diff options
Diffstat (limited to 'source/use_case')
88 files changed, 285 insertions, 8334 deletions
diff --git a/source/use_case/ad/include/AdMelSpectrogram.hpp b/source/use_case/ad/include/AdMelSpectrogram.hpp deleted file mode 100644 index 05c5bfc..0000000 --- a/source/use_case/ad/include/AdMelSpectrogram.hpp +++ /dev/null @@ -1,97 +0,0 @@ -/* - * 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. - */ -#ifndef ADMELSPECTROGRAM_HPP -#define ADMELSPECTROGRAM_HPP - -#include "MelSpectrogram.hpp" - -namespace arm { -namespace app { -namespace audio { - - /* Class to provide anomaly detection specific Mel Spectrogram calculation requirements */ - class AdMelSpectrogram : public MelSpectrogram { - - public: - static constexpr uint32_t ms_defaultSamplingFreq = 16000; - static constexpr uint32_t ms_defaultNumFbankBins = 64; - static constexpr uint32_t ms_defaultMelLoFreq = 0; - static constexpr uint32_t ms_defaultMelHiFreq = 8000; - static constexpr bool ms_defaultUseHtkMethod = false; - - explicit AdMelSpectrogram(const size_t frameLen) - : MelSpectrogram(MelSpecParams( - ms_defaultSamplingFreq, ms_defaultNumFbankBins, - ms_defaultMelLoFreq, ms_defaultMelHiFreq, - frameLen, ms_defaultUseHtkMethod)) - {} - - AdMelSpectrogram() = delete; - ~AdMelSpectrogram() = default; - - protected: - - /** - * @brief Overrides base class implementation of this function. - * @param[in] fftVec Vector populated with FFT magnitudes - * @param[in] melFilterBank 2D Vector with filter bank weights - * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank - * to be used for each bin. - * @param[in] filterBankFilterLast Vector containing the last indices of filter bank - * to be used for each bin. - * @param[out] melEnergies Pre-allocated vector of MEL energies to be - * populated. - * @return true if successful, false otherwise - */ - virtual bool ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) override; - - /** - * @brief Override for the base class implementation convert mel - * energies to logarithmic scale. The difference from - * default behaviour is that the power is converted to dB - * and subsequently clamped. - * @param[in,out] melEnergies - 1D vector of Mel energies - **/ - virtual void ConvertToLogarithmicScale(std::vector<float>& melEnergies) override; - - /** - * @brief Given the low and high Mel values, get the normaliser - * for weights to be applied when populating the filter - * bank. Override for the base class implementation. - * @param[in] leftMel - low Mel frequency value - * @param[in] rightMel - high Mel frequency value - * @param[in] useHTKMethod - bool to signal if HTK method is to be - * used for calculation - * @return Return float value to be applied - * when populating the filter bank. - */ - virtual float GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod) override; - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* ADMELSPECTROGRAM_HPP */ diff --git a/source/use_case/ad/include/AdModel.hpp b/source/use_case/ad/include/AdModel.hpp deleted file mode 100644 index 2195a7c..0000000 --- a/source/use_case/ad/include/AdModel.hpp +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef AD_MODEL_HPP -#define AD_MODEL_HPP - -#include "Model.hpp" - -extern const int g_FrameLength; -extern const int g_FrameStride; -extern const float g_ScoreThreshold; -extern const float g_TrainingMean; - -namespace arm { -namespace app { - - class AdModel : public Model { - - public: - /* Indices for the expected model - based on input tensor shape */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - - protected: - /** @brief Gets the reference to op resolver interface class */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted */ - static constexpr int ms_maxOpCnt = 6; - - /* A mutable op resolver instance */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* AD_MODEL_HPP */ diff --git a/source/use_case/ad/include/AdProcessing.hpp b/source/use_case/ad/include/AdProcessing.hpp deleted file mode 100644 index 9abf6f1..0000000 --- a/source/use_case/ad/include/AdProcessing.hpp +++ /dev/null @@ -1,230 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef AD_PROCESSING_HPP -#define AD_PROCESSING_HPP - -#include "BaseProcessing.hpp" -#include "AudioUtils.hpp" -#include "AdMelSpectrogram.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for anomaly detection use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class AdPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor for AdPreProcess class objects - * @param[in] inputTensor input tensor pointer from the tensor arena. - * @param[in] melSpectrogramFrameLen MEL spectrogram's frame length - * @param[in] melSpectrogramFrameStride MEL spectrogram's frame stride - * @param[in] adModelTrainingMean Training mean for the Anomaly detection model being used. - */ - explicit AdPreProcess(TfLiteTensor* inputTensor, - uint32_t melSpectrogramFrameLen, - uint32_t melSpectrogramFrameStride, - float adModelTrainingMean); - - ~AdPreProcess() = default; - - /** - * @brief Function to invoke pre-processing and populate the input vector - * @param input pointer to input data. For anomaly detection, this is the pointer to - * the audio data. - * @param inputSize Size of the data being passed in for pre-processing. - * @return True if successful, false otherwise. - */ - bool DoPreProcess(const void* input, size_t inputSize) override; - - /** - * @brief Getter function for audio window size computed when constructing - * the class object. - * @return Audio window size as 32 bit unsigned integer. - */ - uint32_t GetAudioWindowSize(); - - /** - * @brief Getter function for audio window stride computed when constructing - * the class object. - * @return Audio window stride as 32 bit unsigned integer. - */ - uint32_t GetAudioDataStride(); - - /** - * @brief Setter function for current audio index. This is only used for evaluating - * if previously computed features can be re-used from cache. - */ - void SetAudioWindowIndex(uint32_t idx); - - private: - bool m_validInstance{false}; /**< Indicates the current object is valid. */ - uint32_t m_melSpectrogramFrameLen{}; /**< MEL spectrogram's window frame length */ - uint32_t m_melSpectrogramFrameStride{}; /**< MEL spectrogram's window frame stride */ - uint8_t m_inputResizeScale{}; /**< Downscaling factor for the MEL energy matrix. */ - uint32_t m_numMelSpecVectorsInAudioStride{}; /**< Number of frames to move across the audio. */ - uint32_t m_audioDataWindowSize{}; /**< Audio window size computed based on other parameters. */ - uint32_t m_audioDataStride{}; /**< Audio window stride computed. */ - uint32_t m_numReusedFeatureVectors{}; /**< Number of MEL vectors that can be re-used */ - uint32_t m_audioWindowIndex{}; /**< Current audio window index (from audio's sliding window) */ - - audio::SlidingWindow<const int16_t> m_melWindowSlider; /**< Internal MEL spectrogram window slider */ - audio::AdMelSpectrogram m_melSpec; /**< MEL spectrogram computation object */ - std::function<void - (std::vector<int16_t>&, int, bool, size_t, size_t)> m_featureCalc; /**< Feature calculator object */ - }; - - class AdPostProcess : public BasePostProcess { - public: - /** - * @brief Constructor for AdPostProcess object. - * @param[in] outputTensor Output tensor pointer. - */ - explicit AdPostProcess(TfLiteTensor* outputTensor); - - ~AdPostProcess() = default; - - /** - * @brief Function to do the post-processing on the output tensor. - * @return True if successful, false otherwise. - */ - bool DoPostProcess() override; - - /** - * @brief Getter function for an element from the de-quantised output vector. - * @param index Index of the element to be retrieved. - * @return index represented as a 32 bit floating point number. - */ - float GetOutputValue(uint32_t index); - - private: - TfLiteTensor* m_outputTensor{}; /**< Output tensor pointer */ - std::vector<float> m_dequantizedOutputVec{}; /**< Internal output vector */ - - /** - * @brief De-quantizes and flattens the output tensor into a vector. - * @tparam T template parameter to indicate data type. - * @return True if successful, false otherwise. - */ - template<typename T> - bool Dequantize() - { - TfLiteTensor* tensor = this->m_outputTensor; - if (tensor == nullptr) { - printf_err("Invalid output tensor.\n"); - return false; - } - T* tensorData = tflite::GetTensorData<T>(tensor); - - uint32_t totalOutputSize = 1; - for (int inputDim = 0; inputDim < tensor->dims->size; inputDim++){ - totalOutputSize *= tensor->dims->data[inputDim]; - } - - /* For getting the floating point values, we need quantization parameters */ - QuantParams quantParams = GetTensorQuantParams(tensor); - - this->m_dequantizedOutputVec = std::vector<float>(totalOutputSize, 0); - - for (size_t i = 0; i < totalOutputSize; ++i) { - this->m_dequantizedOutputVec[i] = quantParams.scale * (tensorData[i] - quantParams.offset); - } - - return true; - } - }; - - /* Templated instances available: */ - template bool AdPostProcess::Dequantize<int8_t>(); - - /** - * @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<float>(TfLiteTensor *inputTensor, - size_t cacheSize, - std::function<std::vector<float>(std::vector<int16_t>&)> compute); - - std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)> - GetFeatureCalculator(audio::AdMelSpectrogram& melSpec, - TfLiteTensor* inputTensor, - size_t cacheSize, - float trainingMean); - -} /* namespace app */ -} /* namespace arm */ - -#endif /* AD_PROCESSING_HPP */ diff --git a/source/use_case/ad/include/MelSpectrogram.hpp b/source/use_case/ad/include/MelSpectrogram.hpp deleted file mode 100644 index d3ea3f7..0000000 --- a/source/use_case/ad/include/MelSpectrogram.hpp +++ /dev/null @@ -1,234 +0,0 @@ -/* - * 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. - */ -#ifndef MELSPECTROGRAM_HPP -#define MELSPECTROGRAM_HPP - -#include "PlatformMath.hpp" - -#include <vector> -#include <cstdint> -#include <cmath> -#include <limits> -#include <string> - -namespace arm { -namespace app { -namespace audio { - - /* Mel Spectrogram consolidated parameters */ - class MelSpecParams { - public: - float m_samplingFreq; - uint32_t m_numFbankBins; - float m_melLoFreq; - float m_melHiFreq; - uint32_t m_frameLen; - uint32_t m_frameLenPadded; - bool m_useHtkMethod; - - /** @brief Constructor */ - MelSpecParams(const float samplingFreq, const uint32_t numFbankBins, - const float melLoFreq, const float melHiFreq, - const uint32_t frameLen, const bool useHtkMethod); - - MelSpecParams() = delete; - ~MelSpecParams() = default; - - /** @brief String representation of parameters */ - std::string Str() const; - }; - - /** - * @brief Class for Mel Spectrogram feature extraction. - * Based on https://github.com/ARM-software/ML-KWS-for-MCU/blob/master/Deployment/Source/MFCC/mfcc.cpp - * This class is designed to be generic and self-sufficient but - * certain calculation routines can be overridden to accommodate - * use-case specific requirements. - */ - class MelSpectrogram { - - public: - /** - * @brief Extract Mel Spectrogram for one single small frame of - * audio data e.g. 640 samples. - * @param[in] audioData Vector of audio samples to calculate - * features for. - * @param[in] trainingMean Value to subtract from the the computed mel spectrogram, default 0. - * @return Vector of extracted Mel Spectrogram features. - **/ - std::vector<float> ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean = 0); - - /** - * @brief Constructor - * @param[in] params Mel Spectrogram parameters - */ - explicit MelSpectrogram(const MelSpecParams& params); - - MelSpectrogram() = delete; - ~MelSpectrogram() = default; - - /** @brief Initialise */ - void Init(); - - /** - * @brief Extract Mel Spectrogram features and quantise for one single small - * frame of audio data e.g. 640 samples. - * @param[in] audioData Vector of audio samples to calculate - * features for. - * @param[in] quantScale quantisation scale. - * @param[in] quantOffset quantisation offset. - * @param[in] trainingMean training mean. - * @return Vector of extracted quantised Mel Spectrogram features. - **/ - template<typename T> - std::vector<T> MelSpecComputeQuant(const std::vector<int16_t>& audioData, - const float quantScale, - const int quantOffset, - float trainingMean = 0) - { - this->ComputeMelSpec(audioData, trainingMean); - float minVal = std::numeric_limits<T>::min(); - float maxVal = std::numeric_limits<T>::max(); - - std::vector<T> melSpecOut(this->m_params.m_numFbankBins); - const size_t numFbankBins = this->m_params.m_numFbankBins; - - /* Quantize to T. */ - for (size_t k = 0; k < numFbankBins; ++k) { - auto quantizedEnergy = std::round(((this->m_melEnergies[k]) / quantScale) + quantOffset); - melSpecOut[k] = static_cast<T>(std::min<float>(std::max<float>(quantizedEnergy, minVal), maxVal)); - } - - return melSpecOut; - } - - /* Constants */ - static constexpr float ms_logStep = /*logf(6.4)*/ 1.8562979903656 / 27.0; - static constexpr float ms_freqStep = 200.0 / 3; - static constexpr float ms_minLogHz = 1000.0; - static constexpr float ms_minLogMel = ms_minLogHz / ms_freqStep; - - protected: - /** - * @brief Project input frequency to Mel Scale. - * @param[in] freq input frequency in floating point - * @param[in] useHTKMethod bool to signal if HTK method is to be - * used for calculation - * @return Mel transformed frequency in floating point - **/ - static float MelScale(const float freq, - const bool useHTKMethod = true); - - /** - * @brief Inverse Mel transform - convert MEL warped frequency - * back to normal frequency - * @param[in] melFreq Mel frequency in floating point - * @param[in] useHTKMethod bool to signal if HTK method is to be - * used for calculation - * @return Real world frequency in floating point - **/ - static float InverseMelScale(const float melFreq, - const bool useHTKMethod = true); - - /** - * @brief Populates MEL energies after applying the MEL filter - * bank weights and adding them up to be placed into - * bins, according to the filter bank's first and last - * indices (pre-computed for each filter bank element - * by CreateMelFilterBank function). - * @param[in] fftVec Vector populated with FFT magnitudes - * @param[in] melFilterBank 2D Vector with filter bank weights - * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank - * to be used for each bin. - * @param[in] filterBankFilterLast Vector containing the last indices of filter bank - * to be used for each bin. - * @param[out] melEnergies Pre-allocated vector of MEL energies to be - * populated. - * @return true if successful, false otherwise - */ - virtual bool ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies); - - /** - * @brief Converts the Mel energies for logarithmic scale - * @param[in,out] melEnergies 1D vector of Mel energies - **/ - virtual void ConvertToLogarithmicScale(std::vector<float>& melEnergies); - - /** - * @brief Given the low and high Mel values, get the normaliser - * for weights to be applied when populating the filter - * bank. - * @param[in] leftMel low Mel frequency value - * @param[in] rightMel high Mel frequency value - * @param[in] useHTKMethod bool to signal if HTK method is to be - * used for calculation - * @return Return float value to be applied - * when populating the filter bank. - */ - virtual float GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod); - - private: - MelSpecParams m_params; - std::vector<float> m_frame; - std::vector<float> m_buffer; - std::vector<float> m_melEnergies; - std::vector<float> m_windowFunc; - std::vector<std::vector<float>> m_melFilterBank; - std::vector<uint32_t> m_filterBankFilterFirst; - std::vector<uint32_t> m_filterBankFilterLast; - bool m_filterBankInitialised; - arm::app::math::FftInstance m_fftInstance; - - /** - * @brief Initialises the filter banks. - **/ - void InitMelFilterBank(); - - /** - * @brief Signals whether the instance of MelSpectrogram has had its - * required buffers initialised - * @return True if initialised, false otherwise - **/ - bool IsMelFilterBankInited() const; - - /** - * @brief Create mel filter banks for Mel Spectrogram calculation. - * @return 2D vector of floats - **/ - std::vector<std::vector<float>> CreateMelFilterBank(); - - /** - * @brief Computes the magnitude from an interleaved complex array - **/ - void