MLECO-2599: Replace DSCNN with MicroNet for KWS
Added SoftMax function to Mathutils to allow MicroNet
to output probability as it does not nativelu have this layer.
Minor refactoring to accommodate Softmax Calculations
Extensive renaming and updating of documentation and resource download script.
Added SoftMax function to Mathutils to allow MicroNet
to output probability.
Change-Id: I7cbbda1024d14b85c9ac1beea7ca8fbffd0b6eb5
Signed-off-by: Liam Barry <liam.barry@arm.com>
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index 3d95753..8085af7 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -18,9 +18,9 @@
#include "InputFiles.hpp"
#include "Classifier.hpp"
-#include "DsCnnModel.hpp"
+#include "MicroNetKwsModel.hpp"
#include "hal.h"
-#include "DsCnnMfcc.hpp"
+#include "MicroNetKwsMfcc.hpp"
#include "AudioUtils.hpp"
#include "UseCaseCommonUtils.hpp"
#include "KwsResult.hpp"
@@ -59,7 +59,7 @@
* @return Function to be called providing audio sample and sliding window index.
*/
static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
- GetFeatureCalculator(audio::DsCnnMFCC& mfcc,
+ GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc,
TfLiteTensor* inputTensor,
size_t cacheSize);
@@ -72,8 +72,8 @@
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
constexpr int minTensorDims = static_cast<int>(
- (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
- arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+ (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
+ arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
auto& model = ctx.Get<Model&>("model");
@@ -105,10 +105,10 @@
}
TfLiteIntArray* inputShape = model.GetInputShape(0);
- const uint32_t kNumCols = inputShape->data[arm::app::DsCnnModel::ms_inputColsIdx];
- const uint32_t kNumRows = inputShape->data[arm::app::DsCnnModel::ms_inputRowsIdx];
+ const uint32_t kNumCols = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+ const uint32_t kNumRows = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
- audio::DsCnnMFCC mfcc = audio::DsCnnMFCC(kNumCols, frameLength);
+ audio::MicroNetKwsMFCC mfcc = audio::MicroNetKwsMFCC(kNumCols, frameLength);
mfcc.Init();
/* Deduce the data length required for 1 inference from the network parameters. */
@@ -132,7 +132,7 @@
/* We expect to be sampling 1 second worth of data at a time.
* NOTE: This is only used for time stamp calculation. */
- const float secondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+ const float secondsPerSample = 1.0/audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
do {
platform.data_psn->clear(COLOR_BLACK);
@@ -208,7 +208,7 @@
std::vector<ClassificationResult> classificationResult;
auto& classifier = ctx.Get<KwsClassifier&>("classifier");
classifier.GetClassificationResults(outputTensor, classificationResult,
- ctx.Get<std::vector<std::string>&>("labels"), 1);
+ ctx.Get<std::vector<std::string>&>("labels"), 1, true);
results.emplace_back(kws::KwsResult(classificationResult,
audioDataSlider.Index() * secondsPerSample * audioDataStride,
@@ -240,7 +240,6 @@
return true;
}
-
static bool PresentInferenceResult(hal_platform& platform,
const std::vector<arm::app::kws::KwsResult>& results)
{
@@ -259,7 +258,6 @@
std::string topKeyword{"<none>"};
float score = 0.f;
-
if (!results[i].m_resultVec.empty()) {
topKeyword = results[i].m_resultVec[0].m_label;
score = results[i].m_resultVec[0].m_normalisedVal;
@@ -366,7 +364,7 @@
static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
- GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+ GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
{
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;