| /* |
| * 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 "AdModel.hpp" |
| #include "InputFiles.hpp" |
| #include "Classifier.hpp" |
| #include "hal.h" |
| #include "AdMelSpectrogram.hpp" |
| #include "AudioUtils.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "AdPostProcessing.hpp" |
| |
| 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] result average sum of classification results |
| * @param[in] threshold if larger than this value we have an anomaly |
| * @return true if successful, false otherwise |
| **/ |
| static bool PresentInferenceResult(hal_platform& platform, float result, float threshold); |
| |
| /** |
| * @brief Returns a function to perform feature calculation and populates input tensor data with |
| * MelSpe 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] melSpec MFCC feature calculator. |
| * @param[in,out] inputTensor Input tensor pointer to store calculated features. |
| * @param[in] cacheSize Size of the feture vectors cache (number of feature vectors). |
| * @param[in] trainingMean Training mean. |
| * @return function function to be called providing audio sample and sliding window index. |
| */ |
| static std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)> |
| GetFeatureCalculator(audio::AdMelSpectrogram& melSpec, |
| TfLiteTensor* inputTensor, |
| size_t cacheSize, |
| float trainingMean); |
| |
| /* Vibration classification handler */ |
| bool ClassifyVibrationHandler(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; |
| |
| 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"); |
| const auto trainingMean = ctx.Get<float>("trainingMean"); |
| 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; |
| } |
| |
| TfLiteIntArray* inputShape = model.GetInputShape(0); |
| const uint32_t kNumRows = inputShape->data[1]; |
| const uint32_t kNumCols = inputShape->data[2]; |
| |
| audio::AdMelSpectrogram melSpec = audio::AdMelSpectrogram(frameLength); |
| melSpec.Init(); |
| |
| /* Deduce the data length required for 1 inference from the network parameters. */ |
| const uint8_t inputResizeScale = 2; |
| const uint32_t audioDataWindowSize = (((inputResizeScale * kNumCols) - 1) * frameStride) + frameLength; |
| |
| /* We are choosing to move by 20 frames across the audio for each inference. */ |
| const uint8_t nMelSpecVectorsInAudioStride = 20; |
| |
| auto audioDataStride = nMelSpecVectorsInAudioStride * frameStride; |
| |
| do { |
| 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)); |
| if (machineOutputIndex == -1) { |
| return false; |
| } |
| |
| /* Creating a Mel Spectrogram sliding window for the data required for 1 inference. |
| * "resizing" done here by multiplying stride by resize scale. */ |
| auto audioMelSpecWindowSlider = audio::SlidingWindow<const int16_t>( |
| get_audio_array(currentIndex), |
| audioDataWindowSize, frameLength, |
| frameStride * inputResizeScale); |
| |
| /* 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 taking into account resizing. |
| * These feature vectors will be reused.*/ |
| auto numberOfReusedFeatureVectors = kNumRows - (nMelSpecVectorsInAudioStride / inputResizeScale); |
| |
| /* Construct feature calculation function. */ |
| auto melSpecFeatureCalc = GetFeatureCalculator(melSpec, inputTensor, |
| numberOfReusedFeatureVectors, trainingMean); |
| if (!melSpecFeatureCalc){ |
| return false; |
| } |
| |
| /* Result is an averaged sum over inferences. */ |
| float result = 0; |
| |
| /* 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. */ |
| audioMelSpecWindowSlider.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 (audioMelSpecWindowSlider.HasNext()) { |
| const int16_t *melSpecWindow = audioMelSpecWindowSlider.Next(); |
| std::vector<int16_t> melSpecAudioData = std::vector<int16_t>(melSpecWindow, |
| melSpecWindow + frameLength); |
| |
| /* Compute features for this window and write them to input tensor. */ |
| melSpecFeatureCalc(melSpecAudioData, audioMelSpecWindowSlider.Index(), |
| useCache, nMelSpecVectorsInAudioStride, inputResizeScale); |
| } |
| |
| 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; |
| } |
| |
| /* Use the negative softmax score of the corresponding index as the outlier score */ |
| std::vector<float> dequantOutput = Dequantize<int8_t>(outputTensor); |
