Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
new file mode 100644
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+++ b/source/use_case/kws/src/UseCaseHandler.cc
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+/*
+ * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "UseCaseHandler.hpp"
+
+#include "InputFiles.hpp"
+#include "Classifier.hpp"
+#include "DsCnnModel.hpp"
+#include "hal.h"
+#include "DsCnnMfcc.hpp"
+#include "AudioUtils.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "KwsResult.hpp"
+
+#include <vector>
+#include <functional>
+
+using KwsClassifier = arm::app::Classifier;
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Helper function to increment current audio clip index.
+ * @param[in,out] ctx Pointer to the application context object.
+ **/
+ static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+
+ /**
+ * @brief Helper function to set the audio clip index.
+ * @param[in,out] ctx Pointer to the application context object.
+ * @param[in] idx Value to be set.
+ * @return true if index is set, false otherwise.
+ **/
+ static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+
+ /**
+ * @brief Presents inference results using the data presentation
+ * object.
+ * @param[in] platform Reference to the hal platform object.
+ * @param[in] results Vector of classification results to be displayed.
+ * @param[in] infTimeMs Inference time in milliseconds, if available,
+ * otherwise, this can be passed in as 0.
+ * @return true if successful, false otherwise.
+ **/
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results);
+
+ /**
+ * @brief Returns a function to perform feature calculation and populates input tensor data with
+ * MFCC data.
+ *
+ * Input tensor data type check is performed to choose correct MFCC feature data type.
+ * If tensor has an integer data type then original features are quantised.
+ *
+ * Warning: MFCC calculator provided as input must have the same life scope as returned function.
+ *
+ * @param[in] mfcc MFCC feature calculator.
+ * @param[in,out] inputTensor Input tensor pointer to store calculated features.
+ * @param[in] cacheSize Size of the feature vectors cache (number of feature vectors).
+ * @return Function to be called providing audio sample and sliding window index.
+ */
+ static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+ GetFeatureCalculator(audio::DsCnnMFCC& mfcc,
+ TfLiteTensor* inputTensor,
+ size_t cacheSize);
+
+ /* Audio inference handler. */
+ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
+ {
+ auto& platform = ctx.Get<hal_platform&>("platform");
+
+ constexpr uint32_t dataPsnTxtInfStartX = 20;
+ constexpr uint32_t dataPsnTxtInfStartY = 40;
+ constexpr int minTensorDims = static_cast<int>(
+ (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
+ arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+
+ platform.data_psn->clear(COLOR_BLACK);
+
+ auto& model = ctx.Get<Model&>("model");
+
+ /* If the request has a valid size, set the audio index. */
+ if (clipIndex < NUMBER_OF_FILES) {
+ if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ return false;
+ }
+ }
+ if (!model.IsInited()) {
+ printf_err("Model is not initialised! Terminating processing.\n");
+ return false;
+ }
+
+ const auto frameLength = ctx.Get<int>("frameLength");
+ const auto frameStride = ctx.Get<int>("frameStride");
+ const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+ auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
+
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+
+ if (!inputTensor->dims) {
+ printf_err("Invalid input tensor dims\n");
+ return false;
+ } else if (inputTensor->dims->size < minTensorDims) {
+ printf_err("Input tensor dimension should be >= %d\n", minTensorDims);
+ return false;
+ }
+
+ TfLiteIntArray* inputShape = model.GetInputShape(0);
+ const uint32_t kNumCols = inputShape->data[arm::app::DsCnnModel::ms_inputColsIdx];
+ const uint32_t kNumRows = inputShape->data[arm::app::DsCnnModel::ms_inputRowsIdx];
+
+ audio::DsCnnMFCC mfcc = audio::DsCnnMFCC(kNumCols, frameLength);
+ mfcc.Init();
+
+ /* Deduce the data length required for 1 inference from the network parameters. */
+ auto audioDataWindowSize = kNumRows * frameStride + (frameLength - frameStride);
+ auto mfccWindowSize = frameLength;
+ auto mfccWindowStride = frameStride;
+
+ /* We choose to move by half the window size => for a 1 second window size
+ * there is an overlap of 0.5 seconds. */
+ auto audioDataStride = audioDataWindowSize / 2;
+
+ /* To have the previously calculated features re-usable, stride must be multiple
+ * of MFCC features window stride. */
+ if (0 != audioDataStride % mfccWindowStride) {
+
+ /* Reduce the stride. */
+ audioDataStride -= audioDataStride % mfccWindowStride;
+ }
+
+ auto nMfccVectorsInAudioStride = audioDataStride/mfccWindowStride;
+
+ /* 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::DsCnnMFCC::ms_defaultSamplingFreq;
+
+ do {
+ auto currentIndex = ctx.Get<uint32_t>("clipIndex");
+
+ /* Creating a mfcc features sliding window for the data required for 1 inference. */
+ auto audioMFCCWindowSlider = audio::SlidingWindow<const int16_t>(
+ get_audio_array(currentIndex),
+ audioDataWindowSize, mfccWindowSize,
+ mfccWindowStride);
+
+ /* 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),
+ audioDataWindowSize, audioDataStride);
+
+ /* Calculate number of the feature vectors in the window overlap region.
