MLECO-3174: Minor refactoring to implemented use case APIS
Looks large but it is mainly just many small adjustments
Removed the inference runner code as it wasn't used
Fixes to doc strings
Consistent naming e.g. Asr/Kws instead of ASR/KWS
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I43b620b5c51d7910a29a63b509ac4d8a82c3a8fc
diff --git a/source/use_case/kws/include/KwsProcessing.hpp b/source/use_case/kws/include/KwsProcessing.hpp
index ddf38c1..d3de3b3 100644
--- a/source/use_case/kws/include/KwsProcessing.hpp
+++ b/source/use_case/kws/include/KwsProcessing.hpp
@@ -33,18 +33,21 @@
* Implements methods declared by BasePreProcess and anything else needed
* to populate input tensors ready for inference.
*/
- class KWSPreProcess : public BasePreProcess {
+ class KwsPreProcess : public BasePreProcess {
public:
/**
* @brief Constructor
- * @param[in] model Pointer to the KWS Model object.
- * @param[in] numFeatures How many MFCC features to use.
- * @param[in] mfccFrameLength Number of audio samples used to calculate one set of MFCC values when
- * sliding a window through the audio sample.
- * @param[in] mfccFrameStride Number of audio samples between consecutive windows.
+ * @param[in] inputTensor Pointer to the TFLite Micro input Tensor.
+ * @param[in] numFeatures How many MFCC features to use.
+ * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated
+ * for an inference.
+ * @param[in] mfccFrameLength Number of audio samples used to calculate one set of MFCC values when
+ * sliding a window through the audio sample.
+ * @param[in] mfccFrameStride Number of audio samples between consecutive windows.
**/
- explicit KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride);
+ explicit KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numFeatureFrames,
+ int mfccFrameLength, int mfccFrameStride);
/**
* @brief Should perform pre-processing of 'raw' input audio data and load it into
@@ -60,8 +63,10 @@
size_t m_audioDataStride; /* Amount of audio to stride across if doing >1 inference in longer clips. */
private:
+ TfLiteTensor* m_inputTensor; /* Model input tensor. */
const int m_mfccFrameLength;
const int m_mfccFrameStride;
+ const size_t m_numMfccFrames; /* How many sets of m_numMfccFeats. */
audio::MicroNetKwsMFCC m_mfcc;
audio::SlidingWindow<const int16_t> m_mfccSlidingWindow;
@@ -99,22 +104,23 @@
* Implements methods declared by BasePostProcess and anything else needed
* to populate result vector.
*/
- class KWSPostProcess : public BasePostProcess {
+ class KwsPostProcess : public BasePostProcess {
private:
- Classifier& m_kwsClassifier;
- const std::vector<std::string>& m_labels;
- std::vector<ClassificationResult>& m_results;
+ TfLiteTensor* m_outputTensor; /* Model output tensor. */
+ Classifier& m_kwsClassifier; /* KWS Classifier object. */
+ const std::vector<std::string>& m_labels; /* KWS Labels. */
+ std::vector<ClassificationResult>& m_results; /* Results vector for a single inference. */
public:
/**
* @brief Constructor
- * @param[in] classifier Classifier object used to get top N results from classification.
- * @param[in] model Pointer to the KWS Model object.
- * @param[in] labels Vector of string labels to identify each output of the model.
- * @param[in/out] results Vector of classification results to store decoded outputs.
+ * @param[in] outputTensor Pointer to the TFLite Micro output Tensor.
+ * @param[in] classifier Classifier object used to get top N results from classification.
+ * @param[in] labels Vector of string labels to identify each output of the model.
+ * @param[in/out] results Vector of classification results to store decoded outputs.
**/
- KWSPostProcess(Classifier& classifier, Model* model,
+ KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
const std::vector<std::string>& labels,
std::vector<ClassificationResult>& results);
diff --git a/source/use_case/kws/include/KwsResult.hpp b/source/use_case/kws/include/KwsResult.hpp
index 5a26ce1..38f32b4 100644
--- a/source/use_case/kws/include/KwsResult.hpp
+++ b/source/use_case/kws/include/KwsResult.hpp
@@ -25,7 +25,7 @@
namespace app {
namespace kws {
- using ResultVec = std::vector < arm::app::ClassificationResult >;
+ using ResultVec = std::vector<arm::app::ClassificationResult>;
/* Structure for holding kws result. */
class KwsResult {
diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc
index 14f9fce..328709d 100644
--- a/source/use_case/kws/src/KwsProcessing.cc
+++ b/source/use_case/kws/src/KwsProcessing.cc
@@ -22,22 +22,19 @@
namespace arm {
namespace app {
- KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride):
+ KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames,
+ int mfccFrameLength, int mfccFrameStride
+ ):
+ m_inputTensor{inputTensor},
m_mfccFrameLength{mfccFrameLength},
m_mfccFrameStride{mfccFrameStride},
+ m_numMfccFrames{numMfccFrames},
m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)}
{
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
this->m_mfcc.Init();
- TfLiteIntArray* inputShape = model->GetInputShape(0);
- const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
-
/* Deduce the data length required for 1 inference from the network parameters. */
- this->m_audioDataWindowSize = numMfccFrames * this->m_mfccFrameStride +
+ this->m_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride +
(this->m_mfccFrameLength - this->m_mfccFrameStride);
/* Creating an MFCC feature sliding window for the data required for 1 inference. */
@@ -62,7 +59,7 @@
- this->m_numMfccVectorsInAudioStride;
/* Construct feature calculation function. */
- this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0),
+ this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor,
this->m_numReusedMfccVectors);
if (!this->m_mfccFeatureCalculator) {
@@ -70,7 +67,7 @@
}
}
- bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize)
+ bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize)
{
UNUSED(inputSize);
if (data == nullptr) {
@@ -116,8 +113,8 @@
*/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
- KWSPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
