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-rw-r--r--source/application/main/UseCaseCommonUtils.cc165
-rw-r--r--source/use_case/ad/src/UseCaseHandler.cc89
-rw-r--r--source/use_case/asr/src/UseCaseHandler.cc107
-rw-r--r--source/use_case/img_class/src/UseCaseHandler.cc64
-rw-r--r--source/use_case/kws/src/UseCaseHandler.cc104
-rw-r--r--source/use_case/kws_asr/src/UseCaseHandler.cc245
-rw-r--r--source/use_case/noise_reduction/src/UseCaseHandler.cc191
-rw-r--r--source/use_case/object_detection/src/UseCaseHandler.cc116
-rw-r--r--source/use_case/vww/src/UseCaseHandler.cc68
9 files changed, 618 insertions, 531 deletions
diff --git a/source/application/main/UseCaseCommonUtils.cc b/source/application/main/UseCaseCommonUtils.cc
index d1276ab..7d0a2ca 100644
--- a/source/application/main/UseCaseCommonUtils.cc
+++ b/source/application/main/UseCaseCommonUtils.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.
@@ -21,7 +21,6 @@
#include <cinttypes>
-
void DisplayCommonMenu()
{
printf("\n\n");
@@ -36,7 +35,7 @@ void DisplayCommonMenu()
fflush(stdout);
}
-bool PresentInferenceResult(const std::vector<arm::app::ClassificationResult> &results)
+bool PresentInferenceResult(const std::vector<arm::app::ClassificationResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 150;
constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -44,7 +43,7 @@ bool PresentInferenceResult(const std::vector<arm::app::ClassificationResult> &r
constexpr uint32_t dataPsnTxtStartX2 = 10;
constexpr uint32_t dataPsnTxtStartY2 = 150;
- constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
hal_lcd_set_text_color(COLOR_GREEN);
@@ -56,31 +55,28 @@ bool PresentInferenceResult(const std::vector<arm::app::ClassificationResult> &r
info("Total number of inferences: 1\n");
for (uint32_t i = 0; i < results.size(); ++i) {
- std::string resultStr =
- std::to_string(i + 1) + ") " +
- std::to_string(results[i].m_labelIdx) +
- " (" + std::to_string(results[i].m_normalisedVal) + ")";
+ std::string resultStr = std::to_string(i + 1) + ") " +
+ std::to_string(results[i].m_labelIdx) + " (" +
+ std::to_string(results[i].m_normalisedVal) + ")";
hal_lcd_display_text(
- resultStr.c_str(), resultStr.size(),
- dataPsnTxtStartX1, rowIdx1, false);
+ resultStr.c_str(), resultStr.size(), dataPsnTxtStartX1, rowIdx1, false);
rowIdx1 += dataPsnTxtYIncr;
resultStr = std::to_string(i + 1) + ") " + results[i].m_label;
- hal_lcd_display_text(
- resultStr.c_str(), resultStr.size(),
- dataPsnTxtStartX2, rowIdx2, 0);
+ hal_lcd_display_text(resultStr.c_str(), resultStr.size(), dataPsnTxtStartX2, rowIdx2, 0);
rowIdx2 += dataPsnTxtYIncr;
- info("%" PRIu32 ") %" PRIu32 " (%f) -> %s\n", i,
- results[i].m_labelIdx, results[i].m_normalisedVal,
- results[i].m_label.c_str());
+ info("%" PRIu32 ") %" PRIu32 " (%f) -> %s\n",
+ i,
+ results[i].m_labelIdx,
+ results[i].m_normalisedVal,
+ results[i].m_label.c_str());
}
return true;
}
-
void IncrementAppCtxIfmIdx(arm::app::ApplicationContext& ctx, const std::string& useCase)
{
#if NUMBER_OF_FILES > 0
@@ -92,7 +88,7 @@ void IncrementAppCtxIfmIdx(arm::app::ApplicationContext& ctx, const std::string&
}
++curImIdx;
ctx.Set<uint32_t>(useCase, curImIdx);
-#else /* NUMBER_OF_FILES > 0 */
+#else /* NUMBER_OF_FILES > 0 */
UNUSED(ctx);
UNUSED(useCase);
#endif /* NUMBER_OF_FILES > 0 */
@@ -102,13 +98,12 @@ bool SetAppCtxIfmIdx(arm::app::ApplicationContext& ctx, uint32_t idx, const std:
{
#if NUMBER_OF_FILES > 0
if (idx >= NUMBER_OF_FILES) {
- printf_err("Invalid idx %" PRIu32 " (expected less than %u)\n",
- idx, NUMBER_OF_FILES);
+ printf_err("Invalid idx %" PRIu32 " (expected less than %u)\n", idx, NUMBER_OF_FILES);
return false;
}
ctx.Set<uint32_t>(ctxIfmName, idx);
return true;
-#else /* NUMBER_OF_FILES > 0 */
+#else /* NUMBER_OF_FILES > 0 */
UNUSED(ctx);
UNUSED(idx);
UNUSED(ctxIfmName);
@@ -119,83 +114,76 @@ bool SetAppCtxIfmIdx(arm::app::ApplicationContext& ctx, uint32_t idx, const std:
namespace arm {
namespace app {
+ bool RunInference(arm::app::Model& model, Profiler& profiler)
+ {
+ profiler.StartProfiling("Inference");
+ bool runInf = model.RunInference();
+ profiler.StopProfiling();
-bool RunInference(arm::app::Model& model, Profiler& profiler)
-{
- profiler.StartProfiling("Inference");
- bool runInf = model.RunInference();
- profiler.StopProfiling();
-
- return runInf;
-}
-
-int ReadUserInputAsInt()
-{
- char chInput[128];
- memset(chInput, 0, sizeof(chInput));
+ return runInf;
+ }
- hal_get_user_input(chInput, sizeof(chInput));
- return atoi(chInput);
-}
+ int ReadUserInputAsInt()
+ {
+ char chInput[128];
+ memset(chInput, 0, sizeof(chInput));
-void DumpTensorData(const uint8_t* tensorData,
- size_t size,
- size_t lineBreakForNumElements)
-{
- char strhex[8];
- std::string strdump;
+ hal_get_user_input(chInput, sizeof(chInput));
+ return atoi(chInput);
+ }
- for (size_t i = 0; i < size; ++i) {
- if (0 == i % lineBreakForNumElements) {
- printf("%s\n\t", strdump.c_str());
- strdump.clear();
+ void DumpTensorData(const uint8_t* tensorData, size_t size, size_t lineBreakForNumElements)
+ {
+ char strhex[8];
+ std::string strdump;
+
+ for (size_t i = 0; i < size; ++i) {
+ if (0 == i % lineBreakForNumElements) {
+ printf("%s\n\t", strdump.c_str());
+ strdump.clear();
+ }
+ snprintf(strhex, sizeof(strhex) - 1, "0x%02x, ", tensorData[i]);
+ strdump += std::string(strhex);
}
- snprintf(strhex, sizeof(strhex) - 1,
- "0x%02x, ", tensorData[i]);
- strdump += std::string(strhex);
- }
- if (!strdump.empty()) {
- printf("%s\n", strdump.c_str());
+ if (!strdump.empty()) {
+ printf("%s\n", strdump.c_str());
+ }
}
-}
-void DumpTensor(const TfLiteTensor* tensor, const size_t lineBreakForNumElements)
-{
- if (!tensor) {
- printf_err("invalid tensor\n");
- return;
- }
+ void DumpTensor(const TfLiteTensor* tensor, const size_t lineBreakForNumElements)
+ {
+ if (!tensor) {
+ printf_err("invalid tensor\n");
+ return;
+ }
- const uint32_t tensorSz = tensor->bytes;
- const auto* tensorData = tflite::GetTensorData<uint8_t>(tensor);
+ const uint32_t tensorSz = tensor->bytes;
+ const auto* tensorData = tflite::GetTensorData<uint8_t>(tensor);
- DumpTensorData(tensorData, tensorSz, lineBreakForNumElements);
-}
+ DumpTensorData(tensorData, tensorSz, lineBreakForNumElements);
+ }
-bool ListFilesHandler(ApplicationContext& ctx)
-{
- auto& model = ctx.Get<Model&>("model");
+ bool ListFilesHandler(ApplicationContext& ctx)
+ {
+ auto& model = ctx.Get<Model&>("model");
- constexpr uint32_t dataPsnTxtStartX = 20;
- constexpr uint32_t dataPsnTxtStartY = 40;
+ constexpr uint32_t dataPsnTxtStartX = 20;
+ constexpr uint32_t dataPsnTxtStartY = 40;
- if (!model.IsInited()) {
- printf_err("Model is not initialised! Terminating processing.\n");
- return false;
- }
+ if (!model.IsInited()) {
+ printf_err("Model is not initialised! Terminating processing.\n");
+ return false;
+ }
- /* Clear the LCD */
- hal_lcd_clear(COLOR_BLACK);
+ /* Clear the LCD */
+ hal_lcd_clear(COLOR_BLACK);
- /* Show the total number of embedded files. */
- std::string strNumFiles = std::string{"Total Number of Files: "} +
- std::to_string(NUMBER_OF_FILES);
- hal_lcd_display_text(strNumFiles.c_str(),
- strNumFiles.size(),
- dataPsnTxtStartX,
- dataPsnTxtStartY,
- false);
+ /* Show the total number of embedded files. */
+ std::string strNumFiles =
+ std::string{"Total Number of Files: "} + std::to_string(NUMBER_OF_FILES);
+ hal_lcd_display_text(
+ strNumFiles.c_str(), strNumFiles.size(), dataPsnTxtStartX, dataPsnTxtStartY, false);
#if NUMBER_OF_FILES > 0
constexpr uint32_t dataPsnTxtYIncr = 16;
@@ -203,17 +191,16 @@ bool ListFilesHandler(ApplicationContext& ctx)
uint32_t yVal = dataPsnTxtStartY + dataPsnTxtYIncr;
for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i, yVal += dataPsnTxtYIncr) {
- std::string currentFilename{get_filename(i)};
- hal_lcd_display_text(currentFilename.c_str(),
- currentFilename.size(),
- dataPsnTxtStartX, yVal, false);
+ std::string currentFilename{GetFilename(i)};
+ hal_lcd_display_text(
+ currentFilename.c_str(), currentFilename.size(), dataPsnTxtStartX, yVal, false);
info("\t%" PRIu32 " => %s\n", i, currentFilename.c_str());
}
#endif /* NUMBER_OF_FILES > 0 */
return true;
-}
+ }
} /* namespace app */
} /* namespace arm */
diff --git a/source/use_case/ad/src/UseCaseHandler.cc b/source/use_case/ad/src/UseCaseHandler.cc
index 222aaf3..c71fdeb 100644
--- a/source/use_case/ad/src/UseCaseHandler.cc
+++ b/source/use_case/ad/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 "AdModel.hpp"
-#include "InputFiles.hpp"
-#include "Classifier.hpp"
-#include "hal.h"
#include "AdMelSpectrogram.hpp"
+#include "AdModel.hpp"
+#include "AdProcessing.hpp"
#include "AudioUtils.hpp"
+#include "Classifier.hpp"
#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
#include "log_macros.h"
-#include "AdProcessing.hpp"
namespace arm {
namespace app {
@@ -44,7 +44,7 @@ namespace app {
* File name should be in format anything_goes_XX_here.wav
* where XX is the machine ID e.g. 00, 02, 04 or 06
* @return AD model output index as 8 bit integer.
