIndex = 0; seqIndex < inputSentences.size(); seqIndex++)
{
std::stringstream inputStream;
std::vector<size_t> seqData;
// build one-hot index for the input sequences.
inputStream.str(inputSentences[seqIndex]);
while (inputStream >> word)
{
inputWordsList[seqIndex].push_back(word);
index = vocabToIndex.at(word);
seqData.push_back(index);
}
inputBatch.push_back(seqData);
seqStartFlagBatch.push_back(true);
}
// Create input value representing the batch data and input data map
ValuePtr inputVal = Value::CreateBatchOfSequences<float>(vocabSize, inputBatch, seqStartFlagBatch, device);
std::unordered_map<Variable, ValuePtr> inputDataMap = { { inputVar, inputVal } };
// The model has only one output.
// If the model has more than one output, use modelFunc->Outputs to get the list of output variables.
Variable outputVar = modelFunc->Output();
// 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);
// output the result
size_t outputSampleSize = outputVar.Shape().TotalSize();
if (outputData.size() != inputBatch.size())
{
throw("The number of sequence in output does not match that in input.");
}
printf("The number of sequences in the batch: %d\n", (int)outputData.size());
for (size_t seqno = 0; seqno < outputData.size(); seqno++)
{
std::vector<float> slotSeq = outputData[seqno];
printf("Sequence %d:\n", (int)seqno);
if (slotSeq.size() % outputSampleSize != 0)
{
throw("The number of elements in the slot sequence is not a multiple of sample size");
}
size_t numOfSlotsInOutput = slotSeq.size() / outputSampleSize;
if (inputWordsList[seqno].size() != numOfSlotsInOutput)
{
throw("The number of input words and the number of output slots do not match");
}
for (size_t i = 0; i < numOfSlotsInOutput; i++)
{
float max = slotSeq[i * outputSampleSize];
size_t maxIndex = 0;
for (size_t j = 1; j < outputSampleSize; j++)
{
if (slotSeq[i * outputSampleSize + j] > max)
{
max = slotSeq[i * outputSampleSize + j];
maxIndex = j;
}
}
printf(" %10s ---- %s\n", inputWordsList[seqno][i].c_str(), indexToSlots[maxIndex].c_str());
}
printf("\n");
}
}