MLECO-1868: Code static analyzer warnings fixes
Signed-off-by: alexander <alexander.efremov@arm.com>
Change-Id: Ie423e9cad3fabec6ab077ded7236813fe4933dea
diff --git a/docs/sections/customizing.md b/docs/sections/customizing.md
index 323ffb5..b4b5bba 100644
--- a/docs/sections/customizing.md
+++ b/docs/sections/customizing.md
@@ -410,10 +410,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 5;
+ static constexpr int ms_maxOpCnt = 5;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
} /* namespace arm */
diff --git a/source/application/hal/hal.c b/source/application/hal/hal.c
index dbf94ba..9c2ce32 100644
--- a/source/application/hal/hal.c
+++ b/source/application/hal/hal.c
@@ -32,7 +32,7 @@
* @brief Initialises the Arm Ethos-U55 NPU
* @return 0 if successful, error code otherwise
**/
-static int _arm_npu_init(void);
+static int arm_npu_init(void);
#endif /* ARM_NPU */
@@ -54,7 +54,7 @@
/**
* @brief Local helper function to clean the slate for current platform.
**/
-static void _hal_platform_clear(hal_platform* platform)
+static void hal_platform_clear(hal_platform* platform)
{
assert(platform);
platform->inited = 0;
@@ -64,7 +64,7 @@
{
int state;
assert(platform && platform->platform_init);
- _hal_platform_clear(platform);
+ hal_platform_clear(platform);
/* Initialise platform */
if (0 != (state = platform->platform_init())) {
@@ -94,7 +94,7 @@
#if defined(ARM_NPU)
/* If Arm Ethos-U55 NPU is to be used, we initialise it here */
- if (0 != (state = _arm_npu_init())) {
+ if (0 != (state = arm_npu_init())) {
return state;
}
@@ -120,7 +120,7 @@
data_acq_channel_release(platform->data_acq);
data_psn_system_release(platform->data_psn);
- _hal_platform_clear(platform);
+ hal_platform_clear(platform);
info("releasing platform %s\n", platform->plat_name);
platform->platform_release();
}
@@ -130,7 +130,7 @@
* @brief Defines the Ethos-U interrupt handler: just a wrapper around the default
* implementation.
**/
-static void _arm_npu_irq_handler(void)
+static void arm_npu_irq_handler(void)
{
/* Call the default interrupt handler from the NPU driver */
ethosu_irq_handler();
@@ -139,19 +139,19 @@
/**
* @brief Initialises the NPU IRQ
**/
-static void _arm_npu_irq_init(void)
+static void arm_npu_irq_init(void)
{
const IRQn_Type ethosu_irqnum = (IRQn_Type)EthosU_IRQn;
/* Register the EthosU IRQ handler in our vector table.
* Note, this handler comes from the EthosU driver */
- NVIC_SetVector(ethosu_irqnum, (uint32_t)_arm_npu_irq_handler);
+ NVIC_SetVector(ethosu_irqnum, (uint32_t)arm_npu_irq_handler);
/* Enable the IRQ */
NVIC_EnableIRQ(ethosu_irqnum);
debug("EthosU IRQ#: %u, Handler: 0x%p\n",
- ethosu_irqnum, _arm_npu_irq_handler);
+ ethosu_irqnum, arm_npu_irq_handler);
}
static int _arm_npu_timing_adapter_init(void)
@@ -213,7 +213,7 @@
return 0;
}
-static int _arm_npu_init(void)
+static int arm_npu_init(void)
{
int err = 0;
@@ -224,7 +224,7 @@
}
/* Initialise the IRQ */
- _arm_npu_irq_init();
+ arm_npu_irq_init();
/* Initialise Ethos-U55 device */
const void * ethosu_base_address = (void *)(SEC_ETHOS_U55_BASE);
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
index bc2c378..9a47f3d 100644
--- a/source/application/main/Classifier.cc
+++ b/source/application/main/Classifier.cc
@@ -28,10 +28,46 @@
namespace app {
template<typename T>
- bool Classifier::_GetTopNResults(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount,
- const std::vector <std::string>& labels)
+ void SetVectorResults(std::set<std::pair<T, uint32_t>>& topNSet,
+ std::vector<ClassificationResult>& vecResults,
+ TfLiteTensor* tensor,
+ const std::vector <std::string>& labels) {
+
+ /* For getting the floating point values, we need quantization parameters. */
+ QuantParams quantParams = GetTensorQuantParams(tensor);
+
+ /* Reset the iterator to the largest element - use reverse iterator. */
+ auto topNIter = topNSet.rbegin();
+ for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
+ T score = topNIter->first;
+ vecResults[i].m_normalisedVal = quantParams.scale * (score - quantParams.offset);
+ vecResults[i].m_label = labels[topNIter->second];
+ vecResults[i].m_labelIdx = topNIter->second;
+ }
+
+ }
+
+ template<>
+ void SetVectorResults<float>(std::set<std::pair<float, uint32_t>>& topNSet,
+ std::vector<ClassificationResult>& vecResults,
+ TfLiteTensor* tensor,
+ const std::vector <std::string>& labels) {
+ UNUSED(tensor);
+ /* Reset the iterator to the largest element - use reverse iterator. */
+ auto topNIter = topNSet.rbegin();
+ for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
+ vecResults[i].m_normalisedVal = topNIter->first;
+ vecResults[i].m_label = labels[topNIter->second];
+ vecResults[i].m_labelIdx = topNIter->second;
+ }
+
+ }
+
+ template<typename T>
+ bool Classifier::GetTopNResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount,
+ const std::vector <std::string>& labels)
{
std::set<std::pair<T, uint32_t>> sortedSet;
@@ -59,82 +95,18 @@
/* Final results' container. */
vecResults = std::vector<ClassificationResult>(topNCount);
- /* For getting the floating point values, we need quantization parameters. */
- QuantParams quantParams = GetTensorQuantParams(tensor);
-
- /* Reset the iterator to the largest element - use reverse iterator. */
- auto setRevIter = sortedSet.rbegin();
-
- /* Populate results
- * Note: we could combine this loop with the loop above, but that
- * would, involve more multiplications and other operations.
- **/
- for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
- double score = static_cast<int> (setRevIter->first);
- vecResults[i].m_normalisedVal = quantParams.scale *
- (score - quantParams.offset);
- vecResults[i].m_label = labels[setRevIter->second];
- vecResults[i].m_labelIdx = setRevIter->second;
- }
+ SetVectorResults<T>(sortedSet, vecResults, tensor, labels);
return true;
}
- template<>
- bool Classifier::_GetTopNResults<float>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount,
- const std::vector <std::string>& labels)
- {
- std::set<std::pair<float, uint32_t>> sortedSet;
+ template bool Classifier::GetTopNResults<uint8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount, const std::vector <std::string>& labels);
- /* NOTE: inputVec's size verification against labels should be
- * checked by the calling/public function. */
- float* tensorData = tflite::GetTensorData<float>(tensor);
-
- /* Set initial elements. */
- for (uint32_t i = 0; i < topNCount; ++i) {
- sortedSet.insert({tensorData[i], i});
- }
-
- /* Initialise iterator. */
- auto setFwdIter = sortedSet.begin();
-
- /* Scan through the rest of elements with compare operations. */
- for (uint32_t i = topNCount; i < labels.size(); ++i) {
- if (setFwdIter->first < tensorData[i]) {
- sortedSet.erase(*setFwdIter);
- sortedSet.insert({tensorData[i], i});
- setFwdIter = sortedSet.begin();
- }
- }
-
- /* Final results' container. */
- vecResults = std::vector<ClassificationResult>(topNCount);
-
- /* Reset the iterator to the largest element - use reverse iterator. */
- auto setRevIter = sortedSet.rbegin();
-
- /* Populate results
- * Note: we could combine this loop with the loop above, but that
- * would, involve more multiplications and other operations.
- **/
- for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
- vecResults[i].m_normalisedVal = setRevIter->first;
- vecResults[i].m_label = labels[setRevIter->second];
- vecResults[i].m_labelIdx = setRevIter->second;
- }
-
- return true;
- }
-
- template bool Classifier::_GetTopNResults<uint8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount, const std::vector <std::string>& labels);
-
- template bool Classifier::_GetTopNResults<int8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount, const std::vector <std::string>& labels);
+ template bool Classifier::GetTopNResults<int8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount, const std::vector <std::string>& labels);
bool Classifier::GetClassificationResults(
TfLiteTensor* outputTensor,
@@ -158,6 +130,9 @@
} else if (totalOutputSize != labels.size()) {
printf_err("Output size doesn't match the labels' size\n");
return false;
+ } else if (topNCount == 0) {
+ printf_err("Top N results cannot be zero\n");
+ return false;
}
bool resultState;
@@ -166,13 +141,13 @@
/* Get the top N results. */
switch (outputTensor->type) {
case kTfLiteUInt8:
- resultState = _GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+ resultState = GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
break;
case kTfLiteInt8:
- resultState = _GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+ resultState = GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
break;
case kTfLiteFloat32:
- resultState = _GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+ resultState = GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
break;
default:
printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
@@ -180,7 +155,7 @@
}
if (!resultState) {
- printf_err("Failed to get sorted set\n");
+ printf_err("Failed to get top N results set\n");
return false;
}
diff --git a/source/application/main/Mfcc.cc b/source/application/main/Mfcc.cc
index bf16159..9ddcb5d 100644
--- a/source/application/main/Mfcc.cc
+++ b/source/application/main/Mfcc.cc
@@ -44,7 +44,7 @@
m_useHtkMethod(useHtkMethod)
{}
- std::string MfccParams::Str()
+ std::string MfccParams::Str() const
{
char strC[1024];
snprintf(strC, sizeof(strC) - 1, "\n \
@@ -74,7 +74,7 @@
this->_m_params.m_numFbankBins, 0.0);
this->_m_windowFunc = std::vector<float>(this->_m_params.m_frameLen);
- const float multiplier = 2 * M_PI / this->_m_params.m_frameLen;
+ const auto multiplier = static_cast<float>(2 * M_PI / this->_m_params.m_frameLen);
/* Create window function. */
for (size_t i = 0; i < this->_m_params.m_frameLen; i++) {
@@ -88,7 +88,7 @@
void MFCC::Init()
{
- this->_InitMelFilterBank();
+ this->InitMelFilterBank();
}
float MFCC::MelScale(const float freq, const bool useHTKMethod)
@@ -126,8 +126,8 @@
bool MFCC::ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies)
{
const size_t numBanks = melEnergies.size();
@@ -140,11 +140,12 @@
for (size_t bin = 0; bin < numBanks; ++bin) {
