MLECO-1870: Cherry pick profiling changes from dev to open source repo

* Documentation update

Change-Id: If85e7ebc44498840b291c408f14e66a5a5faa424
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>

12 files changed, 71 insertions, 33 deletions

diff --git a/source/use_case/ad/src/MainLoop.cc b/source/use_case/ad/src/MainLoop.cc
index 5455b43..6a7cbe0 100644
--- a/source/use_case/ad/src/MainLoop.cc
+++ b/source/use_case/ad/src/MainLoop.cc
@@ -56,6 +56,8 @@ void main_loop(hal_platform& platform)
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
+    arm::app::Profiler profiler{&platform, "ad"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("clipIndex", 0);
diff --git a/source/use_case/ad/src/UseCaseHandler.cc b/source/use_case/ad/src/UseCaseHandler.cc
index c18a0a4..1c15595 100644
--- a/source/use_case/ad/src/UseCaseHandler.cc
+++ b/source/use_case/ad/src/UseCaseHandler.cc
@@ -76,6 +76,7 @@ namespace app {
     bool ClassifyVibrationHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
     {
         auto& platform = ctx.Get<hal_platform&>("platform");
+        auto& profiler = ctx.Get<Profiler&>("profiler");
 
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -98,7 +99,7 @@ namespace app {
         const auto frameLength = ctx.Get<int>("frameLength");
         const auto frameStride = ctx.Get<int>("frameStride");
         const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
-        const float trainingMean = ctx.Get<float>("trainingMean");
+        const auto trainingMean = ctx.Get<float>("trainingMean");
         auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
 
         TfLiteTensor* outputTensor = model.GetOutputTensor(0);
@@ -193,7 +194,7 @@ namespace app {
                      audioDataSlider.TotalStrides() + 1);
 
                 /* Run inference over this audio clip sliding window */
-                arm::app::RunInference(platform, model);
+                arm::app::RunInference(model, profiler);
 
                 /* Use the negative softmax score of the corresponding index as the outlier score */
                 std::vector<float> dequantOutput = Dequantize<int8_t>(outputTensor);
@@ -219,6 +220,8 @@ namespace app {
                 return false;
             }
 
+            profiler.PrintProfilingResult();
+
             _IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
diff --git a/source/use_case/asr/src/MainLoop.cc b/source/use_case/asr/src/MainLoop.cc
index ca777be..c5a26a4 100644
--- a/source/use_case/asr/src/MainLoop.cc
+++ b/source/use_case/asr/src/MainLoop.cc
@@ -96,6 +96,8 @@ void main_loop(hal_platform& platform)
     GetLabelsVector(labels);
     arm::app::AsrClassifier classifier;  /* Classifier wrapper object. */
 
+    arm::app::Profiler profiler{&platform, "asr"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("clipIndex", 0);
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index e706eb8..5d3157a 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -67,6 +67,8 @@ namespace app {
         auto& platform = ctx.Get<hal_platform&>("platform");
         platform.data_psn->clear(COLOR_BLACK);
 
+        auto& profiler = ctx.Get<Profiler&>("profiler");
+
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
             if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
@@ -168,18 +170,11 @@ namespace app {
                 info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                      static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
-                Profiler prepProfiler{&platform, "pre-processing"};
-                prepProfiler.StartProfiling();
-
                 /* Calculate MFCCs, deltas and populate the input tensor. */
                 prep.Invoke(inferenceWindow, inferenceWindowLen, inputTensor);
 
-                prepProfiler.StopProfiling();
-                std::string prepProfileResults = prepProfiler.GetResultsAndReset();
-                info("%s\n", prepProfileResults.c_str());
-
                 /* Run inference over this audio clip sliding window. */
-                arm::app::RunInference(platform, model);
+                arm::app::RunInference(model, profiler);
 
                 /* Post-process. */
                 postp.Invoke(outputTensor, reductionAxis, !audioDataSlider.HasNext());
@@ -216,6 +211,8 @@ namespace app {
                 return false;
             }
 
+            profiler.PrintProfilingResult();
+
             _IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
@@ -256,6 +253,8 @@ namespace app {
 
         platform.data_psn->set_text_color(COLOR_GREEN);
 
+        info("Final results:\n");
+        info("Total number of inferences: %zu\n", results.size());
         /* Results from multiple inferences should be combined before processing. */
         std::vector<arm::app::ClassificationResult> combinedResults;
         for (auto& result : results) {
@@ -268,8 +267,9 @@ namespace app {
         for (const auto & result : results) {
             std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
 
-            info("Result for inf %u: %s\n", result.m_inferenceNumber,
-                                            infResultStr.c_str());
+            info("For timestamp: %f (inference #: %u); label: %s\n",
+                 result.m_timeStamp, result.m_inferenceNumber,
+                 infResultStr.c_str());
         }
 
         /* Get the decoded result for the combined result. */
@@ -280,7 +280,7 @@ namespace app {
                             dataPsnTxtStartX1, dataPsnTxtStartY1,
                             allow_multiple_lines);
 
