MLECO-1868: Code static analyzer warnings fixes
Signed-off-by: alexander <alexander.efremov@arm.com>
Change-Id: Ie423e9cad3fabec6ab077ded7236813fe4933dea
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