- **/
+ **/
static int8_t OutputIndexFromFileName(std::string wavFileName);
/* Anomaly Detection inference handler */
@@ -57,7 +57,7 @@ namespace app {
/* 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;
}
}
@@ -66,26 +66,22 @@ namespace app {
return false;
}
- auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
const auto melSpecFrameLength = ctx.Get<uint32_t>("frameLength");
const auto melSpecFrameStride = ctx.Get<uint32_t>("frameStride");
- const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
- const auto trainingMean = ctx.Get<float>("trainingMean");
- auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
+ const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+ const auto trainingMean = ctx.Get<float>("trainingMean");
+ auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
- TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
}
- AdPreProcess preProcess{
- inputTensor,
- melSpecFrameLength,
- melSpecFrameStride,
- trainingMean};
+ AdPreProcess preProcess{inputTensor, melSpecFrameLength, melSpecFrameStride, trainingMean};
AdPostProcess postProcess{outputTensor};
@@ -95,17 +91,17 @@ namespace app {
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* Get the output index to look at based on id in the filename. */
- int8_t machineOutputIndex = OutputIndexFromFileName(get_filename(currentIndex));
+ int8_t machineOutputIndex = OutputIndexFromFileName(GetFilename(currentIndex));
if (machineOutputIndex == -1) {
return false;
}
/* 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.GetAudioWindowSize(),
- preProcess.GetAudioDataStride());
+ auto audioDataSlider =
+ audio::SlidingWindow<const int16_t>(GetAudioArray(currentIndex),
+ GetAudioArraySize(currentIndex),
+ preProcess.GetAudioWindowSize(),
+ preProcess.GetAudioDataStride());
/* Result is an averaged sum over inferences. */
float result = 0;
@@ -113,11 +109,11 @@ namespace app {
/* 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);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
info("Running inference on audio clip %" PRIu32 " => %s\n",
- currentIndex, get_filename(currentIndex));
+ currentIndex,
+ GetFilename(currentIndex));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
@@ -126,7 +122,8 @@ namespace app {
preProcess.SetAudioWindowIndex(audioDataSlider.Index());
preProcess.DoPreProcess(inferenceWindow, preProcess.GetAudioWindowSize());
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Run inference over this audio clip sliding window */
@@ -139,7 +136,7 @@ namespace app {
#if VERIFY_TEST_OUTPUT
DumpTensor(outputTensor);
-#endif /* VERIFY_TEST_OUTPUT */
+#endif /* VERIFY_TEST_OUTPUT */
} /* while (audioDataSlider.HasNext()) */
/* Use average over whole clip as final score. */
@@ -148,8 +145,7 @@ namespace app {
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
ctx.Set<float>("result", result);
if (!PresentInferenceResult(result, scoreThreshold)) {
@@ -158,7 +154,7 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"clipIndex");
+ IncrementAppCtxIfmIdx(ctx, "clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
@@ -176,8 +172,10 @@ namespace app {
/* Display each result */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
- std::string anomalyScore = std::string{"Average anomaly score is: "} + std::to_string(result);
- std::string anomalyThreshold = std::string("Anomaly threshold is: ") + std::to_string(threshold);
+ std::string anomalyScore =
+ std::string{"Average anomaly score is: "} + std::to_string(result);
+ std::string anomalyThreshold =
+ std::string("Anomaly threshold is: ") + std::to_string(threshold);
std::string anomalyResult;
if (result > threshold) {
@@ -187,8 +185,7 @@ namespace app {
}
hal_lcd_display_text(
- anomalyScore.c_str(), anomalyScore.size(),
- dataPsnTxtStartX1, rowIdx1, false);
+ anomalyScore.c_str(), anomalyScore.size(), dataPsnTxtStartX1, rowIdx1, false);
info("%s\n", anomalyScore.c_str());
info("%s\n", anomalyThreshold.c_str());
@@ -200,26 +197,28 @@ namespace app {
static int8_t OutputIndexFromFileName(std::string wavFileName)
{
/* Filename is assumed in the form machine_id_00.wav */
- std::string delimiter = "_"; /* First character used to split the file name up. */
+ std::string delimiter = "_"; /* First character used to split the file name up. */
size_t delimiterStart;
std::string subString;
- size_t machineIdxInString = 3; /* Which part of the file name the machine id should be at. */
+ size_t machineIdxInString =
+ 3; /* Which part of the file name the machine id should be at. */
for (size_t i = 0; i < machineIdxInString; ++i) {
delimiterStart = wavFileName.find(delimiter);
- subString = wavFileName.substr(0, delimiterStart);
+ subString = wavFileName.substr(0, delimiterStart);
wavFileName.erase(0, delimiterStart + delimiter.length());
}
/* At this point substring should be 00.wav */
- delimiter = "."; /* Second character used to split the file name up. */
+ delimiter = "."; /* Second character used to split the file name up. */
delimiterStart = subString.find(delimiter);
- subString = (delimiterStart != std::string::npos) ? subString.substr(0, delimiterStart) : subString;
+ subString =
+ (delimiterStart != std::string::npos) ? subString.substr(0, delimiterStart) : subString;
- auto is_number = [](const std::string& str) -> bool
- {
+ auto is_number = [](const std::string& str) -> bool {
std::string::const_iterator it = str.begin();
- while (it != str.end() && std::isdigit(*it)) ++it;
+ while (it != str.end() && std::isdigit(*it))
+ ++it;
return !str.empty() && it == str.end();
};
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 76409b6..d13a03a 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/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.