ConvertToPowerSpectrum(); - - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - - -#endif /* MELSPECTROGRAM_HPP */ diff --git a/source/use_case/ad/src/AdMelSpectrogram.cc b/source/use_case/ad/src/AdMelSpectrogram.cc deleted file mode 100644 index 14b9323..0000000 --- a/source/use_case/ad/src/AdMelSpectrogram.cc +++ /dev/null @@ -1,93 +0,0 @@ -/* - * 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 "AdMelSpectrogram.hpp" -#include "PlatformMath.hpp" -#include "log_macros.h" - -#include <cfloat> - -namespace arm { -namespace app { -namespace audio { - - bool AdMelSpectrogram::ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) - { - const size_t numBanks = melEnergies.size(); - - if (numBanks != filterBankFilterFirst.size() || - numBanks != filterBankFilterLast.size()) { - printf_err("unexpected filter bank lengths\n"); - return false; - } - - for (size_t bin = 0; bin < numBanks; ++bin) { - auto filterBankIter = melFilterBank[bin].begin(); - auto end = melFilterBank[bin].end(); - float melEnergy = FLT_MIN; /* Avoid log of zero at later stages. */ - const uint32_t firstIndex = filterBankFilterFirst[bin]; - const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1); - - for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) { - melEnergy += (*filterBankIter++ * fftVec[i]); - } - - melEnergies[bin] = melEnergy; - } - - return true; - } - - void AdMelSpectrogram::ConvertToLogarithmicScale( - std::vector<float>& melEnergies) - { - /* Container for natural logarithms of mel energies */ - std::vector <float> vecLogEnergies(melEnergies.size(), 0.f); - - /* Because we are taking natural logs, we need to multiply by log10(e). - * Also, for wav2letter model, we scale our log10 values by 10 */ - constexpr float multiplier = 10.0 * /* default scalar */ - 0.4342944819032518; /* log10f(std::exp(1.0))*/ - - /* Take log of the whole vector */ - math::MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies); - - /* Scale the log values. */ - for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin(); - iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) { - - *iterM = *iterL * multiplier; - } - } - - float AdMelSpectrogram::GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod) - { - /* Slaney normalization for mel weights. */ - return (2.0f / (AdMelSpectrogram::InverseMelScale(rightMel, useHTKMethod) - - AdMelSpectrogram::InverseMelScale(leftMel, useHTKMethod))); - } - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/ad/src/AdModel.cc b/source/use_case/ad/src/AdModel.cc deleted file mode 100644 index a2ef260..0000000 --- a/source/use_case/ad/src/AdModel.cc +++ /dev/null @@ -1,54 +0,0 @@ -/* - * 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 "AdModel.hpp" -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::AdModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::AdModel::EnlistOperations() -{ - this->m_opResolver.AddAveragePool2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddRelu6(); - this->m_opResolver.AddReshape(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::AdModel::ModelPointer() -{ - return GetModelPointer(); -} -extern size_t GetModelLen(); -size_t arm::app::AdModel::ModelSize() -{ - return GetModelLen(); -} diff --git a/source/use_case/ad/src/AdProcessing.cc b/source/use_case/ad/src/AdProcessing.cc deleted file mode 100644 index a33131c..0000000 --- a/source/use_case/ad/src/AdProcessing.cc +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Copyright (c) 2022 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 "AdProcessing.hpp" - -#include "AdModel.hpp" - -namespace arm { -namespace app { - -AdPreProcess::AdPreProcess(TfLiteTensor* inputTensor, - uint32_t melSpectrogramFrameLen, - uint32_t melSpectrogramFrameStride, - float adModelTrainingMean): - m_validInstance{false}, - m_melSpectrogramFrameLen{melSpectrogramFrameLen}, - m_melSpectrogramFrameStride{melSpectrogramFrameStride}, - /**< Model is trained on features downsampled 2x */ - m_inputResizeScale{2}, - /**< We are choosing to move by 20 frames across the audio for each inference. */ - m_numMelSpecVectorsInAudioStride{20}, - m_audioDataStride{m_numMelSpecVectorsInAudioStride * melSpectrogramFrameStride}, - m_melSpec{melSpectrogramFrameLen} -{ - if (!inputTensor) { - printf_err("Invalid input tensor provided to pre-process\n"); - return; - } - - TfLiteIntArray* inputShape = inputTensor->dims; - - if (!inputShape) { - printf_err("Invalid input tensor dims\n"); - return; - } - - const uint32_t kNumRows = inputShape->data[AdModel::ms_inputRowsIdx]; - const uint32_t kNumCols = inputShape->data[AdModel::ms_inputColsIdx]; - - /* Deduce the data length required for 1 inference from the network parameters. */ - this->m_audioDataWindowSize = (((this->m_inputResizeScale * kNumCols) - 1) * - melSpectrogramFrameStride) + - melSpectrogramFrameLen; - this->m_numReusedFeatureVectors = kNumRows - - (this->m_numMelSpecVectorsInAudioStride / - this->m_inputResizeScale); - this->m_melSpec.Init(); - - /* Creating a Mel Spectrogram sliding window for the data required for 1 inference. - * "resizing" done here by multiplying stride by resize scale. */ - this->m_melWindowSlider = audio::SlidingWindow<const int16_t>( - nullptr, /* to be populated later. */ - this->m_audioDataWindowSize, - melSpectrogramFrameLen, - melSpectrogramFrameStride * this->m_inputResizeScale); - - /* Construct feature calculation function. */ - this->m_featureCalc = GetFeatureCalculator(this->m_melSpec, inputTensor, - this->m_numReusedFeatureVectors, - adModelTrainingMean); - this->m_validInstance = true; -} - -bool AdPreProcess::DoPreProcess(const void* input, size_t inputSize) -{ - /* Check that we have a valid instance. */ - if (!this->m_validInstance) { - printf_err("Invalid pre-processor instance\n"); - return false; - } - - /* We expect that we can traverse the size with which the MEL spectrogram - * sliding window was initialised with. */ - if (!input || inputSize < this->m_audioDataWindowSize) { - printf_err("Invalid input provided for pre-processing\n"); - return false; - } - - /* We moved to the next window - set the features sliding to the new address. */ - this->m_melWindowSlider.Reset(static_cast<const int16_t*>(input)); - - /* The first window does not have cache ready. */ - const bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedFeatureVectors > 0; - - /* Start calculating features inside one audio sliding window. */ - while (this->m_melWindowSlider.HasNext()) { - const int16_t* melSpecWindow = this->m_melWindowSlider.Next(); - std::vector<int16_t> melSpecAudioData = std::vector<int16_t>( - melSpecWindow, - melSpecWindow + this->m_melSpectrogramFrameLen); - - /* Compute features for this window and write them to input tensor. */ - this->m_featureCalc(melSpecAudioData, - this->m_melWindowSlider.Index(), - useCache, - this->m_numMelSpecVectorsInAudioStride, - this->m_inputResizeScale); - } - - return true; -} - -uint32_t AdPreProcess::GetAudioWindowSize() -{ - return this->m_audioDataWindowSize; -} - -uint32_t AdPreProcess::GetAudioDataStride() -{ - return this->m_audioDataStride; -} - -void AdPreProcess::SetAudioWindowIndex(uint32_t idx) -{ - this->m_audioWindowIndex = idx; -} - -AdPostProcess::AdPostProcess(TfLiteTensor* outputTensor) : - m_outputTensor {outputTensor} -{} - -bool AdPostProcess::DoPostProcess() -{ - switch (this->m_outputTensor->type) { - case kTfLiteInt8: - this->Dequantize<int8_t>(); - break; - default: - printf_err("Unsupported tensor type"); - return false; - } - - math::MathUtils::SoftmaxF32(this->m_dequantizedOutputVec); - return true; -} - -float AdPostProcess::GetOutputValue(uint32_t index) -{ - if (index < this->m_dequantizedOutputVec.size()) { - return this->m_dequantizedOutputVec[index]; - } - printf_err("Invalid index for output\n"); - return 0.0; -} - -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 = static_cast<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; - } - default: - printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); - } - } else { - melSpecFeatureCalc = FeatureCalc<float>( - inputTensor, - cacheSize, - [=, &melSpec]( - std::vector<int16_t>& audioDataWindow) { - return melSpec.ComputeMelSpec( - audioDataWindow, - trainingMean); - }); - } - return melSpecFeatureCalc; -} - -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/ad/src/MainLoop.cc b/source/use_case/ad/src/MainLoop.cc index 140359b..e9f7b4e 100644 --- a/source/use_case/ad/src/MainLoop.cc +++ b/source/use_case/ad/src/MainLoop.cc @@ -18,7 +18,17 @@ #include "AdModel.hpp" /* Model class for running inference */ #include "UseCaseCommonUtils.hpp" /* Utils functions */ #include "UseCaseHandler.hpp" /* Handlers for different user options */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); enum opcodes { @@ -49,12 +59,23 @@ void main_loop() arm::app::AdModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; diff --git a/source/use_case/ad/src/MelSpectrogram.cc b/source/use_case/ad/src/MelSpectrogram.cc deleted file mode 100644 index ff0c536..0000000 --- a/source/use_case/ad/src/MelSpectrogram.cc +++ /dev/null @@ -1,316 +0,0 @@ -/* - * 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 "MelSpectrogram.hpp" - -#include "PlatformMath.hpp" -#include "log_macros.h" - -#include <cfloat> -#include <cinttypes> - -namespace arm { -namespace app { -namespace audio { - - MelSpecParams::MelSpecParams( - const float samplingFreq, - const uint32_t numFbankBins, - const float melLoFreq, - const float melHiFreq, - const uint32_t frameLen, - const bool useHtkMethod): - m_samplingFreq(samplingFreq), - m_numFbankBins(numFbankBins), - m_melLoFreq(melLoFreq), - m_melHiFreq(melHiFreq), - m_frameLen(frameLen), - - /* Smallest power of 2 >= frame length. */ - m_frameLenPadded(pow(2, ceil((log(frameLen)/log(2))))), - m_useHtkMethod(useHtkMethod) - {} - - std::string MelSpecParams::Str() const - { - char strC[1024]; - snprintf(strC, sizeof(strC) - 1, "\n \ - \n\t Sampling frequency: %f\ - \n\t Number of filter banks: %" PRIu32 "\ - \n\t Mel frequency limit (low): %f\ - \n\t Mel frequency limit (high): %f\ - \n\t Frame length: %" PRIu32 "\ - \n\t Padded frame length: %" PRIu32 "\ - \n\t Using HTK for Mel scale: %s\n", - this->m_samplingFreq, this->m_numFbankBins, this->m_melLoFreq, - this->m_melHiFreq, this->m_frameLen, - this->m_frameLenPadded, this->m_useHtkMethod ? "yes" : "no"); - return std::string{strC}; - } - - MelSpectrogram::MelSpectrogram(const MelSpecParams& params): - m_params(params), - m_filterBankInitialised(false) - { - this->m_buffer = std::vector<float>( - this->m_params.m_frameLenPadded, 0.0); - this->m_frame = std::vector<float>( - this->m_params.m_frameLenPadded, 0.0); - this->m_melEnergies = std::vector<float>( - this->m_params.m_numFbankBins, 0.0); - - this->m_windowFunc = std::vector<float>(this->m_params.m_frameLen); - const auto multiplier = static_cast<float>(2 * M_PI / this->m_params.m_frameLen); - - /* Create window function. */ - for (size_t i = 0; i < this->m_params.m_frameLen; ++i) { - this->m_windowFunc[i] = (0.5 - (0.5 * - math::MathUtils::CosineF32(static_cast<float>(i) * multiplier))); - } - - math::MathUtils::FftInitF32(this->m_params.m_frameLenPadded, this->m_fftInstance); - debug("Instantiated Mel Spectrogram object: %s\n", this->m_params.Str().c_str()); - } - - void MelSpectrogram::Init() - { - this->InitMelFilterBank(); - } - - float MelSpectrogram::MelScale(const float freq, const bool useHTKMethod) - { - if (useHTKMethod) { - return 1127.0f * logf (1.0f + freq / 700.0f); - } else { - /* Slaney formula for mel scale. */ - float mel = freq / ms_freqStep; - - if (freq >= ms_minLogHz) { - mel = ms_minLogMel + logf(freq / ms_minLogHz) / ms_logStep; - } - return mel; - } - } - - float MelSpectrogram::InverseMelScale(const float melFreq, const bool useHTKMethod) - { - if (useHTKMethod) { - return 700.0f * (expf (melFreq / 1127.0f) - 1.0f); - } else { - /* Slaney formula for inverse mel scale. */ - float freq = ms_freqStep * melFreq; - - if (melFreq >= ms_minLogMel) { - freq = ms_minLogHz * expf(ms_logStep * (melFreq - ms_minLogMel)); - } - return freq; - } - } - - bool MelSpectrogram::ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) - { - const size_t numBanks = melEnergies.size(); - - if (numBanks != filterBankFilterFirst.size() || - numBanks != filterBankFilterLast.size()) { - printf_err("unexpected filter bank lengths\n"); - return false; - } - - for (size_t bin = 0; bin < numBanks; ++bin) { - auto filterBankIter = melFilterBank[bin].begin(); - auto end = melFilterBank[bin].end(); - float melEnergy = FLT_MIN; /* Avoid log of zero at later stages */ - const uint32_t firstIndex = filterBankFilterFirst[bin]; - const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1); - - for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) { - float energyRep = math::MathUtils::SqrtF32(fftVec[i]); - melEnergy += (*filterBankIter++ * energyRep); - } - - melEnergies[bin] = melEnergy; - } - - return true; - } - - void MelSpectrogram::ConvertToLogarithmicScale(std::vector<float>& melEnergies) - { - for (float& melEnergy : melEnergies) { - melEnergy = logf(melEnergy); - } - } - - void MelSpectrogram::ConvertToPowerSpectrum() - { - const uint32_t halfDim = this->m_buffer.size() / 2; - - /* Handle this special case. */ - float firstEnergy = this->m_buffer[0] * this->m_buffer[0]; - float lastEnergy = this->m_buffer[1] * this->m_buffer[1]; - - math::MathUtils::ComplexMagnitudeSquaredF32( - this->m_buffer.data(), - this->m_buffer.size(), - this->m_buffer.data(), - this->m_buffer.size()/2); - - this->m_buffer[0] = firstEnergy; - this->m_buffer[halfDim] = lastEnergy; - } - - float MelSpectrogram::GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod) - { - UNUSED(leftMel); - UNUSED(rightMel); - UNUSED(useHTKMethod); - - /* By default, no normalisation => return 1 */ - return 1.f; - } - - void MelSpectrogram::InitMelFilterBank() - { - if (!this->IsMelFilterBankInited()) { - this->m_melFilterBank = this->CreateMelFilterBank(); - this->m_filterBankInitialised = true; - } - } - - bool MelSpectrogram::IsMelFilterBankInited() const - { - return this->m_filterBankInitialised; - } - - std::vector<float> MelSpectrogram::ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean) - { - this->InitMelFilterBank(); - - /* TensorFlow way of normalizing .wav data to (-1, 1). */ - constexpr float normaliser = 1.0/(1<<15); - for (size_t i = 0; i < this->m_params.m_frameLen; ++i) { - this->m_frame[i] = static_cast<float>(audioData[i]) * normaliser; - } - - /* Apply window function to input frame. */ - for(size_t i = 0; i < this->m_params.m_frameLen; ++i) { - this->m_frame[i] *= this->m_windowFunc[i]; - } - - /* Set remaining frame values to 0. */ - std::fill(this->m_frame.begin() + this->m_params.m_frameLen,this->m_frame.end(), 0); - - /* Compute FFT. */ - math::MathUtils::FftF32(this->m_frame, this->m_buffer, this->m_fftInstance); - - /* Convert to power spectrum. */ - this->ConvertToPowerSpectrum(); - - /* Apply mel filterbanks. */ - if (!this->ApplyMelFilterBank(this->m_buffer, - this->m_melFilterBank, - this->m_filterBankFilterFirst, - this->m_filterBankFilterLast, - this->m_melEnergies)) { - printf_err("Failed to apply MEL filter banks\n"); - } - - /* Convert to logarithmic scale */ - this->ConvertToLogarithmicScale(this->m_melEnergies); - - /* Perform mean subtraction. */ - for (auto& energy:this->m_melEnergies) { - energy -= trainingMean; - } - - return this->m_melEnergies; - } - - std::vector<std::vector<float>> MelSpectrogram::CreateMelFilterBank() - { - size_t numFftBins = this->m_params.m_frameLenPadded / 2; - float fftBinWidth = static_cast<float>(this->m_params.m_samplingFreq) / this->m_params.m_frameLenPadded; - - float melLowFreq = MelSpectrogram::MelScale(this->m_params.m_melLoFreq, - this->m_params.m_useHtkMethod); - float melHighFreq = MelSpectrogram::MelScale(this->m_params.m_melHiFreq, - this->m_params.m_useHtkMethod); - float melFreqDelta = (melHighFreq - melLowFreq) / (this->m_params.m_numFbankBins + 1); - - std::vector<float> thisBin = std::vector<float>(numFftBins); - std::vector<std::vector<float>> melFilterBank( - this->m_params.m_numFbankBins); - this->m_filterBankFilterFirst = - std::vector<uint32_t>(this->m_params.m_numFbankBins); - this->m_filterBankFilterLast = - std::vector<uint32_t>(this->m_params.m_numFbankBins); - - for (size_t bin = 0; bin < this->m_params.m_numFbankBins; bin++) { - float leftMel = melLowFreq + bin * melFreqDelta; - float centerMel = melLowFreq + (bin + 1) * melFreqDelta; - float rightMel = melLowFreq + (bin + 2) * melFreqDelta; - - uint32_t firstIndex = 0; - uint32_t lastIndex = 0; - bool firstIndexFound = false; - const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->m_params.m_useHtkMethod); - - for (size_t i = 0; i < numFftBins; ++i) { - float freq = (fftBinWidth * i); /* Center freq of this fft bin. */ - float mel = MelSpectrogram::MelScale(freq, this->m_params.m_useHtkMethod); - thisBin[i] = 0.0; - - if (mel > leftMel && mel < rightMel) { - float weight; - if (mel <= centerMel) { - weight = (mel - leftMel) / (centerMel - leftMel); - } else { - weight = (rightMel - mel) / (rightMel - centerMel); - } - - thisBin[i] = weight * normaliser; - if (!firstIndexFound) { - firstIndex = i; - firstIndexFound = true; - } - lastIndex = i; - } - } - - this->m_filterBankFilterFirst[bin] = firstIndex; - this->m_filterBankFilterLast[bin] = lastIndex; - - /* Copy the part we care about. */ - for (uint32_t i = firstIndex; i <= lastIndex; ++i) { - melFilterBank[bin].push_back(thisBin[i]); - } - } - - return melFilterBank; - } - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/ad/usecase.cmake b/source/use_case/ad/usecase.cmake index 23b4c32..06d7681 100644 --- a/source/use_case/ad/usecase.cmake +++ b/source/use_case/ad/usecase.cmake @@ -15,6 +15,9 @@ # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "ad") + USER_OPTION(${use_case}_FILE_PATH "Directory with custom WAV input files, or path to a single input WAV file, to use in the evaluation application." ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ PATH_OR_FILE) diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp deleted file mode 100644 index a07a721..0000000 --- a/source/use_case/asr/include/AsrClassifier.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_CLASSIFIER_HPP -#define ASR_CLASSIFIER_HPP - -#include "Classifier.hpp" - -namespace arm { -namespace app { - - class AsrClassifier : public Classifier { - public: - /** - * @brief Gets the top N classification results from the - * output vector. - * @param[in] outputTensor Inference output tensor from an NN model. - * @param[out] vecResults A vector of classification results - * populated by this function. - * @param[in] labels Labels vector to match classified classes - * @param[in] topNCount Number of top classifications to pick. - * @param[in] use_softmax Whether softmax scaling should be applied to model output. - * @return true if successful, false otherwise. - **/ - bool GetClassificationResults(TfLiteTensor* outputTensor, - std::vector<ClassificationResult>& vecResults, - const std::vector<std::string>& labels, - uint32_t topNCount, bool use_softmax = false) override; - - private: - /** - * @brief Utility function that gets the top 1 classification results from the - * output tensor (vector of vector). - * @param[in] tensor Inference output tensor from an NN model. - * @param[out] vecResults Vector of classification results populated by this function. - * @param[in] labels Labels vector to match classified classes. - * @param[in] scale Quantization scale. - * @param[in] zeroPoint Quantization zero point. - * @return true if successful, false otherwise. - **/ - template<typename T> - bool GetTopResults(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector<std::string>& labels, double scale, double zeroPoint); - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_CLASSIFIER_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/AsrResult.hpp b/source/use_case/asr/include/AsrResult.hpp deleted file mode 100644 index ed826d0..0000000 --- a/source/use_case/asr/include/AsrResult.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_RESULT_HPP -#define ASR_RESULT_HPP - -#include "ClassificationResult.hpp" - -#include <vector> - -namespace arm { -namespace app { -namespace asr { - - using ResultVec = std::vector<arm::app::ClassificationResult>; - - /* Structure for holding ASR result. */ - class AsrResult { - - public: - ResultVec m_resultVec; /* Container for "thresholded" classification results. */ - float m_timeStamp; /* Audio timestamp for this result. */ - uint32_t m_inferenceNumber; /* Corresponding inference number. */ - float m_threshold; /* Threshold value for `m_resultVec.` */ - - AsrResult() = delete; - AsrResult(ResultVec& resultVec, - const float timestamp, - const uint32_t inferenceIdx, - const float scoreThreshold) { - - this->m_threshold = scoreThreshold; - this->m_timeStamp = timestamp; - this->m_inferenceNumber = inferenceIdx; - - this->m_resultVec = ResultVec(); - for (auto& i : resultVec) { - if (i.m_normalisedVal >= this->m_threshold) { - this->m_resultVec.emplace_back(i); - } - } - } - ~AsrResult() = default; - }; - -} /* namespace asr */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_RESULT_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/OutputDecode.hpp b/source/use_case/asr/include/OutputDecode.hpp deleted file mode 100644 index 9d39057..0000000 --- a/source/use_case/asr/include/OutputDecode.hpp +++ /dev/null @@ -1,40 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_OUTPUT_DECODE_HPP -#define ASR_OUTPUT_DECODE_HPP - -#include "AsrClassifier.hpp" - -namespace arm { -namespace app { -namespace audio { -namespace asr { - - /** - * @brief Gets the top N classification results from the - * output vector. - * @param[in] vecResults Label output from classifier. - * @return true if successful, false otherwise. - **/ - std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults); - -} /* namespace asr */ -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_OUTPUT_DECODE_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/Wav2LetterMfcc.hpp b/source/use_case/asr/include/Wav2LetterMfcc.hpp deleted file mode 100644 index b5a21d3..0000000 --- a/source/use_case/asr/include/Wav2LetterMfcc.hpp +++ /dev/null @@ -1,109 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_WAV2LETTER_MFCC_HPP -#define ASR_WAV2LETTER_MFCC_HPP - -#include "Mfcc.hpp" - -namespace arm { -namespace app { -namespace audio { - - /* Class to provide Wav2Letter specific MFCC calculation requirements. */ - class Wav2LetterMFCC : public MFCC { - - public: - static constexpr uint32_t ms_defaultSamplingFreq = 16000; - static constexpr uint32_t ms_defaultNumFbankBins = 128; - static constexpr uint32_t ms_defaultMelLoFreq = 0; - static constexpr uint32_t ms_defaultMelHiFreq = 8000; - static constexpr bool ms_defaultUseHtkMethod = false; - - explicit Wav2LetterMFCC(const size_t numFeats, const size_t frameLen) - : MFCC(MfccParams( - ms_defaultSamplingFreq, ms_defaultNumFbankBins, - ms_defaultMelLoFreq, ms_defaultMelHiFreq, - numFeats, frameLen, ms_defaultUseHtkMethod)) - {} - - Wav2LetterMFCC() = delete; - ~Wav2LetterMFCC() = default; - - protected: - - /** - * @brief Overrides base class implementation of this function. - * @param[in] fftVec Vector populated with FFT magnitudes - * @param[in] melFilterBank 2D Vector with filter bank weights - * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank - * to be used for each bin. - * @param[in] filterBankFilterLast Vector containing the last indices of filter bank - * to be used for each bin. - * @param[out] melEnergies Pre-allocated vector of MEL energies to be - * populated. - * @return true if successful, false otherwise - */ - bool ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) override; - - /** - * @brief Override for the base class implementation convert mel - * energies to logarithmic scale. The difference from - * default behaviour is that the power is converted to dB - * and subsequently clamped. - * @param[in,out] melEnergies 1D vector of Mel energies - **/ - void ConvertToLogarithmicScale(std::vector<float>& melEnergies) override; - - /** - * @brief Create a matrix used to calculate Discrete Cosine - * Transform. Override for the base class' default - * implementation as the first and last elements - * use a different normaliser. - * @param[in] inputLength input length of the buffer on which - * DCT will be performed - * @param[in] coefficientCount Total coefficients per input length. - * @return 1D vector with inputLength x coefficientCount elements - * populated with DCT coefficients. - */ - std::vector<float> CreateDCTMatrix(int32_t inputLength, - int32_t coefficientCount) override; - - /** - * @brief Given the low and high Mel values, get the normaliser - * for weights to be applied when populating the filter - * bank. Override for the base class implementation. - * @param[in] leftMel Low Mel frequency value. - * @param[in] rightMel High Mel frequency value. - * @param[in] useHTKMethod bool to signal if HTK method is to be - * used for calculation. - * @return Value to use for normalising. - */ - float GetMelFilterBankNormaliser(const float& leftMel, - const float& rightMel, - bool useHTKMethod) override; - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_WAV2LETTER_MFCC_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp deleted file mode 100644 index bec70ab..0000000 --- a/source/use_case/asr/include/Wav2LetterModel.hpp +++ /dev/null @@ -1,65 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_WAV2LETTER_MODEL_HPP -#define ASR_WAV2LETTER_MODEL_HPP - -#include "Model.hpp" - -extern const int g_FrameLength; -extern const int g_FrameStride; -extern const float g_ScoreThreshold; -extern const int g_ctxLen; - -namespace arm { -namespace app { - - class Wav2LetterModel : public Model { - - public: - /* Indices for the expected model - based on input and output tensor shapes */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_outputRowsIdx = 2; - static constexpr uint32_t ms_outputColsIdx = 3; - - /* Model specific constants. */ - static constexpr uint32_t ms_blankTokenIdx = 28; - static constexpr uint32_t ms_numMfccFeatures = 13; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 5; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_WAV2LETTER_MODEL_HPP */ diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp deleted file mode 100644 index 446014d..0000000 --- a/source/use_case/asr/include/Wav2LetterPostprocess.hpp +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef ASR_WAV2LETTER_POSTPROCESS_HPP -#define ASR_WAV2LETTER_POSTPROCESS_HPP - -#include "TensorFlowLiteMicro.hpp" /* TensorFlow headers. */ -#include "BaseProcessing.hpp" -#include "AsrClassifier.hpp" -#include "AsrResult.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - /** - * @brief Helper class to manage tensor post-processing for "wav2letter" - * output. - */ - class AsrPostProcess : public BasePostProcess { - public: - bool m_lastIteration = false; /* Flag to set if processing the last set of data for a clip. */ - - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Object used to get top N results from classification. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[in/out] result Vector of classification results to store decoded outputs. - * @param[in] outputContextLen Left/right context length for output tensor. - * @param[in] blankTokenIdx Index in the labels that the "Blank token" takes. - * @param[in] reductionAxis The axis that the logits of each time step is on. - **/ - AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier, - const std::vector<std::string>& labels, asr::ResultVec& result, - uint32_t outputContextLen, - uint32_t blankTokenIdx, uint32_t reductionAxis); - - /** - * @brief Should perform post-processing of the result of inference then - * populate ASR result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - - /** @brief Gets the output inner length for post-processing. */ - static uint32_t GetOutputInnerLen(const TfLiteTensor*, uint32_t outputCtxLen); - - /** @brief Gets the output context length (left/right) for post-processing. */ - static uint32_t GetOutputContextLen(const Model& model, uint32_t inputCtxLen); - - /** @brief Gets the number of feature vectors to be computed. */ - static uint32_t GetNumFeatureVectors(const Model& model); - - private: - AsrClassifier& m_classifier; /* ASR Classifier object. */ - TfLiteTensor* m_outputTensor; /* Model output tensor. */ - const std::vector<std::string>& m_labels; /* ASR Labels. */ - asr::ResultVec & m_results; /* Results vector for a single inference. */ - uint32_t m_outputContextLen; /* lengths of left/right contexts for output. */ - uint32_t m_outputInnerLen; /* Length of output inner context. */ - uint32_t m_totalLen; /* Total length of the required axis. */ - uint32_t m_countIterations; /* Current number of iterations. */ - uint32_t m_blankTokenIdx; /* Index of the labels blank token. */ - uint32_t m_reductionAxisIdx; /* Axis containing output logits for a single step. */ - - /** - * @brief Checks if the tensor and axis index are valid - * inputs to the object - based on how it has been initialised. - * @return true if valid, false otherwise. - */ - bool IsInputValid(TfLiteTensor* tensor, - uint32_t axisIdx) const; - - /** - * @brief Gets the tensor data element size in bytes based - * on the tensor type. - * @return Size in bytes, 0 if not supported. - */ - static uint32_t GetTensorElementSize(TfLiteTensor* tensor); - - /** - * @brief Erases sections from the data assuming row-wise - * arrangement along the context axis. - * @return true if successful, false otherwise. - */ - bool EraseSectionsRowWise(uint8_t* ptrData, - uint32_t strideSzBytes, - bool lastIteration); - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_WAV2LETTER_POSTPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp deleted file mode 100644 index dc9a415..0000000 --- a/source/use_case/asr/include/Wav2LetterPreprocess.hpp +++ /dev/null @@ -1,182 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef ASR_WAV2LETTER_PREPROCESS_HPP -#define ASR_WAV2LETTER_PREPROCESS_HPP - -#include "Wav2LetterModel.hpp" -#include "Wav2LetterMfcc.hpp" -#include "AudioUtils.hpp" -#include "DataStructures.hpp" -#include "BaseProcessing.