+ * These feature vectors will be reused.*/
+ auto numberOfReusedFeatureVectors = audioMFCCWindowSlider.TotalStrides() + 1
+ - nMfccVectorsInAudioStride;
+
+ /* Construct feature calculation function. */
+ auto mfccFeatureCalc = GetFeatureCalculator(mfcc, inputTensor,
+ numberOfReusedFeatureVectors);
+
+ if (!mfccFeatureCalc){
+ return false;
+ }
+
+ /* Declare a container for results. */
+ std::vector<arm::app::kws::KwsResult> results;
+
+ /* Display message on the LCD - inference running. */
+ std::string str_inf{"Running inference... "};
+ platform.data_psn->present_data_text(
+ str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ info("Running inference on audio clip %u => %s\n", currentIndex,
+ get_filename(currentIndex));
+
+ /* Start sliding through audio clip. */
+ while (audioDataSlider.HasNext()) {
+ const int16_t *inferenceWindow = audioDataSlider.Next();
+
+ /* We moved to the next window - set the features sliding to the new address. */
+ audioMFCCWindowSlider.Reset(inferenceWindow);
+
+ /* The first window does not have cache ready. */
+ bool useCache = audioDataSlider.Index() > 0 && numberOfReusedFeatureVectors > 0;
+
+ /* Start calculating features inside one audio sliding window. */
+ while (audioMFCCWindowSlider.HasNext()) {
+ const int16_t *mfccWindow = audioMFCCWindowSlider.Next();
+ std::vector<int16_t> mfccAudioData = std::vector<int16_t>(mfccWindow,
+ mfccWindow + mfccWindowSize);
+ /* Compute features for this window and write them to input tensor. */
+ mfccFeatureCalc(mfccAudioData,
+ audioMFCCWindowSlider.Index(),
+ useCache,
+ nMfccVectorsInAudioStride);
+ }
+
+ info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ audioDataSlider.TotalStrides() + 1);
+
+ /* Run inference over this audio clip sliding window. */
+ arm::app::RunInference(platform, model);
+
+ std::vector<ClassificationResult> classificationResult;
+ auto& classifier = ctx.Get<KwsClassifier&>("classifier");
+ classifier.GetClassificationResults(outputTensor, classificationResult,
+ ctx.Get<std::vector<std::string>&>("labels"), 1);
+
+ results.emplace_back(kws::KwsResult(classificationResult,
+ audioDataSlider.Index() * secondsPerSample * audioDataStride,
+ audioDataSlider.Index(), scoreThreshold));
+
+#if VERIFY_TEST_OUTPUT
+ arm::app::DumpTensor(outputTensor);
+#endif /* VERIFY_TEST_OUTPUT */
+ } /* while (audioDataSlider.HasNext()) */
+
+ /* Erase. */
+ str_inf = std::string(str_inf.size(), ' ');
+ platform.data_psn->present_data_text(
+ str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+
+ ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
+
+ if (!_PresentInferenceResult(platform, results)) {
+ return false;
+ }
+
+ _IncrementAppCtxClipIdx(ctx);
+
+ } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
+
+ return true;
+ }
+
+ static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ {
+ auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
+
+ if (curAudioIdx + 1 >= NUMBER_OF_FILES) {
+ ctx.Set<uint32_t>("clipIndex", 0);
+ return;
+ }
+ ++curAudioIdx;
+ ctx.Set<uint32_t>("clipIndex", curAudioIdx);
+ }
+
+ static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ {
+ if (idx >= NUMBER_OF_FILES) {
+ printf_err("Invalid idx %u (expected less than %u)\n",
+ idx, NUMBER_OF_FILES);
+ return false;
+ }
+ ctx.Set<uint32_t>("clipIndex", idx);
+ return true;
+ }
+
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results)
+ {
+ constexpr uint32_t dataPsnTxtStartX1 = 20;
+ constexpr uint32_t dataPsnTxtStartY1 = 30;
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+
+ platform.data_psn->set_text_color(COLOR_GREEN);
+
+ /* Display each result */
+ uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
+
+ for (uint32_t i = 0; i < results.size(); ++i) {
+
+ std::string topKeyword{"<none>"};
+ float score = 0.f;
+