auto filterBankIter = melFilterBank[bin].begin();
+ auto end = melFilterBank[bin].end();
float melEnergy = FLT_MIN; /* Avoid log of zero at later stages */
- int32_t firstIndex = filterBankFilterFirst[bin];
- int32_t lastIndex = filterBankFilterLast[bin];
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
- for (int i = firstIndex; i <= lastIndex; i++) {
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; i++) {
float energyRep = math::MathUtils::SqrtF32(fftVec[i]);
melEnergy += (*filterBankIter++ * energyRep);
}
@@ -157,14 +158,14 @@
void MFCC::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
{
- for (size_t bin = 0; bin < melEnergies.size(); ++bin) {
- melEnergies[bin] = logf(melEnergies[bin]);
+ for (float& melEnergy : melEnergies) {
+ melEnergy = logf(melEnergy);
}
}
- void MFCC::_ConvertToPowerSpectrum()
+ void MFCC::ConvertToPowerSpectrum()
{
- const uint32_t halfDim = this->_m_params.m_frameLenPadded / 2;
+ const uint32_t halfDim = this->_m_buffer.size() / 2;
/* Handle this special case. */
float firstEnergy = this->_m_buffer[0] * this->_m_buffer[0];
@@ -193,7 +194,7 @@
for (int32_t k = 0, m = 0; k < coefficientCount; k++, m += inputLength) {
for (int32_t n = 0; n < inputLength; n++) {
dctMatix[m+n] = normalizer *
- math::MathUtils::CosineF32((n + 0.5) * angle);
+ math::MathUtils::CosineF32((n + 0.5f) * angle);
}
angle += angleIncr;
}
@@ -214,10 +215,10 @@
return 1.f;
}
- void MFCC::_InitMelFilterBank()
+ void MFCC::InitMelFilterBank()
{
- if (!this->_IsMelFilterBankInited()) {
- this->_m_melFilterBank = this->_CreateMelFilterBank();
+ if (!this->IsMelFilterBankInited()) {
+ this->_m_melFilterBank = this->CreateMelFilterBank();
this->_m_dctMatrix = this->CreateDCTMatrix(
this->_m_params.m_numFbankBins,
this->_m_params.m_numMfccFeatures);
@@ -225,17 +226,17 @@
}
}
- bool MFCC::_IsMelFilterBankInited()
+ bool MFCC::IsMelFilterBankInited() const
{
return this->_m_filterBankInitialised;
}
- void MFCC::_MfccComputePreFeature(const std::vector<int16_t>& audioData)
+ void MFCC::MfccComputePreFeature(const std::vector<int16_t>& audioData)
{
- this->_InitMelFilterBank();
+ this->InitMelFilterBank();
/* TensorFlow way of normalizing .wav data to (-1, 1). */
- constexpr float normaliser = 1.0/(1<<15);
+ constexpr float normaliser = 1.0/(1u<<15u);
for (size_t i = 0; i < this->_m_params.m_frameLen; i++) {
this->_m_frame[i] = static_cast<float>(audioData[i]) * normaliser;
}
@@ -252,7 +253,7 @@
math::MathUtils::FftF32(this->_m_frame, this->_m_buffer, this->_m_fftInstance);
/* Convert to power spectrum. */
- this->_ConvertToPowerSpectrum();
+ this->ConvertToPowerSpectrum();
/* Apply mel filterbanks. */
if (!this->ApplyMelFilterBank(this->_m_buffer,
@@ -269,7 +270,7 @@
std::vector<float> MFCC::MfccCompute(const std::vector<int16_t>& audioData)
{
- this->_MfccComputePreFeature(audioData);
+ this->MfccComputePreFeature(audioData);
std::vector<float> mfccOut(this->_m_params.m_numMfccFeatures);
@@ -288,7 +289,7 @@
return mfccOut;
}
- std::vector<std::vector<float>> MFCC::_CreateMelFilterBank()
+ std::vector<std::vector<float>> MFCC::CreateMelFilterBank()
{
size_t numFftBins = this->_m_params.m_frameLenPadded / 2;
float fftBinWidth = static_cast<float>(this->_m_params.m_samplingFreq) / this->_m_params.m_frameLenPadded;
@@ -303,17 +304,18 @@
std::vector<std::vector<float>> melFilterBank(
this->_m_params.m_numFbankBins);
this->_m_filterBankFilterFirst =
- std::vector<int32_t>(this->_m_params.m_numFbankBins);
+ std::vector<uint32_t>(this->_m_params.m_numFbankBins);
this->_m_filterBankFilterLast =
- std::vector<int32_t>(this->_m_params.m_numFbankBins);
+ std::vector<uint32_t>(this->_m_params.m_numFbankBins);
for (size_t bin = 0; bin < this->_m_params.m_numFbankBins; bin++) {
float leftMel = melLowFreq + bin * melFreqDelta;
float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
- int32_t firstIndex = -1;
- int32_t lastIndex = -1;
+ uint32_t firstIndex = 0;
+ uint32_t lastIndex = 0;
+ bool firstIndexFound = false;
const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->_m_params.m_useHtkMethod);
for (size_t i = 0; i < numFftBins; i++) {
@@ -330,8 +332,9 @@
}
thisBin[i] = weight * normaliser;
- if (firstIndex == -1) {
+ if (!firstIndexFound) {
firstIndex = i;
+ firstIndexFound = true;
}
lastIndex = i;
}
@@ -341,7 +344,7 @@
this->_m_filterBankFilterLast[bin] = lastIndex;
/* Copy the part we care about. */
- for (int32_t i = firstIndex; i <= lastIndex; i++) {
+ for (uint32_t i = firstIndex; i <= lastIndex; i++) {
melFilterBank[bin].push_back(thisBin[i]);
}
}
diff --git a/source/application/main/PlatformMath.cc b/source/application/main/PlatformMath.cc
index a9f5049..9d18151 100644
--- a/source/application/main/PlatformMath.cc
+++ b/source/application/main/PlatformMath.cc
@@ -121,7 +121,7 @@
float sumReal = 0, sumImag = 0;
for (int t = 0; t < inputLength; t++) {
- float angle = 2 * M_PI * t * k / inputLength;
+ auto angle = static_cast<float>(2 * M_PI * t * k / inputLength);
sumReal += input[t] * cosf(angle);
sumImag += -input[t] * sinf(angle);
}
@@ -147,7 +147,7 @@
output.size());
#else /* ARM_DSP_AVAILABLE */
for (auto in = input.begin(), out = output.begin();
- in != input.end(); ++in, ++out) {
+ in != input.end() && out != output.end(); ++in, ++out) {
*out = logf(*in);
}
#endif /* ARM_DSP_AVAILABLE */
diff --git a/source/application/main/Profiler.cc b/source/application/main/Profiler.cc
index 0456ba4..10a828a 100644
--- a/source/application/main/Profiler.cc
+++ b/source/application/main/Profiler.cc
@@ -54,7 +54,7 @@
this->_m_tstampEnd = this->_m_pPlatform->timer->stop_profiling();
this->_m_started = false;
- this->_AddProfilingUnit(this->_m_tstampSt, this->_m_tstampEnd, this->_m_name);
+ this->AddProfilingUnit(this->_m_tstampSt, this->_m_tstampEnd, this->_m_name);
return true;
}
@@ -238,8 +238,8 @@
this->_m_name = std::string(str);
}
- void Profiler::_AddProfilingUnit(time_counter start, time_counter end,
- const std::string& name)
+ void Profiler::AddProfilingUnit(time_counter start, time_counter end,
+ const std::string& name)
{
platform_timer * timer = this->_m_pPlatform->timer;
diff --git a/source/application/main/include/Classifier.hpp b/source/application/main/include/Classifier.hpp
index 510e6f9..3ee3148 100644
--- a/source/application/main/include/Classifier.hpp
+++ b/source/application/main/include/Classifier.hpp
@@ -62,7 +62,7 @@
* @return true if successful, false otherwise.
**/
template<typename T>
- bool _GetTopNResults(TfLiteTensor* tensor,
+ bool GetTopNResults(TfLiteTensor* tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount,
const std::vector <std::string>& labels);
diff --git a/source/application/main/include/DataStructures.hpp b/source/application/main/include/DataStructures.hpp
index 5cc8b5e..2f267c0 100644
--- a/source/application/main/include/DataStructures.hpp
+++ b/source/application/main/include/DataStructures.hpp
@@ -47,15 +47,13 @@
* @param[in] rows Number of rows.
* @param[in] cols Number of columns.
*/
- Array2d(unsigned rows, unsigned cols)
+ Array2d(unsigned rows, unsigned cols): _m_rows(rows), _m_cols(cols)
{
if (rows == 0 || cols == 0) {
printf_err("Array2d constructor has 0 size.\n");
_m_data = nullptr;
return;
}
- _m_rows = rows;
- _m_cols = cols;
_m_data = new T[rows * cols];
}
diff --git a/source/application/main/include/Mfcc.hpp b/source/application/main/include/Mfcc.hpp
index 6379fab..dcafe62 100644
--- a/source/application/main/include/Mfcc.hpp
+++ b/source/application/main/include/Mfcc.hpp
@@ -52,7 +52,7 @@
~MfccParams() = default;
/** @brief String representation of parameters */
- std::string Str();
+ std::string Str() const;
};
/**
@@ -100,7 +100,7 @@
const float quantScale,
const int quantOffset)
{
- this->_MfccComputePreFeature(audioData);
+ this->MfccComputePreFeature(audioData);
float minVal = std::numeric_limits<T>::min();
float maxVal = std::numeric_limits<T>::max();
@@ -154,7 +154,7 @@
* bank weights and adding them up to be placed into
* bins, according to the filter bank's first and last
* indices (pre-computed for each filter bank element
- * by _CreateMelFilterBank function).
+ * by CreateMelFilterBank function).
* @param[in] fftVec Vector populated with FFT magnitudes.
* @param[in] melFilterBank 2D Vector with filter bank weights.
* @param[in] filterBankFilterFirst Vector containing the first indices of filter bank
@@ -168,8 +168,8 @@
virtual bool ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies);
/**
@@ -214,37 +214,37 @@
std::vector<float> _m_windowFunc;
std::vector<std::vector<float>> _m_melFilterBank;
std::vector<float> _m_dctMatrix;
- std::vector<int32_t> _m_filterBankFilterFirst;
- std::vector<int32_t> _m_filterBankFilterLast;
+ std::vector<uint32_t> _m_filterBankFilterFirst;
+ std::vector<uint32_t> _m_filterBankFilterLast;
bool _m_filterBankInitialised;
arm::app::math::FftInstance _m_fftInstance;
/**
* @brief Initialises the filter banks and the DCT matrix. **/
- void _InitMelFilterBank();
+ void InitMelFilterBank();
/**
* @brief Signals whether the instance of MFCC has had its
* required buffers initialised.
* @return true if initialised, false otherwise.
**/
- bool _IsMelFilterBankInited();
+ bool IsMelFilterBankInited() const;
/**
* @brief Create mel filter banks for MFCC calculation.
* @return 2D vector of floats.
**/
- std::vector<std::vector<float>> _CreateMelFilterBank();
+ std::vector<std::vector<float>> CreateMelFilterBank();
/**
* @brief Computes and populates internal memeber buffers used
* in MFCC feature calculation
* @param[in] audioData 1D vector of 16-bit audio data.
*/
- void _MfccComputePreFeature(const std::vector<int16_t>& audioData);
+ void MfccComputePreFeature(const std::vector<int16_t>& audioData);
/** @brief Computes the magnitude from an interleaved complex array. */
- void _ConvertToPowerSpectrum();
+ void ConvertToPowerSpectrum();
};
diff --git a/source/application/main/include/Profiler.hpp b/source/application/main/include/Profiler.hpp
index d93b257..c5f77e7 100644
--- a/source/application/main/include/Profiler.hpp
+++ b/source/application/main/include/Profiler.hpp
@@ -125,8 +125,8 @@
* @param[in] name Name for the profiling unit series to be
* appended to.