-        info("Final result: %s\n", finalResultStr.c_str());
+        info("Complete recognition: %s\n", finalResultStr.c_str());
         return true;
     }
 
diff --git a/source/use_case/img_class/src/MainLoop.cc b/source/use_case/img_class/src/MainLoop.cc
index 469907c..66d7064 100644
--- a/source/use_case/img_class/src/MainLoop.cc
+++ b/source/use_case/img_class/src/MainLoop.cc
@@ -58,6 +58,8 @@ void main_loop(hal_platform& platform)
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
+    arm::app::Profiler profiler{&platform, "img_class"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("imgIndex", 0);
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index a412fec..f7e83f5 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -74,6 +74,7 @@ namespace app {
     bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
     {
         auto& platform = ctx.Get<hal_platform&>("platform");
+        auto& profiler = ctx.Get<Profiler&>("profiler");
 
         constexpr uint32_t dataPsnImgDownscaleFactor = 2;
         constexpr uint32_t dataPsnImgStartX = 10;
@@ -144,7 +145,7 @@ namespace app {
             info("Running inference on image %u => %s\n", ctx.Get<uint32_t>("imgIndex"),
                 get_filename(ctx.Get<uint32_t>("imgIndex")));
 
-            RunInference(platform, model);
+            RunInference(model, profiler);
 
             /* Erase. */
             str_inf = std::string(str_inf.size(), ' ');
@@ -167,6 +168,8 @@ namespace app {
                 return false;
             }
 
+            profiler.PrintProfilingResult();
+
             _IncrementAppCtxImageIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
@@ -230,6 +233,8 @@ namespace app {
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
         uint32_t rowIdx2 = dataPsnTxtStartY2;
 
+        info("Final results:\n");
+        info("Total number of inferences: 1\n");
         for (uint32_t i = 0; i < results.size(); ++i) {
             std::string resultStr =
                 std::to_string(i + 1) + ") " +
diff --git a/source/use_case/inference_runner/src/MainLoop.cc b/source/use_case/inference_runner/src/MainLoop.cc
index b110a24..26a20de 100644
--- a/source/use_case/inference_runner/src/MainLoop.cc
+++ b/source/use_case/inference_runner/src/MainLoop.cc
@@ -38,6 +38,9 @@ void main_loop(hal_platform& platform)
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
+    arm::app::Profiler profiler{&platform, "inference_runner"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
+
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("imgIndex", 0);
diff --git a/source/use_case/inference_runner/src/UseCaseHandler.cc b/source/use_case/inference_runner/src/UseCaseHandler.cc
index ac4ea47..a2b3fb7 100644
--- a/source/use_case/inference_runner/src/UseCaseHandler.cc
+++ b/source/use_case/inference_runner/src/UseCaseHandler.cc
@@ -28,6 +28,7 @@ namespace app {
     bool RunInferenceHandler(ApplicationContext& ctx)
     {
         auto& platform = ctx.Get<hal_platform&>("platform");
+        auto& profiler = ctx.Get<Profiler&>("profiler");
         auto& model = ctx.Get<Model&>("model");
 
         constexpr uint32_t dataPsnTxtInfStartX = 150;
@@ -67,7 +68,7 @@ namespace app {
                                 str_inf.c_str(), str_inf.size(),
                                 dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
 
-        RunInference(platform, model);
+        RunInference(model, profiler);
 
         /* Erase. */
         str_inf = std::string(str_inf.size(), ' ');
@@ -75,6 +76,9 @@ namespace app {
                                 str_inf.c_str(), str_inf.size(),
                                 dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
 
+        info("Final results:\n");
+        profiler.PrintProfilingResult();
+
 #if VERIFY_TEST_OUTPUT
         for (size_t outputIndex = 0; outputIndex < model.GetNumOutputs(); outputIndex++) {
             arm::app::DumpTensor(model.GetOutputTensor(outputIndex));
diff --git a/source/use_case/kws/src/MainLoop.cc b/source/use_case/kws/src/MainLoop.cc
index 24cb939..f971c30 100644
--- a/source/use_case/kws/src/MainLoop.cc
+++ b/source/use_case/kws/src/MainLoop.cc
@@ -58,6 +58,9 @@ void main_loop(hal_platform& platform)
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
+    arm::app::Profiler profiler{&platform, "kws"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
+
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("model", model);
     caseContext.Set<uint32_t>("clipIndex", 0);
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index 872d323..d2cba55 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -82,6 +82,7 @@ namespace app {
     bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
     {
         auto& platform = ctx.Get<hal_platform&>("platform");
+        auto& profiler = ctx.Get<Profiler&>("profiler");
 
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -215,7 +216,7 @@ namespace app {
                      audioDataSlider.TotalStrides() + 1);
 