@@ -16,17 +16,17 @@
*/
#include "UseCaseHandler.hpp"
-#include "InputFiles.hpp"
#include "AsrClassifier.hpp"
-#include "Wav2LetterModel.hpp"
-#include "hal.h"
+#include "AsrResult.hpp"
#include "AudioUtils.hpp"
#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
+#include "OutputDecode.hpp"
#include "UseCaseCommonUtils.hpp"
-#include "AsrResult.hpp"
-#include "Wav2LetterPreprocess.hpp"
+#include "Wav2LetterModel.hpp"
#include "Wav2LetterPostprocess.hpp"
-#include "OutputDecode.hpp"
+#include "Wav2LetterPreprocess.hpp"
+#include "hal.h"
#include "log_macros.h"
namespace arm {
@@ -42,19 +42,19 @@ namespace app {
/* ASR inference handler. */
bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
{
- auto& model = ctx.Get<Model&>("model");
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto mfccFrameLen = ctx.Get<uint32_t>("frameLength");
+ auto& model = ctx.Get<Model&>("model");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto mfccFrameLen = ctx.Get<uint32_t>("frameLength");
auto mfccFrameStride = ctx.Get<uint32_t>("frameStride");
- auto scoreThreshold = ctx.Get<float>("scoreThreshold");
- auto inputCtxLen = ctx.Get<uint32_t>("ctxLen");
+ auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+ auto inputCtxLen = ctx.Get<uint32_t>("ctxLen");
/* 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;
}
}
- auto initialClipIdx = ctx.Get<uint32_t>("clipIndex");
+ auto initialClipIdx = ctx.Get<uint32_t>("clipIndex");
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -63,7 +63,7 @@ namespace app {
return false;
}
- TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
/* Get input shape. Dimensions of the tensor should have been verified by
@@ -81,18 +81,21 @@ namespace app {
const float secondsPerSample = (1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
/* Set up pre and post-processing objects. */
- AsrPreProcess preProcess = AsrPreProcess(inputTensor, Wav2LetterModel::ms_numMfccFeatures,
+ AsrPreProcess preProcess = AsrPreProcess(inputTensor,
+ Wav2LetterModel::ms_numMfccFeatures,
inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
- mfccFrameLen, mfccFrameStride);
+ mfccFrameLen,
+ mfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(model, inputCtxLen);
- AsrPostProcess postProcess = AsrPostProcess(
- outputTensor, ctx.Get<AsrClassifier&>("classifier"),
- ctx.Get<std::vector<std::string>&>("labels"),
- singleInfResult, outputCtxLen,
- Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
- );
+ AsrPostProcess postProcess = AsrPostProcess(outputTensor,
+ ctx.Get<AsrClassifier&>("classifier"),
+ ctx.Get<std::vector<std::string>&>("labels"),
+ singleInfResult,
+ outputCtxLen,
+ Wav2LetterModel::ms_blankTokenIdx,
+ Wav2LetterModel::ms_outputRowsIdx);
/* Loop to process audio clips. */
do {
@@ -102,8 +105,8 @@ namespace app {
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* Get the current audio buffer and respective size. */
- const int16_t* audioArr = get_audio_array(currentIndex);
- const uint32_t audioArrSize = get_audio_array_size(currentIndex);
+ const int16_t* audioArr = GetAudioArray(currentIndex);
+ const uint32_t audioArrSize = GetAudioArraySize(currentIndex);
if (!audioArr) {
printf_err("Invalid audio array pointer.\n");
@@ -119,19 +122,19 @@ namespace app {
/* Creating a sliding window through the whole audio clip. */
auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>(
- audioArr, audioArrSize,
- audioDataWindowLen, audioDataWindowStride);
+ audioArr, audioArrSize, audioDataWindowLen, audioDataWindowStride);
/* Declare a container for final results. */
std::vector<asr::AsrResult> 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);
+ 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));
+ info("Running inference on audio clip %" PRIu32 " => %s\n",
+ currentIndex,
+ GetFilename(currentIndex));
size_t inferenceWindowLen = audioDataWindowLen;
@@ -146,7 +149,8 @@ namespace app {
const int16_t* inferenceWindow = audioDataSlider.Next();
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
/* Run the pre-processing, inference and post-processing. */
@@ -168,20 +172,22 @@ namespace app {
}
/* Add results from this window to our final results vector. */
- finalResults.emplace_back(asr::AsrResult(singleInfResult,
- (audioDataSlider.Index() * secondsPerSample * audioDataWindowStride),
- audioDataSlider.Index(), scoreThreshold));
+ finalResults.emplace_back(asr::AsrResult(
+ singleInfResult,
+ (audioDataSlider.Index() * secondsPerSample * audioDataWindowStride),
+ audioDataSlider.Index(),
+ scoreThreshold));
#if VERIFY_TEST_OUTPUT
armDumpTensor(outputTensor,
- outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
-#endif /* VERIFY_TEST_OUTPUT */
+ outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
+#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, 0);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
ctx.Set<std::vector<asr::AsrResult>>("results", finalResults);
@@ -191,19 +197,18 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"clipIndex");
+ IncrementAppCtxIfmIdx(ctx, "clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
return true;
}
-
static bool PresentInferenceResult(const std::vector<asr::AsrResult>& results)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 60;
- constexpr bool allow_multiple_lines = true;
+ constexpr bool allow_multiple_lines = true;
hal_lcd_set_text_color(COLOR_GREEN);
@@ -212,9 +217,8 @@ namespace app {
/* Results from multiple inferences should be combined before processing. */
std::vector<ClassificationResult> combinedResults;
for (const auto& result : results) {
- combinedResults.insert(combinedResults.end(),
- result.m_resultVec.begin(),
- result.m_resultVec.end());
+ combinedResults.insert(
+ combinedResults.end(), result.m_resultVec.begin(), result.m_resultVec.end());
}
/* Get each inference result string using the decoder. */
@@ -222,16 +226,19 @@ namespace app {
std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
info("For timestamp: %f (inference #: %" PRIu32 "); label: %s\n",
- result.m_timeStamp, result.m_inferenceNumber,
+ result.m_timeStamp,
+ result.m_inferenceNumber,
infResultStr.c_str());
}
/* Get the decoded result for the combined result. */
std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
- hal_lcd_display_text(finalResultStr.c_str(), finalResultStr.size(),
- dataPsnTxtStartX1, dataPsnTxtStartY1,
- allow_multiple_lines);
+ hal_lcd_display_text(finalResultStr.c_str(),
+ finalResultStr.size(),
+ dataPsnTxtStartX1,
+ dataPsnTxtStartY1,
+ allow_multiple_lines);
info("Complete recognition: %s\n", finalResultStr.c_str());
return true;
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index 4732064..52c42f3 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/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.
@@ -17,13 +17,13 @@
#include "UseCaseHandler.hpp"
#include "Classifier.hpp"
+#include "ImageUtils.hpp"
+#include "ImgClassProcessing.hpp"
#include "InputFiles.hpp"
#include "MobileNetModel.hpp"
-#include "ImageUtils.hpp"
#include "UseCaseCommonUtils.hpp"
#include "hal.h"
#include "log_macros.h"
-#include "ImgClassProcessing.hpp"
#include <cinttypes>
@@ -36,7 +36,7 @@ namespace app {
bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
- auto& model = ctx.Get<Model&>("model");
+ auto& model = ctx.Get<Model&>("model");
/* If the request has a valid size, set the image index as it might not be set. */
if (imgIndex < NUMBER_OF_FILES) {
if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
@@ -46,19 +46,18 @@ namespace app {
auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
constexpr uint32_t dataPsnImgDownscaleFactor = 2;
- constexpr uint32_t dataPsnImgStartX = 10;
- constexpr uint32_t dataPsnImgStartY = 35;
+ constexpr uint32_t dataPsnImgStartX = 10;
+ constexpr uint32_t dataPsnImgStartY = 35;
constexpr uint32_t dataPsnTxtInfStartX = 150;
constexpr uint32_t dataPsnTxtInfStartY = 40;
-
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
- 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");
@@ -70,17 +69,19 @@ namespace app {
/* Get input shape for displaying the image. */
TfLiteIntArray* inputShape = model.GetInputShape(0);
- const uint32_t nCols = inputShape->data[arm::app::MobileNetModel::ms_inputColsIdx];
- const uint32_t nRows = inputShape->data[arm::app::MobileNetModel::ms_inputRowsIdx];
+ const uint32_t nCols = inputShape->data[arm::app::MobileNetModel::ms_inputColsIdx];
+ const uint32_t nRows = inputShape->data[arm::app::MobileNetModel::ms_inputRowsIdx];
const uint32_t nChannels = inputShape->data[arm::app::MobileNetModel::ms_inputChannelsIdx];
/* Set up pre and post-processing. */
ImgClassPreProcess preProcess = ImgClassPreProcess(inputTensor, model.IsDataSigned());
std::vector<ClassificationResult> results;
- ImgClassPostProcess postProcess = ImgClassPostProcess(outputTensor,
- ctx.Get<ImgClassClassifier&>("classifier"), ctx.Get<std::vector<std::string>&>("labels"),
- results);
+ ImgClassPostProcess postProcess =
+ ImgClassPostProcess(outputTensor,
+ ctx.Get<ImgClassClassifier&>("classifier"),
+ ctx.Get<std::vector<std::string>&>("labels"),
+ results);
do {
hal_lcd_clear(COLOR_BLACK);
@@ -88,29 +89,34 @@ namespace app {
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
- const uint8_t* imgSrc = get_img_array(ctx.Get<uint32_t>("imgIndex"));
+ const uint8_t* imgSrc = GetImgArray(ctx.Get<uint32_t>("imgIndex"));
if (nullptr == imgSrc) {
- printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", ctx.Get<uint32_t>("imgIndex"),
+ printf_err("Failed to get image index %" PRIu32 " (max: %u)\n",
+ ctx.Get<uint32_t>("imgIndex"),
NUMBER_OF_FILES - 1);
return false;
}
/* Display this image on the LCD. */
- hal_lcd_display_image(
- imgSrc,
- nCols, nRows, nChannels,
- dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
+ hal_lcd_display_image(imgSrc,
+ nCols,
+ nRows,
+ nChannels,
+ dataPsnImgStartX,
+ dataPsnImgStartY,
+ dataPsnImgDownscaleFactor);
/* Display message on the LCD - inference running. */
- 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);
/* Select the image to run inference with. */
- info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"),
- get_filename(ctx.Get<uint32_t>("imgIndex")));
+ info("Running inference on image %" PRIu32 " => %s\n",
+ ctx.Get<uint32_t>("imgIndex"),
+ GetFilename(ctx.Get<uint32_t>("imgIndex")));
- const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
- inputTensor->bytes : IMAGE_DATA_SIZE;
+ const size_t imgSz =
+ inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
/* Run the pre-processing, inference and post-processing. */
if (!preProcess.DoPreProcess(imgSrc, imgSz)) {
@@ -130,8 +136,8 @@ namespace app {
/* 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);
/* Add results to context for access outside handler. */
ctx.Set<std::vector<ClassificationResult>>("results", results);
@@ -146,7 +152,7 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"imgIndex");
+ IncrementAppCtxIfmIdx(ctx, "imgIndex");
} while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
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 "AudioUtils.hpp"
+#include "ImageUtils.hpp"
#include "InputFiles.hpp"
#include "KwsClassifier.hpp"
+#include "KwsProcessing.hpp"
+#include "KwsResult.hpp"
#include "MicroNetKwsModel.hpp"
-#include "hal.h"
-#include "AudioUtils.hpp"
-#include "ImageUtils.hpp"
#include "UseCaseCommonUtils.hpp"
-#include "KwsResult.hpp"
+#include "hal.h"
#include "log_macros.h"
-#include "KwsProcessing.hpp"
#include <vector>
@@ -42,15 +42,15 @@ namespace app {
/* 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 @@ namespace app {
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 @@ namespace app {
}
/* 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 @@ namespace app {
}
/* 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 @@ namespace app {
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 @@ namespace app {
}
/* 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 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"clipIndex");
+ IncrementAppCtxIfmIdx(ctx, "clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
@@ -175,7 +183,7 @@ namespace app {
{
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 @@ namespace app {
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(),
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index e733605..8a024b7 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/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.