**/
- void _AddProfilingUnit(time_counter start, time_counter end,
- const std::string& name);
+ void AddProfilingUnit(time_counter start, time_counter end,
+ const std::string& name);
};
} /* namespace app */
diff --git a/source/application/tensorflow-lite-micro/Model.cc b/source/application/tensorflow-lite-micro/Model.cc
index 0775467..abf97b6 100644
--- a/source/application/tensorflow-lite-micro/Model.cc
+++ b/source/application/tensorflow-lite-micro/Model.cc
@@ -317,7 +317,7 @@
}
namespace arm {
namespace app {
- static uint8_t _tensor_arena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+ static uint8_t tensor_arena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
} /* namespace app */
} /* namespace arm */
@@ -328,5 +328,5 @@
uint8_t *arm::app::Model::GetTensorArena()
{
- return _tensor_arena;
+ return tensor_arena;
}
\ No newline at end of file
diff --git a/source/use_case/ad/include/AdMelSpectrogram.hpp b/source/use_case/ad/include/AdMelSpectrogram.hpp
index cf8a1d4..30a77c1 100644
--- a/source/use_case/ad/include/AdMelSpectrogram.hpp
+++ b/source/use_case/ad/include/AdMelSpectrogram.hpp
@@ -60,8 +60,8 @@
virtual bool ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies) override;
/**
diff --git a/source/use_case/ad/include/AdModel.hpp b/source/use_case/ad/include/AdModel.hpp
index 2d83455..bbdf91c 100644
--- a/source/use_case/ad/include/AdModel.hpp
+++ b/source/use_case/ad/include/AdModel.hpp
@@ -41,10 +41,10 @@
private:
/* Maximum number of individual operations that can be enlisted */
- static constexpr int _ms_maxOpCnt = 6;
+ static constexpr int ms_maxOpCnt = 6;
/* A mutable op resolver instance */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/ad/include/MelSpectrogram.hpp b/source/use_case/ad/include/MelSpectrogram.hpp
index c1dd61e..22b5d29 100644
--- a/source/use_case/ad/include/MelSpectrogram.hpp
+++ b/source/use_case/ad/include/MelSpectrogram.hpp
@@ -49,7 +49,7 @@
~MelSpecParams() = default;
/** @brief String representation of parameters */
- std::string Str();
+ std::string Str() const;
};
/**
@@ -76,7 +76,7 @@
* @brief Constructor
* @param[in] params - Mel Spectrogram parameters
*/
- MelSpectrogram(const MelSpecParams& params);
+ explicit MelSpectrogram(const MelSpecParams& params);
MelSpectrogram() = delete;
~MelSpectrogram() = default;
@@ -148,7 +148,7 @@
* bank weights and adding them up to be placed into
* bins, according to the filter bank's first and last
* indices (pre-computed for each filter bank element
- * by _CreateMelFilterBank function).
+ * by CreateMelFilterBank function).
* @param[in] fftVec Vector populated with FFT magnitudes
* @param[in] melFilterBank 2D Vector with filter bank weights
* @param[in] filterBankFilterFirst Vector containing the first indices of filter bank
@@ -162,8 +162,8 @@
virtual bool ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies);
/**
@@ -195,33 +195,33 @@
std::vector<float> _m_melEnergies;
std::vector<float> _m_windowFunc;
std::vector<std::vector<float>> _m_melFilterBank;
- std::vector<int32_t> _m_filterBankFilterFirst;
- std::vector<int32_t> _m_filterBankFilterLast;
+ std::vector<uint32_t> _m_filterBankFilterFirst;
+ std::vector<uint32_t> _m_filterBankFilterLast;
bool _m_filterBankInitialised;
arm::app::math::FftInstance _m_fftInstance;
/**
* @brief Initialises the filter banks.
**/
- void _InitMelFilterBank();
+ void InitMelFilterBank();
/**
* @brief Signals whether the instance of MelSpectrogram has had its
* required buffers initialised
* @return True if initialised, false otherwise
**/
- bool _IsMelFilterBankInited();
+ bool IsMelFilterBankInited() const;
/**
* @brief Create mel filter banks for Mel Spectrogram calculation.
* @return 2D vector of floats
**/
- std::vector<std::vector<float>> _CreateMelFilterBank();
+ std::vector<std::vector<float>> CreateMelFilterBank();
/**
* @brief Computes the magnitude from an interleaved complex array
**/
- void _ConvertToPowerSpectrum();
+ void ConvertToPowerSpectrum();
};
diff --git a/source/use_case/ad/src/AdMelSpectrogram.cc b/source/use_case/ad/src/AdMelSpectrogram.cc
index 183c05c..e070eb8 100644
--- a/source/use_case/ad/src/AdMelSpectrogram.cc
+++ b/source/use_case/ad/src/AdMelSpectrogram.cc
@@ -18,6 +18,8 @@
#include "PlatformMath.hpp"
+#include <cfloat>
+
namespace arm {
namespace app {
namespace audio {
@@ -25,8 +27,8 @@
bool AdMelSpectrogram::ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies)
{
const size_t numBanks = melEnergies.size();
@@ -39,11 +41,12 @@
for (size_t bin = 0; bin < numBanks; ++bin) {
auto filterBankIter = melFilterBank[bin].begin();
- float melEnergy = 1e-10; /* Avoid log of zero at later stages. */
- const int32_t firstIndex = filterBankFilterFirst[bin];
- const int32_t lastIndex = filterBankFilterLast[bin];
+ auto end = melFilterBank[bin].end();
+ float melEnergy = FLT_MIN; /* Avoid log of zero at later stages. */
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
- for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
melEnergy += (*filterBankIter++ * fftVec[i]);
}
@@ -69,7 +72,7 @@
/* Scale the log values. */
for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
- iterM != melEnergies.end(); ++iterM, ++iterL) {
+ iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
*iterM = *iterL * multiplier;
}
diff --git a/source/use_case/ad/src/MelSpectrogram.cc b/source/use_case/ad/src/MelSpectrogram.cc
index 86d57e6..372ebd8 100644
--- a/source/use_case/ad/src/MelSpectrogram.cc
+++ b/source/use_case/ad/src/MelSpectrogram.cc
@@ -42,7 +42,7 @@
m_useHtkMethod(useHtkMethod)
{}
- std::string MelSpecParams::Str()
+ std::string MelSpecParams::Str() const
{
char strC[1024];
snprintf(strC, sizeof(strC) - 1, "\n \
@@ -71,7 +71,7 @@
this->_m_params.m_numFbankBins, 0.0);
this->_m_windowFunc = std::vector<float>(this->_m_params.m_frameLen);
- const float multiplier = 2 * M_PI / this->_m_params.m_frameLen;
+ const auto multiplier = static_cast<float>(2 * M_PI / this->_m_params.m_frameLen);
/* Create window function. */
for (size_t i = 0; i < this->_m_params.m_frameLen; ++i) {
@@ -85,7 +85,7 @@
void MelSpectrogram::Init()
{
- this->_InitMelFilterBank();
+ this->InitMelFilterBank();
}
float MelSpectrogram::MelScale(const float freq, const bool useHTKMethod)
@@ -121,8 +121,8 @@
bool MelSpectrogram::ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies)
{
const size_t numBanks = melEnergies.size();
@@ -135,11 +135,12 @@
for (size_t bin = 0; bin < numBanks; ++bin) {
auto filterBankIter = melFilterBank[bin].begin();
+ auto end = melFilterBank[bin].end();
float melEnergy = FLT_MIN; /* Avoid log of zero at later stages */
- int32_t firstIndex = filterBankFilterFirst[bin];
- int32_t lastIndex = filterBankFilterLast[bin];
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
- for (int i = firstIndex; i <= lastIndex; ++i) {
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
float energyRep = math::MathUtils::SqrtF32(fftVec[i]);
melEnergy += (*filterBankIter++ * energyRep);
}
@@ -152,14 +153,14 @@
void MelSpectrogram::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
{
- for (size_t bin = 0; bin < melEnergies.size(); ++bin) {
- melEnergies[bin] = logf(melEnergies[bin]);
+ for (float& melEnergy : melEnergies) {
+ melEnergy = logf(melEnergy);
}
}
- void MelSpectrogram::_ConvertToPowerSpectrum()
+ void MelSpectrogram::ConvertToPowerSpectrum()
{
- const uint32_t halfDim = this->_m_params.m_frameLenPadded / 2;
+ const uint32_t halfDim = this->_m_buffer.size() / 2;
/* Handle this special case. */
float firstEnergy = this->_m_buffer[0] * this->_m_buffer[0];
@@ -188,22 +189,22 @@
return 1.f;
}
- void MelSpectrogram::_InitMelFilterBank()
+ void MelSpectrogram::InitMelFilterBank()
{
- if (!this->_IsMelFilterBankInited()) {
- this->_m_melFilterBank = this->_CreateMelFilterBank();
+ if (!this->IsMelFilterBankInited()) {
+ this->_m_melFilterBank = this->CreateMelFilterBank();
this->_m_filterBankInitialised = true;
}
}
- bool MelSpectrogram::_IsMelFilterBankInited()
+ bool MelSpectrogram::IsMelFilterBankInited() const
{
return this->_m_filterBankInitialised;
}
std::vector<float> MelSpectrogram::ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean)
{
- this->_InitMelFilterBank();
+ this->InitMelFilterBank();
/* TensorFlow way of normalizing .wav data to (-1, 1). */
constexpr float normaliser = 1.0/(1<<15);
@@ -223,7 +224,7 @@
math::MathUtils::FftF32(this->_m_frame, this->_m_buffer, this->_m_fftInstance);
/* Convert to power spectrum. */
- this->_ConvertToPowerSpectrum();
+ this->ConvertToPowerSpectrum();
/* Apply mel filterbanks. */
if (!this->ApplyMelFilterBank(this->_m_buffer,
@@ -245,7 +246,7 @@
return this->_m_melEnergies;
}
- std::vector<std::vector<float>> MelSpectrogram::_CreateMelFilterBank()
+ std::vector<std::vector<float>> MelSpectrogram::CreateMelFilterBank()
{
size_t numFftBins = this->_m_params.m_frameLenPadded / 2;
float fftBinWidth = static_cast<float>(this->_m_params.m_samplingFreq) / this->_m_params.m_frameLenPadded;
@@ -260,17 +261,18 @@
std::vector<std::vector<float>> melFilterBank(
this->_m_params.m_numFbankBins);
this->_m_filterBankFilterFirst =
- std::vector<int32_t>(this->_m_params.m_numFbankBins);
+ std::vector<uint32_t>(this->_m_params.m_numFbankBins);
this->_m_filterBankFilterLast =
- std::vector<int32_t>(this->_m_params.m_numFbankBins);
+ std::vector<uint32_t>(this->_m_params.m_numFbankBins);
for (size_t bin = 0; bin < this->_m_params.m_numFbankBins; bin++) {
float leftMel = melLowFreq + bin * melFreqDelta;
float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
- int32_t firstIndex = -1;
- int32_t lastIndex = -1;
+ uint32_t firstIndex = 0;
+ uint32_t lastIndex = 0;
+ bool firstIndexFound = false;
const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->_m_params.m_useHtkMethod);
for (size_t i = 0; i < numFftBins; ++i) {
@@ -287,8 +289,9 @@
}
thisBin[i] = weight * normaliser;
- if (firstIndex == -1) {
+ if (!firstIndexFound) {
firstIndex = i;
+ firstIndexFound = true;
}
lastIndex = i;
}
@@ -298,7 +301,7 @@
this->_m_filterBankFilterLast[bin] = lastIndex;
/* Copy the part we care about. */
- for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+ for (uint32_t i = firstIndex; i <= lastIndex; ++i) {