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - /* Class to facilitate pre-processing calculation for Wav2Letter model - * for ASR. */ - using AudioWindow = audio::SlidingWindow<const int16_t>; - - class AsrPreProcess : public BasePreProcess { - public: - /** - * @brief Constructor. - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] numMfccFeatures Number of MFCC features per window. - * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated - * for an inference. - * @param[in] mfccWindowLen Number of audio elements to calculate MFCC features per window. - * @param[in] mfccWindowStride Stride (in number of elements) for moving the MFCC window. - */ - AsrPreProcess(TfLiteTensor* inputTensor, - uint32_t numMfccFeatures, - uint32_t numFeatureFrames, - uint32_t mfccWindowLen, - uint32_t mfccWindowStride); - - /** - * @brief Calculates the features required from audio data. This - * includes MFCC, first and second order deltas, - * normalisation and finally, quantisation. The tensor is - * populated with features from a given window placed along - * in a single row. - * @param[in] audioData Pointer to the first element of audio data. - * @param[in] audioDataLen Number of elements in the audio data. - * @return true if successful, false in case of error. - */ - bool DoPreProcess(const void* audioData, size_t audioDataLen) override; - - protected: - /** - * @brief Computes the first and second order deltas for the - * MFCC buffers - they are assumed to be populated. - * - * @param[in] mfcc MFCC buffers. - * @param[out] delta1 Result of the first diff computation. - * @param[out] delta2 Result of the second diff computation. - * @return true if successful, false otherwise. - */ - static bool ComputeDeltas(Array2d<float>& mfcc, - Array2d<float>& delta1, - Array2d<float>& delta2); - - /** - * @brief Given a 2D vector of floats, rescale it to have mean of 0 and - * standard deviation of 1. - * @param[in,out] vec Vector of vector of floats. - */ - static void StandardizeVecF32(Array2d<float>& vec); - - /** - * @brief Standardizes all the MFCC and delta buffers to have mean 0 and std. dev 1. - */ - void Standarize(); - - /** - * @brief Given the quantisation and data type limits, computes - * the quantised values of a floating point input data. - * @param[in] elem Element to be quantised. - * @param[in] quantScale Scale. - * @param[in] quantOffset Offset. - * @param[in] minVal Numerical limit - minimum. - * @param[in] maxVal Numerical limit - maximum. - * @return Floating point quantised value. - */ - static float GetQuantElem( - float elem, - float quantScale, - int quantOffset, - float minVal, - float maxVal); - - /** - * @brief Quantises the MFCC and delta buffers, and places them - * in the output buffer. While doing so, it transposes - * the data. Reason: Buffers in this class are arranged - * for "time" axis to be row major. Primary reason for - * this being the convolution speed up (as we can use - * contiguous memory). The output, however, requires the - * time axis to be in column major arrangement. - * @param[in] outputBuf Pointer to the output buffer. - * @param[in] outputBufSz Output buffer's size. - * @param[in] quantScale Quantisation scale. - * @param[in] quantOffset Quantisation offset. - */ - template <typename T> - bool Quantise( - T* outputBuf, - const uint32_t outputBufSz, - const float quantScale, - const int quantOffset) - { - /* Check the output size will fit everything. */ - if (outputBufSz < (this->m_mfccBuf.size(0) * 3 * sizeof(T))) { - printf_err("Tensor size too small for features\n"); - return false; - } - - /* Populate. */ - T* outputBufMfcc = outputBuf; - T* outputBufD1 = outputBuf + this->m_numMfccFeats; - T* outputBufD2 = outputBufD1 + this->m_numMfccFeats; - const uint32_t ptrIncr = this->m_numMfccFeats * 2; /* (3 vectors - 1 vector) */ - - const float minVal = std::numeric_limits<T>::min(); - const float maxVal = std::numeric_limits<T>::max(); - - /* Need to transpose while copying and concatenating the tensor. */ - for (uint32_t j = 0; j < this->m_numFeatureFrames; ++j) { - for (uint32_t i = 0; i < this->m_numMfccFeats; ++i) { - *outputBufMfcc++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_mfccBuf(i, j), quantScale, - quantOffset, minVal, maxVal)); - *outputBufD1++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_delta1Buf(i, j), quantScale, - quantOffset, minVal, maxVal)); - *outputBufD2++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_delta2Buf(i, j), quantScale, - quantOffset, minVal, maxVal)); - } - outputBufMfcc += ptrIncr; - outputBufD1 += ptrIncr; - outputBufD2 += ptrIncr; - } - - return true; - } - - private: - audio::Wav2LetterMFCC m_mfcc; /* MFCC instance. */ - TfLiteTensor* m_inputTensor; /* Model input tensor. */ - - /* Actual buffers to be populated. */ - Array2d<float> m_mfccBuf; /* Contiguous buffer 1D: MFCC */ - Array2d<float> m_delta1Buf; /* Contiguous buffer 1D: Delta 1 */ - Array2d<float> m_delta2Buf; /* Contiguous buffer 1D: Delta 2 */ - - uint32_t m_mfccWindowLen; /* Window length for MFCC. */ - uint32_t m_mfccWindowStride; /* Window stride len for MFCC. */ - uint32_t m_numMfccFeats; /* Number of MFCC features per window. */ - uint32_t m_numFeatureFrames; /* How many sets of m_numMfccFeats. */ - AudioWindow m_mfccSlidingWindow; /* Sliding window to calculate MFCCs. */ - - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_WAV2LETTER_PREPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc deleted file mode 100644 index 4ba8c7b..0000000 --- a/source/use_case/asr/src/AsrClassifier.cc +++ /dev/null @@ -1,144 +0,0 @@ -/* - * 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 "AsrClassifier.hpp" - -#include "log_macros.h" -#include "TensorFlowLiteMicro.hpp" -#include "Wav2LetterModel.hpp" - -namespace arm { -namespace app { - - template<typename T> - bool AsrClassifier::GetTopResults(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, double scale, double zeroPoint) - { - const uint32_t nElems = tensor->dims->data[Wav2LetterModel::ms_outputRowsIdx]; - const uint32_t nLetters = tensor->dims->data[Wav2LetterModel::ms_outputColsIdx]; - - if (nLetters != labels.size()) { - printf("Output size doesn't match the labels' size\n"); - return false; - } - - /* NOTE: tensor's size verification against labels should be - * checked by the calling/public function. */ - if (nLetters < 1) { - return false; - } - - /* Final results' container. */ - vecResults = std::vector<ClassificationResult>(nElems); - - T* tensorData = tflite::GetTensorData<T>(tensor); - - /* Get the top 1 results. */ - for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) { - std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0); - - for (uint32_t j = 1; j < nLetters; ++j) { - if (top_1.first < tensorData[row + j]) { - top_1.first = tensorData[row + j]; - top_1.second = j; - } - } - - double score = static_cast<int> (top_1.first); - vecResults[i].m_normalisedVal = scale * (score - zeroPoint); - vecResults[i].m_label = labels[top_1.second]; - vecResults[i].m_labelIdx = top_1.second; - } - - return true; - } - template bool AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, - double scale, double zeroPoint); - template bool AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, - double scale, double zeroPoint); - - bool AsrClassifier::GetClassificationResults( - TfLiteTensor* outputTensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax) - { - UNUSED(use_softmax); - vecResults.clear(); - - constexpr int minTensorDims = static_cast<int>( - (Wav2LetterModel::ms_outputRowsIdx > Wav2LetterModel::ms_outputColsIdx)? - Wav2LetterModel::ms_outputRowsIdx : Wav2LetterModel::ms_outputColsIdx); - - constexpr uint32_t outColsIdx = Wav2LetterModel::ms_outputColsIdx; - - /* Sanity checks. */ - if (outputTensor == nullptr) { - printf_err("Output vector is null pointer.\n"); - return false; - } else if (outputTensor->dims->size < minTensorDims) { - printf_err("Output tensor expected to be %dD\n", minTensorDims); - return false; - } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) { - printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount); - return false; - } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) { - printf("Output size doesn't match the labels' size\n"); - return false; - } - - if (topNCount != 1) { - warn("TopNCount value ignored in this implementation\n"); - } - - /* To return the floating point values, we need quantization parameters. */ - QuantParams quantParams = GetTensorQuantParams(outputTensor); - - bool resultState; - - switch (outputTensor->type) { - case kTfLiteUInt8: - resultState = this->GetTopResults<uint8_t>( - outputTensor, vecResults, - labels, quantParams.scale, - quantParams.offset); - break; - case kTfLiteInt8: - resultState = this->GetTopResults<int8_t>( - outputTensor, vecResults, - labels, quantParams.scale, - quantParams.offset); - break; - default: - printf_err("Tensor type %s not supported by classifier\n", - TfLiteTypeGetName(outputTensor->type)); - return false; - } - - if (!resultState) { - printf_err("Failed to get sorted set\n"); - return false; - } - - return true; - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/src/MainLoop.cc b/source/use_case/asr/src/MainLoop.cc index a1a9540..7acd319 100644 --- a/source/use_case/asr/src/MainLoop.cc +++ b/source/use_case/asr/src/MainLoop.cc @@ -20,7 +20,18 @@ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ #include "AsrClassifier.hpp" /* Classifier. */ #include "InputFiles.hpp" /* Generated audio clip header. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { +namespace app { +namespace asr { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + extern uint8_t* GetModelPointer(); + extern size_t GetModelLen(); +} /* namespace asr */ +} /* namespace app */ +} /* namespace arm */ enum opcodes { @@ -53,7 +64,10 @@ void main_loop() arm::app::Wav2LetterModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::asr::tensorArena, + sizeof(arm::app::asr::tensorArena), + arm::app::asr::GetModelPointer(), + arm::app::asr::GetModelLen())) { printf_err("Failed to initialise model\n"); return; } else if (!VerifyTensorDimensions(model)) { @@ -61,6 +75,14 @@ void main_loop() return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; std::vector <std::string> labels; @@ -71,10 +93,10 @@ void main_loop() caseContext.Set<arm::app::Profiler&>("profiler", profiler); caseContext.Set<arm::app::Model&>("model", model); caseContext.Set<uint32_t>("clipIndex", 0); - caseContext.Set<uint32_t>("frameLength", g_FrameLength); - caseContext.Set<uint32_t>("frameStride", g_FrameStride); - caseContext.Set<float>("scoreThreshold", g_ScoreThreshold); /* Score threshold. */ - caseContext.Set<uint32_t>("ctxLen", g_ctxLen); /* Left and right context length (MFCC feat vectors). */ + caseContext.Set<uint32_t>("frameLength", arm::app::asr::g_FrameLength); + caseContext.Set<uint32_t>("frameStride", arm::app::asr::g_FrameStride); + caseContext.Set<float>("scoreThreshold", arm::app::asr::g_ScoreThreshold); /* Score threshold. */ + caseContext.Set<uint32_t>("ctxLen", arm::app::asr::g_ctxLen); /* Left and right context length (MFCC feat vectors). */ caseContext.Set<const std::vector <std::string>&>("labels", labels); caseContext.Set<arm::app::AsrClassifier&>("classifier", classifier); diff --git a/source/use_case/asr/src/OutputDecode.cc b/source/use_case/asr/src/OutputDecode.cc deleted file mode 100644 index 41fbe07..0000000 --- a/source/use_case/asr/src/OutputDecode.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* - * 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 "OutputDecode.hpp" - -namespace arm { -namespace app { -namespace audio { -namespace asr { - - std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults) - { - std::string CleanOutputBuffer; - - for (size_t i = 0; i < vecResults.size(); ++i) /* For all elements in vector. */ - { - while (i+1 < vecResults.size() && - vecResults[i].m_label == vecResults[i+1].m_label) /* While the current element is equal to the next, ignore it and move on. */ - { - ++i; - } - if (vecResults[i].m_label != "$") /* $ is a character used to represent unknown and double characters so should not be in output. */ - { - CleanOutputBuffer += vecResults[i].m_label; /* If the element is different to the next, it will be appended to CleanOutputBuffer. */ - } - } - - return CleanOutputBuffer; /* Return string type containing clean output. */ - } - -} /* namespace asr */ -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/asr/src/Wav2LetterMfcc.cc b/source/use_case/asr/src/Wav2LetterMfcc.cc deleted file mode 100644 index bb29b0f..0000000 --- a/source/use_case/asr/src/Wav2LetterMfcc.cc +++ /dev/null @@ -1,141 +0,0 @@ -/* - * 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 "Wav2LetterMfcc.hpp" - -#include "PlatformMath.hpp" -#include "log_macros.h" - -#include <cfloat> - -namespace arm { -namespace app { -namespace audio { - - bool Wav2LetterMFCC::ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) - { - const size_t numBanks = melEnergies.size(); - - if (numBanks != filterBankFilterFirst.size() || - numBanks != filterBankFilterLast.size()) { - printf_err("Unexpected filter bank lengths\n"); - return false; - } - - for (size_t bin = 0; bin < numBanks; ++bin) { - auto filterBankIter = melFilterBank[bin].begin(); - auto end = melFilterBank[bin].end(); - /* Avoid log of zero at later stages, same value used in librosa. - * The number was used during our default wav2letter model training. */ - float melEnergy = 1e-10; - const uint32_t firstIndex = filterBankFilterFirst[bin]; - const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1); - - for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) { - melEnergy += (*filterBankIter++ * fftVec[i]); - } - - melEnergies[bin] = melEnergy; - } - - return true; - } - - void Wav2LetterMFCC::ConvertToLogarithmicScale( - std::vector<float>& melEnergies) - { - float maxMelEnergy = -FLT_MAX; - - /* Container for natural logarithms of mel energies. */ - std::vector <float> vecLogEnergies(melEnergies.size(), 0.f); - - /* Because we are taking natural logs, we need to multiply by log10(e). - * Also, for wav2letter model, we scale our log10 values by 10. */ - constexpr float multiplier = 10.0 * /* Default scalar. */ - 0.4342944819032518; /* log10f(std::exp(1.0)) */ - - /* Take log of the whole vector. */ - math::MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies); - - /* Scale the log values and get the max. */ - for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin(); - iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) { - - *iterM = *iterL * multiplier; - - /* Save the max mel energy. */ - if (*iterM > maxMelEnergy) { - maxMelEnergy = *iterM; - } - } - - /* Clamp the mel energies. */ - constexpr float maxDb = 80.0; - const float clampLevelLowdB = maxMelEnergy - maxDb; - for (float& melEnergy : melEnergies) { - melEnergy = std::max(melEnergy, clampLevelLowdB); - } - } - - std::vector<float> Wav2LetterMFCC::CreateDCTMatrix( - const int32_t inputLength, - const int32_t coefficientCount) - { - std::vector<float> dctMatix(inputLength * coefficientCount); - - /* Orthonormal normalization. */ - const float normalizerK0 = 2 * math::MathUtils::SqrtF32(1.0f / - static_cast<float>(4*inputLength)); - const float normalizer = 2 * math::MathUtils::SqrtF32(1.0f / - static_cast<float>(2*inputLength)); - - const float angleIncr = M_PI / inputLength; - float angle = angleIncr; /* We start using it at k = 1 loop. */ - - /* First row of DCT will use normalizer K0. */ - for (int32_t n = 0; n < inputLength; ++n) { - dctMatix[n] = normalizerK0 /* cos(0) = 1 */; - } - - /* Second row (index = 1) onwards, we use standard normalizer. */ - for (int32_t k = 1, m = inputLength; k < coefficientCount; ++k, m += inputLength) { - for (int32_t n = 0; n < inputLength; ++n) { - dctMatix[m+n] = normalizer * - math::MathUtils::CosineF32((n + 0.5f) * angle); - } - angle += angleIncr; - } - return dctMatix; - } - - float Wav2LetterMFCC::GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod) - { - /* Slaney normalization for mel weights. */ - return (2.0f / (MFCC::InverseMelScale(rightMel, useHTKMethod) - - MFCC::InverseMelScale(leftMel, useHTKMethod))); - } - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/asr/src/Wav2LetterModel.cc b/source/use_case/asr/src/Wav2LetterModel.cc deleted file mode 100644 index 8b38f4f..0000000 --- a/source/use_case/asr/src/Wav2LetterModel.cc +++ /dev/null @@ -1,57 +0,0 @@ -/* - * 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 "Wav2LetterModel.hpp" - -#include "log_macros.h" - - -const tflite::MicroOpResolver& arm::app::Wav2LetterModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::Wav2LetterModel::EnlistOperations() -{ - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddReshape(); - this->m_opResolver.AddLeakyRelu(); - this->m_opResolver.AddSoftmax(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::Wav2LetterModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::Wav2LetterModel::ModelSize() -{ - return GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc deleted file mode 100644 index 42f434e..0000000 --- a/source/use_case/asr/src/Wav2LetterPostprocess.cc +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "Wav2LetterPostprocess.hpp" - -#include "Wav2LetterModel.hpp" -#include "log_macros.h" - -#include <cmath> - -namespace arm { -namespace app { - - AsrPostProcess::AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier, - const std::vector<std::string>& labels, std::vector<ClassificationResult>& results, - const uint32_t outputContextLen, - const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx - ): - m_classifier(classifier), - m_outputTensor(outputTensor), - m_labels{labels}, - m_results(results), - m_outputContextLen(outputContextLen), - m_countIterations(0), - m_blankTokenIdx(blankTokenIdx), - m_reductionAxisIdx(reductionAxisIdx) - { - this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen); - this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen); - } - - bool AsrPostProcess::DoPostProcess() - { - /* Basic checks. */ - if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) { - return false; - } - - /* Irrespective of tensor type, we use unsigned "byte" */ - auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor); - const uint32_t elemSz = AsrPostProcess::GetTensorElementSize(this->m_outputTensor); - - /* Other sanity checks. */ - if (0 == elemSz) { - printf_err("Tensor type not supported for post processing\n"); - return false; - } else