@@ -16,35 +16,34 @@
*/
#include "UseCaseHandler.hpp"
-#include "hal.h"
-#include "InputFiles.hpp"
+#include "AsrClassifier.hpp"
+#include "AsrResult.hpp"
#include "AudioUtils.hpp"
-#include "ImageUtils.hpp"
-#include "UseCaseCommonUtils.hpp"
-#include "MicroNetKwsModel.hpp"
-#include "MicroNetKwsMfcc.hpp"
#include "Classifier.hpp"
+#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
+#include "KwsProcessing.hpp"
#include "KwsResult.hpp"
-#include "Wav2LetterModel.hpp"
+#include "MicroNetKwsMfcc.hpp"
+#include "MicroNetKwsModel.hpp"
+#include "OutputDecode.hpp"
+#include "UseCaseCommonUtils.hpp"
#include "Wav2LetterMfcc.hpp"
-#include "Wav2LetterPreprocess.hpp"
+#include "Wav2LetterModel.hpp"
#include "Wav2LetterPostprocess.hpp"
-#include "KwsProcessing.hpp"
-#include "AsrResult.hpp"
-#include "AsrClassifier.hpp"
-#include "OutputDecode.hpp"
+#include "Wav2LetterPreprocess.hpp"
+#include "hal.h"
#include "log_macros.h"
-
using KwsClassifier = arm::app::Classifier;
namespace arm {
namespace app {
struct KWSOutput {
- bool executionSuccess = false;
+ bool executionSuccess = false;
const int16_t* asrAudioStart = nullptr;
- int32_t asrAudioSamples = 0;
+ int32_t asrAudioSamples = 0;
};
/**
@@ -69,23 +68,24 @@ namespace app {
**/
static KWSOutput doKws(ApplicationContext& ctx)
{
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto& kwsModel = ctx.Get<Model&>("kwsModel");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto& kwsModel = ctx.Get<Model&>("kwsModel");
const auto kwsMfccFrameLength = ctx.Get<int>("kwsFrameLength");
const auto kwsMfccFrameStride = ctx.Get<int>("kwsFrameStride");
- const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold");
+ const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold");
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
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);
/* Output struct from doing KWS. */
- KWSOutput output {};
+ KWSOutput output{};
if (!kwsModel.IsInited()) {
printf_err("KWS model has not been initialised\n");
@@ -93,7 +93,7 @@ namespace app {
}
/* Get Input and Output tensors for pre/post processing. */
- TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0);
+ TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0);
TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(0);
if (!kwsInputTensor->dims) {
printf_err("Invalid input tensor dims\n");
@@ -104,28 +104,30 @@ namespace app {
}
/* Get input shape for feature extraction. */
- TfLiteIntArray* inputShape = kwsModel.GetInputShape(0);
+ TfLiteIntArray* inputShape = kwsModel.GetInputShape(0);
const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
- const uint32_t numMfccFrames = inputShape->data[MicroNetKwsModel::ms_inputRowsIdx];
+ const uint32_t numMfccFrames = inputShape->data[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 kwsAudioParamsSecondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
+ const float kwsAudioParamsSecondsPerSample =
+ 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
/* Set up pre and post-processing. */
- KwsPreProcess preProcess = KwsPreProcess(kwsInputTensor, numMfccFeatures, numMfccFrames,
- kwsMfccFrameLength, kwsMfccFrameStride);
+ KwsPreProcess preProcess = KwsPreProcess(
+ kwsInputTensor, numMfccFeatures, numMfccFrames, kwsMfccFrameLength, kwsMfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
- KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor, ctx.Get<KwsClassifier &>("kwsClassifier"),
+ KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor,
+ ctx.Get<KwsClassifier&>("kwsClassifier"),
ctx.Get<std::vector<std::string>&>("kwsLabels"),
singleInfResult);
/* 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 kws results from across the whole audio clip. */
std::vector<kws::KwsResult> finalResults;
@@ -133,11 +135,11 @@ namespace app {
/* Display message on the LCD - inference running. */
std::string str_inf{"Running KWS inference... "};
hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
info("Running KWS inference on audio clip %" PRIu32 " => %s\n",
- currentIndex, get_filename(currentIndex));
+ currentIndex,
+ GetFilename(currentIndex));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
@@ -159,22 +161,26 @@ namespace app {
return output;
}
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Add results from this window to our final results vector. */
finalResults.emplace_back(
- kws::KwsResult(singleInfResult,
- audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * preProcess.m_audioDataStride,
- audioDataSlider.Index(), kwsScoreThreshold));
+ kws::KwsResult(singleInfResult,
+ audioDataSlider.Index() * kwsAudioParamsSecondsPerSample *
+ preProcess.m_audioDataStride,
+ audioDataSlider.Index(),
+ kwsScoreThreshold));
/* Break out when trigger keyword is detected. */
- if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword")
- && singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
+ if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword") &&
+ singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
output.asrAudioStart = inferenceWindow + preProcess.m_audioDataWindowSize;
- output.asrAudioSamples = get_audio_array_size(currentIndex) -
- (audioDataSlider.NextWindowStartIndex() -
- preProcess.m_audioDataStride + preProcess.m_audioDataWindowSize);
+ output.asrAudioSamples =
+ GetAudioArraySize(currentIndex) -
+ (audioDataSlider.NextWindowStartIndex() - preProcess.m_audioDataStride +
+ preProcess.m_audioDataWindowSize);
break;
}
@@ -186,8 +192,8 @@ namespace app {
/* 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);
if (!PresentInferenceResult(finalResults)) {
return output;
@@ -208,12 +214,12 @@ namespace app {
**/
static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput)
{
- auto& asrModel = ctx.Get<Model&>("asrModel");
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength");
+ auto& asrModel = ctx.Get<Model&>("asrModel");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength");
auto asrMfccFrameStride = ctx.Get<uint32_t>("asrFrameStride");
- auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold");
- auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen");
+ auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold");
+ auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen");
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -226,31 +232,32 @@ namespace app {
hal_lcd_clear(COLOR_BLACK);
/* Get Input and Output tensors for pre/post processing. */
- TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0);
+ TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0);
TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0);
/* Get input shape. Dimensions of the tensor should have been verified by
- * the callee. */
+ * the callee. */
TfLiteIntArray* inputShape = asrModel.GetInputShape(0);
-
const uint32_t asrInputRows = asrInputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx];
const uint32_t asrInputInnerLen = asrInputRows - (2 * asrInputCtxLen);
/* Make sure the input tensor supports the above context and inner lengths. */
if (asrInputRows <= 2 * asrInputCtxLen || asrInputRows <= asrInputInnerLen) {
printf_err("ASR input rows not compatible with ctx length %" PRIu32 "\n",
- asrInputCtxLen);
+ asrInputCtxLen);
return false;
}
/* Audio data stride corresponds to inputInnerLen feature vectors. */
- const uint32_t asrAudioDataWindowLen = (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
+ const uint32_t asrAudioDataWindowLen =
+ (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
const uint32_t asrAudioDataWindowStride = asrInputInnerLen * asrMfccFrameStride;
- const float asrAudioParamsSecondsPerSample = 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
+ const float asrAudioParamsSecondsPerSample =