melFilterBank[bin].push_back(thisBin[i]);
}
}
diff --git a/source/use_case/ad/src/UseCaseHandler.cc b/source/use_case/ad/src/UseCaseHandler.cc
index 1c15595..8f86966 100644
--- a/source/use_case/ad/src/UseCaseHandler.cc
+++ b/source/use_case/ad/src/UseCaseHandler.cc
@@ -32,7 +32,7 @@
* @brief Helper function to increment current audio clip index
* @param[in/out] ctx pointer to the application context object
**/
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
/**
* @brief Helper function to set the audio clip index
@@ -40,7 +40,7 @@
* @param[in] idx value to be set
* @return true if index is set, false otherwise
**/
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
/**
* @brief Presents inference results using the data presentation
@@ -50,7 +50,7 @@
* @param[in] threhsold if larger than this value we have an anomaly
* @return true if successful, false otherwise
**/
- static bool _PresentInferenceResult(hal_platform& platform, float result, float threshold);
+ static bool PresentInferenceResult(hal_platform& platform, float result, float threshold);
/**
* @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -87,7 +87,7 @@
/* If the request has a valid size, set the audio index */
if (clipIndex < NUMBER_OF_FILES) {
- if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ if (!SetAppCtxClipIdx(ctx, clipIndex)) {
return false;
}
}
@@ -216,20 +216,20 @@
dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
ctx.Set<float>("result", result);
- if (!_PresentInferenceResult(platform, result, scoreThreshold)) {
+ if (!PresentInferenceResult(platform, result, scoreThreshold)) {
return false;
}
profiler.PrintProfilingResult();
- _IncrementAppCtxClipIdx(ctx);
+ IncrementAppCtxClipIdx(ctx);
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
{
auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
@@ -241,7 +241,7 @@
ctx.Set<uint32_t>("clipIndex", curAudioIdx);
}
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
{
if (idx >= NUMBER_OF_FILES) {
printf_err("Invalid idx %u (expected less than %u)\n",
@@ -252,7 +252,7 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform, float result, float threshold)
+ static bool PresentInferenceResult(hal_platform& platform, float result, float threshold)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -275,7 +275,7 @@
platform.data_psn->present_data_text(
resultStr.c_str(), resultStr.size(),
- dataPsnTxtStartX1, rowIdx1, 0);
+ dataPsnTxtStartX1, rowIdx1, false);
info("%s\n", resultStr.c_str());
@@ -297,8 +297,8 @@
*/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)>
- _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
- std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ 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);
@@ -335,24 +335,24 @@
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
- _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ 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, size_t)>
+ FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
size_t cacheSize,
- std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+ std::function<std::vector<uint8_t> (std::vector<int16_t>&)> compute);
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
- _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<uint8_t> (std::vector<int16_t>&)> compute);
-
- template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
- _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<int16_t> (std::vector<int16_t>&)> compute);
+ FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int16_t> (std::vector<int16_t>&)> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t, size_t)>
- _FeatureCalc<float>(TfLiteTensor *inputTensor,
- size_t cacheSize,
- std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+ FeatureCalc<float>(TfLiteTensor *inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
static std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)>
@@ -370,38 +370,41 @@
switch (inputTensor->type) {
case kTfLiteInt8: {
- melSpecFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
- cacheSize,
- [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
- return melSpec.MelSpecComputeQuant<int8_t>(audioDataWindow,
- quantScale,
- quantOffset,
- trainingMean);
- }
+ melSpecFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+ return melSpec.MelSpecComputeQuant<int8_t>(
+ audioDataWindow,
+ quantScale,
+ quantOffset,
+ trainingMean);
+ }
);
break;
}
case kTfLiteUInt8: {
- melSpecFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
- cacheSize,
- [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
- return melSpec.MelSpecComputeQuant<uint8_t>(audioDataWindow,
- quantScale,
- quantOffset,
- trainingMean);
- }
+ melSpecFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+ cacheSize,
+ [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+ return melSpec.MelSpecComputeQuant<uint8_t>(
+ audioDataWindow,
+ quantScale,
+ quantOffset,
+ trainingMean);
+ }
);
break;
}
case kTfLiteInt16: {
- melSpecFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
- cacheSize,
- [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
- return melSpec.MelSpecComputeQuant<int16_t>(audioDataWindow,
- quantScale,
- quantOffset,
- trainingMean);
- }
+ melSpecFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+ cacheSize,
+ [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+ return melSpec.MelSpecComputeQuant<int16_t>(
+ audioDataWindow,
+ quantScale,
+ quantOffset,
+ trainingMean);
+ }
);
break;
}
@@ -411,12 +414,14 @@
} else {
- melSpecFeatureCalc = melSpecFeatureCalc = _FeatureCalc<float>(inputTensor,
- cacheSize,
- [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
- return melSpec.ComputeMelSpec(audioDataWindow,
- trainingMean);
- });
+ melSpecFeatureCalc = melSpecFeatureCalc = FeatureCalc<float>(inputTensor,
+ cacheSize,
+ [=, &melSpec](
+ std::vector<int16_t>& audioDataWindow) {
+ return melSpec.ComputeMelSpec(
+ audioDataWindow,
+ trainingMean);
+ });
}
return melSpecFeatureCalc;
}
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp
index 1a63814..2c97a39 100644
--- a/source/use_case/asr/include/AsrClassifier.hpp
+++ b/source/use_case/asr/include/AsrClassifier.hpp
@@ -51,9 +51,9 @@
* @return true if successful, false otherwise.
**/
template<typename T>
- bool _GetTopResults(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
+ bool GetTopResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
};
} /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterMfcc.hpp b/source/use_case/asr/include/Wav2LetterMfcc.hpp
index 3cb43b9..b5a21d3 100644
--- a/source/use_case/asr/include/Wav2LetterMfcc.hpp
+++ b/source/use_case/asr/include/Wav2LetterMfcc.hpp
@@ -60,8 +60,8 @@
bool ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies) override;
/**
diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp
index b801e10..4c62578 100644
--- a/source/use_case/asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/asr/include/Wav2LetterModel.hpp
@@ -49,10 +49,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 5;
+ static constexpr int ms_maxOpCnt = 5;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
index 69567a3..e16d35b 100644
--- a/source/use_case/asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
@@ -72,33 +72,33 @@
* initialised.
* @return true if valid, false otherwise.
*/
- bool _IsInputValid(TfLiteTensor* tensor,
- uint32_t axisIdx) const;
+ bool IsInputValid(TfLiteTensor* tensor,
+ const uint32_t axisIdx) const;
/**
* @brief Gets the tensor data element size in bytes based
* on the tensor type.
* @return Size in bytes, 0 if not supported.
*/
- uint32_t _GetTensorElementSize(TfLiteTensor* tensor);
+ static uint32_t GetTensorElementSize(TfLiteTensor* tensor);
/**
* @brief Erases sections from the data assuming row-wise
* arrangement along the context axis.
* @return true if successful, false otherwise.
*/
- bool _EraseSectionsRowWise(uint8_t* ptrData,
- uint32_t strideSzBytes,
- bool lastIteration);
+ bool EraseSectionsRowWise(uint8_t* ptrData,
+ const uint32_t strideSzBytes,
+ const bool lastIteration);
/**
* @brief Erases sections from the data assuming col-wise
* arrangement along the context axis.
* @return true if successful, false otherwise.
*/
- bool _EraseSectionsColWise(uint8_t* ptrData,
- uint32_t strideSzBytes,
- bool lastIteration);
+ static bool EraseSectionsColWise(const uint8_t* ptrData,
+ const uint32_t strideSzBytes,
+ const bool lastIteration);
};
} /* namespace asr */
diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
index 8a4e0b7..10512b9 100644
--- a/source/use_case/asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
@@ -74,16 +74,16 @@
* @param[out] delta2 Result of the second diff computation.
* @return true if successful, false otherwise.
*/
- static bool _ComputeDeltas(Array2d<float>& mfcc,
- Array2d<float>& delta1,
- Array2d<float>& delta2);
+ static bool ComputeDeltas(Array2d<float>& mfcc,
+ Array2d<float>& delta1,
+ Array2d<float>& delta2);
/**
* @brief Given a 2D vector of floats, computes the mean.
* @param[in] vec Vctor of vector of floats.
* @return Mean value.
*/
- static float _GetMean(Array2d<float>& vec);
+ static float GetMean(Array2d<float>& vec);
/**
* @brief Given a 2D vector of floats, computes the stddev.
@@ -91,20 +91,20 @@
* @param[in] mean Mean value of the vector passed in.
* @return stddev value.
*/
- static float _GetStdDev(Array2d<float>& vec,
- float mean);
+ static float GetStdDev(Array2d<float>& vec,
+ const float mean);
/**
* @brief Given a 2D vector of floats, normalises it using
* the mean and the stddev.
* @param[in,out] vec Vector of vector of floats.
*/
- static void _NormaliseVec(Array2d<float>& vec);
+ static void NormaliseVec(Array2d<float>& vec);
/**
* @brief Normalises the MFCC and delta buffers.
*/
- void _Normalise();
+ void Normalise();
/**
* @brief Given the quantisation and data type limits, computes
@@ -116,7 +116,7 @@
* @param[in] maxVal Numerical limit - maximum.
* @return Floating point quantised value.
*/
- static float _GetQuantElem(
+ static float GetQuantElem(
float elem,
float quantScale,
int quantOffset,
@@ -137,7 +137,7 @@
* @param[in] quantOffset Quantisation offset.
