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Diffstat (limited to 'source/use_case/kws/src/UseCaseHandler.cc')
| -rw-r--r-- | source/use_case/kws/src/UseCaseHandler.cc | 452 |
1 files changed, 452 insertions, 0 deletions
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc new file mode 100644 index 0000000..872d323 --- /dev/null +++ b/source/use_case/kws/src/UseCaseHandler.cc @@ -0,0 +1,452 @@ +/* + * 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 */
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