MLECO-3096: Removing data_acq and data_psn
Further to the HAL refactoring done in previous commits,
this CR simpifies HAL by removing data_acq and data_psn
"modules". The associated function pointers have been
removed.
Change-Id: I04c194c08dfe0aff98ce4e0f0f056bac254c137d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index 8dd7724..e04cefc 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -35,16 +35,14 @@
namespace arm {
namespace app {
-
+
/**
* @brief Presents inference results using the data presentation
* object.
- * @param[in] platform Reference to the hal platform object.
* @param[in] results Vector of classification results to be displayed.
* @return true if successful, false otherwise.
**/
- static bool PresentInferenceResult(hal_platform& platform,
- const std::vector<arm::app::kws::KwsResult>& results);
+ static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results);
/**
* @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -68,7 +66,6 @@
/* Audio inference handler. */
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;
@@ -137,7 +134,7 @@
const float secondsPerSample = 1.0/audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
do {
- platform.data_psn->clear(COLOR_BLACK);
+ hal_lcd_clear(COLOR_BLACK);
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
@@ -171,7 +168,7 @@
/* Display message on the LCD - inference running. */
std::string str_inf{"Running inference... "};
- platform.data_psn->present_data_text(
+ hal_lcd_display_text(
str_inf.c_str(), str_inf.size(),
dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
info("Running inference on audio clip %" PRIu32 " => %s\n", currentIndex,
@@ -223,13 +220,13 @@
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
- platform.data_psn->present_data_text(
+ hal_lcd_display_text(
str_inf.c_str(), str_inf.size(),
dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
- if (!PresentInferenceResult(platform, results)) {
+ if (!PresentInferenceResult(results)) {
return false;
}
@@ -242,61 +239,6 @@
return true;
}
- static bool PresentInferenceResult(hal_platform& platform,
- const std::vector<arm::app::kws::KwsResult>& results)
- {
- constexpr uint32_t dataPsnTxtStartX1 = 20;
- constexpr uint32_t dataPsnTxtStartY1 = 30;
- 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;
-
- for (uint32_t i = 0; i < results.size(); ++i) {
-
- 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;
- }
-
- std::string resultStr =
- std::string{"@"} + std::to_string(results[i].m_timeStamp) +
- std::string{"s: "} + topKeyword + std::string{" ("} +
- std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
-
- platform.data_psn->present_data_text(
- resultStr.c_str(), resultStr.size(),
- dataPsnTxtStartX1, rowIdx1, false);
- rowIdx1 += dataPsnTxtYIncr;
-
- if (results[i].m_resultVec.empty()) {
- info("For timestamp: %f (inference #: %" PRIu32
- "); 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 #: %" PRIu32
- "); 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);
- }
- }
- }
-
- return true;
- }
-
/**
* @brief Generic feature calculator factory.
*
@@ -344,6 +286,60 @@
};
}
+ static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results)
+ {
+ constexpr uint32_t dataPsnTxtStartX1 = 20;
+ constexpr uint32_t dataPsnTxtStartY1 = 30;
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+
+ hal_lcd_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;
+
+ for (uint32_t i = 0; i < results.size(); ++i) {
+
+ 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;
+ }
+
+ std::string resultStr =
+ std::string{"@"} + std::to_string(results[i].m_timeStamp) +
+ std::string{"s: "} + topKeyword + std::string{" ("} +
+ std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
+
+ hal_lcd_display_text(
+ resultStr.c_str(), resultStr.size(),
+ dataPsnTxtStartX1, rowIdx1, false);
+ rowIdx1 += dataPsnTxtYIncr;
+
+ if (results[i].m_resultVec.empty()) {
+ info("For timestamp: %f (inference #: %" PRIu32
+ "); 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 #: %" PRIu32
+ "); 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);
+ }
+ }
+ }
+
+ return true;
+ }
+
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
size_t cacheSize,