                 /* Run inference over this audio clip sliding window. */
-                arm::app::RunInference(platform, model);
+                arm::app::RunInference(model, profiler);
 
                 std::vector<ClassificationResult> classificationResult;
                 auto& classifier = ctx.Get<KwsClassifier&>("classifier");
@@ -243,6 +244,8 @@ namespace app {
                 return false;
             }
 
+            profiler.PrintProfilingResult();
+
             _IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
@@ -281,6 +284,8 @@ namespace app {
         constexpr uint32_t dataPsnTxtYIncr   = 16;  /* Row index increment. */
 
         platform.data_psn->set_text_color(COLOR_GREEN);
+        info("Final results:\n");
+        info("Total number of inferences: %zu\n", results.size());
 
         /* Display each result */
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
@@ -290,7 +295,7 @@ namespace app {
             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;
             }
@@ -305,13 +310,20 @@ namespace app {
                                     dataPsnTxtStartX1, rowIdx1, false);
             rowIdx1 += dataPsnTxtYIncr;
 
-            info("For timestamp: %f (inference #: %u); threshold: %f\n",
-                    results[i].m_timeStamp, results[i].m_inferenceNumber,
-                    results[i].m_threshold);
-            for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) {
-                info("\t\tlabel @ %u: %s, score: %f\n", j,
-                    results[i].m_resultVec[j].m_label.c_str(),
-                    results[i].m_resultVec[j].m_normalisedVal);
+            if (results[i].m_resultVec.empty()) {
+                info("For timestamp: %f (inference #: %u); label: %s; threshold: %f\n",
+                     results[i].m_timeStamp, results[i].m_inferenceNumber,
+                     topKeyword.c_str(),
+                     results[i].m_threshold);
+            } else {
+                for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) {
+                    info("For timestamp: %f (inference #: %u); label: %s, score: %f; threshold: %f\n",
+                         results[i].m_timeStamp,
+                         results[i].m_inferenceNumber,
+                         results[i].m_resultVec[j].m_label.c_str(),
+                         results[i].m_resultVec[j].m_normalisedVal,
+                         results[i].m_threshold);
+                }
             }
         }
 
diff --git a/source/use_case/kws_asr/src/MainLoop.cc b/source/use_case/kws_asr/src/MainLoop.cc
index 37146c9..95e5a8f 100644
--- a/source/use_case/kws_asr/src/MainLoop.cc
+++ b/source/use_case/kws_asr/src/MainLoop.cc
@@ -101,6 +101,9 @@ void main_loop(hal_platform& platform)
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
+    arm::app::Profiler profiler{&platform, "kws_asr"};
+    caseContext.Set<arm::app::Profiler&>("profiler", profiler);
+
     caseContext.Set<hal_platform&>("platform", platform);
     caseContext.Set<arm::app::Model&>("kwsmodel", kwsModel);
     caseContext.Set<arm::app::Model&>("asrmodel", asrModel);
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index c50796f..a428210 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -127,6 +127,7 @@ namespace app {
 
         KWSOutput output;
 
+        auto& profiler = ctx.Get<Profiler&>("profiler");
         auto& kwsModel = ctx.Get<Model&>("kwsmodel");
         if (!kwsModel.IsInited()) {
             printf_err("KWS model has not been initialised\n");
@@ -243,7 +244,7 @@ namespace app {
                  audioDataSlider.TotalStrides() + 1);
 
             /* Run inference over this audio clip sliding window. */
-            arm::app::RunInference(platform, kwsModel);
+            arm::app::RunInference(kwsModel, profiler);
 
             std::vector<ClassificationResult> kwsClassificationResult;
             auto& kwsClassifier = ctx.Get<KwsClassifier&>("kwsclassifier");
@@ -284,6 +285,8 @@ namespace app {
             return output;
         }
 
+        profiler.PrintProfilingResult();
+
         output.executionSuccess = true;
         return output;
     }
@@ -300,6 +303,7 @@ namespace app {
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
+        auto& profiler = ctx.Get<Profiler&>("profiler");
         auto& platform = ctx.Get<hal_platform&>("platform");
         platform.data_psn->clear(COLOR_BLACK);
 
@@ -389,18 +393,11 @@ namespace app {
             info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                 static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
-            Profiler prepProfiler{&platform, "pre-processing"};
-            prepProfiler.StartProfiling();
-
             /* Calculate MFCCs, deltas and populate the input tensor. */
             asrPrep.Invoke(asrInferenceWindow, asrInferenceWindowLen, asrInputTensor);
 
-            prepProfiler.StopProfiling();
-            std::string prepProfileResults = prepProfiler.GetResultsAndReset();
-            info("%s\n", prepProfileResults.c_str());
-
             /* Run inference over this audio clip sliding window. */
-            arm::app::RunInference(platform, asrModel);
+            arm::app::RunInference(asrModel, profiler);
 
             /* Post-process. */
             asrPostp.Invoke(asrOutputTensor, reductionAxis, !audioDataSlider.HasNext());
@@ -432,6 +429,8 @@ namespace app {
             return false;
         }
 
+        profiler.PrintProfilingResult();
+
         return true;
     }