if (elemSz * this->m_totalLen > this->m_outputTensor->bytes) { - printf_err("Insufficient number of tensor bytes\n"); - return false; - } - - /* Which axis do we need to process? */ - switch (this->m_reductionAxisIdx) { - case Wav2LetterModel::ms_outputRowsIdx: - this->EraseSectionsRowWise( - ptrData, elemSz * this->m_outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx], - this->m_lastIteration); - break; - default: - printf_err("Unsupported axis index: %" PRIu32 "\n", this->m_reductionAxisIdx); - return false; - } - this->m_classifier.GetClassificationResults(this->m_outputTensor, - this->m_results, this->m_labels, 1); - - return true; - } - - bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const - { - if (nullptr == tensor) { - return false; - } - - if (static_cast<int>(axisIdx) >= tensor->dims->size) { - printf_err("Invalid axis index: %" PRIu32 "; Max: %d\n", - axisIdx, tensor->dims->size); - return false; - } - - if (static_cast<int>(this->m_totalLen) != - tensor->dims->data[axisIdx]) { - printf_err("Unexpected tensor dimension for axis %d, got %d, \n", - axisIdx, tensor->dims->data[axisIdx]); - return false; - } - - return true; - } - - uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor) - { - switch(tensor->type) { - case kTfLiteUInt8: - case kTfLiteInt8: - return 1; - case kTfLiteInt16: - return 2; - case kTfLiteInt32: - case kTfLiteFloat32: - return 4; - default: - printf_err("Unsupported tensor type %s\n", - TfLiteTypeGetName(tensor->type)); - } - - return 0; - } - - bool AsrPostProcess::EraseSectionsRowWise( - uint8_t* ptrData, - const uint32_t strideSzBytes, - const bool lastIteration) - { - /* In this case, the "zero-ing" is quite simple as the region - * to be zeroed sits in contiguous memory (row-major). */ - const uint32_t eraseLen = strideSzBytes * this->m_outputContextLen; - - /* Erase left context? */ - if (this->m_countIterations > 0) { - /* Set output of each classification window to the blank token. */ - std::memset(ptrData, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - ptrData[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - /* Erase right context? */ - if (false == lastIteration) { - uint8_t* rightCtxPtr = ptrData + (strideSzBytes * (this->m_outputContextLen + this->m_outputInnerLen)); - /* Set output of each classification window to the blank token. */ - std::memset(rightCtxPtr, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - rightCtxPtr[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - if (lastIteration) { - this->m_countIterations = 0; - } else { - ++this->m_countIterations; - } - - return true; - } - - uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model) - { - TfLiteTensor* inputTensor = model.GetInputTensor(0); - const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0); - if (inputRows == 0) { - printf_err("Error getting number of input rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_inputRowsIdx); - } - return inputRows; - } - - uint32_t AsrPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen) - { - const uint32_t outputRows = std::max(outputTensor->dims->data[Wav2LetterModel::ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - } - - /* Watching for underflow. */ - int innerLen = (outputRows - (2 * outputCtxLen)); - - return std::max(innerLen, 0); - } - - uint32_t AsrPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen) - { - const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model); - const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); - constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx; - - /* Check to make sure that the input tensor supports the above - * context and inner lengths. */ - if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) { - printf_err("Input rows not compatible with ctx of %" PRIu32 "\n", - inputCtxLen); - return 0; - } - - TfLiteTensor* outputTensor = model.GetOutputTensor(0); - const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - return 0; - } - - const float inOutRowRatio = static_cast<float>(inputRows) / - static_cast<float>(outputRows); - - return std::round(static_cast<float>(inputCtxLen) / inOutRowRatio); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc deleted file mode 100644 index 92b0631..0000000 --- a/source/use_case/asr/src/Wav2LetterPreprocess.cc +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "Wav2LetterPreprocess.hpp" - -#include "PlatformMath.hpp" -#include "TensorFlowLiteMicro.hpp" - -#include <algorithm> -#include <cmath> - -namespace arm { -namespace app { - - AsrPreProcess::AsrPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures, - const uint32_t numFeatureFrames, const uint32_t mfccWindowLen, - const uint32_t mfccWindowStride - ): - m_mfcc(numMfccFeatures, mfccWindowLen), - m_inputTensor(inputTensor), - m_mfccBuf(numMfccFeatures, numFeatureFrames), - m_delta1Buf(numMfccFeatures, numFeatureFrames), - m_delta2Buf(numMfccFeatures, numFeatureFrames), - m_mfccWindowLen(mfccWindowLen), - m_mfccWindowStride(mfccWindowStride), - m_numMfccFeats(numMfccFeatures), - m_numFeatureFrames(numFeatureFrames) - { - if (numMfccFeatures > 0 && mfccWindowLen > 0) { - this->m_mfcc.Init(); - } - } - - bool AsrPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen) - { - this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>( - static_cast<const int16_t*>(audioData), audioDataLen, - this->m_mfccWindowLen, this->m_mfccWindowStride); - - uint32_t mfccBufIdx = 0; - - std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f); - std::fill(m_delta1Buf.begin(), m_delta1Buf.end(), 0.f); - std::fill(m_delta2Buf.begin(), m_delta2Buf.end(), 0.f); - - /* While we can slide over the audio. */ - while (this->m_mfccSlidingWindow.HasNext()) { - const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next(); - auto mfccAudioData = std::vector<int16_t>( - mfccWindow, - mfccWindow + this->m_mfccWindowLen); - auto mfcc = this->m_mfcc.MfccCompute(mfccAudioData); - for (size_t i = 0; i < this->m_mfccBuf.size(0); ++i) { - this->m_mfccBuf(i, mfccBufIdx) = mfcc[i]; - } - ++mfccBufIdx; - } - - /* Pad MFCC if needed by adding MFCC for zeros. */ - if (mfccBufIdx != this->m_numFeatureFrames) { - std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_mfccWindowLen, 0); - std::vector<float> mfccZeros = this->m_mfcc.MfccCompute(zerosWindow); - - while (mfccBufIdx != this->m_numFeatureFrames) { - memcpy(&this->m_mfccBuf(0, mfccBufIdx), - mfccZeros.data(), sizeof(float) * m_numMfccFeats); - ++mfccBufIdx; - } - } - - /* Compute first and second order deltas from MFCCs. */ - AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf); - - /* Standardize calculated features. */ - this->Standarize(); - - /* Quantise. */ - QuantParams quantParams = GetTensorQuantParams(this->m_inputTensor); - - if (0 == quantParams.scale) { - printf_err("Quantisation scale can't be 0\n"); - return false; - } - - switch(this->m_inputTensor->type) { - case kTfLiteUInt8: - return this->Quantise<uint8_t>( - tflite::GetTensorData<uint8_t>(this->m_inputTensor), this->m_inputTensor->bytes, - quantParams.scale, quantParams.offset); - case kTfLiteInt8: - return this->Quantise<int8_t>( - tflite::GetTensorData<int8_t>(this->m_inputTensor), this->m_inputTensor->bytes, - quantParams.scale, quantParams.offset); - default: - printf_err("Unsupported tensor type %s\n", - TfLiteTypeGetName(this->m_inputTensor->type)); - } - - return false; - } - - bool AsrPreProcess::ComputeDeltas(Array2d<float>& mfcc, - Array2d<float>& delta1, - Array2d<float>& delta2) - { - const std::vector <float> delta1Coeffs = - {6.66666667e-02, 5.00000000e-02, 3.33333333e-02, - 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, - -3.33333333e-02, -5.00000000e-02, -6.66666667e-02}; - - const std::vector <float> delta2Coeffs = - {0.06060606, 0.01515152, -0.01731602, - -0.03679654, -0.04329004, -0.03679654, - -0.01731602, 0.01515152, 0.06060606}; - - if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) || - mfcc.size(0) == 0 || mfcc.size(1) == 0) { - return false; - } - - /* Get the middle index; coeff vec len should always be odd. */ - const size_t coeffLen = delta1Coeffs.size(); - const size_t fMidIdx = (coeffLen - 1)/2; - const size_t numFeatures = mfcc.size(0); - const size_t numFeatVectors = mfcc.size(1); - - /* Iterate through features in MFCC vector. */ - for (size_t i = 0; i < numFeatures; ++i) { - /* For each feature, iterate through time (t) samples representing feature evolution and - * calculate d/dt and d^2/dt^2, using 1D convolution with differential kernels. - * Convolution padding = valid, result size is `time length - kernel length + 1`. - * The result is padded with 0 from both sides to match the size of initial time samples data. - * - * For the small filter, conv1D implementation as a simple loop is efficient enough. - * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32. - */ - - for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) { - float d1 = 0; - float d2 = 0; - const size_t mfccStIdx = j - fMidIdx; - - for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) { - - d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m]; - d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m]; - } - - delta1(i,j) = d1; - delta2(i,j) = d2; - } - } - - return true; - } - - void AsrPreProcess::StandardizeVecF32(Array2d<float>& vec) - { - auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize()); - auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); - - debug("Mean: %f, Stddev: %f\n", mean, stddev); - if (stddev == 0) { - std::fill(vec.begin(), vec.end(), 0); - } else { - const float stddevInv = 1.f/stddev; - const float normalisedMean = mean/stddev; - - auto NormalisingFunction = [=](float& value) { - value = value * stddevInv - normalisedMean; - }; - std::for_each(vec.begin(), vec.end(), NormalisingFunction); - } - } - - void AsrPreProcess::Standarize() - { - AsrPreProcess::StandardizeVecF32(this->m_mfccBuf); - AsrPreProcess::StandardizeVecF32(this->m_delta1Buf); - AsrPreProcess::StandardizeVecF32(this->m_delta2Buf); - } - - float AsrPreProcess::GetQuantElem( - const float elem, - const float quantScale, - const int quantOffset, - const float minVal, - const float maxVal) - { - float val = std::round((elem/quantScale) + quantOffset); - return std::min<float>(std::max<float>(val, minVal), maxVal); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/usecase.cmake b/source/use_case/asr/usecase.cmake index 50e7e26..2a2178b 100644 --- a/source/use_case/asr/usecase.cmake +++ b/source/use_case/asr/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "asr") USER_OPTION(${use_case}_FILE_PATH "Directory with custom WAV input files, or path to a single WAV file, to use in the evaluation application." ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ @@ -98,4 +100,4 @@ generate_tflite_code( MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH} DESTINATION ${SRC_GEN_DIR} EXPRESSIONS ${EXTRA_MODEL_CODE} - ) + NAMESPACE "arm" "app" "asr") diff --git a/source/use_case/img_class/include/ImgClassProcessing.hpp b/source/use_case/img_class/include/ImgClassProcessing.hpp deleted file mode 100644 index e931b7d..0000000 --- a/source/use_case/img_class/include/ImgClassProcessing.hpp +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef IMG_CLASS_PROCESSING_HPP -#define IMG_CLASS_PROCESSING_HPP - -#include "BaseProcessing.hpp" -#include "Model.hpp" -#include "Classifier.hpp" - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Image Classification use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class ImgClassPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] convertToInt8 Should the image be converted to Int8 range. - **/ - explicit ImgClassPreProcess(TfLiteTensor* inputTensor, bool convertToInt8); - - /** - * @brief Should perform pre-processing of 'raw' input image data and load it into - * TFLite Micro input tensors ready for inference - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - private: - TfLiteTensor* m_inputTensor; - bool m_convertToInt8; - }; - - /** - * @brief Post-processing class for Image Classification use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class ImgClassPostProcess : public BasePostProcess { - - public: - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Classifier object used to get top N results from classification. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[in] results Vector of classification results to store decoded outputs. - **/ - ImgClassPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results); - - /** - * @brief Should perform post-processing of the result of inference then - * populate classification result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - - private: - TfLiteTensor* m_outputTensor; - Classifier& m_imgClassifier; - const std::vector<std::string>& m_labels; - std::vector<ClassificationResult>& m_results; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* IMG_CLASS_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/img_class/include/MobileNetModel.hpp b/source/use_case/img_class/include/MobileNetModel.hpp deleted file mode 100644 index 503f1ac..0000000 --- a/source/use_case/img_class/include/MobileNetModel.hpp +++ /dev/null @@ -1,55 +0,0 @@ -/* - * 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. - */ -#ifndef IMG_CLASS_MOBILENETMODEL_HPP -#define IMG_CLASS_MOBILENETMODEL_HPP - -#include "Model.hpp" - -namespace arm { -namespace app { - - class MobileNetModel : public Model { - - public: - /* Indices for the expected model - based on input tensor shape */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_inputChannelsIdx = 3; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 7; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* IMG_CLASS_MOBILENETMODEL_HPP */
\ No newline at end of file diff --git a/source/use_case/img_class/src/ImgClassProcessing.cc b/source/use_case/img_class/src/ImgClassProcessing.cc deleted file mode 100644 index adf9794..0000000 --- a/source/use_case/img_class/src/ImgClassProcessing.cc +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2022 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 "ImgClassProcessing.hpp" -#include "ImageUtils.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - ImgClassPreProcess::ImgClassPreProcess(TfLiteTensor* inputTensor, bool convertToInt8) - :m_inputTensor{inputTensor}, - m_convertToInt8{convertToInt8} - {} - - bool ImgClassPreProcess::DoPreProcess(const void* data, size_t inputSize) - { - if (data == nullptr) { - printf_err("Data pointer is null"); - return false; - } - - auto input = static_cast<const uint8_t*>(data); - - std::memcpy(this->m_inputTensor->data.data, input, inputSize); - debug("Input tensor populated \n"); - - if (this->m_convertToInt8) { - image::ConvertImgToInt8(this->m_inputTensor->data.data, this->m_inputTensor->bytes); - } - - return true; - } - - ImgClassPostProcess::ImgClassPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results) - :m_outputTensor{outputTensor}, - m_imgClassifier{classifier}, - m_labels{labels}, - m_results{results} - {} - - bool ImgClassPostProcess::DoPostProcess() - { - return this->m_imgClassifier.GetClassificationResults( - this->m_outputTensor, this->m_results, - this->m_labels, 5, false); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/img_class/src/MainLoop.cc b/source/use_case/img_class/src/MainLoop.cc index d9fb925..de3779f 100644 --- a/source/use_case/img_class/src/MainLoop.cc +++ b/source/use_case/img_class/src/MainLoop.cc @@ -21,7 +21,16 @@ #include "MobileNetModel.hpp" /* Model class for running inference. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ -#include "log_macros.h" +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); using ImgClassClassifier = arm::app::Classifier; @@ -30,11 +39,22 @@ void main_loop() arm::app::MobileNetModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("Failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; diff --git a/source/use_case/img_class/src/MobileNetModel.cc b/source/use_case/img_class/src/MobileNetModel.cc deleted file mode 100644 index 2e48f3b..0000000 --- a/source/use_case/img_class/src/MobileNetModel.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * 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 "MobileNetModel.hpp" -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::MobileNetModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::MobileNetModel::EnlistOperations() -{ - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddAveragePool2D(); - this->m_opResolver.AddAdd(); - this->m_opResolver.AddReshape(); - this->m_opResolver.AddSoftmax(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::MobileNetModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::MobileNetModel::ModelSize() -{ - return GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/img_class/usecase.cmake b/source/use_case/img_class/usecase.cmake index dafdbbf..2a8be09 100644 --- a/source/use_case/img_class/usecase.cmake +++ b/source/use_case/img_class/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "img_class") USER_OPTION(${use_case}_FILE_PATH "Directory with custom image files to use, or path to a single image, in the evaluation application" ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ diff --git a/source/use_case/inference_runner/include/TestModel.hpp b/source/use_case/inference_runner/include/TestModel.hpp deleted file mode 100644 index 0846bd4..0000000 --- a/source/use_case/inference_runner/include/TestModel.hpp +++ /dev/null @@ -1,47 +0,0 @@ -/* - * 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. - */ -#ifndef INF_RUNNER_TESTMODEL_HPP -#define INF_RUNNER_TESTMODEL_HPP - -#include "Model.hpp" - -namespace arm { -namespace app { - - class TestModel : public Model { - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::AllOpsResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance, not needed as using AllOpsResolver. */ - bool EnlistOperations() override {return false;} - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - - /* No need to define individual ops at the cost of extra memory. */ - tflite::AllOpsResolver m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* INF_RUNNER_TESTMODEL_HPP */