+ 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
/* Get the remaining audio buffer and respective size from KWS results. */
- const int16_t* audioArr = kwsOutput.asrAudioStart;
+ const int16_t* audioArr = kwsOutput.asrAudioStart;
const uint32_t audioArrSize = kwsOutput.asrAudioSamples;
/* Audio clip must have enough samples to produce 1 MFCC feature. */
@@ -262,35 +269,40 @@ namespace app {
}
/* Initialise an audio slider. */
- auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>(
- audioBuffer.data(),
- audioBuffer.size(),
- asrAudioDataWindowLen,
- asrAudioDataWindowStride);
+ auto audioDataSlider =
+ audio::FractionalSlidingWindow<const int16_t>(audioBuffer.data(),
+ audioBuffer.size(),
+ asrAudioDataWindowLen,
+ asrAudioDataWindowStride);
/* Declare a container for results. */
std::vector<asr::AsrResult> asrResults;
/* Display message on the LCD - inference running. */
std::string str_inf{"Running ASR inference... "};
- 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);
size_t asrInferenceWindowLen = asrAudioDataWindowLen;
/* Set up pre and post-processing objects. */
- AsrPreProcess asrPreProcess = AsrPreProcess(asrInputTensor, arm::app::Wav2LetterModel::ms_numMfccFeatures,
- inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
- asrMfccFrameLen, asrMfccFrameStride);
+ AsrPreProcess asrPreProcess =
+ AsrPreProcess(asrInputTensor,
+ arm::app::Wav2LetterModel::ms_numMfccFeatures,
+ inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
+ asrMfccFrameLen,
+ asrMfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(asrModel, asrInputCtxLen);
- AsrPostProcess asrPostProcess = AsrPostProcess(
- asrOutputTensor, ctx.Get<AsrClassifier&>("asrClassifier"),
- ctx.Get<std::vector<std::string>&>("asrLabels"),
- singleInfResult, outputCtxLen,
- Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
- );
+ AsrPostProcess asrPostProcess =
+ AsrPostProcess(asrOutputTensor,
+ ctx.Get<AsrClassifier&>("asrClassifier"),
+ ctx.Get<std::vector<std::string>&>("asrLabels"),
+ singleInfResult,
+ outputCtxLen,
+ Wav2LetterModel::ms_blankTokenIdx,
+ Wav2LetterModel::ms_outputRowsIdx);
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
@@ -302,8 +314,9 @@ namespace app {
const int16_t* asrInferenceWindow = audioDataSlider.Next();
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
- static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
+ static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
/* Run the pre-processing, inference and post-processing. */
if (!asrPreProcess.DoPreProcess(asrInferenceWindow, asrInferenceWindowLen)) {
@@ -327,25 +340,27 @@ namespace app {
/* Get results. */
std::vector<ClassificationResult> asrClassificationResult;
auto& asrClassifier = ctx.Get<AsrClassifier&>("asrClassifier");
- asrClassifier.GetClassificationResults(
- asrOutputTensor, asrClassificationResult,
- ctx.Get<std::vector<std::string>&>("asrLabels"), 1);
-
- asrResults.emplace_back(asr::AsrResult(asrClassificationResult,
- (audioDataSlider.Index() *
- asrAudioParamsSecondsPerSample *
- asrAudioDataWindowStride),
- audioDataSlider.Index(), asrScoreThreshold));
+ asrClassifier.GetClassificationResults(asrOutputTensor,
+ asrClassificationResult,
+ ctx.Get<std::vector<std::string>&>("asrLabels"),
+ 1);
+
+ asrResults.emplace_back(
+ asr::AsrResult(asrClassificationResult,
+ (audioDataSlider.Index() * asrAudioParamsSecondsPerSample *
+ asrAudioDataWindowStride),
+ audioDataSlider.Index(),
+ asrScoreThreshold));
#if VERIFY_TEST_OUTPUT
- armDumpTensor(asrOutputTensor, asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
+ armDumpTensor(asrOutputTensor,
+ asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
#endif /* VERIFY_TEST_OUTPUT */
/* Erase */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
}
if (!PresentInferenceResult(asrResults)) {
return false;
@@ -363,7 +378,7 @@ namespace app {
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
- if (!SetAppCtxIfmIdx(ctx, clipIndex,"kws_asr")) {
+ if (!SetAppCtxIfmIdx(ctx, clipIndex, "kws_asr")) {
return false;
}
}
@@ -379,13 +394,13 @@ namespace app {
if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) {
info("Trigger keyword spotted\n");
- if(!doAsr(ctx, kwsOutput)) {
+ if (!doAsr(ctx, kwsOutput)) {
printf_err("ASR failed\n");
return false;
}
}
- IncrementAppCtxIfmIdx(ctx,"kws_asr");
+ IncrementAppCtxIfmIdx(ctx, "kws_asr");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
@@ -396,36 +411,38 @@ namespace app {
{
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);
/* Display each result. */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
- for (auto & result : results) {
+ for (auto& result : results) {
std::string topKeyword{"<none>"};
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, 0);
+ hal_lcd_display_text(
+ resultStr.c_str(), resultStr.size(), dataPsnTxtStartX1, rowIdx1, 0);
rowIdx1 += dataPsnTxtYIncr;
info("For timestamp: %f (inference #: %" PRIu32 "); threshold: %f\n",
- result.m_timeStamp, result.m_inferenceNumber,
+ result.m_timeStamp,
+ result.m_inferenceNumber,
result.m_threshold);
for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
- info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n", j,
+ info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n",
+ j,
result.m_resultVec[j].m_label.c_str(),
result.m_resultVec[j].m_normalisedVal);
}
@@ -438,30 +455,32 @@ namespace app {
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 80;
- constexpr bool allow_multiple_lines = true;
+ constexpr bool allow_multiple_lines = true;
hal_lcd_set_text_color(COLOR_GREEN);
/* Results from multiple inferences should be combined before processing. */
std::vector<arm::app::ClassificationResult> combinedResults;
for (auto& result : results) {
- combinedResults.insert(combinedResults.end(),
- result.m_resultVec.begin(),
- result.m_resultVec.end());
+ combinedResults.insert(
+ combinedResults.end(), result.m_resultVec.begin(), result.m_resultVec.end());
}
for (auto& result : results) {
/* Get the final result string using the decoder. */
std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
- info("Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber,
- infResultStr.c_str());
+ info(
+ "Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber, infResultStr.c_str());
}
std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
- hal_lcd_display_text(finalResultStr.c_str(), finalResultStr.size(),
- dataPsnTxtStartX1, dataPsnTxtStartY1, allow_multiple_lines);
+ hal_lcd_display_text(finalResultStr.c_str(),
+ finalResultStr.size(),
+ dataPsnTxtStartX1,
+ dataPsnTxtStartY1,
+ allow_multiple_lines);
info("Final result: %s\n", finalResultStr.c_str());
return true;
diff --git a/source/use_case/noise_reduction/src/UseCaseHandler.cc b/source/use_case/noise_reduction/src/UseCaseHandler.cc
index 0aef600..0c5ff39 100644
--- a/source/use_case/noise_reduction/src/UseCaseHandler.cc
+++ b/source/use_case/noise_reduction/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.
@@ -14,24 +14,24 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-#include "hal.h"
#include "UseCaseHandler.hpp"
-#include "UseCaseCommonUtils.hpp"
#include "AudioUtils.hpp"
#include "ImageUtils.hpp"
#include "InputFiles.hpp"
-#include "RNNoiseModel.hpp"
#include "RNNoiseFeatureProcessor.hpp"
+#include "RNNoiseModel.hpp"
#include "RNNoiseProcessing.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
#include "log_macros.h"
namespace arm {
namespace app {
/**
- * @brief Helper function to increment current audio clip features index.
- * @param[in,out] ctx Pointer to the application context object.
- **/
+ * @brief Helper function to increment current audio clip features index.
+ * @param[in,out] ctx Pointer to the application context object.
