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

1 files changed, 24 insertions, 22 deletions

diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index e73a2c3..61c6eb6 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -34,13 +34,12 @@ using KwsClassifier = arm::app::Classifier;
 namespace arm {
 namespace app {
 
-
     /**
      * @brief           Presents KWS inference results.
      * @param[in]       results     Vector of KWS classification results to be displayed.
      * @return          true if successful, false otherwise.
      **/
-    static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results);
+    static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results);
 
     /* KWS inference handler. */
     bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
@@ -50,6 +49,7 @@ namespace app {
         const auto mfccFrameLength = ctx.Get<int>("frameLength");
         const auto mfccFrameStride = ctx.Get<int>("frameStride");
         const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
             if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) {
@@ -61,16 +61,17 @@ namespace app {
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
         constexpr int minTensorDims = static_cast<int>(
-            (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
-             arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
-
+            (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)?
+             MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx);
 
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
         }
 
+        /* Get Input and Output tensors for pre/post processing. */
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -81,22 +82,23 @@ namespace app {
 
         /* Get input shape for feature extraction. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-        const uint32_t numMfccFeatures = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
 
         /* We expect to be sampling 1 second worth of data at a time.
          * NOTE: This is only used for time stamp calculation. */
         const float secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
 
         /* Set up pre and post-processing. */
-        KWSPreProcess preprocess = KWSPreProcess(&model, numMfccFeatures, mfccFrameLength, mfccFrameStride);
+        KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames,
+                                                 mfccFrameLength, mfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
-        KWSPostProcess postprocess = KWSPostProcess(ctx.Get<KwsClassifier &>("classifier"), &model,
+        KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"),
                                                     ctx.Get<std::vector<std::string>&>("labels"),
                                                     singleInfResult);
 
-        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
-
+        /* Loop to process audio clips. */
         do {
             hal_lcd_clear(COLOR_BLACK);
 
@@ -106,7 +108,7 @@ namespace app {
             auto audioDataSlider = audio::SlidingWindow<const int16_t>(
                     get_audio_array(currentIndex),
                     get_audio_array_size(currentIndex),
-                    preprocess.m_audioDataWindowSize, preprocess.m_audioDataStride);
+                    preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride);
 
             /* Declare a container to hold results from across the whole audio clip. */
             std::vector<kws::KwsResult> finalResults;
@@ -123,34 +125,34 @@ namespace app {
                 const int16_t* inferenceWindow = audioDataSlider.Next();
 
                 /* The first window does not have cache ready. */
-                preprocess.m_audioWindowIndex = audioDataSlider.Index();
+                preProcess.m_audioWindowIndex = audioDataSlider.Index();
 
                 info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                      audioDataSlider.TotalStrides() + 1);
 
                 /* Run the pre-processing, inference and post-processing. */
-                if (!runner.PreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+                if (!preProcess.DoPreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+                    printf_err("Pre-processing failed.");
                     return false;
                 }
 
-                profiler.StartProfiling("Inference");
-                if (!runner.RunInference()) {
+                if (!RunInference(model, profiler)) {
+                    printf_err("Inference failed.");
                     return false;
                 }
-                profiler.StopProfiling();
 
-                if (!runner.PostProcess()) {
+                if (!postProcess.DoPostProcess()) {
+                    printf_err("Post-processing failed.");
                     return false;
                 }
 
                 /* Add results from this window to our final results vector. */
                 finalResults.emplace_back(kws::KwsResult(singleInfResult,
-                        audioDataSlider.Index() * secondsPerSample * preprocess.m_audioDataStride,
+                        audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride,
                         audioDataSlider.Index(), scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-                arm::app::DumpTensor(outputTensor);
+                DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
             } /* while (audioDataSlider.HasNext()) */
 
@@ -174,7 +176,7 @@ namespace app {
         return true;
     }
 
-    static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results)
+    static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -187,7 +189,7 @@ namespace app {
         /* Display each result */
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
 
-        for (const auto & result : results) {
+        for (const auto& result : results) {
 
             std::string topKeyword{"<none>"};
             float score = 0.f;