\ No newline at end of file diff --git a/source/use_case/inference_runner/src/MainLoop.cc b/source/use_case/inference_runner/src/MainLoop.cc index ddff40c..0991b7b 100644 --- a/source/use_case/inference_runner/src/MainLoop.cc +++ b/source/use_case/inference_runner/src/MainLoop.cc @@ -18,7 +18,37 @@ #include "TestModel.hpp" /* Model class for running inference. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +#if defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) + +static uint8_t* GetModelPointer() +{ + info("Model pointer: 0x%08x\n", DYNAMIC_MODEL_BASE); + return reinterpret_cast<uint8_t *>(DYNAMIC_MODEL_BASE); +} + +static size_t GetModelLen() +{ + /* TODO: Can we get the actual model size here somehow? + * Currently we return the reserved space. It is possible to do + * so by reading the memory pattern but it will not be reliable. */ + return static_cast<size_t>(DYNAMIC_MODEL_SIZE); +} + +#else /* defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); + +#endif /* defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) */ enum opcodes { @@ -31,11 +61,22 @@ void main_loop() arm::app::TestModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("Failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; diff --git a/source/use_case/inference_runner/src/TestModel.cc b/source/use_case/inference_runner/src/TestModel.cc deleted file mode 100644 index 3e72119..0000000 --- a/source/use_case/inference_runner/src/TestModel.cc +++ /dev/null @@ -1,55 +0,0 @@ -/* - * 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 "TestModel.hpp" -#include "log_macros.h" - -const tflite::AllOpsResolver& arm::app::TestModel::GetOpResolver() -{ - return this->m_opResolver; -} - -#if defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) - - const uint8_t* arm::app::TestModel::ModelPointer() - { - info("Model pointer: 0x%08x\n", DYNAMIC_MODEL_BASE); - return reinterpret_cast<uint8_t *>(DYNAMIC_MODEL_BASE); - } - - size_t arm::app::TestModel::ModelSize() - { - /* TODO: Can we get the actual model size here somehow? - * Currently we return the reserved space. It is possible to do - * so by reading the memory pattern but it will not be reliable. */ - return static_cast<size_t>(DYNAMIC_MODEL_SIZE); - } - -#else /* defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) */ - - extern uint8_t* GetModelPointer(); - const uint8_t* arm::app::TestModel::ModelPointer() - { - return GetModelPointer(); - } - - extern size_t GetModelLen(); - size_t arm::app::TestModel::ModelSize() - { - return GetModelLen(); - } - -#endif /* defined(DYNAMIC_MODEL_BASE) && defined(DYNAMIC_MODEL_SIZE) */ diff --git a/source/use_case/inference_runner/usecase.cmake b/source/use_case/inference_runner/usecase.cmake index 7d12120..c70be71 100644 --- a/source/use_case/inference_runner/usecase.cmake +++ b/source/use_case/inference_runner/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "inference_runner") USER_OPTION(${use_case}_ACTIVATION_BUF_SZ "Activation buffer size for the chosen model" 0x00200000 diff --git a/source/use_case/kws/include/KwsProcessing.hpp b/source/use_case/kws/include/KwsProcessing.hpp deleted file mode 100644 index d3de3b3..0000000 --- a/source/use_case/kws/include/KwsProcessing.hpp +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef KWS_PROCESSING_HPP -#define KWS_PROCESSING_HPP - -#include <AudioUtils.hpp> -#include "BaseProcessing.hpp" -#include "Model.hpp" -#include "Classifier.hpp" -#include "MicroNetKwsMfcc.hpp" - -#include <functional> - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Keyword Spotting use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class KwsPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] numFeatures How many MFCC features to use. - * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated - * for an inference. - * @param[in] mfccFrameLength Number of audio samples used to calculate one set of MFCC values when - * sliding a window through the audio sample. - * @param[in] mfccFrameStride Number of audio samples between consecutive windows. - **/ - explicit KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numFeatureFrames, - int mfccFrameLength, int mfccFrameStride); - - /** - * @brief Should perform pre-processing of 'raw' input audio data and load it into - * TFLite Micro input tensors ready for inference. - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - size_t m_audioWindowIndex = 0; /* Index of audio slider, used when caching features in longer clips. */ - size_t m_audioDataWindowSize; /* Amount of audio needed for 1 inference. */ - size_t m_audioDataStride; /* Amount of audio to stride across if doing >1 inference in longer clips. */ - - private: - TfLiteTensor* m_inputTensor; /* Model input tensor. */ - const int m_mfccFrameLength; - const int m_mfccFrameStride; - const size_t m_numMfccFrames; /* How many sets of m_numMfccFeats. */ - - audio::MicroNetKwsMFCC m_mfcc; - audio::SlidingWindow<const int16_t> m_mfccSlidingWindow; - size_t m_numMfccVectorsInAudioStride; - size_t m_numReusedMfccVectors; - std::function<void (std::vector<int16_t>&, int, bool, size_t)> m_mfccFeatureCalculator; - - /** - * @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. - */ - std::function<void (std::vector<int16_t>&, int, bool, size_t)> - GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, - TfLiteTensor* inputTensor, - size_t cacheSize); - - 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); - }; - - /** - * @brief Post-processing class for Keyword Spotting use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class KwsPostProcess : public BasePostProcess { - - private: - TfLiteTensor* m_outputTensor; /* Model output tensor. */ - Classifier& m_kwsClassifier; /* KWS Classifier object. */ - const std::vector<std::string>& m_labels; /* KWS Labels. */ - std::vector<ClassificationResult>& m_results; /* Results vector for a single inference. */ - - public: - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Classifier object used to get top N results from classification. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[in/out] results Vector of classification results to store decoded outputs. - **/ - KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results); - - /** - * @brief Should perform post-processing of the result of inference then - * populate KWS result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/kws/include/KwsResult.hpp b/source/use_case/kws/include/KwsResult.hpp deleted file mode 100644 index 38f32b4..0000000 --- a/source/use_case/kws/include/KwsResult.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_RESULT_HPP -#define KWS_RESULT_HPP - -#include "ClassificationResult.hpp" - -#include <vector> - -namespace arm { -namespace app { -namespace kws { - - using ResultVec = std::vector<arm::app::ClassificationResult>; - - /* Structure for holding kws result. */ - class KwsResult { - - public: - ResultVec m_resultVec; /* Container for "thresholded" classification results. */ - float m_timeStamp; /* Audio timestamp for this result. */ - uint32_t m_inferenceNumber; /* Corresponding inference number. */ - float m_threshold; /* Threshold value for `m_resultVec`. */ - - KwsResult() = delete; - KwsResult(ResultVec& resultVec, - const float timestamp, - const uint32_t inferenceIdx, - const float scoreThreshold) { - - this->m_threshold = scoreThreshold; - this->m_timeStamp = timestamp; - this->m_inferenceNumber = inferenceIdx; - - this->m_resultVec = ResultVec(); - for (auto & i : resultVec) { - if (i.m_normalisedVal >= this->m_threshold) { - this->m_resultVec.emplace_back(i); - } - } - } - ~KwsResult() = default; - }; - -} /* namespace kws */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_RESULT_HPP */
\ No newline at end of file diff --git a/source/use_case/kws/include/MicroNetKwsMfcc.hpp b/source/use_case/kws/include/MicroNetKwsMfcc.hpp deleted file mode 100644 index b2565a3..0000000 --- a/source/use_case/kws/include/MicroNetKwsMfcc.hpp +++ /dev/null @@ -1,50 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_MICRONET_MFCC_HPP -#define KWS_MICRONET_MFCC_HPP - -#include "Mfcc.hpp" - -namespace arm { -namespace app { -namespace audio { - - /* Class to provide MicroNet specific MFCC calculation requirements. */ - class MicroNetKwsMFCC : public MFCC { - - public: - static constexpr uint32_t ms_defaultSamplingFreq = 16000; - static constexpr uint32_t ms_defaultNumFbankBins = 40; - static constexpr uint32_t ms_defaultMelLoFreq = 20; - static constexpr uint32_t ms_defaultMelHiFreq = 4000; - static constexpr bool ms_defaultUseHtkMethod = true; - - explicit MicroNetKwsMFCC(const size_t numFeats, const size_t frameLen) - : MFCC(MfccParams( - ms_defaultSamplingFreq, ms_defaultNumFbankBins, - ms_defaultMelLoFreq, ms_defaultMelHiFreq, - numFeats, frameLen, ms_defaultUseHtkMethod)) - {} - MicroNetKwsMFCC() = delete; - ~MicroNetKwsMFCC() = default; - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_MICRONET_MFCC_HPP */
\ No newline at end of file diff --git a/source/use_case/kws/include/MicroNetKwsModel.hpp b/source/use_case/kws/include/MicroNetKwsModel.hpp deleted file mode 100644 index 3259c45..0000000 --- a/source/use_case/kws/include/MicroNetKwsModel.hpp +++ /dev/null @@ -1,59 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_MICRONETMODEL_HPP -#define KWS_MICRONETMODEL_HPP - -#include "Model.hpp" - -extern const int g_FrameLength; -extern const int g_FrameStride; -extern const float g_ScoreThreshold; - -namespace arm { -namespace app { - - class MicroNetKwsModel : public Model { - public: - /* Indices for the expected model - based on input and output tensor shapes */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_outputRowsIdx = 2; - static constexpr uint32_t ms_outputColsIdx = 3; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 7; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_MICRONETMODEL_HPP */ diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc deleted file mode 100644 index 328709d..0000000 --- a/source/use_case/kws/src/KwsProcessing.cc +++ /dev/null @@ -1,212 +0,0 @@ -/* - * Copyright (c) 2022 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 "KwsProcessing.hpp" -#include "ImageUtils.hpp" -#include "log_macros.h" -#include "MicroNetKwsModel.hpp" - -namespace arm { -namespace app { - - KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames, - int mfccFrameLength, int mfccFrameStride - ): - m_inputTensor{inputTensor}, - m_mfccFrameLength{mfccFrameLength}, - m_mfccFrameStride{mfccFrameStride}, - m_numMfccFrames{numMfccFrames}, - m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)} - { - this->m_mfcc.Init(); - - /* Deduce the data length required for 1 inference from the network parameters. */ - this->m_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride + - (this->m_mfccFrameLength - this->m_mfccFrameStride); - - /* Creating an MFCC feature sliding window for the data required for 1 inference. */ - this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize, - this->m_mfccFrameLength, this->m_mfccFrameStride); - - /* For longer audio clips we choose to move by half the audio window size - * => for a 1 second window size there is an overlap of 0.5 seconds. */ - this->m_audioDataStride = this->m_audioDataWindowSize / 2; - - /* To have the previously calculated features re-usable, stride must be multiple - * of MFCC features window stride. Reduce stride through audio if needed. */ - if (0 != this->m_audioDataStride % this->m_mfccFrameStride) { - this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride; - } - - this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride; - - /* Calculate number of the feature vectors in the window overlap region. - * These feature vectors will be reused.*/ - this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1 - - this->m_numMfccVectorsInAudioStride; - - /* Construct feature calculation function. */ - this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor, - this->m_numReusedMfccVectors); - - if (!this->m_mfccFeatureCalculator) { - printf_err("Feature calculator not initialized."); - } - } - - bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize) - { - UNUSED(inputSize); - if (data == nullptr) { - printf_err("Data pointer is null"); - } - - /* Set the features sliding window to the new address. */ - auto input = static_cast<const int16_t*>(data); - this->m_mfccSlidingWindow.Reset(input); - - /* Cache is only usable if we have more than 1 inference in an audio clip. */ - bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0; - - /* Use a sliding window to calculate MFCC features frame by frame. */ - while (this->m_mfccSlidingWindow.HasNext()) { - const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next(); - - std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow, - mfccWindow + this->m_mfccFrameLength); - - /* Compute features for this window and write them to input tensor. */ - this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(), - useCache, this->m_numMfccVectorsInAudioStride); - } - - debug("Input tensor populated \n"); - - 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)> - KwsPreProcess::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)> - KwsPreProcess::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)> - KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor, - size_t cacheSize, - std::function<std::vector<float>(std::vector<int16_t>&)> compute); - - - std::function<void (std::vector<int16_t>&, int, bool, size_t)> - KwsPreProcess::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 = this->FeatureCalc<int8_t>(inputTensor, - cacheSize, - [=, &mfcc](std::vector<int16_t>& audioDataWindow) { - return mfcc.MfccComputeQuant<int8_t>(audioDataWindow, - quantScale, - quantOffset); - } - ); - break; - } - default: - printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); - } - } else { - mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize, - [&mfcc](std::vector<int16_t>& audioDataWindow) { - return mfcc.MfccCompute(audioDataWindow); } - ); - } - return mfccFeatureCalc; - } - - KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results) - :m_outputTensor{outputTensor}, - m_kwsClassifier{classifier}, - m_labels{labels}, - m_results{results} - {} - - bool KwsPostProcess::DoPostProcess() - { - return this->m_kwsClassifier.GetClassificationResults( - this->m_outputTensor, this->m_results, - this->m_labels, 1, true); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/kws/src/MainLoop.cc b/source/use_case/kws/src/MainLoop.cc index e590c4a..3c35a7f 100644 --- a/source/use_case/kws/src/MainLoop.cc +++ b/source/use_case/kws/src/MainLoop.cc @@ -21,7 +21,18 @@ #include "Labels.hpp" /* For label strings. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { +namespace app { +namespace kws { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + extern uint8_t *GetModelPointer(); + extern size_t GetModelLen(); +} /* namespace kws */ +} /* namespace app */ +} /* namespace arm */ using KwsClassifier = arm::app::Classifier; @@ -53,11 +64,22 @@ void main_loop() arm::app::MicroNetKwsModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::kws::tensorArena, + sizeof(arm::app::kws::tensorArena), + arm::app::kws::GetModelPointer(), + arm::app::kws::GetModelLen())) { printf_err("Failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; @@ -65,9 +87,9 @@ void main_loop() caseContext.Set<arm::app::Profiler&>("profiler", profiler); caseContext.Set<arm::app::Model&>("model", model); caseContext.Set<uint32_t>("clipIndex", 0); - caseContext.Set<int>("frameLength", g_FrameLength); - caseContext.Set<int>("frameStride", g_FrameStride); - caseContext.Set<float>("scoreThreshold", g_ScoreThreshold); /* Normalised score threshold. */ + caseContext.Set<int>("frameLength", arm::app::kws::g_FrameLength); + caseContext.Set<int>("frameStride", arm::app::kws::g_FrameStride); + caseContext.Set<float>("scoreThreshold", arm::app::kws::g_ScoreThreshold); /* Normalised score threshold. */ KwsClassifier classifier; /* classifier wrapper object. */ caseContext.Set<arm::app::Classifier&>("classifier", classifier); @@ -114,4 +136,4 @@ void main_loop() } } while (executionSuccessful && bUseMenu); info("Main loop terminated.\n"); -}