*/
template <typename T>
- bool _Quantise(
+ bool Quantise(
T * outputBuf,
const uint32_t outputBufSz,
const float quantScale,
@@ -161,15 +161,15 @@
/* Need to transpose while copying and concatenating the tensor. */
for (uint32_t j = 0; j < this->_m_numFeatVectors; ++j) {
for (uint32_t i = 0; i < this->_m_numMfccFeats; ++i) {
- *outputBufMfcc++ = static_cast<T>(Preprocess::_GetQuantElem(
- this->_m_mfccBuf(i, j), quantScale,
- quantOffset, minVal, maxVal));
- *outputBufD1++ = static_cast<T>(Preprocess::_GetQuantElem(
- this->_m_delta1Buf(i, j), quantScale,
- quantOffset, minVal, maxVal));
- *outputBufD2++ = static_cast<T>(Preprocess::_GetQuantElem(
- this->_m_delta2Buf(i, j), quantScale,
- quantOffset, minVal, maxVal));
+ *outputBufMfcc++ = static_cast<T>(Preprocess::GetQuantElem(
+ this->_m_mfccBuf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
+ *outputBufD1++ = static_cast<T>(Preprocess::GetQuantElem(
+ this->_m_delta1Buf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
+ *outputBufD2++ = static_cast<T>(Preprocess::GetQuantElem(
+ this->_m_delta2Buf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
}
outputBufMfcc += ptrIncr;
outputBufD1 += ptrIncr;
diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc
index 7377d30..df26a7f 100644
--- a/source/use_case/asr/src/AsrClassifier.cc
+++ b/source/use_case/asr/src/AsrClassifier.cc
@@ -21,13 +21,18 @@
#include "Wav2LetterModel.hpp"
template<typename T>
-bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint)
+bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint)
{
const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx];
const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx];
+ if (nLetters != labels.size()) {
+ printf("Output size doesn't match the labels' size\n");
+ return false;
+ }
+
/* NOTE: tensor's size verification against labels should be
* checked by the calling/public function. */
if (nLetters < 1) {
@@ -58,12 +63,12 @@
return true;
}
-template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
-template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
bool arm::app::AsrClassifier::GetClassificationResults(
TfLiteTensor* outputTensor,
@@ -104,16 +109,16 @@
switch (outputTensor->type) {
case kTfLiteUInt8:
- resultState = this->_GetTopResults<uint8_t>(
- outputTensor, vecResults,
- labels, quantParams.scale,
- quantParams.offset);
+ resultState = this->GetTopResults<uint8_t>(
+ outputTensor, vecResults,
+ labels, quantParams.scale,
+ quantParams.offset);
break;
case kTfLiteInt8:
- resultState = this->_GetTopResults<int8_t>(
- outputTensor, vecResults,
- labels, quantParams.scale,
- quantParams.offset);
+ resultState = this->GetTopResults<int8_t>(
+ outputTensor, vecResults,
+ labels, quantParams.scale,
+ quantParams.offset);
break;
default:
printf_err("Tensor type %s not supported by classifier\n",
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 5d3157a..efaefc2 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -35,7 +35,7 @@
* @brief Helper function to increment current audio clip index.
* @param[in,out] ctx Pointer to the application context object.
**/
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
/**
* @brief Helper function to set the audio clip index.
@@ -43,7 +43,7 @@
* @param[in] idx Value to be set.
* @return true if index is set, false otherwise.
**/
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
/**
* @brief Presents inference results using the data presentation
@@ -54,7 +54,7 @@
* otherwise, this can be passed in as 0.
* @return true if successful, false otherwise.
**/
- static bool _PresentInferenceResult(
+ static bool PresentInferenceResult(
hal_platform& platform,
const std::vector<arm::app::asr::AsrResult>& results);
@@ -71,7 +71,7 @@
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
- if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ if (!SetAppCtxClipIdx(ctx, clipIndex)) {
return false;
}
}
@@ -207,20 +207,20 @@
ctx.Set<std::vector<arm::app::asr::AsrResult>>("results", results);
- if (!_PresentInferenceResult(platform, results)) {
+ if (!PresentInferenceResult(platform, results)) {
return false;
}
profiler.PrintProfilingResult();
- _IncrementAppCtxClipIdx(ctx);
+ IncrementAppCtxClipIdx(ctx);
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
{
auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
@@ -232,7 +232,7 @@
ctx.Set<uint32_t>("clipIndex", curAudioIdx);
}
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
{
if (idx >= NUMBER_OF_FILES) {
printf_err("Invalid idx %u (expected less than %u)\n",
@@ -244,8 +244,8 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform,
- const std::vector<arm::app::asr::AsrResult>& results)
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::asr::AsrResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 60;
diff --git a/source/use_case/asr/src/Wav2LetterMfcc.cc b/source/use_case/asr/src/Wav2LetterMfcc.cc
index 92c91bc..0eb152a 100644
--- a/source/use_case/asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/asr/src/Wav2LetterMfcc.cc
@@ -27,8 +27,8 @@
bool Wav2LetterMFCC::ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies)
{
const size_t numBanks = melEnergies.size();
@@ -41,11 +41,14 @@
for (size_t bin = 0; bin < numBanks; ++bin) {
auto filterBankIter = melFilterBank[bin].begin();
- float melEnergy = 1e-10; /* Avoid log of zero at later stages, same value used in librosa. */
- const int32_t firstIndex = filterBankFilterFirst[bin];
- const int32_t lastIndex = filterBankFilterLast[bin];
+ auto end = melFilterBank[bin].end();
+ /* Avoid log of zero at later stages, same value used in librosa.
+ * The number was used during our default wav2letter model training. */
+ float melEnergy = 1e-10;
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
- for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
melEnergy += (*filterBankIter++ * fftVec[i]);
}
@@ -73,7 +76,7 @@
/* Scale the log values and get the max. */
for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
- iterM != melEnergies.end(); ++iterM, ++iterL) {
+ iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
*iterM = *iterL * multiplier;
@@ -86,8 +89,8 @@
/* Clamp the mel energies. */
constexpr float maxDb = 80.0;
const float clampLevelLowdB = maxMelEnergy - maxDb;
- for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) {
- *iter = std::max(*iter, clampLevelLowdB);
+ for (float& melEnergy : melEnergies) {
+ melEnergy = std::max(melEnergy, clampLevelLowdB);
}
}
diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc
index 60ee51e..9157a6f 100644
--- a/source/use_case/asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc
@@ -39,13 +39,13 @@
const bool lastIteration)
{
/* Basic checks. */
- if (!this->_IsInputValid(tensor, axisIdx)) {
+ if (!this->IsInputValid(tensor, axisIdx)) {
return false;
}
/* Irrespective of tensor type, we use unsigned "byte" */
uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor);
- const uint32_t elemSz = this->_GetTensorElementSize(tensor);
+ const uint32_t elemSz = this->GetTensorElementSize(tensor);
/* Other sanity checks. */
if (0 == elemSz) {
@@ -59,13 +59,15 @@
/* Which axis do we need to process? */
switch (axisIdx) {
case arm::app::Wav2LetterModel::ms_outputRowsIdx:
- return this->_EraseSectionsRowWise(ptrData,
- elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
- lastIteration);
+ return this->EraseSectionsRowWise(ptrData,
+ elemSz *
+ tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
+ lastIteration);
case arm::app::Wav2LetterModel::ms_outputColsIdx:
- return this->_EraseSectionsColWise(ptrData,
- elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx],
- lastIteration);
+ return this->EraseSectionsColWise(ptrData,
+ elemSz *
+ tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx],
+ lastIteration);
default:
printf_err("Unsupported axis index: %u\n", axisIdx);
}
@@ -73,8 +75,8 @@
return false;
}
- bool Postprocess::_IsInputValid(TfLiteTensor* tensor,
- const uint32_t axisIdx) const
+ bool Postprocess::IsInputValid(TfLiteTensor* tensor,
+ const uint32_t axisIdx) const
{
if (nullptr == tensor) {
return false;
@@ -96,17 +98,15 @@
return true;
}
- uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor* tensor)
+ uint32_t Postprocess::GetTensorElementSize(TfLiteTensor* tensor)
{
switch(tensor->type) {
case kTfLiteUInt8:
- return 1;
case kTfLiteInt8:
return 1;
case kTfLiteInt16:
return 2;
case kTfLiteInt32:
- return 4;
case kTfLiteFloat32:
return 4;
default:
@@ -117,7 +117,7 @@
return 0;
}
- bool Postprocess::_EraseSectionsRowWise(
+ bool Postprocess::EraseSectionsRowWise(
uint8_t* ptrData,
const uint32_t strideSzBytes,
const bool lastIteration)
@@ -154,8 +154,8 @@
return true;
}
- bool Postprocess::_EraseSectionsColWise(
- uint8_t* ptrData,
+ bool Postprocess::EraseSectionsColWise(
+ const uint8_t* ptrData,
const uint32_t strideSzBytes,
const bool lastIteration)
{
diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc
index e46cca3..d65ea75 100644
--- a/source/use_case/asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc
@@ -88,12 +88,12 @@
}
/* Compute first and second order deltas from MFCCs. */
- this->_ComputeDeltas(this->_m_mfccBuf,
- this->_m_delta1Buf,
- this->_m_delta2Buf);
+ Preprocess::ComputeDeltas(this->_m_mfccBuf,
+ this->_m_delta1Buf,
+ this->_m_delta2Buf);
/* Normalise. */
- this->_Normalise();
+ this->Normalise();
/* Quantise. */
QuantParams quantParams = GetTensorQuantParams(tensor);
@@ -105,11 +105,11 @@
switch(tensor->type) {
case kTfLiteUInt8:
- return this->_Quantise<uint8_t>(
+ return this->Quantise<uint8_t>(
tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
quantParams.scale, quantParams.offset);
case kTfLiteInt8:
- return this->_Quantise<int8_t>(
+ return this->Quantise<int8_t>(
tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
quantParams.scale, quantParams.offset);
default:
@@ -120,9 +120,9 @@
return false;
}
- bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
- Array2d<float>& delta1,
- Array2d<float>& delta2)
+ bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+ Array2d<float>& delta1,
+ Array2d<float>& delta2)
{
const std::vector <float> delta1Coeffs =
{6.66666667e-02, 5.00000000e-02, 3.33333333e-02,
@@ -175,20 +175,20 @@
return true;
}
- float Preprocess::_GetMean(Array2d<float>& vec)
+ float Preprocess::GetMean(Array2d<float>& vec)
{
return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
}
- float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+ float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
{
return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
}
- void Preprocess::_NormaliseVec(Array2d<float>& vec)
+ void Preprocess::NormaliseVec(Array2d<float>& vec)
{
- auto mean = Preprocess::_GetMean(vec);
- auto stddev = Preprocess::_GetStdDev(vec, mean);
+ auto mean = Preprocess::GetMean(vec);
+ auto stddev = Preprocess::GetStdDev(vec, mean);
debug("Mean: %f, Stddev: %f\n", mean, stddev);
if (stddev == 0) {
@@ -204,14 +204,14 @@
}
}
- void Preprocess::_Normalise()
+ void Preprocess::Normalise()
{
- Preprocess::_NormaliseVec(this->_m_mfccBuf);
- Preprocess::_NormaliseVec(this->_m_delta1Buf);
- Preprocess::_NormaliseVec(this->_m_delta2Buf);
+ Preprocess::NormaliseVec(this->_m_mfccBuf);
+ Preprocess::NormaliseVec(this->_m_delta1Buf);
+ Preprocess::NormaliseVec(this->_m_delta2Buf);
}
- float Preprocess::_GetQuantElem(
+ float Preprocess::GetQuantElem(
const float elem,
const float quantScale,
const int quantOffset,
diff --git a/source/use_case/img_class/include/MobileNetModel.hpp b/source/use_case/img_class/include/MobileNetModel.hpp
index f0521ce..2540564 100644
--- a/source/use_case/img_class/include/MobileNetModel.hpp
+++ b/source/use_case/img_class/include/MobileNetModel.hpp
@@ -43,10 +43,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 7;
+ static constexpr int ms_maxOpCnt = 7;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index f7e83f5..ffeb860 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -35,13 +35,13 @@
* @param[out] inputTensor Pointer to the input tensor to be populated.
* @return true if tensor is loaded, false otherwise.
**/
- static bool _LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor);
+ static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor);
/**
* @brief Helper function to increment current image index.
* @param[in,out] ctx Pointer to the application context object.
**/
- static void _IncrementAppCtxImageIdx(ApplicationContext& ctx);
+ static void IncrementAppCtxImageIdx(ApplicationContext& ctx);
/**
* @brief Helper function to set the image index.