+ **/
static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
/* Noise reduction inference handler. */
@@ -41,17 +41,18 @@ namespace app {
constexpr uint32_t dataPsnTxtInfStartY = 40;
/* Variables used for memory dumping. */
- size_t memDumpMaxLen = 0;
- uint8_t* memDumpBaseAddr = nullptr;
+ size_t memDumpMaxLen = 0;
+ uint8_t* memDumpBaseAddr = nullptr;
size_t undefMemDumpBytesWritten = 0;
- size_t* pMemDumpBytesWritten = &undefMemDumpBytesWritten;
- if (ctx.Has("MEM_DUMP_LEN") && ctx.Has("MEM_DUMP_BASE_ADDR") && ctx.Has("MEM_DUMP_BYTE_WRITTEN")) {
- memDumpMaxLen = ctx.Get<size_t>("MEM_DUMP_LEN");
- memDumpBaseAddr = ctx.Get<uint8_t*>("MEM_DUMP_BASE_ADDR");
+ size_t* pMemDumpBytesWritten = &undefMemDumpBytesWritten;
+ if (ctx.Has("MEM_DUMP_LEN") && ctx.Has("MEM_DUMP_BASE_ADDR") &&
+ ctx.Has("MEM_DUMP_BYTE_WRITTEN")) {
+ memDumpMaxLen = ctx.Get<size_t>("MEM_DUMP_LEN");
+ memDumpBaseAddr = ctx.Get<uint8_t*>("MEM_DUMP_BASE_ADDR");
pMemDumpBytesWritten = ctx.Get<size_t*>("MEM_DUMP_BYTE_WRITTEN");
}
std::reference_wrapper<size_t> memDumpBytesWritten = std::ref(*pMemDumpBytesWritten);
- auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
/* Get model reference. */
auto& model = ctx.Get<RNNoiseModel&>("model");
@@ -61,15 +62,16 @@ namespace app {
}
/* Populate Pre-Processing related parameters. */
- auto audioFrameLen = ctx.Get<uint32_t>("frameLength");
- auto audioFrameStride = ctx.Get<uint32_t>("frameStride");
+ auto audioFrameLen = ctx.Get<uint32_t>("frameLength");
+ auto audioFrameStride = ctx.Get<uint32_t>("frameStride");
auto nrNumInputFeatures = ctx.Get<uint32_t>("numInputFeatures");
TfLiteTensor* inputTensor = model.GetInputTensor(0);
if (nrNumInputFeatures != inputTensor->bytes) {
printf_err("Input features size must be equal to input tensor size."
" Feature size = %" PRIu32 ", Tensor size = %zu.\n",
- nrNumInputFeatures, inputTensor->bytes);
+ nrNumInputFeatures,
+ inputTensor->bytes);
return false;
}
@@ -78,49 +80,55 @@ namespace app {
/* Initial choice of index for WAV file. */
auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
- std::function<const int16_t* (const uint32_t)> audioAccessorFunc = get_audio_array;
+ std::function<const int16_t*(const uint32_t)> audioAccessorFunc = GetAudioArray;
if (ctx.Has("features")) {
- audioAccessorFunc = ctx.Get<std::function<const int16_t* (const uint32_t)>>("features");
+ audioAccessorFunc = ctx.Get<std::function<const int16_t*(const uint32_t)>>("features");
}
- std::function<uint32_t (const uint32_t)> audioSizeAccessorFunc = get_audio_array_size;
+ std::function<uint32_t(const uint32_t)> audioSizeAccessorFunc = GetAudioArraySize;
if (ctx.Has("featureSizes")) {
- audioSizeAccessorFunc = ctx.Get<std::function<uint32_t (const uint32_t)>>("featureSizes");
+ audioSizeAccessorFunc =
+ ctx.Get<std::function<uint32_t(const uint32_t)>>("featureSizes");
}
- std::function<const char*(const uint32_t)> audioFileAccessorFunc = get_filename;
+ std::function<const char*(const uint32_t)> audioFileAccessorFunc = GetFilename;
if (ctx.Has("featureFileNames")) {
- audioFileAccessorFunc = ctx.Get<std::function<const char*(const uint32_t)>>("featureFileNames");
+ audioFileAccessorFunc =
+ ctx.Get<std::function<const char*(const uint32_t)>>("featureFileNames");
}
do {
hal_lcd_clear(COLOR_BLACK);
auto startDumpAddress = memDumpBaseAddr + memDumpBytesWritten;
- auto currentIndex = ctx.Get<uint32_t>("clipIndex");
+ auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* Creating a sliding window through the audio. */
- auto audioDataSlider = audio::SlidingWindow<const int16_t>(
- audioAccessorFunc(currentIndex),
- audioSizeAccessorFunc(currentIndex), audioFrameLen,
- audioFrameStride);
-
- info("Running inference on input feature map %" PRIu32 " => %s\n", currentIndex,
+ auto audioDataSlider =
+ audio::SlidingWindow<const int16_t>(audioAccessorFunc(currentIndex),
+ audioSizeAccessorFunc(currentIndex),
+ audioFrameLen,
+ audioFrameStride);
+
+ info("Running inference on input feature map %" PRIu32 " => %s\n",
+ currentIndex,
audioFileAccessorFunc(currentIndex));
- memDumpBytesWritten += DumpDenoisedAudioHeader(audioFileAccessorFunc(currentIndex),
- (audioDataSlider.TotalStrides() + 1) * audioFrameLen,
- memDumpBaseAddr + memDumpBytesWritten,
- memDumpMaxLen - memDumpBytesWritten);
+ memDumpBytesWritten +=
+ DumpDenoisedAudioHeader(audioFileAccessorFunc(currentIndex),
+ (audioDataSlider.TotalStrides() + 1) * audioFrameLen,
+ memDumpBaseAddr + memDumpBytesWritten,
+ memDumpMaxLen - memDumpBytesWritten);
/* Set up pre and post-processing. */
std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor =
- std::make_shared<rnn::RNNoiseFeatureProcessor>();
+ std::make_shared<rnn::RNNoiseFeatureProcessor>();
std::shared_ptr<rnn::FrameFeatures> frameFeatures =
- std::make_shared<rnn::FrameFeatures>();
+ std::make_shared<rnn::FrameFeatures>();
- RNNoisePreProcess preProcess = RNNoisePreProcess(inputTensor, featureProcessor, frameFeatures);
+ RNNoisePreProcess preProcess =
+ RNNoisePreProcess(inputTensor, featureProcessor, frameFeatures);
std::vector<int16_t> denoisedAudioFrame(audioFrameLen);
- RNNoisePostProcess postProcess = RNNoisePostProcess(outputTensor, denoisedAudioFrame,
- featureProcessor, frameFeatures);
+ RNNoisePostProcess postProcess = RNNoisePostProcess(
+ outputTensor, denoisedAudioFrame, featureProcessor, frameFeatures);
bool resetGRU = true;
@@ -133,11 +141,12 @@ namespace app {
}
/* Reset or copy over GRU states first to avoid TFLu memory overlap issues. */
- if (resetGRU){
+ if (resetGRU) {
model.ResetGruState();
} else {
/* Copying gru state outputs to gru state inputs.