| Softmax(dequantOutput); |
| result += -dequantOutput[machineOutputIndex]; |
| |
| #if VERIFY_TEST_OUTPUT |
| arm::app::DumpTensor(outputTensor); |
| #endif /* VERIFY_TEST_OUTPUT */ |
| } /* while (audioDataSlider.HasNext()) */ |
| |
| /* Use average over whole clip as final score. */ |
| result /= (audioDataSlider.TotalStrides() + 1); |
| |
| /* Erase. */ |
| str_inf = std::string(str_inf.size(), ' '); |
| platform.data_psn->present_data_text( |
| str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| |
| ctx.Set<float>("result", result); |
| if (!PresentInferenceResult(platform, result, scoreThreshold)) { |
| return false; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| 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, uint32_t idx) |
| { |
| if (idx >= NUMBER_OF_FILES) { |
| printf_err("Invalid idx %" PRIu32 " (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, float result, float threshold) |
| { |
| 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; |
| |
| std::string resultStr = std::string{"Average anomaly score is: "} + std::to_string(result) + |
| std::string("\n") + std::string("Anomaly threshold is: ") + std::to_string(threshold) + |
| std::string("\n"); |
| |
| if (result > threshold) { |
| resultStr += std::string("Anomaly detected!"); |
| } else { |
| resultStr += std::string("Everything fine, no anomaly detected!"); |
| } |
| |
| platform.data_psn->present_data_text( |
| resultStr.c_str(), resultStr.size(), |
| dataPsnTxtStartX1, rowIdx1, false); |
| |
| info("%s\n", resultStr.c_str()); |
| |
| 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, 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, |
| size_t resizeScale) |
| { |
| 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() / resizeScale; |
| auto sizeBytes = sizeof(T); |
| |
| /* Input should be transposed and "resized" by skipping elements. */ |
| for (size_t outIndex = 0; outIndex < size; outIndex++) { |
| std::memcpy(tensorData + (outIndex*size) + index, &features[outIndex*resizeScale], sizeBytes); |
| } |
| |
| /* Start renewing cache as soon iteration goes out of the windows overlap. */ |
| if (index >= featuresOverlapIndex / resizeScale) { |
| featureCache[index - featuresOverlapIndex / resizeScale] = std::move(features); |
| } |
| }; |
| } |
| |
| template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, 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, 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, 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, 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, size_t)> |
| GetFeatureCalculator(audio::AdMelSpectrogram& melSpec, TfLiteTensor* inputTensor, size_t cacheSize, float trainingMean) |
| { |
| std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)> melSpecFeatureCalc; |
| |
| 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: { |
| melSpecFeatureCalc = FeatureCalc<int8_t>(inputTensor, |
| cacheSize, |
| [=, &melSpec](std::vector<int16_t>& audioDataWindow) { |
| return melSpec.MelSpecComputeQuant<int8_t>( |
| audioDataWindow, |
| quantScale, |
| quantOffset, |
| trainingMean); |
| } |
| ); |
| break; |
| } |
| case kTfLiteUInt8: { |
| melSpecFeatureCalc = FeatureCalc<uint8_t>(inputTensor, |
| cacheSize, |
| [=, &melSpec](std::vector<int16_t>& audioDataWindow) { |
| return melSpec.MelSpecComputeQuant<uint8_t>( |
| audioDataWindow, |
| quantScale, |
| quantOffset, |
| trainingMean); |
| } |
| ); |
| break; |
| } |
| case kTfLiteInt16: { |
| melSpecFeatureCalc = FeatureCalc<int16_t>(inputTensor, |
| cacheSize, |
| [=, &melSpec](std::vector<int16_t>& audioDataWindow) { |
| return melSpec.MelSpecComputeQuant<int16_t>( |
| audioDataWindow, |
| quantScale, |
| quantOffset, |
| trainingMean); |
| } |
| ); |
| break; |
| } |
| default: |
| printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); |
| } |
| |
| |
| } else { |
| melSpecFeatureCalc = melSpecFeatureCalc = FeatureCalc<float>(inputTensor, |
| cacheSize, |
| [=, &melSpec]( |
| std::vector<int16_t>& audioDataWindow) { |
| return melSpec.ComputeMelSpec( |
| audioDataWindow, |
| trainingMean); |
| }); |
| } |
| return melSpecFeatureCalc; |
| } |
| |
| } /* namespace app */ |
| } /* namespace arm */ |