@@ -49,7 +49,7 @@
* @param[in] idx Value to be set.
* @return true if index is set, false otherwise.
**/
- static bool _SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
+ static bool SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
/**
* @brief Presents inference results using the data presentation
@@ -60,8 +60,8 @@
* otherwise, this can be passed in as 0.
* @return true if successful, false otherwise.
**/
- static bool _PresentInferenceResult(hal_platform& platform,
- const std::vector<ClassificationResult>& results);
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<ClassificationResult>& results);
/**
* @brief Helper function to convert a UINT8 image to INT8 format.
@@ -89,7 +89,7 @@
/* If the request has a valid size, set the image index. */
if (imgIndex < NUMBER_OF_FILES) {
- if (!_SetAppCtxImageIdx(ctx, imgIndex)) {
+ if (!SetAppCtxImageIdx(ctx, imgIndex)) {
return false;
}
}
@@ -124,7 +124,7 @@
std::string str_inf{"Running inference... "};
/* Copy over the data. */
- _LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
+ LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
/* Display this image on the LCD. */
platform.data_psn->present_data_image(
@@ -164,20 +164,20 @@
arm::app::DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
- if (!_PresentInferenceResult(platform, results)) {
+ if (!PresentInferenceResult(platform, results)) {
return false;
}
profiler.PrintProfilingResult();
- _IncrementAppCtxImageIdx(ctx);
+ IncrementAppCtxImageIdx(ctx);
} while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
return true;
}
- static bool _LoadImageIntoTensor(const uint32_t imIdx, TfLiteTensor* inputTensor)
+ static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor)
{
const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
inputTensor->bytes : IMAGE_DATA_SIZE;
@@ -193,7 +193,7 @@
return true;
}
- static void _IncrementAppCtxImageIdx(ApplicationContext& ctx)
+ static void IncrementAppCtxImageIdx(ApplicationContext& ctx)
{
auto curImIdx = ctx.Get<uint32_t>("imgIndex");
@@ -205,7 +205,7 @@
ctx.Set<uint32_t>("imgIndex", curImIdx);
}
- static bool _SetAppCtxImageIdx(ApplicationContext& ctx, const uint32_t idx)
+ static bool SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx)
{
if (idx >= NUMBER_OF_FILES) {
printf_err("Invalid idx %u (expected less than %u)\n",
@@ -216,8 +216,8 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform,
- const std::vector<ClassificationResult>& results)
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<ClassificationResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 150;
constexpr uint32_t dataPsnTxtStartY1 = 30;
diff --git a/source/use_case/kws/include/DsCnnModel.hpp b/source/use_case/kws/include/DsCnnModel.hpp
index a4e7110..e9ac18c 100644
--- a/source/use_case/kws/include/DsCnnModel.hpp
+++ b/source/use_case/kws/include/DsCnnModel.hpp
@@ -47,10 +47,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 8;
+ static constexpr int ms_maxOpCnt = 8;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index d2cba55..4011df6 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -37,7 +37,7 @@
* @brief Helper function to increment current audio clip index.
* @param[in,out] ctx Pointer to the application context object.
**/
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
/**
* @brief Helper function to set the audio clip index.
@@ -45,7 +45,7 @@
* @param[in] idx Value to be set.
* @return true if index is set, false otherwise.
**/
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
/**
* @brief Presents inference results using the data presentation
@@ -56,8 +56,8 @@
* otherwise, this can be passed in as 0.
* @return true if successful, false otherwise.
**/
- static bool _PresentInferenceResult(hal_platform& platform,
- const std::vector<arm::app::kws::KwsResult>& results);
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results);
/**
* @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -96,7 +96,7 @@
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
- if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ if (!SetAppCtxClipIdx(ctx, clipIndex)) {
return false;
}
}
@@ -240,20 +240,20 @@
ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
- if (!_PresentInferenceResult(platform, results)) {
+ if (!PresentInferenceResult(platform, results)) {
return false;
}
profiler.PrintProfilingResult();
- _IncrementAppCtxClipIdx(ctx);
+ IncrementAppCtxClipIdx(ctx);
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
{
auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
@@ -265,7 +265,7 @@
ctx.Set<uint32_t>("clipIndex", curAudioIdx);
}
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
{
if (idx >= NUMBER_OF_FILES) {
printf_err("Invalid idx %u (expected less than %u)\n",
@@ -276,8 +276,8 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform,
- const std::vector<arm::app::kws::KwsResult>& results)
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -345,8 +345,8 @@
*/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
- _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
- std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ 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);
@@ -378,24 +378,24 @@
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ 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)>
- _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
+ FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+ FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
- _FeatureCalc<float>(TfLiteTensor *inputTensor,
- size_t cacheSize,
- std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+ FeatureCalc<float>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
@@ -413,19 +413,19 @@
switch (inputTensor->type) {
case kTfLiteInt8: {
- mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
- cacheSize,
- [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
- quantScale,
- quantOffset);
- }
+ mfccFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
);
break;
}
case kTfLiteUInt8: {
- mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
- cacheSize,
+ mfccFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+ cacheSize,
[=, &mfcc](std::vector<int16_t>& audioDataWindow) {
return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
quantScale,
@@ -435,13 +435,13 @@
break;
}
case kTfLiteInt16: {
- mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
- cacheSize,
- [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
- quantScale,
- quantOffset);
- }
+ mfccFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
);
break;
}
@@ -451,11 +451,11 @@
} else {
- mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
- cacheSize,
- [&mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccCompute(audioDataWindow);
- });
+ mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
+ cacheSize,
+ [&mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccCompute(audioDataWindow);
+ });
}
return mfccFeatureCalc;
}
diff --git a/source/use_case/kws_asr/include/AsrClassifier.hpp b/source/use_case/kws_asr/include/AsrClassifier.hpp
index de18aa8..7dbb6e9 100644
--- a/source/use_case/kws_asr/include/AsrClassifier.hpp
+++ b/source/use_case/kws_asr/include/AsrClassifier.hpp
@@ -53,9 +53,9 @@
* @return true if successful, false otherwise.
**/
template<typename T>
- bool _GetTopResults(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
+ bool GetTopResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
};
} /* namespace app */
diff --git a/source/use_case/kws_asr/include/DsCnnModel.hpp b/source/use_case/kws_asr/include/DsCnnModel.hpp
index 150a48c..f9d4357 100644
--- a/source/use_case/kws_asr/include/DsCnnModel.hpp
+++ b/source/use_case/kws_asr/include/DsCnnModel.hpp
@@ -55,10 +55,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 10;
+ static constexpr int ms_maxOpCnt = 10;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
index 0852cbf..75d75da 100644
--- a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
@@ -60,8 +60,8 @@
bool ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies) override;
/**
@@ -103,6 +103,7 @@
const float& leftMel,
const float& rightMel,
bool useHTKMethod) override;
+
};
} /* namespace audio */
diff --git a/source/use_case/kws_asr/include/Wav2LetterModel.hpp b/source/use_case/kws_asr/include/Wav2LetterModel.hpp
index fb701ea..9a86bd9 100644
--- a/source/use_case/kws_asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterModel.hpp
@@ -55,10 +55,10 @@
private:
/* Maximum number of individual operations that can be enlisted. */
- static constexpr int _ms_maxOpCnt = 5;
+ static constexpr int ms_maxOpCnt = 5;
/* A mutable op resolver instance. */
- tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
};
} /* namespace app */
diff --git a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
index 3a9d401..fe60923 100644
--- a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
@@ -72,24 +72,24 @@
* initialised.
* @return true if valid, false otherwise.
*/
- bool _IsInputValid(TfLiteTensor* tensor,
- uint32_t axisIdx) const;
+ bool IsInputValid(TfLiteTensor* tensor,
+ const uint32_t axisIdx) const;
/**
* @brief Gets the tensor data element size in bytes based
* on the tensor type.
* @return Size in bytes, 0 if not supported.
*/
- uint32_t _GetTensorElementSize(TfLiteTensor* tensor);
+ uint32_t GetTensorElementSize(TfLiteTensor* tensor);
/**
* @brief Erases sections from the data assuming row-wise
* arrangement along the context axis.
* @return true if successful, false otherwise.
*/
- bool _EraseSectionsRowWise(uint8_t* ptrData,
- uint32_t strideSzBytes,
- bool lastIteration);
+ bool EraseSectionsRowWise(uint8_t* ptrData,
+ const uint32_t strideSzBytes,
+ const bool lastIteration);
};
diff --git a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
index 3ffabb4..cf40fa8 100644
--- a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
@@ -75,16 +75,16 @@
*
* @return true if successful, false otherwise.
*/
- static bool _ComputeDeltas(Array2d<float>& mfcc,
- Array2d<float>& delta1,
- Array2d<float>& delta2);
+ static bool ComputeDeltas(Array2d<float>& mfcc,
+ Array2d<float>& delta1,
+ Array2d<float>& delta2);
/**
* @brief Given a 2D vector of floats, computes the mean.
* @param[in] vec Vector of vector of floats.
* @return Mean value.
*/
- static float _GetMean(Array2d<float>& vec);
+ static float GetMean(Array2d<float>& vec);
/**
* @brief Given a 2D vector of floats, computes the stddev.
@@ -92,20 +92,20 @@
* @param[in] mean Mean value of the vector passed in.
* @return stddev value.
*/
- static float _GetStdDev(Array2d<float>& vec,
- float mean);
+ static float GetStdDev(Array2d<float>& vec,
+ const float mean);
/**
* @brief Given a 2D vector of floats, normalises it using
* the mean and the stddev
* @param[in,out] vec Vector of vector of floats.
*/
- static void _NormaliseVec(Array2d<float>& vec);
+ static void NormaliseVec(Array2d<float>& vec);
/**
* @brief Normalises the MFCC and delta buffers.
*/
- void _Normalise();
+ void Normalise();
/**
* @brief Given the quantisation and data type limits, computes
@@ -117,7 +117,7 @@
* @param[in] maxVal Numerical limit - maximum.
* @return Floating point quantised value.
*/
- static float _GetQuantElem(
+ static float GetQuantElem(
float elem,
float quantScale,
int quantOffset,
@@ -138,7 +138,7 @@
* @param[in] quantOffset Quantisation offset.