\ No newline at end of file +} diff --git a/source/use_case/kws/src/MicroNetKwsModel.cc b/source/use_case/kws/src/MicroNetKwsModel.cc deleted file mode 100644 index 1c38525..0000000 --- a/source/use_case/kws/src/MicroNetKwsModel.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * 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 "MicroNetKwsModel.hpp" -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::MicroNetKwsModel::EnlistOperations() -{ - this->m_opResolver.AddReshape(); - this->m_opResolver.AddAveragePool2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddFullyConnected(); - this->m_opResolver.AddRelu(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::MicroNetKwsModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::MicroNetKwsModel::ModelSize() -{ - return GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/kws/usecase.cmake b/source/use_case/kws/usecase.cmake index 9f3736e..d9985c7 100644 --- a/source/use_case/kws/usecase.cmake +++ b/source/use_case/kws/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "kws") USER_OPTION(${use_case}_FILE_PATH "Directory with custom WAV input files, or path to a single WAV file, to use in the evaluation application." ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ @@ -96,4 +98,5 @@ generate_tflite_code( MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH} DESTINATION ${SRC_GEN_DIR} EXPRESSIONS ${EXTRA_MODEL_CODE} + NAMESPACE "arm" "app" "kws" ) diff --git a/source/use_case/kws_asr/include/AsrClassifier.hpp b/source/use_case/kws_asr/include/AsrClassifier.hpp deleted file mode 100644 index 6ab9685..0000000 --- a/source/use_case/kws_asr/include/AsrClassifier.hpp +++ /dev/null @@ -1,66 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_CLASSIFIER_HPP -#define ASR_CLASSIFIER_HPP - -#include "Classifier.hpp" - -namespace arm { -namespace app { - - class AsrClassifier : public Classifier { - public: - /** - * @brief Gets the top N classification results from the - * output vector. - * @param[in] outputTensor Inference output tensor from an NN model. - * @param[out] vecResults A vector of classification results - * populated by this function. - * @param[in] labels Labels vector to match classified classes - * @param[in] topNCount Number of top classifications to pick. - * @param[in] use_softmax Whether softmax scaling should be applied to model output. - * @return true if successful, false otherwise. - **/ - bool GetClassificationResults( - TfLiteTensor* outputTensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, uint32_t topNCount, - bool use_softmax = false) override; - - private: - - /** - * @brief Utility function that gets the top 1 classification results from the - * output tensor (vector of vector). - * @param[in] tensor Inference output tensor from an NN model. - * @param[out] vecResults A vector of classification results - * populated by this function. - * @param[in] labels Labels vector to match classified classes. - * @param[in] scale Quantization scale. - * @param[in] zeroPoint Quantization zero point. - * @return true if successful, false otherwise. - **/ - template<typename T> - bool GetTopResults(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, double scale, double zeroPoint); - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_CLASSIFIER_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/AsrResult.hpp b/source/use_case/kws_asr/include/AsrResult.hpp deleted file mode 100644 index 25fa9e8..0000000 --- a/source/use_case/kws_asr/include/AsrResult.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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. - */ -#ifndef ASR_RESULT_HPP -#define ASR_RESULT_HPP - -#include "ClassificationResult.hpp" - -#include <vector> - -namespace arm { -namespace app { -namespace asr { - - using ResultVec = std::vector<arm::app::ClassificationResult>; - - /* Structure for holding asr result. */ - class AsrResult { - - public: - ResultVec m_resultVec; /* Container for "thresholded" classification results. */ - float m_timeStamp; /* Audio timestamp for this result. */ - uint32_t m_inferenceNumber; /* Corresponding inference number. */ - float m_threshold; /* Threshold value for `m_resultVec` */ - - AsrResult() = delete; - AsrResult(ResultVec& resultVec, - const float timestamp, - const uint32_t inferenceIdx, - const float scoreThreshold) { - - this->m_threshold = scoreThreshold; - this->m_timeStamp = timestamp; - this->m_inferenceNumber = inferenceIdx; - - this->m_resultVec = ResultVec(); - for (auto& i : resultVec) { - if (i.m_normalisedVal >= this->m_threshold) { - this->m_resultVec.emplace_back(i); - } - } - } - ~AsrResult() = default; - }; - -} /* namespace asr */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* ASR_RESULT_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/KwsProcessing.hpp b/source/use_case/kws_asr/include/KwsProcessing.hpp deleted file mode 100644 index d3de3b3..0000000 --- a/source/use_case/kws_asr/include/KwsProcessing.hpp +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef KWS_PROCESSING_HPP -#define KWS_PROCESSING_HPP - -#include <AudioUtils.hpp> -#include "BaseProcessing.hpp" -#include "Model.hpp" -#include "Classifier.hpp" -#include "MicroNetKwsMfcc.hpp" - -#include <functional> - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Keyword Spotting use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class KwsPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] numFeatures How many MFCC features to use. - * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated - * for an inference. - * @param[in] mfccFrameLength Number of audio samples used to calculate one set of MFCC values when - * sliding a window through the audio sample. - * @param[in] mfccFrameStride Number of audio samples between consecutive windows. - **/ - explicit KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numFeatureFrames, - int mfccFrameLength, int mfccFrameStride); - - /** - * @brief Should perform pre-processing of 'raw' input audio data and load it into - * TFLite Micro input tensors ready for inference. - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - size_t m_audioWindowIndex = 0; /* Index of audio slider, used when caching features in longer clips. */ - size_t m_audioDataWindowSize; /* Amount of audio needed for 1 inference. */ - size_t m_audioDataStride; /* Amount of audio to stride across if doing >1 inference in longer clips. */ - - private: - TfLiteTensor* m_inputTensor; /* Model input tensor. */ - const int m_mfccFrameLength; - const int m_mfccFrameStride; - const size_t m_numMfccFrames; /* How many sets of m_numMfccFeats. */ - - audio::MicroNetKwsMFCC m_mfcc; - audio::SlidingWindow<const int16_t> m_mfccSlidingWindow; - size_t m_numMfccVectorsInAudioStride; - size_t m_numReusedMfccVectors; - std::function<void (std::vector<int16_t>&, int, bool, size_t)> m_mfccFeatureCalculator; - - /** - * @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. - */ - std::function<void (std::vector<int16_t>&, int, bool, size_t)> - GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, - TfLiteTensor* inputTensor, - size_t cacheSize); - - 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); - }; - - /** - * @brief Post-processing class for Keyword Spotting use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class KwsPostProcess : public BasePostProcess { - - private: - TfLiteTensor* m_outputTensor; /* Model output tensor. */ - Classifier& m_kwsClassifier; /* KWS Classifier object. */ - const std::vector<std::string>& m_labels; /* KWS Labels. */ - std::vector<ClassificationResult>& m_results; /* Results vector for a single inference. */ - - public: - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Classifier object used to get top N results from classification. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[in/out] results Vector of classification results to store decoded outputs. - **/ - KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results); - - /** - * @brief Should perform post-processing of the result of inference then - * populate KWS result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/KwsResult.hpp b/source/use_case/kws_asr/include/KwsResult.hpp deleted file mode 100644 index 45bb790..0000000 --- a/source/use_case/kws_asr/include/KwsResult.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_RESULT_HPP -#define KWS_RESULT_HPP - -#include "ClassificationResult.hpp" - -#include <vector> - -namespace arm { -namespace app { -namespace kws { - - using ResultVec = std::vector < arm::app::ClassificationResult >; - - /* Structure for holding kws result. */ - class KwsResult { - - public: - ResultVec m_resultVec; /* Container for "thresholded" classification results. */ - float m_timeStamp; /* Audio timestamp for this result. */ - uint32_t m_inferenceNumber; /* Corresponding inference number. */ - float m_threshold; /* Threshold value for `m_resultVec.` */ - - KwsResult() = delete; - KwsResult(ResultVec& resultVec, - const float timestamp, - const uint32_t inferenceIdx, - const float scoreThreshold) { - - this->m_threshold = scoreThreshold; - this->m_timeStamp = timestamp; - this->m_inferenceNumber = inferenceIdx; - - this->m_resultVec = ResultVec(); - for (auto & i : resultVec) { - if (i.m_normalisedVal >= this->m_threshold) { - this->m_resultVec.emplace_back(i); - } - } - } - ~KwsResult() = default; - }; - -} /* namespace kws */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_RESULT_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp b/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp deleted file mode 100644 index af6ba5f..0000000 --- a/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef KWS_ASR_MICRONET_MFCC_HPP -#define KWS_ASR_MICRONET_MFCC_HPP - -#include "Mfcc.hpp" - -namespace arm { -namespace app { -namespace audio { - - /* Class to provide MicroNet specific MFCC calculation requirements. */ - class MicroNetKwsMFCC : public MFCC { - - public: - static constexpr uint32_t ms_defaultSamplingFreq = 16000; - static constexpr uint32_t ms_defaultNumFbankBins = 40; - static constexpr uint32_t ms_defaultMelLoFreq = 20; - static constexpr uint32_t ms_defaultMelHiFreq = 4000; - static constexpr bool ms_defaultUseHtkMethod = true; - - - explicit MicroNetKwsMFCC(const size_t numFeats, const size_t frameLen) - : MFCC(MfccParams( - ms_defaultSamplingFreq, ms_defaultNumFbankBins, - ms_defaultMelLoFreq, ms_defaultMelHiFreq, - numFeats, frameLen, ms_defaultUseHtkMethod)) - {} - MicroNetKwsMFCC() = delete; - ~MicroNetKwsMFCC() = default; - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_MICRONET_MFCC_HPP */ diff --git a/source/use_case/kws_asr/include/MicroNetKwsModel.hpp b/source/use_case/kws_asr/include/MicroNetKwsModel.hpp deleted file mode 100644 index 22cf916..0000000 --- a/source/use_case/kws_asr/include/MicroNetKwsModel.hpp +++ /dev/null @@ -1,66 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_ASR_MICRONETMODEL_HPP -#define KWS_ASR_MICRONETMODEL_HPP - -#include "Model.hpp" - -namespace arm { -namespace app { -namespace kws { - extern const int g_FrameLength; - extern const int g_FrameStride; - extern const float g_ScoreThreshold; - extern const uint32_t g_NumMfcc; - extern const uint32_t g_NumAudioWins; -} /* namespace kws */ -} /* namespace app */ -} /* namespace arm */ - -namespace arm { -namespace app { - class MicroNetKwsModel : public Model { - public: - /* Indices for the expected model - based on input and output tensor shapes */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_outputRowsIdx = 2; - static constexpr uint32_t ms_outputColsIdx = 3; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 7; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_MICRONETMODEL_HPP */ diff --git a/source/use_case/kws_asr/include/OutputDecode.hpp b/source/use_case/kws_asr/include/OutputDecode.hpp deleted file mode 100644 index cea2c33..0000000 --- a/source/use_case/kws_asr/include/OutputDecode.hpp +++ /dev/null @@ -1,40 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_ASR_OUTPUT_DECODE_HPP -#define KWS_ASR_OUTPUT_DECODE_HPP - -#include "AsrClassifier.hpp" - -namespace arm { -namespace app { -namespace audio { -namespace asr { - - /** - * @brief Gets the top N classification results from the - * output vector. - * @param[in] vecResults Label output from classifier. - * @return true if successful, false otherwise. - **/ - std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults); - -} /* namespace asr */ -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_OUTPUT_DECODE_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp deleted file mode 100644 index 75d75da..0000000 --- a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp +++ /dev/null @@ -1,113 +0,0 @@ -/* - * 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. - */ -#ifndef KWS_ASR_WAV2LET_MFCC_HPP -#define KWS_ASR_WAV2LET_MFCC_HPP - -#include "Mfcc.hpp" - -namespace arm { -namespace app { -namespace audio { - - /* Class to provide Wav2Letter specific MFCC calculation requirements. */ - class Wav2LetterMFCC : public MFCC { - - public: - static constexpr uint32_t ms_defaultSamplingFreq = 16000; - static constexpr uint32_t ms_defaultNumFbankBins = 128; - static constexpr uint32_t ms_defaultMelLoFreq = 0; - static constexpr uint32_t ms_defaultMelHiFreq = 8000; - static constexpr bool ms_defaultUseHtkMethod = false; - - explicit Wav2LetterMFCC(const size_t numFeats, const size_t frameLen) - : MFCC(MfccParams( - ms_defaultSamplingFreq, ms_defaultNumFbankBins, - ms_defaultMelLoFreq, ms_defaultMelHiFreq, - numFeats, frameLen, ms_defaultUseHtkMethod)) - {} - - Wav2LetterMFCC() = delete; - ~Wav2LetterMFCC() = default; - - protected: - - /** - * @brief Overrides base class implementation of this function. - * @param[in] fftVec Vector populated with FFT magnitudes. - * @param[in] melFilterBank 2D Vector with filter bank weights. - * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank - * to be used for each bin. - * @param[in] filterBankFilterLast Vector containing the last indices of filter bank - * to be used for each bin. - * @param[out] melEnergies Pre-allocated vector of MEL energies to be - * populated. - * @return true if successful, false otherwise. - */ - bool ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) override; - - /** - * @brief Override for the base class implementation convert mel - * energies to logarithmic scale. The difference from - * default behaviour is that the power is converted to dB - * and subsequently