MLECO-3174: Minor refactoring to implemented use case APIS
Looks large but it is mainly just many small adjustments
Removed the inference runner code as it wasn't used
Fixes to doc strings
Consistent naming e.g. Asr/Kws instead of ASR/KWS
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I43b620b5c51d7910a29a63b509ac4d8a82c3a8fc
diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc
index 14f9fce..328709d 100644
--- a/source/use_case/kws/src/KwsProcessing.cc
+++ b/source/use_case/kws/src/KwsProcessing.cc
@@ -22,22 +22,19 @@
namespace arm {
namespace app {
- KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride):
+ KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames,
+ int mfccFrameLength, int mfccFrameStride
+ ):
+ m_inputTensor{inputTensor},
m_mfccFrameLength{mfccFrameLength},
m_mfccFrameStride{mfccFrameStride},
+ m_numMfccFrames{numMfccFrames},
m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)}
{
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
this->m_mfcc.Init();
- TfLiteIntArray* inputShape = model->GetInputShape(0);
- const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
-
/* Deduce the data length required for 1 inference from the network parameters. */
- this->m_audioDataWindowSize = numMfccFrames * this->m_mfccFrameStride +
+ this->m_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride +
(this->m_mfccFrameLength - this->m_mfccFrameStride);
/* Creating an MFCC feature sliding window for the data required for 1 inference. */
@@ -62,7 +59,7 @@
- this->m_numMfccVectorsInAudioStride;
/* Construct feature calculation function. */
- this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0),
+ this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor,
this->m_numReusedMfccVectors);
if (!this->m_mfccFeatureCalculator) {
@@ -70,7 +67,7 @@
}
}
- bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize)
+ bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize)
{
UNUSED(inputSize);
if (data == nullptr) {
@@ -116,8 +113,8 @@
*/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
- KWSPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
- std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ KwsPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+ std::function<std::vector<T> (std::vector<int16_t>& )> compute)
{
/* Feature cache to be captured by lambda function. */
static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -149,18 +146,18 @@
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
- KWSPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+ KwsPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
- KWSPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
- size_t cacheSize,
- std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+ KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
std::function<void (std::vector<int16_t>&, int, bool, size_t)>
- KWSPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+ KwsPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
{
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
@@ -195,23 +192,19 @@
return mfccFeatureCalc;
}
- KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model,
+ KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
const std::vector<std::string>& labels,
std::vector<ClassificationResult>& results)
- :m_kwsClassifier{classifier},
+ :m_outputTensor{outputTensor},
+ m_kwsClassifier{classifier},
m_labels{labels},
m_results{results}
- {
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
- }
+ {}
- bool KWSPostProcess::DoPostProcess()
+ bool KwsPostProcess::DoPostProcess()
{
return this->m_kwsClassifier.GetClassificationResults(
- this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_outputTensor, this->m_results,
this->m_labels, 1, true);
}