*/
template <typename T>
- bool _Quantise(
+ bool Quantise(
T * outputBuf,
const uint32_t outputBufSz,
const float quantScale,
@@ -163,15 +163,15 @@
* the tensor. */
for (uint32_t j = 0; j < this->_m_numFeatVectors; ++j) {
for (uint32_t i = 0; i < this->_m_numMfccFeats; ++i) {
- *outputBufMfcc++ = static_cast<T>(this->_GetQuantElem(
- this->_m_mfccBuf(i, j), quantScale,
- quantOffset, minVal, maxVal));
- *outputBufD1++ = static_cast<T>(this->_GetQuantElem(
- this->_m_delta1Buf(i, j), quantScale,
- quantOffset, minVal, maxVal));
- *outputBufD2++ = static_cast<T>(this->_GetQuantElem(
- this->_m_delta2Buf(i, j), quantScale,
- quantOffset, minVal, maxVal));
+ *outputBufMfcc++ = static_cast<T>(this->GetQuantElem(
+ this->_m_mfccBuf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
+ *outputBufD1++ = static_cast<T>(this->GetQuantElem(
+ this->_m_delta1Buf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
+ *outputBufD2++ = static_cast<T>(this->GetQuantElem(
+ this->_m_delta2Buf(i, j), quantScale,
+ quantOffset, minVal, maxVal));
}
outputBufMfcc += ptrIncr;
outputBufD1 += ptrIncr;
diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc
index bc86e09..f1fa6f1 100644
--- a/source/use_case/kws_asr/src/AsrClassifier.cc
+++ b/source/use_case/kws_asr/src/AsrClassifier.cc
@@ -21,13 +21,17 @@
#include "Wav2LetterModel.hpp"
template<typename T>
-bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint)
+bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint)
{
const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx];
const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx];
+ if (nLetters != labels.size()) {
+ printf("Output size doesn't match the labels' size\n");
+ return false;
+ }
/* NOTE: tensor's size verification against labels should be
* checked by the calling/public function. */
@@ -42,7 +46,7 @@
/* Get the top 1 results. */
for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) {
- std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0);
+ std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row], 0);
for (uint32_t j = 1; j < nLetters; ++j) {
if (top_1.first < tensorData[row + j]) {
@@ -59,12 +63,12 @@
return true;
}
-template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
-template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, double scale, double zeroPoint);
bool arm::app::AsrClassifier::GetClassificationResults(
TfLiteTensor* outputTensor,
@@ -105,16 +109,16 @@
switch (outputTensor->type) {
case kTfLiteUInt8:
- resultState = this->_GetTopResults<uint8_t>(
- outputTensor, vecResults,
- labels, quantParams.scale,
- quantParams.offset);
+ resultState = this->GetTopResults<uint8_t>(
+ outputTensor, vecResults,
+ labels, quantParams.scale,
+ quantParams.offset);
break;
case kTfLiteInt8:
- resultState = this->_GetTopResults<int8_t>(
- outputTensor, vecResults,
- labels, quantParams.scale,
- quantParams.offset);
+ resultState = this->GetTopResults<int8_t>(
+ outputTensor, vecResults,
+ labels, quantParams.scale,
+ quantParams.offset);
break;
default:
printf_err("Tensor type %s not supported by classifier\n",
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index a428210..1edc7c4 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -52,13 +52,7 @@
* @brief Helper function to increment current audio clip index
* @param[in,out] ctx pointer to the application context object
**/
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
-
- /**
- * @brief Helper function to increment current audio clip index
- * @param[in,out] ctx pointer to the application context object
- **/
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
/**
* @brief Helper function to set the audio clip index
@@ -66,7 +60,7 @@
* @param[in] idx value to be set
* @return true if index is set, false otherwise
**/
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
/**
* @brief Presents kws inference results using the data presentation
@@ -77,7 +71,7 @@
* Otherwise, this can be passed in as 0.
* @return true if successful, false otherwise
**/
- static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
+ static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
/**
* @brief Presents asr inference results using the data presentation
@@ -88,7 +82,7 @@
* Otherwise, this can be passed in as 0.
* @return true if successful, false otherwise
**/
- static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
+ static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
/**
* @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -212,7 +206,7 @@
std::string str_inf{"Running KWS inference... "};
platform.data_psn->present_data_text(
str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
info("Running KWS inference on audio clip %u => %s\n",
currentIndex, get_filename(currentIndex));
@@ -279,9 +273,9 @@
str_inf = std::string(str_inf.size(), ' ');
platform.data_psn->present_data_text(
str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
- if (!_PresentInferenceResult(platform, kwsResults)) {
+ if (!PresentInferenceResult(platform, kwsResults)) {
return output;
}
@@ -375,7 +369,7 @@
std::string str_inf{"Running ASR inference... "};
platform.data_psn->present_data_text(
str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
size_t asrInferenceWindowLen = asrAudioParamsWinLen;
@@ -425,7 +419,7 @@
str_inf.c_str(), str_inf.size(),
dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
}
- if (!_PresentInferenceResult(platform, asrResults)) {
+ if (!PresentInferenceResult(platform, asrResults)) {
return false;
}
@@ -442,7 +436,7 @@
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
- if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ if (!SetAppCtxClipIdx(ctx, clipIndex)) {
return false;
}
}
@@ -463,14 +457,14 @@
}
}
- _IncrementAppCtxClipIdx(ctx);
+ IncrementAppCtxClipIdx(ctx);
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
- static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
{
auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
@@ -482,7 +476,7 @@
ctx.Set<uint32_t>("clipIndex", curAudioIdx);
}
- static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
{
if (idx >= NUMBER_OF_FILES) {
printf_err("Invalid idx %u (expected less than %u)\n",
@@ -493,8 +487,8 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform,
- std::vector<arm::app::kws::KwsResult>& results)
+ static bool PresentInferenceResult(hal_platform& platform,
+ std::vector<arm::app::kws::KwsResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -510,7 +504,7 @@
std::string topKeyword{"<none>"};
float score = 0.f;
- if (results[i].m_resultVec.size()) {
+ if (!results[i].m_resultVec.empty()) {
topKeyword = results[i].m_resultVec[0].m_label;
score = results[i].m_resultVec[0].m_normalisedVal;
}
@@ -538,7 +532,7 @@
return true;
}
- static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
+ static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 80;
@@ -587,8 +581,8 @@
**/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
- _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
- std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ 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);
@@ -621,24 +615,24 @@
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ 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)>
+ FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
size_t cacheSize,
- std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+ std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
-
- template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+ FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
- _FeatureCalc<float>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+ FeatureCalc<float>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
@@ -656,35 +650,35 @@
switch (inputTensor->type) {
case kTfLiteInt8: {
- mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
- cacheSize,
- [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
- quantScale,
- quantOffset);
- }
+ mfccFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
);
break;
}
case kTfLiteUInt8: {
- mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
- cacheSize,
- [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
- quantScale,
- quantOffset);
- }
+ mfccFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
);
break;
}
case kTfLiteInt16: {
- mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
- cacheSize,
- [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
- quantScale,
- quantOffset);
- }
+ mfccFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
);
break;
}
@@ -694,11 +688,11 @@
} else {
- mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
- cacheSize,
- [&mfcc](std::vector<int16_t>& audioDataWindow) {
- return mfcc.MfccCompute(audioDataWindow);
- });
+ mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
+ cacheSize,
+ [&mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccCompute(audioDataWindow);
+ });
}
return mfccFeatureCalc;
}
diff --git a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
index 80e4a26..ae9e57a 100644
--- a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
@@ -27,8 +27,8 @@
bool Wav2LetterMFCC::ApplyMelFilterBank(
std::vector<float>& fftVec,
std::vector<std::vector<float>>& melFilterBank,
- std::vector<int32_t>& filterBankFilterFirst,
- std::vector<int32_t>& filterBankFilterLast,
+ std::vector<uint32_t>& filterBankFilterFirst,
+ std::vector<uint32_t>& filterBankFilterLast,
std::vector<float>& melEnergies)
{
const size_t numBanks = melEnergies.size();
@@ -41,11 +41,14 @@
for (size_t bin = 0; bin < numBanks; ++bin) {
auto filterBankIter = melFilterBank[bin].begin();
- float melEnergy = 1e-10; /* Avoid log of zero at later stages, same value used in librosa. */
- const int32_t firstIndex = filterBankFilterFirst[bin];
- const int32_t lastIndex = filterBankFilterLast[bin];
+ auto end = melFilterBank[bin].end();
+ /* Avoid log of zero at later stages, same value used in librosa.
+ * The number was used during our default wav2letter model training. */
+ float melEnergy = 1e-10;
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
- for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
melEnergy += (*filterBankIter++ * fftVec[i]);
}
@@ -73,7 +76,7 @@
/* Scale the log values and get the max. */
for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
- iterM != melEnergies.end(); ++iterM, ++iterL) {
+ iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
*iterM = *iterL * multiplier;
@@ -86,8 +89,8 @@
/* Clamp the mel energies. */
constexpr float maxDb = 80.0;
const float clampLevelLowdB = maxMelEnergy - maxDb;
- for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) {
- *iter = std::max(*iter, clampLevelLowdB);
+ for (float & melEnergie : melEnergies) {
+ melEnergie = std::max(melEnergie, clampLevelLowdB);
}
}
diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
index b173968..ee3aba0 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
@@ -38,13 +38,13 @@
const bool lastIteration)
{
/* Basic checks. */
- if (!this->_IsInputValid(tensor, axisIdx)) {
+ if (!this->IsInputValid(tensor, axisIdx)) {
return false;
}
/* Irrespective of tensor type, we use unsigned "byte" */
uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor);
- const uint32_t elemSz = this->_GetTensorElementSize(tensor);
+ const uint32_t elemSz = this->GetTensorElementSize(tensor);
/* Other sanity checks. */
if (0 == elemSz) {
@@ -58,9 +58,10 @@
/* Which axis do we need to process? */
switch (axisIdx) {
case arm::app::Wav2LetterModel::ms_outputRowsIdx:
- return this->_EraseSectionsRowWise(ptrData,
- elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
- lastIteration);
+ return this->EraseSectionsRowWise(ptrData,
+ elemSz *
+ tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
+ lastIteration);
default:
printf_err("Unsupported axis index: %u\n", axisIdx);
}
@@ -68,8 +69,8 @@
return false;
}
- bool Postprocess::_IsInputValid(TfLiteTensor* tensor,
- const uint32_t axisIdx) const
+ bool Postprocess::IsInputValid(TfLiteTensor* tensor,
+ const uint32_t axisIdx) const
{
if (nullptr == tensor) {
return false;
@@ -91,7 +92,7 @@
return true;
}
- uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor* tensor)
+ uint32_t Postprocess::GetTensorElementSize(TfLiteTensor* tensor)
{
switch(tensor->type) {
case kTfLiteUInt8:
@@ -112,7 +113,7 @@
return 0;
}
- bool Postprocess::_EraseSectionsRowWise(
+ bool Postprocess::EraseSectionsRowWise(
uint8_t* ptrData,
const uint32_t strideSzBytes,
const bool lastIteration)
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
index 613ddb0..8251396 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
@@ -88,12 +88,12 @@
}
/* Compute first and second order deltas from MFCCs. */
- this->_ComputeDeltas(this->_m_mfccBuf,
- this->_m_delta1Buf,
- this->_m_delta2Buf);
+ this->ComputeDeltas(this->_m_mfccBuf,
+ this->_m_delta1Buf,
+ this->_m_delta2Buf);
/* Normalise. */
- this->_Normalise();
+ this->Normalise();
/* Quantise. */
QuantParams quantParams = GetTensorQuantParams(tensor);
@@ -105,11 +105,11 @@
switch(tensor->type) {
case kTfLiteUInt8:
- return this->_Quantise<uint8_t>(
+ return this->Quantise<uint8_t>(
tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
quantParams.scale, quantParams.offset);
case kTfLiteInt8:
- return this->_Quantise<int8_t>(
+ return this->Quantise<int8_t>(
tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
quantParams.scale, quantParams.offset);
default:
@@ -120,9 +120,9 @@
return false;
}
- bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
- Array2d<float>& delta1,
- Array2d<float>& delta2)
+ bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+ Array2d<float>& delta1,
+ Array2d<float>& delta2)
{
const std::vector <float> delta1Coeffs =
{6.66666667e-02, 5.00000000e-02, 3.33333333e-02,
@@ -175,20 +175,20 @@
return true;
}
- float Preprocess::_GetMean(Array2d<float>& vec)
+ float Preprocess::GetMean(Array2d<float>& vec)
{
return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
}
- float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+ float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
{
return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
}
- void Preprocess::_NormaliseVec(Array2d<float>& vec)
+ void Preprocess::NormaliseVec(Array2d<float>& vec)
{
- auto mean = Preprocess::_GetMean(vec);
- auto stddev = Preprocess::_GetStdDev(vec, mean);
+ auto mean = Preprocess::GetMean(vec);
+ auto stddev = Preprocess::GetStdDev(vec, mean);
debug("Mean: %f, Stddev: %f\n", mean, stddev);
if (stddev == 0) {
@@ -204,14 +204,14 @@
}
}
- void Preprocess::_Normalise()
+ void Preprocess::Normalise()
{
- Preprocess::_NormaliseVec(this->_m_mfccBuf);
- Preprocess::_NormaliseVec(this->_m_delta1Buf);
- Preprocess::_NormaliseVec(this->_m_delta2Buf);
+ Preprocess::NormaliseVec(this->_m_mfccBuf);
+ Preprocess::NormaliseVec(this->_m_delta1Buf);
+ Preprocess::NormaliseVec(this->_m_delta2Buf);
}
- float Preprocess::_GetQuantElem(
+ float Preprocess::GetQuantElem(
const float elem,
const float quantScale,
const int quantOffset,
diff --git a/tests/common/ClassifierTests.cc b/tests/common/ClassifierTests.cc
index f08a09a..a04e4c2 100644
--- a/tests/common/ClassifierTests.cc
+++ b/tests/common/ClassifierTests.cc
@@ -18,6 +18,31 @@
#include <catch.hpp>
+
+template<typename T>
+void test_classifier_result(std::vector<std::pair<uint32_t, T>>& selectedResults, T defaultTensorValue) {
+ const int dimArray[] = {1, 1001};
+ std::vector <std::string> labels(1001);
+ std::vector<T> outputVec(1001, defaultTensorValue);
+ TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
+ TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(outputVec.data(), dims, 1, 0);
+ TfLiteTensor* outputTensor = &tfTensor;
+
+ std::vector <arm::app::ClassificationResult> resultVec;
+
+ for (auto& selectedResult : selectedResults) {
+ outputVec[selectedResult.first] = selectedResult.second;
+ }
+
+ arm::app::Classifier classifier;
+ REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
+ REQUIRE(5 == resultVec.size());
+
+ for (size_t i = 0; i < resultVec.size(); ++i) {
+ REQUIRE(resultVec[i].m_labelIdx == selectedResults[i].first);
+ }
+}
+
TEST_CASE("Common classifier")
{
SECTION("Test invalid classifier")
@@ -28,49 +53,31 @@
REQUIRE(!classifier.GetClassificationResults(outputTens, resultVec, {}, 5));
}
- SECTION("Test valid classifier UINT8")
+ SECTION("Test classification results")
{
- const int dimArray[] = {1, 1001};
- std::vector <std::string> labels(1001);
- std::vector <uint8_t> outputVec(1001);
- TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
- TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
- outputVec.data(), dims, 1, 0, "test");
- TfLiteTensor* outputTensor = &tfTensor;
- std::vector <arm::app::ClassificationResult> resultVec;
- arm::app::Classifier classifier;
- REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
- REQUIRE(5 == resultVec.size());
- }
+ SECTION("uint8") {
+ /* Set the top five results <position, score>. */
+ std::vector<std::pair<uint32_t, uint8_t>> selectedResults {
+ {1000, 10}, {15, 9}, {0, 8}, {20, 7}, {10, 7} };
- SECTION("Get classification results")
- {
- const int dimArray[] = {1, 1001};
- std::vector <std::string> labels(1001);
- std::vector<uint8_t> outputVec(1001, static_cast<uint8_t>(5));
- TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
- TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
- outputVec.data(), dims, 1, 0, "test");
- TfLiteTensor* outputTensor = &tfTensor;
-
- std::vector <arm::app::ClassificationResult> resultVec;
-
- /* Set the top five results. */
- std::vector<std::pair<uint32_t, uint8_t>> selectedResults {
- {0, 8}, {20, 7}, {10, 7}, {15, 9}, {1000, 10}};
-
- for (size_t i = 0; i < selectedResults.size(); ++i) {
- outputVec[selectedResults[i].first] = selectedResults[i].second;
+ test_classifier_result(selectedResults, static_cast<uint8_t>(5));
}
- arm::app::Classifier classifier;
- REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
- REQUIRE(5 == resultVec.size());
+ SECTION("int8") {
+ /* Set the top five results <position, score>. */
+ std::vector<std::pair<uint32_t, int8_t>> selectedResults {
+ {1000, 10}, {15, 9}, {0, 8}, {20, -7}, {10, -7} };
- REQUIRE(resultVec[0].m_labelIdx == 1000);
- REQUIRE(resultVec[1].m_labelIdx == 15);
- REQUIRE(resultVec[2].m_labelIdx == 0);
- REQUIRE(resultVec[3].m_labelIdx == 20);
- REQUIRE(resultVec[4].m_labelIdx == 10);
+ test_classifier_result(selectedResults, static_cast<int8_t>(-100));
+ }
+
+ SECTION("float") {
+ /* Set the top five results <position, score>. */
+ std::vector<std::pair<uint32_t, float>> selectedResults {
+ {1000, 10.9f}, {15, 9.8f}, {0, 8.7f}, {20, -7.0f}, {10, -7.1f} };
+
+ test_classifier_result(selectedResults, -100.0f);
+ }
+
}
}
diff --git a/tests/use_case/asr/AsrClassifierTests.cc b/tests/use_case/asr/AsrClassifierTests.cc
index 7c71912..12523aa 100644
--- a/tests/use_case/asr/AsrClassifierTests.cc
+++ b/tests/use_case/asr/AsrClassifierTests.cc
@@ -35,7 +35,7 @@
std::vector <uint8_t> outputVec(7134);
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
- outputVec.data(), dims, 1, 0, "test");
+ outputVec.data(), dims, 1, 0);
TfLiteTensor* outputTensor = &tfTensor;
std::vector <arm::app::ClassificationResult> resultVec;
arm::app::AsrClassifier classifier;
@@ -51,7 +51,7 @@
std::vector<uint8_t> outputVec(150, static_cast<uint8_t>(1));
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
- outputVec.data(), dims, 1, 0, "test");
+ outputVec.data(), dims, 1, 0);
TfLiteTensor* outputTensor = &tfTensor;
std::vector <arm::app::ClassificationResult> resultVec(10);
diff --git a/tests/use_case/asr/AsrFeaturesTests.cc b/tests/use_case/asr/AsrFeaturesTests.cc
index 9401f40..59fe29b 100644
--- a/tests/use_case/asr/AsrFeaturesTests.cc
+++ b/tests/use_case/asr/AsrFeaturesTests.cc
@@ -25,30 +25,27 @@
class TestPreprocess : public arm::app::audio::asr::Preprocess {
public:
- TestPreprocess()
- : arm::app::audio::asr::Preprocess(0,0,0,0)
- {}
- bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
+ static bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
arm::app::Array2d<float>& delta1,
arm::app::Array2d<float>& delta2)
{
- return this->_ComputeDeltas(mfcc, delta1, delta2);
+ return Preprocess::ComputeDeltas(mfcc, delta1, delta2);
}
- float GetMean(arm::app::Array2d<float>& vec)
+ static float GetMean(arm::app::Array2d<float>& vec)
{
- return this->_GetMean(vec);
+ return Preprocess::GetMean(vec);
}
- float GetStdDev(arm::app::Array2d<float>& vec, const float mean)
+ static float GetStdDev(arm::app::Array2d<float>& vec, const float mean)
{
- return this->_GetStdDev(vec, mean);
+ return Preprocess::GetStdDev(vec, mean);
}
- void NormaliseVec(arm::app::Array2d<float>& vec)
+ static void NormaliseVec(arm::app::Array2d<float>& vec)
{
- return this->_NormaliseVec(vec);
+ return Preprocess::NormaliseVec(vec);
}
};
@@ -86,7 +83,6 @@
TEST_CASE("Floating point asr features calculation", "[ASR]")
{
- TestPreprocess tp;
SECTION("First and second diff")
{
@@ -109,7 +105,7 @@
std::fill(delta1Buf.begin(), delta1Buf.end(), 0.f);
std::fill(delta2Buf.begin(), delta2Buf.end(), 0.f);
- tp.ComputeDeltas(mfccBuf, delta1Buf, delta2Buf);
+ TestPreprocess::ComputeDeltas(mfccBuf, delta1Buf, delta2Buf);
/* First 4 and last 4 values are different because we pad AFTER diff calculated. */
for (size_t i = 0; i < numMfccFeats; ++i) {
@@ -136,37 +132,37 @@
{-1, -2}};
arm::app::Array2d<float> mean1(2,2); /* {{1, 2},{-1, -2}} */
populateArray2dWithVectorOfVector(mean1vec, mean1);
- REQUIRE(0 == Approx(tp.GetMean(mean1)));
+ REQUIRE(0 == Approx(TestPreprocess::GetMean(mean1)));
arm::app::Array2d<float> mean2(2, 2);
std::fill(mean2.begin(), mean2.end(), 0.f);
- REQUIRE(0 == Approx(tp.GetMean(mean2)));
+ REQUIRE(0 == Approx(TestPreprocess::GetMean(mean2)));
arm::app::Array2d<float> mean3(3,3);
std::fill(mean3.begin(), mean3.end(), 1.f);
- REQUIRE(1 == Approx(tp.GetMean(mean3)));
+ REQUIRE(1 == Approx(TestPreprocess::GetMean(mean3)));
}
SECTION("Std")
{
arm::app::Array2d<float> std1(2, 2);
std::fill(std1.begin(), std1.end(), 0.f); /* {{0, 0}, {0, 0}} */
- REQUIRE(0 == Approx(tp.GetStdDev(std1, 0)));
+ REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std1, 0)));
std::vector<std::vector<float>> std2vec{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 0}};
arm::app::Array2d<float> std2(2,5);
populateArray2dWithVectorOfVector(std2vec, std2);
- const float mean = tp.GetMean(std2);
- REQUIRE(2.872281323 == Approx(tp.GetStdDev(std2, mean)));
+ const float mean = TestPreprocess::GetMean(std2);
+ REQUIRE(2.872281323 == Approx(TestPreprocess::GetStdDev(std2, mean)));
arm::app::Array2d<float> std3(2,2);
std::fill(std3.begin(), std3.end(), 1.f); /* std3{{1, 1}, {1, 1}}; */
- REQUIRE(0 == Approx(tp.GetStdDev(std3, 1)));
+ REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std3, 1)));
}
SECTION("Norm") {
auto checker = [&](arm::app::Array2d<float>& d, std::vector<float>& g) {
- tp.NormaliseVec(d);
+ TestPreprocess::NormaliseVec(d);
std::vector<float> d_vec(d.begin(), d.end());
REQUIRE_THAT(g, Catch::Approx(d_vec));
};