MLECO-3611: Formatting fixes for generated files.
Template files updated for generated files to adhere to
coding guidelines and clang format configuration. There
will still be unavoidable violations, but most of the
others have been fixed.
Change-Id: Ia03db40f8c62a369f2b07fe02eea65e41993a523
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 c20c32b..ce99ed3 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -1,6 +1,6 @@
/*
- * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@@ -16,16 +16,16 @@
*/
#include "UseCaseHandler.hpp"
-#include "InputFiles.hpp"
-#include "KwsClassifier.hpp"
-#include "MicroNetKwsModel.hpp"
-#include "hal.h"
#include "AudioUtils.hpp"
#include "ImageUtils.hpp"
-#include "UseCaseCommonUtils.hpp"
-#include "KwsResult.hpp"
-#include "log_macros.h"
+#include "InputFiles.hpp"
+#include "KwsClassifier.hpp"
#include "KwsProcessing.hpp"
+#include "KwsResult.hpp"
+#include "MicroNetKwsModel.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
+#include "log_macros.h"
#include <vector>
@@ -42,15 +42,15 @@
/* KWS inference handler. */
bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
{
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto& model = ctx.Get<Model&>("model");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto& model = ctx.Get<Model&>("model");
const auto mfccFrameLength = ctx.Get<int>("frameLength");
const auto mfccFrameStride = ctx.Get<int>("frameStride");
- const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+ 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")) {
+ if (!SetAppCtxIfmIdx(ctx, clipIndex, "clipIndex")) {
return false;
}
}
@@ -58,9 +58,10 @@
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
- constexpr int minTensorDims = static_cast<int>(
- (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)?
- MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx);
+ constexpr int minTensorDims =
+ static_cast<int>((MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)
+ ? MicroNetKwsModel::ms_inputRowsIdx
+ : MicroNetKwsModel::ms_inputColsIdx);
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
@@ -68,7 +69,7 @@
}
/* Get Input and Output tensors for pre/post processing. */
- TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
@@ -79,20 +80,22 @@
}
/* Get input shape for feature extraction. */
- TfLiteIntArray* inputShape = model.GetInputShape(0);
+ TfLiteIntArray* inputShape = model.GetInputShape(0);
const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
- const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
+ 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(inputTensor, numMfccFeatures, numMfccFrames,
- mfccFrameLength, mfccFrameStride);
+ KwsPreProcess preProcess = KwsPreProcess(
+ inputTensor, numMfccFeatures, numMfccFrames, mfccFrameLength, mfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
- KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"),
+ KwsPostProcess postProcess = KwsPostProcess(outputTensor,
+ ctx.Get<KwsClassifier&>("classifier"),
ctx.Get<std::vector<std::string>&>("labels"),
singleInfResult);
@@ -103,26 +106,29 @@
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* Creating a sliding window through the whole audio clip. */
- auto audioDataSlider = audio::SlidingWindow<const int16_t>(
- get_audio_array(currentIndex),
- get_audio_array_size(currentIndex),
- preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride);
+ auto audioDataSlider =
+ audio::SlidingWindow<const int16_t>(GetAudioArray(currentIndex),
+ GetAudioArraySize(currentIndex),
+ preProcess.m_audioDataWindowSize,
+ preProcess.m_audioDataStride);
/* Declare a container to hold results from across the whole audio clip. */
std::vector<kws::KwsResult> finalResults;
/* Display message on the LCD - inference running. */
std::string str_inf{"Running inference... "};
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
- info("Running inference on audio clip %" PRIu32 " => %s\n", currentIndex,
- get_filename(currentIndex));
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ info("Running inference on audio clip %" PRIu32 " => %s\n",
+ currentIndex,
+ GetFilename(currentIndex));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
const int16_t* inferenceWindow = audioDataSlider.Next();
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Run the pre-processing, inference and post-processing. */
@@ -142,19 +148,21 @@
}
/* Add results from this window to our final results vector. */
- finalResults.emplace_back(kws::KwsResult(singleInfResult,
- audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride,
- audioDataSlider.Index(), scoreThreshold));
+ finalResults.emplace_back(kws::KwsResult(
+ singleInfResult,
+ audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride,
+ audioDataSlider.Index(),
+ scoreThreshold));
#if VERIFY_TEST_OUTPUT
DumpTensor(outputTensor);
-#endif /* VERIFY_TEST_OUTPUT */
+#endif /* VERIFY_TEST_OUTPUT */
} /* while (audioDataSlider.HasNext()) */
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
ctx.Set<std::vector<kws::KwsResult>>("results", finalResults);
@@ -164,7 +172,7 @@
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"clipIndex");
+ IncrementAppCtxIfmIdx(ctx, "clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
@@ -175,7 +183,7 @@
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
- constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
hal_lcd_set_text_color(COLOR_GREEN);
info("Final results:\n");
@@ -190,28 +198,28 @@
float score = 0.f;
if (!result.m_resultVec.empty()) {
topKeyword = result.m_resultVec[0].m_label;
- score = result.m_resultVec[0].m_normalisedVal;
+ score = result.m_resultVec[0].m_normalisedVal;
}
- std::string resultStr =
- std::string{"@"} + std::to_string(result.m_timeStamp) +
- std::string{"s: "} + topKeyword + std::string{" ("} +
- std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
+ std::string resultStr = std::string{"@"} + std::to_string(result.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);
+ hal_lcd_display_text(
+ resultStr.c_str(), resultStr.size(), dataPsnTxtStartX1, rowIdx1, false);
rowIdx1 += dataPsnTxtYIncr;
if (result.m_resultVec.empty()) {
- info("For timestamp: %f (inference #: %" PRIu32
- "); label: %s; threshold: %f\n",
- result.m_timeStamp, result.m_inferenceNumber,
+ info("For timestamp: %f (inference #: %" PRIu32 "); label: %s; threshold: %f\n",
+ result.m_timeStamp,
+ result.m_inferenceNumber,
topKeyword.c_str(),
result.m_threshold);
} else {
for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
info("For timestamp: %f (inference #: %" PRIu32
- "); label: %s, score: %f; threshold: %f\n",
+ "); label: %s, score: %f; threshold: %f\n",
result.m_timeStamp,
result.m_inferenceNumber,
result.m_resultVec[j].m_label.c_str(),