| /* |
| * 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 "MicroNetKwsModel.hpp" |
| #include "hal.h" |
| #include "MicroNetKwsMfcc.hpp" |
| #include "AudioUtils.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "KwsResult.hpp" |
| |
| #include <vector> |
| #include <functional> |
| |
| using KwsClassifier = arm::app::Classifier; |
| |
| namespace arm { |
| namespace app { |
| |
| |
| /** |
| * @brief Presents inference results using the data presentation |
| * object. |
| * @param[in] platform Reference to the hal platform object. |
| * @param[in] results Vector of classification results to be displayed. |
| * @return true if successful, false otherwise. |
| **/ |
| static bool PresentInferenceResult(hal_platform& platform, |
| const std::vector<arm::app::kws::KwsResult>& results); |
| |
| /** |
| * @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::MicroNetKwsMFCC& 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"); |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| |
| constexpr uint32_t dataPsnTxtInfStartX = 20; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| constexpr int minTensorDims = static_cast<int>( |
| (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)? |
| arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx); |
| |
| auto& model = ctx.Get<Model&>("model"); |
| |
| /* If the request has a valid size, set the audio index. */ |
| if (clipIndex < NUMBER_OF_FILES) { |
| if (!SetAppCtxIfmIdx(ctx, clipIndex,"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::MicroNetKwsModel::ms_inputColsIdx]; |
| const uint32_t kNumRows = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx]; |
| |
| audio::MicroNetKwsMFCC mfcc = audio::MicroNetKwsMFCC(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::MicroNetKwsMFCC::ms_defaultSamplingFreq; |
| |
| do { |
| platform.data_psn->clear(COLOR_BLACK); |
| |
| 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 %" PRIu32 " => %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. */ |
| if (!RunInference(model, profiler)) { |
| return false; |
| } |
| |
| std::vector<ClassificationResult> classificationResult; |
| auto& classifier = ctx.Get<KwsClassifier&>("classifier"); |
| classifier.GetClassificationResults(outputTensor, classificationResult, |
| ctx.Get<std::vector<std::string>&>("labels"), 1, true); |
| |
| 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; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| IncrementAppCtxIfmIdx(ctx,"clipIndex"); |
| |
| } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx); |
| |
| return true; |
| } |
| |
| static bool PresentInferenceResult(hal_platform& platform, |
| const std::vector<arm::app::kws::KwsResult>& results) |
| { |
| constexpr uint32_t dataPsnTxtStartX1 = 20; |
| constexpr uint32_t dataPsnTxtStartY1 = 30; |
| constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */ |
| |
| platform.data_psn->set_text_color(COLOR_GREEN); |
| info("Final results:\n"); |
| info("Total number of inferences: %zu\n", results.size()); |
| |
| /* Display each result */ |
| uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr; |
| |
| for (uint32_t i = 0; i < results.size(); ++i) { |
| |
| std::string topKeyword{"<none>"}; |
| float score = 0.f; |
| if (!results[i].m_resultVec.empty()) { |
| topKeyword = results[i].m_resultVec[0].m_label; |
| score = results[i].m_resultVec[0].m_normalisedVal; |
| } |
| |
| std::string resultStr = |
| std::string{"@"} + std::to_string(results[i].m_timeStamp) + |
| std::string{"s: "} + topKeyword + std::string{" ("} + |
| std::to_string(static_cast<int>(score * 100)) + std::string{"%)"}; |
| |
| platform.data_psn->present_data_text( |
| resultStr.c_str(), resultStr.size(), |
| dataPsnTxtStartX1, rowIdx1, false); |
| rowIdx1 += dataPsnTxtYIncr; |
| |
| if (results[i].m_resultVec.empty()) { |
| info("For timestamp: %f (inference #: %" PRIu32 |
| "); label: %s; threshold: %f\n", |
| results[i].m_timeStamp, results[i].m_inferenceNumber, |
| topKeyword.c_str(), |
| results[i].m_threshold); |
| } else { |
| for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) { |
| info("For timestamp: %f (inference #: %" PRIu32 |
| "); label: %s, score: %f; threshold: %f\n", |
| results[i].m_timeStamp, |
| results[i].m_inferenceNumber, |
| results[i].m_resultVec[j].m_label.c_str(), |
| results[i].m_resultVec[j].m_normalisedVal, |
| results[i].m_threshold); |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| /** |
| * @brief Generic feature calculator factory. |
| * |
| * 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[in] inputTensor Model input tensor pointer. |
| * @param[in] cacheSize Number of feature vectors to cache. Defined by the sliding window overlap. |
| * @param[in] 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::MicroNetKwsMFCC& 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 */ |