- std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ KwsPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+ std::function<std::vector<T> (std::vector<int16_t>& )> compute)
{
/* Feature cache to be captured by lambda function. */
static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -149,18 +146,18 @@
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- KWSPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+ KwsPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
- KWSPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+ KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
std::function<void (std::vector<int16_t>&, int, bool, size_t)>
- KWSPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+ KwsPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
{
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
@@ -195,23 +192,19 @@
return mfccFeatureCalc;
}
- KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model,
+ KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
const std::vector<std::string>& labels,
std::vector<ClassificationResult>& results)
- :m_kwsClassifier{classifier},
+ :m_outputTensor{outputTensor},
+ m_kwsClassifier{classifier},
m_labels{labels},
m_results{results}
- {
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
- }
+ {}
- bool KWSPostProcess::DoPostProcess()
+ bool KwsPostProcess::DoPostProcess()
{
return this->m_kwsClassifier.GetClassificationResults(
- this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_outputTensor, this->m_results,
this->m_labels, 1, true);
}
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index e73a2c3..61c6eb6 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -34,13 +34,12 @@
namespace arm {
namespace app {
-
/**
* @brief Presents KWS inference results.
* @param[in] results Vector of KWS classification results to be displayed.
* @return true if successful, false otherwise.
**/
- static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results);
+ static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results);
/* KWS inference handler. */
bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
@@ -50,6 +49,7 @@
const auto mfccFrameLength = ctx.Get<int>("frameLength");
const auto mfccFrameStride = ctx.Get<int>("frameStride");
const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) {
@@ -61,16 +61,17 @@
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
constexpr int minTensorDims = static_cast<int>(
- (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
- arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
-
+ (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)?
+ MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx);
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
+ /* Get Input and Output tensors for pre/post processing. */
TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
@@ -81,22 +82,23 @@
/* Get input shape for feature extraction. */
TfLiteIntArray* inputShape = model.GetInputShape(0);
- const uint32_t numMfccFeatures = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+ const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
+ const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
/* We expect to be sampling 1 second worth of data at a time.
* NOTE: This is only used for time stamp calculation. */
const float secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
/* Set up pre and post-processing. */
- KWSPreProcess preprocess = KWSPreProcess(&model, numMfccFeatures, mfccFrameLength, mfccFrameStride);
+ KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames,
+ mfccFrameLength, mfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
- KWSPostProcess postprocess = KWSPostProcess(ctx.Get<KwsClassifier &>("classifier"), &model,
+ KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"),
ctx.Get<std::vector<std::string>&>("labels"),
singleInfResult);
- UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
-
+ /* Loop to process audio clips. */
do {
hal_lcd_clear(COLOR_BLACK);
@@ -106,7 +108,7 @@
auto audioDataSlider = audio::SlidingWindow<const int16_t>(
get_audio_array(currentIndex),
get_audio_array_size(currentIndex),
- preprocess.m_audioDataWindowSize, preprocess.m_audioDataStride);
+ preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride);
/* Declare a container to hold results from across the whole audio clip. */
std::vector<kws::KwsResult> finalResults;
@@ -123,34 +125,34 @@
const int16_t* inferenceWindow = audioDataSlider.Next();
/* The first window does not have cache ready. */
- preprocess.m_audioWindowIndex = audioDataSlider.Index();
+ preProcess.m_audioWindowIndex = audioDataSlider.Index();
info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Run the pre-processing, inference and post-processing. */
- if (!runner.PreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+ if (!preProcess.DoPreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+ printf_err("Pre-processing failed.");
return false;
}
- profiler.StartProfiling("Inference");
- if (!runner.RunInference()) {
+ if (!RunInference(model, profiler)) {
+ printf_err("Inference failed.");
return false;
}
- profiler.StopProfiling();
- if (!runner.PostProcess()) {
+ if (!postProcess.DoPostProcess()) {
+ printf_err("Post-processing failed.");
return false;
}
/* Add results from this window to our final results vector. */
finalResults.emplace_back(kws::KwsResult(singleInfResult,
- audioDataSlider.Index() * secondsPerSample * preprocess.m_audioDataStride,
+ audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride,
audioDataSlider.Index(), scoreThreshold));
#if VERIFY_TEST_OUTPUT
- TfLiteTensor* outputTensor = model.GetOutputTensor(0);
- arm::app::DumpTensor(outputTensor);
+ DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
} /* while (audioDataSlider.HasNext()) */
@@ -174,7 +176,7 @@
return true;
}
- static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results)
+ static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -187,7 +189,7 @@
/* Display each result */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
- for (const auto & result : results) {
+ for (const auto& result : results) {
std::string topKeyword{"<none>"};
float score = 0.f;