- * Call ResetGruState in between the sequence of inferences on unrelated input data. */
+ * Call ResetGruState in between the sequence of inferences on unrelated input
+ * data. */
model.CopyGruStates();
}
@@ -145,10 +154,15 @@ namespace app {
std::string str_inf{"Running inference... "};
/* Display message on the LCD - inference running. */
- 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);
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, audioDataSlider.TotalStrides() + 1);
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
+ audioDataSlider.TotalStrides() + 1);
/* Run inference over this feature sliding window. */
if (!RunInference(model, profiler)) {
@@ -165,15 +179,18 @@ namespace app {
/* 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);
if (memDumpMaxLen > 0) {
/* Dump final post processed output to memory. */
- memDumpBytesWritten += DumpOutputDenoisedAudioFrame(
- denoisedAudioFrame,
- memDumpBaseAddr + memDumpBytesWritten,
- memDumpMaxLen - memDumpBytesWritten);
+ memDumpBytesWritten +=
+ DumpOutputDenoisedAudioFrame(denoisedAudioFrame,
+ memDumpBaseAddr + memDumpBytesWritten,
+ memDumpMaxLen - memDumpBytesWritten);
}
}
@@ -181,43 +198,54 @@ namespace app {
/* Needed to not let the compiler complain about type mismatch. */
size_t valMemDumpBytesWritten = memDumpBytesWritten;
info("Output memory dump of %zu bytes written at address 0x%p\n",
- valMemDumpBytesWritten, startDumpAddress);
+ valMemDumpBytesWritten,
+ startDumpAddress);
}
/* Finish by dumping the footer. */
- DumpDenoisedAudioFooter(memDumpBaseAddr + memDumpBytesWritten, memDumpMaxLen - memDumpBytesWritten);
+ DumpDenoisedAudioFooter(memDumpBaseAddr + memDumpBytesWritten,
+ memDumpMaxLen - memDumpBytesWritten);
info("All inferences for audio clip complete.\n");
profiler.PrintProfilingResult();
IncrementAppCtxClipIdx(ctx);
std::string clearString{' '};
- hal_lcd_display_text(clearString.c_str(), clearString.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ hal_lcd_display_text(clearString.c_str(),
+ clearString.size(),
+ dataPsnTxtInfStartX,
+ dataPsnTxtInfStartY,
+ false);
std::string completeMsg{"Inference complete!"};
/* Display message on the LCD - inference complete. */
- hal_lcd_display_text(completeMsg.c_str(), completeMsg.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ hal_lcd_display_text(completeMsg.c_str(),
+ completeMsg.size(),
+ dataPsnTxtInfStartX,
+ dataPsnTxtInfStartY,
+ false);
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
- size_t DumpDenoisedAudioHeader(const char* filename, size_t dumpSize,
- uint8_t* memAddress, size_t memSize){
+ size_t DumpDenoisedAudioHeader(const char* filename,
+ size_t dumpSize,
+ uint8_t* memAddress,
+ size_t memSize)
+ {
- if (memAddress == nullptr){
+ if (memAddress == nullptr) {
return 0;
}
int32_t filenameLength = strlen(filename);
size_t numBytesWritten = 0;
size_t numBytesToWrite = 0;
- int32_t dumpSizeByte = dumpSize * sizeof(int16_t);
- bool overflow = false;
+ int32_t dumpSizeByte = dumpSize * sizeof(int16_t);
+ bool overflow = false;
/* Write the filename length */
numBytesToWrite = sizeof(filenameLength);
@@ -231,7 +259,7 @@ namespace app {
/* Write file name */
numBytesToWrite = filenameLength;
- if(memSize - numBytesToWrite > 0) {
+ if (memSize - numBytesToWrite > 0) {
std::memcpy(memAddress + numBytesWritten, filename, numBytesToWrite);
numBytesWritten += numBytesToWrite;
memSize -= numBytesWritten;
@@ -241,7 +269,7 @@ namespace app {
/* Write dumpSize in byte */
numBytesToWrite = sizeof(dumpSizeByte);
- if(memSize - numBytesToWrite > 0) {
+ if (memSize - numBytesToWrite > 0) {
std::memcpy(memAddress + numBytesWritten, &(dumpSizeByte), numBytesToWrite);
numBytesWritten += numBytesToWrite;
memSize -= numBytesWritten;
@@ -249,8 +277,10 @@ namespace app {
overflow = true;
}
- if(false == overflow) {
- info("Audio Clip dump header info (%zu bytes) written to %p\n", numBytesWritten, memAddress);
+ if (false == overflow) {
+ info("Audio Clip dump header info (%zu bytes) written to %p\n",
+ numBytesWritten,
+ memAddress);
} else {
printf_err("Not enough memory to dump Audio Clip header.\n");
}
@@ -258,7 +288,8 @@ namespace app {
return numBytesWritten;
}
- size_t DumpDenoisedAudioFooter(uint8_t* memAddress, size_t memSize){
+ size_t DumpDenoisedAudioFooter(uint8_t* memAddress, size_t memSize)
+ {
if ((memAddress == nullptr) || (memSize < 4)) {
return 0;
}
@@ -266,23 +297,27 @@ namespace app {
std::memcpy(memAddress, &eofMarker, sizeof(int32_t));
return sizeof(int32_t);
- }
+ }
size_t DumpOutputDenoisedAudioFrame(const std::vector<int16_t>& audioFrame,
- uint8_t* memAddress, size_t memSize)
+ uint8_t* memAddress,
+ size_t memSize)
{
if (memAddress == nullptr) {
return 0;
}
size_t numByteToBeWritten = audioFrame.size() * sizeof(int16_t);
- if( numByteToBeWritten > memSize) {
- printf_err("Overflow error: Writing %zu of %zu bytes to memory @ 0x%p.\n", memSize, numByteToBeWritten, memAddress);
+ if (numByteToBeWritten > memSize) {
+ printf_err("Overflow error: Writing %zu of %zu bytes to memory @ 0x%p.\n",
+ memSize,
+ numByteToBeWritten,
+ memAddress);
numByteToBeWritten = memSize;
}
std::memcpy(memAddress, audioFrame.data(), numByteToBeWritten);
- info("Copied %zu bytes to %p\n", numByteToBeWritten, memAddress);
+ info("Copied %zu bytes to %p\n", numByteToBeWritten, memAddress);
return numByteToBeWritten;
}
@@ -290,13 +325,13 @@ namespace app {
size_t DumpOutputTensorsToMemory(Model& model, uint8_t* memAddress, const size_t memSize)
{
const size_t numOutputs = model.GetNumOutputs();
- size_t numBytesWritten = 0;
- uint8_t* ptr = memAddress;
+ size_t numBytesWritten = 0;
+ uint8_t* ptr = memAddress;
/* Iterate over all output tensors. */
for (size_t i = 0; i < numOutputs; ++i) {
const TfLiteTensor* tensor = model.GetOutputTensor(i);
- const auto* tData = tflite::GetTensorData<uint8_t>(tensor);
+ const auto* tData = tflite::GetTensorData<uint8_t>(tensor);
#if VERIFY_TEST_OUTPUT
DumpTensor(tensor);
#endif /* VERIFY_TEST_OUTPUT */
@@ -305,15 +340,13 @@ namespace app {
if (tensor->bytes > 0) {
std::memcpy(ptr, tData, tensor->bytes);
- info("Copied %zu bytes for tensor %zu to 0x%p\n",
- tensor->bytes, i, ptr);
+ info("Copied %zu bytes for tensor %zu to 0x%p\n", tensor->bytes, i, ptr);
numBytesWritten += tensor->bytes;
ptr += tensor->bytes;
}
} else {
- printf_err("Error writing tensor %zu to memory @ 0x%p\n",
- i, memAddress);
+ printf_err("Error writing tensor %zu to memory @ 0x%p\n", i, memAddress);
break;
}
}
diff --git a/source/use_case/object_detection/src/UseCaseHandler.cc b/source/use_case/object_detection/src/UseCaseHandler.cc
index 084059e..9330187 100644
--- a/source/use_case/object_detection/src/UseCaseHandler.cc
+++ b/source/use_case/object_detection/src/UseCaseHandler.cc
@@ -1,6 +1,6 @@
/*
- * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 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.
@@ -15,11 +15,11 @@
* limitations under the License.
*/
#include "UseCaseHandler.hpp"
-#include "InputFiles.hpp"
-#include "YoloFastestModel.hpp"
-#include "UseCaseCommonUtils.hpp"
#include "DetectorPostProcessing.hpp"
#include "DetectorPreProcessing.hpp"
+#include "InputFiles.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "YoloFastestModel.hpp"
#include "hal.h"
#include "log_macros.h"
@@ -34,7 +34,8 @@ namespace app {
* @param[in] results Vector of detection results to be displayed.
* @return true if successful, false otherwise.
**/
- static bool PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results);
+ static bool
+ PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results);
/**
* @brief Draw boxes directly on the LCD for all detected objects.
@@ -43,11 +44,10 @@ namespace app {
* @param[in] imageStartY Y coordinate where the image starts on the LCD.
* @param[in] imgDownscaleFactor How much image has been downscaled on LCD.