+ if (results[i].m_resultVec.size()) {
+ topKeyword = results[i].m_resultVec[0].m_label;
+ score = results[i].m_resultVec[0].m_normalisedVal;
+ }
+
+ std::string resultStr =
+ std::string{"@"} + std::to_string(results[i].m_timeStamp) +
+ std::string{"s: "} + topKeyword + std::string{" ("} +
+ std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
+
+ platform.data_psn->present_data_text(
+ resultStr.c_str(), resultStr.size(),
+ dataPsnTxtStartX1, rowIdx1, false);
+ rowIdx1 += dataPsnTxtYIncr;
+
+ info("For timestamp: %f (inference #: %u); threshold: %f\n",
+ results[i].m_timeStamp, results[i].m_inferenceNumber,
+ results[i].m_threshold);
+ for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) {
+ info("\t\tlabel @ %u: %s, score: %f\n", j,
+ results[i].m_resultVec[j].m_label.c_str(),
+ results[i].m_resultVec[j].m_normalisedVal);
+ }
+ }
+
+ return true;
+ }
+
+ /**
+ * @brief Generic feature calculator factory.
+ *
+ * Returns lambda function to compute features using features cache.
+ * Real features math is done by a lambda function provided as a parameter.
+ * Features are written to input tensor memory.
+ *
+ * @tparam T Feature vector type.
+ * @param inputTensor Model input tensor pointer.
+ * @param cacheSize Number of feature vectors to cache. Defined by the sliding window overlap.
+ * @param compute Features calculator function.
+ * @return Lambda function to compute features.
+ */
+ template<class T>
+ std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
+ _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+ std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ {
+ /* Feature cache to be captured by lambda function. */
+ static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
+
+ return [=](std::vector<int16_t>& audioDataWindow,
+ size_t index,
+ bool useCache,
+ size_t featuresOverlapIndex)
+ {
+ T *tensorData = tflite::GetTensorData<T>(inputTensor);
+ std::vector<T> features;
+
+ /* Reuse features from cache if cache is ready and sliding windows overlap.
+ * Overlap is in the beginning of sliding window with a size of a feature cache. */
+ if (useCache && index < featureCache.size()) {
+ features = std::move(featureCache[index]);
+ } else {
+ features = std::move(compute(audioDataWindow));
+ }
+ auto size = features.size();
+ auto sizeBytes = sizeof(T) * size;
+ std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
+
+ /* Start renewing cache as soon iteration goes out of the windows overlap. */
+ if (index >= featuresOverlapIndex) {
+ featureCache[index - featuresOverlapIndex] = std::move(features);
+ }
+ };
+ }
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
+ _FeatureCalc<float>(TfLiteTensor *inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+
+
+ static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+ GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+ {
+ std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
+
+ TfLiteQuantization quant = inputTensor->quantization;
+
+ if (kTfLiteAffineQuantization == quant.type) {
+
+ auto *quantParams = (TfLiteAffineQuantization *) quant.params;
+ const float quantScale = quantParams->scale->data[0];
+ const int quantOffset = quantParams->zero_point->data[0];
+
+ switch (inputTensor->type) {
+ case kTfLiteInt8: {
+ mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ case kTfLiteUInt8: {
+ mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ case kTfLiteInt16: {
+ mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ default:
+ printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
+ }
+
+
+ } else {
+ mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
+ cacheSize,
+ [&mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccCompute(audioDataWindow);
+ });
+ }
+ return mfccFeatureCalc;
+ }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file