**/
- static void DrawDetectionBoxes(
- const std::vector<object_detection::DetectionResult>& results,
- uint32_t imgStartX,
- uint32_t imgStartY,
- uint32_t imgDownscaleFactor);
+ static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
+ uint32_t imgStartX,
+ uint32_t imgStartY,
+ uint32_t imgDownscaleFactor);
/* Object detection inference handler. */
bool ObjectDetectionHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
@@ -55,8 +55,8 @@ namespace app {
auto& profiler = ctx.Get<Profiler&>("profiler");
constexpr uint32_t dataPsnImgDownscaleFactor = 1;
- constexpr uint32_t dataPsnImgStartX = 10;
- constexpr uint32_t dataPsnImgStartY = 35;
+ constexpr uint32_t dataPsnImgStartX = 10;
+ constexpr uint32_t dataPsnImgStartY = 35;
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 28;
@@ -78,7 +78,7 @@ namespace app {
auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
- TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);
@@ -99,12 +99,14 @@ namespace app {
DetectorPreProcess preProcess = DetectorPreProcess(inputTensor, true, model.IsDataSigned());
std::vector<object_detection::DetectionResult> results;
- const object_detection::PostProcessParams postProcessParams {
- inputImgRows, inputImgCols, object_detection::originalImageSize,
- object_detection::anchor1, object_detection::anchor2
- };
- DetectorPostProcess postProcess = DetectorPostProcess(outputTensor0, outputTensor1,
- results, postProcessParams);
+ const object_detection::PostProcessParams postProcessParams{
+ inputImgRows,
+ inputImgCols,
+ object_detection::originalImageSize,
+ object_detection::anchor1,
+ object_detection::anchor2};
+ DetectorPostProcess postProcess =
+ DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);
do {
/* Ensure there are no results leftover from previous inference when running all. */
results.clear();
@@ -112,11 +114,11 @@ namespace app {
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
- const uint8_t* currImage = get_img_array(ctx.Get<uint32_t>("imgIndex"));
+ const uint8_t* currImage = GetImgArray(ctx.Get<uint32_t>("imgIndex"));
auto dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8);
- const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
- inputTensor->bytes : IMAGE_DATA_SIZE;
+ const size_t copySz =
+ inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
/* Run the pre-processing, inference and post-processing. */
if (!preProcess.DoPreProcess(currImage, copySz)) {
@@ -135,12 +137,13 @@ namespace app {
dataPsnImgDownscaleFactor);
/* Display message on the LCD - inference running. */
- 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);
/* Run inference over this image. */
- info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"),
- get_filename(ctx.Get<uint32_t>("imgIndex")));
+ info("Running inference on image %" PRIu32 " => %s\n",
+ ctx.Get<uint32_t>("imgIndex"),
+ GetFilename(ctx.Get<uint32_t>("imgIndex")));
if (!RunInference(model, profiler)) {
printf_err("Inference failed.");
@@ -154,11 +157,12 @@ namespace app {
/* 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);
/* Draw boxes. */
- DrawDetectionBoxes(results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
+ DrawDetectionBoxes(
+ results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
#if VERIFY_TEST_OUTPUT
DumpTensor(modelOutput0);
@@ -171,14 +175,15 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"imgIndex");
+ IncrementAppCtxIfmIdx(ctx, "imgIndex");
} while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
return true;
}
- static bool PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results)
+ static bool
+ PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results)
{
hal_lcd_set_text_color(COLOR_GREEN);
@@ -187,9 +192,14 @@ namespace app {
info("Total number of inferences: 1\n");
for (uint32_t i = 0; i < results.size(); ++i) {
- info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", i,
- results[i].m_normalisedVal, "Detection box:",
- results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h );
+ info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n",
+ i,
+ results[i].m_normalisedVal,
+ "Detection box:",
+ results[i].m_x0,
+ results[i].m_y0,
+ results[i].m_w,
+ results[i].m_h);
}
return true;
@@ -202,24 +212,34 @@ namespace app {
{
uint32_t lineThickness = 1;
- for (const auto& result: results) {
+ for (const auto& result : results) {
/* Top line. */
- hal_lcd_display_box(imgStartX + result.m_x0/imgDownscaleFactor,
- imgStartY + result.m_y0/imgDownscaleFactor,
- result.m_w/imgDownscaleFactor, lineThickness, COLOR_GREEN);
+ hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
+ imgStartY + result.m_y0 / imgDownscaleFactor,
+ result.m_w / imgDownscaleFactor,
+ lineThickness,
+ COLOR_GREEN);
/* Bot line. */
- hal_lcd_display_box(imgStartX + result.m_x0/imgDownscaleFactor,
- imgStartY + (result.m_y0 + result.m_h)/imgDownscaleFactor - lineThickness,
- result.m_w/imgDownscaleFactor, lineThickness, COLOR_GREEN);
+ hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
+ imgStartY + (result.m_y0 + result.m_h) / imgDownscaleFactor -
+ lineThickness,
+ result.m_w / imgDownscaleFactor,
+ lineThickness,
+ COLOR_GREEN);
/* Left line. */
- hal_lcd_display_box(imgStartX + result.m_x0/imgDownscaleFactor,
- imgStartY + result.m_y0/imgDownscaleFactor,
- lineThickness, result.m_h/imgDownscaleFactor, COLOR_GREEN);
+ hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
+ imgStartY + result.m_y0 / imgDownscaleFactor,
+ lineThickness,
+ result.m_h / imgDownscaleFactor,
+ COLOR_GREEN);
/* Right line. */
- hal_lcd_display_box(imgStartX + (result.m_x0 + result.m_w)/imgDownscaleFactor - lineThickness,
- imgStartY + result.m_y0/imgDownscaleFactor,
- lineThickness, result.m_h/imgDownscaleFactor, COLOR_GREEN);
+ hal_lcd_display_box(imgStartX + (result.m_x0 + result.m_w) / imgDownscaleFactor -
+ lineThickness,
+ imgStartY + result.m_y0 / imgDownscaleFactor,
+ lineThickness,
+ result.m_h / imgDownscaleFactor,
+ COLOR_GREEN);
}
}
diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc
index e2e48d1..4704c97 100644
--- a/source/use_case/vww/src/UseCaseHandler.cc
+++ b/source/use_case/vww/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.
@@ -15,34 +15,34 @@
* limitations under the License.
*/
#include "UseCaseHandler.hpp"
-#include "VisualWakeWordModel.hpp"
#include "Classifier.hpp"
-#include "InputFiles.hpp"
#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
#include "UseCaseCommonUtils.hpp"
+#include "VisualWakeWordModel.hpp"
+#include "VisualWakeWordProcessing.hpp"
#include "hal.h"
#include "log_macros.h"
-#include "VisualWakeWordProcessing.hpp"
namespace arm {
namespace app {
/* Visual Wake Word inference handler. */
- bool ClassifyImageHandler(ApplicationContext &ctx, uint32_t imgIndex, bool runAll)
+ bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
- auto& model = ctx.Get<Model&>("model");
+ auto& model = ctx.Get<Model&>("model");
/* If the request has a valid size, set the image index. */
if (imgIndex < NUMBER_OF_FILES) {
- if (!SetAppCtxIfmIdx(ctx, imgIndex,"imgIndex")) {
+ if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
return false;
}
}
auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
constexpr uint32_t dataPsnImgDownscaleFactor = 1;
- constexpr uint32_t dataPsnImgStartX = 10;
- constexpr uint32_t dataPsnImgStartY = 35;
+ constexpr uint32_t dataPsnImgStartX = 10;
+ constexpr uint32_t dataPsnImgStartY = 35;
constexpr uint32_t dataPsnTxtInfStartX = 150;
constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -52,7 +52,7 @@ namespace app {
return false;
}
- 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");
@@ -66,7 +66,8 @@ namespace app {
TfLiteIntArray* inputShape = model.GetInputShape(0);
const uint32_t nCols = inputShape->data[arm::app::VisualWakeWordModel::ms_inputColsIdx];
const uint32_t nRows = inputShape->data[arm::app::VisualWakeWordModel::ms_inputRowsIdx];
- if (arm::app::VisualWakeWordModel::ms_inputChannelsIdx >= static_cast<uint32_t>(inputShape->size)) {
+ if (arm::app::VisualWakeWordModel::ms_inputChannelsIdx >=
+ static_cast<uint32_t>(inputShape->size)) {
printf_err("Invalid channel index.\n");
return false;
}
@@ -78,9 +79,11 @@ namespace app {
VisualWakeWordPreProcess preProcess = VisualWakeWordPreProcess(inputTensor);
std::vector<ClassificationResult> results;
- VisualWakeWordPostProcess postProcess = VisualWakeWordPostProcess(outputTensor,
- ctx.Get<Classifier&>("classifier"),
- ctx.Get<std::vector<std::string>&>("labels"), results);
+ VisualWakeWordPostProcess postProcess =
+ VisualWakeWordPostProcess(outputTensor,
+ ctx.Get<Classifier&>("classifier"),
+ ctx.Get<std::vector<std::string>&>("labels"),
+ results);
do {
hal_lcd_clear(COLOR_BLACK);
@@ -88,29 +91,34 @@ namespace app {
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
- const uint8_t* imgSrc = get_img_array(ctx.Get<uint32_t>("imgIndex"));
+ const uint8_t* imgSrc = GetImgArray(ctx.Get<uint32_t>("imgIndex"));
if (nullptr == imgSrc) {
- printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", ctx.Get<uint32_t>("imgIndex"),
+ printf_err("Failed to get image index %" PRIu32 " (max: %u)\n",
+ ctx.Get<uint32_t>("imgIndex"),
NUMBER_OF_FILES - 1);
return false;
}
/* Display this image on the LCD. */
- hal_lcd_display_image(
- imgSrc,
- nCols, nRows, displayChannels,
- dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
+ hal_lcd_display_image(imgSrc,
+ nCols,
+ nRows,
+ displayChannels,
+ dataPsnImgStartX,
+ dataPsnImgStartY,
+ dataPsnImgDownscaleFactor);
/* Display message on the LCD - inference running. */
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
/* Run inference over this image. */
- info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"),
- get_filename(ctx.Get<uint32_t>("imgIndex")));
+ info("Running inference on image %" PRIu32 " => %s\n",
+ ctx.Get<uint32_t>("imgIndex"),
+ GetFilename(ctx.Get<uint32_t>("imgIndex")));
- const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
- inputTensor->bytes : IMAGE_DATA_SIZE;
+ const size_t imgSz =
+ inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
/* Run the pre-processing, inference and post-processing. */
if (!preProcess.DoPreProcess(imgSrc, imgSz)) {
@@ -130,8 +138,8 @@ namespace app {
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
/* Add results to context for access outside handler. */
ctx.Set<std::vector<ClassificationResult>>("results", results);
@@ -146,7 +154,7 @@ namespace app {
profiler.PrintProfilingResult();
- IncrementAppCtxIfmIdx(ctx,"imgIndex");
+ IncrementAppCtxIfmIdx(ctx, "imgIndex");
} while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-29 09:15:38 +0000