clamped. - * @param[in,out] melEnergies 1D vector of Mel energies. - **/ - void ConvertToLogarithmicScale( - std::vector<float>& melEnergies) override; - - /** - * @brief Create a matrix used to calculate Discrete Cosine - * Transform. Override for the base class' default - * implementation as the first and last elements - * use a different normaliser. - * @param[in] inputLength Input length of the buffer on which - * DCT will be performed. - * @param[in] coefficientCount Total coefficients per input length. - * @return 1D vector with inputLength x coefficientCount elements - * populated with DCT coefficients. - */ - std::vector<float> CreateDCTMatrix( - int32_t inputLength, - int32_t coefficientCount) override; - - /** - * @brief Given the low and high Mel values, get the normaliser - * for weights to be applied when populating the filter - * bank. Override for the base class implementation. - * @param[in] leftMel Low Mel frequency value. - * @param[in] rightMel High Mel frequency value. - * @param[in] useHTKMethod Bool to signal if HTK method is to be - * used for calculation. - * @return Value to use for normalising. - */ - float GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - bool useHTKMethod) override; - - }; - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_WAV2LET_MFCC_HPP */ diff --git a/source/use_case/kws_asr/include/Wav2LetterModel.hpp b/source/use_case/kws_asr/include/Wav2LetterModel.hpp deleted file mode 100644 index 0e1adc5..0000000 --- a/source/use_case/kws_asr/include/Wav2LetterModel.hpp +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef KWS_ASR_WAV2LETTER_MODEL_HPP -#define KWS_ASR_WAV2LETTER_MODEL_HPP - -#include "Model.hpp" - -namespace arm { -namespace app { -namespace asr { - extern const int g_FrameLength; - extern const int g_FrameStride; - extern const float g_ScoreThreshold; - extern const int g_ctxLen; -} /* namespace asr */ -} /* namespace app */ -} /* namespace arm */ - -namespace arm { -namespace app { - - class Wav2LetterModel : public Model { - - public: - /* Indices for the expected model - based on input and output tensor shapes */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_outputRowsIdx = 2; - static constexpr uint32_t ms_outputColsIdx = 3; - - /* Model specific constants. */ - static constexpr uint32_t ms_blankTokenIdx = 28; - static constexpr uint32_t ms_numMfccFeatures = 13; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 5; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_WAV2LETTER_MODEL_HPP */ diff --git a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp deleted file mode 100644 index d1bc9a2..0000000 --- a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef KWS_ASR_WAV2LETTER_POSTPROCESS_HPP -#define KWS_ASR_WAV2LETTER_POSTPROCESS_HPP - -#include "TensorFlowLiteMicro.hpp" /* TensorFlow headers. */ -#include "BaseProcessing.hpp" -#include "AsrClassifier.hpp" -#include "AsrResult.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - /** - * @brief Helper class to manage tensor post-processing for "wav2letter" - * output. - */ - class AsrPostProcess : public BasePostProcess { - public: - bool m_lastIteration = false; /* Flag to set if processing the last set of data for a clip. */ - - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Object used to get top N results from classification. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[in/out] result Vector of classification results to store decoded outputs. - * @param[in] outputContextLen Left/right context length for output tensor. - * @param[in] blankTokenIdx Index in the labels that the "Blank token" takes. - * @param[in] reductionAxis The axis that the logits of each time step is on. - **/ - AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier, - const std::vector<std::string>& labels, asr::ResultVec& result, - uint32_t outputContextLen, - uint32_t blankTokenIdx, uint32_t reductionAxis); - - /** - * @brief Should perform post-processing of the result of inference then - * populate ASR result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - - /** @brief Gets the output inner length for post-processing. */ - static uint32_t GetOutputInnerLen(const TfLiteTensor*, uint32_t outputCtxLen); - - /** @brief Gets the output context length (left/right) for post-processing. */ - static uint32_t GetOutputContextLen(const Model& model, uint32_t inputCtxLen); - - /** @brief Gets the number of feature vectors to be computed. */ - static uint32_t GetNumFeatureVectors(const Model& model); - - private: - AsrClassifier& m_classifier; /* ASR Classifier object. */ - TfLiteTensor* m_outputTensor; /* Model output tensor. */ - const std::vector<std::string>& m_labels; /* ASR Labels. */ - asr::ResultVec & m_results; /* Results vector for a single inference. */ - uint32_t m_outputContextLen; /* lengths of left/right contexts for output. */ - uint32_t m_outputInnerLen; /* Length of output inner context. */ - uint32_t m_totalLen; /* Total length of the required axis. */ - uint32_t m_countIterations; /* Current number of iterations. */ - uint32_t m_blankTokenIdx; /* Index of the labels blank token. */ - uint32_t m_reductionAxisIdx; /* Axis containing output logits for a single step. */ - - /** - * @brief Checks if the tensor and axis index are valid - * inputs to the object - based on how it has been initialised. - * @return true if valid, false otherwise. - */ - bool IsInputValid(TfLiteTensor* tensor, - uint32_t axisIdx) const; - - /** - * @brief Gets the tensor data element size in bytes based - * on the tensor type. - * @return Size in bytes, 0 if not supported. - */ - static uint32_t GetTensorElementSize(TfLiteTensor* tensor); - - /** - * @brief Erases sections from the data assuming row-wise - * arrangement along the context axis. - * @return true if successful, false otherwise. - */ - bool EraseSectionsRowWise(uint8_t* ptrData, - uint32_t strideSzBytes, - bool lastIteration); - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_WAV2LETTER_POSTPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp deleted file mode 100644 index 1224c23..0000000 --- a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp +++ /dev/null @@ -1,182 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef KWS_ASR_WAV2LETTER_PREPROCESS_HPP -#define KWS_ASR_WAV2LETTER_PREPROCESS_HPP - -#include "Wav2LetterModel.hpp" -#include "Wav2LetterMfcc.hpp" -#include "AudioUtils.hpp" -#include "DataStructures.hpp" -#include "BaseProcessing.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - /* Class to facilitate pre-processing calculation for Wav2Letter model - * for ASR. */ - using AudioWindow = audio::SlidingWindow<const int16_t>; - - class AsrPreProcess : public BasePreProcess { - public: - /** - * @brief Constructor. - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] numMfccFeatures Number of MFCC features per window. - * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated - * for an inference. - * @param[in] mfccWindowLen Number of audio elements to calculate MFCC features per window. - * @param[in] mfccWindowStride Stride (in number of elements) for moving the MFCC window. - */ - AsrPreProcess(TfLiteTensor* inputTensor, - uint32_t numMfccFeatures, - uint32_t numFeatureFrames, - uint32_t mfccWindowLen, - uint32_t mfccWindowStride); - - /** - * @brief Calculates the features required from audio data. This - * includes MFCC, first and second order deltas, - * normalisation and finally, quantisation. The tensor is - * populated with features from a given window placed along - * in a single row. - * @param[in] audioData Pointer to the first element of audio data. - * @param[in] audioDataLen Number of elements in the audio data. - * @return true if successful, false in case of error. - */ - bool DoPreProcess(const void* audioData, size_t audioDataLen) override; - - protected: - /** - * @brief Computes the first and second order deltas for the - * MFCC buffers - they are assumed to be populated. - * - * @param[in] mfcc MFCC buffers. - * @param[out] delta1 Result of the first diff computation. - * @param[out] delta2 Result of the second diff computation. - * @return true if successful, false otherwise. - */ - static bool ComputeDeltas(Array2d<float>& mfcc, - Array2d<float>& delta1, - Array2d<float>& delta2); - - /** - * @brief Given a 2D vector of floats, rescale it to have mean of 0 and - * standard deviation of 1. - * @param[in,out] vec Vector of vector of floats. - */ - static void StandardizeVecF32(Array2d<float>& vec); - - /** - * @brief Standardizes all the MFCC and delta buffers to have mean 0 and std. dev 1. - */ - void Standarize(); - - /** - * @brief Given the quantisation and data type limits, computes - * the quantised values of a floating point input data. - * @param[in] elem Element to be quantised. - * @param[in] quantScale Scale. - * @param[in] quantOffset Offset. - * @param[in] minVal Numerical limit - minimum. - * @param[in] maxVal Numerical limit - maximum. - * @return Floating point quantised value. - */ - static float GetQuantElem( - float elem, - float quantScale, - int quantOffset, - float minVal, - float maxVal); - - /** - * @brief Quantises the MFCC and delta buffers, and places them - * in the output buffer. While doing so, it transposes - * the data. Reason: Buffers in this class are arranged - * for "time" axis to be row major. Primary reason for - * this being the convolution speed up (as we can use - * contiguous memory). The output, however, requires the - * time axis to be in column major arrangement. - * @param[in] outputBuf Pointer to the output buffer. - * @param[in] outputBufSz Output buffer's size. - * @param[in] quantScale Quantisation scale. - * @param[in] quantOffset Quantisation offset. - */ - template <typename T> - bool Quantise( - T* outputBuf, - const uint32_t outputBufSz, - const float quantScale, - const int quantOffset) - { - /* Check the output size will fit everything. */ - if (outputBufSz < (this->m_mfccBuf.size(0) * 3 * sizeof(T))) { - printf_err("Tensor size too small for features\n"); - return false; - } - - /* Populate. */ - T* outputBufMfcc = outputBuf; - T* outputBufD1 = outputBuf + this->m_numMfccFeats; - T* outputBufD2 = outputBufD1 + this->m_numMfccFeats; - const uint32_t ptrIncr = this->m_numMfccFeats * 2; /* (3 vectors - 1 vector) */ - - const float minVal = std::numeric_limits<T>::min(); - const float maxVal = std::numeric_limits<T>::max(); - - /* Need to transpose while copying and concatenating the tensor. */ - for (uint32_t j = 0; j < this->m_numFeatureFrames; ++j) { - for (uint32_t i = 0; i < this->m_numMfccFeats; ++i) { - *outputBufMfcc++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_mfccBuf(i, j), quantScale, - quantOffset, minVal, maxVal)); - *outputBufD1++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_delta1Buf(i, j), quantScale, - quantOffset, minVal, maxVal)); - *outputBufD2++ = static_cast<T>(AsrPreProcess::GetQuantElem( - this->m_delta2Buf(i, j), quantScale, - quantOffset, minVal, maxVal)); - } - outputBufMfcc += ptrIncr; - outputBufD1 += ptrIncr; - outputBufD2 += ptrIncr; - } - - return true; - } - - private: - audio::Wav2LetterMFCC m_mfcc; /* MFCC instance. */ - TfLiteTensor* m_inputTensor; /* Model input tensor. */ - - /* Actual buffers to be populated. */ - Array2d<float> m_mfccBuf; /* Contiguous buffer 1D: MFCC */ - Array2d<float> m_delta1Buf; /* Contiguous buffer 1D: Delta 1 */ - Array2d<float> m_delta2Buf; /* Contiguous buffer 1D: Delta 2 */ - - uint32_t m_mfccWindowLen; /* Window length for MFCC. */ - uint32_t m_mfccWindowStride; /* Window stride len for MFCC. */ - uint32_t m_numMfccFeats; /* Number of MFCC features per window. */ - uint32_t m_numFeatureFrames; /* How many sets of m_numMfccFeats. */ - AudioWindow m_mfccSlidingWindow; /* Sliding window to calculate MFCCs. */ - - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* KWS_ASR_WAV2LETTER_PREPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc deleted file mode 100644 index 9c18b14..0000000 --- a/source/use_case/kws_asr/src/AsrClassifier.cc +++ /dev/null @@ -1,136 +0,0 @@ -/* - * 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 "AsrClassifier.hpp" - -#include "log_macros.h" -#include "TensorFlowLiteMicro.hpp" -#include "Wav2LetterModel.hpp" - -template<typename T> -bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, double scale, double zeroPoint) -{ - const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx]; - const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]; - - if (nLetters != labels.size()) { - printf("Output size doesn't match the labels' size\n"); - return false; - } - - /* NOTE: tensor's size verification against labels should be - * checked by the calling/public function. */ - if (nLetters < 1) { - return false; - } - - /* Final results' container. */ - vecResults = std::vector<ClassificationResult>(nElems); - - T* tensorData = tflite::GetTensorData<T>(tensor); - - /* Get the top 1 results. */ - for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) { - std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row], 0); - - for (uint32_t j = 1; j < nLetters; ++j) { - if (top_1.first < tensorData[row + j]) { - top_1.first = tensorData[row + j]; - top_1.second = j; - } - } - - double score = static_cast<int> (top_1.first); - vecResults[i].m_normalisedVal = scale * (score - zeroPoint); - vecResults[i].m_label = labels[top_1.second]; - vecResults[i].m_labelIdx = top_1.second; - } - - return true; -} -template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, double scale, double zeroPoint); -template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, double scale, double zeroPoint); - -bool arm::app::AsrClassifier::GetClassificationResults( - TfLiteTensor* outputTensor, - std::vector<ClassificationResult>& vecResults, - const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax) -{ - UNUSED(use_softmax); - vecResults.clear(); - - constexpr int minTensorDims = static_cast<int>( - (arm::app::Wav2LetterModel::ms_outputRowsIdx > arm::app::Wav2LetterModel::ms_outputColsIdx)? - arm::app::Wav2LetterModel::ms_outputRowsIdx : arm::app::Wav2LetterModel::ms_outputColsIdx); - - constexpr uint32_t outColsIdx = arm::app::Wav2LetterModel::ms_outputColsIdx; - - /* Sanity checks. */ - if (outputTensor == nullptr) { - printf_err("Output vector is null pointer.\n"); - return false; - } else if (outputTensor->dims->size < minTensorDims) { - printf_err("Output tensor expected to be 3D (1, m, n)\n"); - return false; - } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) { - printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount); - return false; - } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) { - printf("Output size doesn't match the labels' size\n"); - return false; - } - - if (topNCount != 1) { - warn("TopNCount value ignored in this implementation\n"); - } - - /* To return the floating point values, we need quantization parameters. */ - QuantParams quantParams = GetTensorQuantParams(outputTensor); - - bool resultState; - - switch (outputTensor->type) { - case kTfLiteUInt8: - resultState = this->GetTopResults<uint8_t>( - outputTensor, vecResults, - labels, quantParams.scale, - quantParams.offset); - break; - case kTfLiteInt8: - resultState = this->GetTopResults<int8_t>( - outputTensor, vecResults, - labels, quantParams.scale, - quantParams.offset); - break; - default: - printf_err("Tensor type %s not supported by classifier\n", - TfLiteTypeGetName(outputTensor->type)); - return false; - } - - if (!resultState) { - printf_err("Failed to get sorted set\n"); - return false; - } - - return true; -}
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/KwsProcessing.cc b/source/use_case/kws_asr/src/KwsProcessing.cc deleted file mode 100644 index 328709d..0000000 --- a/source/use_case/kws_asr/src/KwsProcessing.cc +++ /dev/null @@ -1,212 +0,0 @@ -/* - * Copyright (c) 2022 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 "KwsProcessing.hpp" -#include "ImageUtils.hpp" -#include "log_macros.h" -#include "MicroNetKwsModel.hpp" - -namespace arm { -namespace app { - - KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames, - int mfccFrameLength, int mfccFrameStride - ): - m_inputTensor{inputTensor}, - m_mfccFrameLength{mfccFrameLength}, - m_mfccFrameStride{mfccFrameStride}, - m_numMfccFrames{numMfccFrames}, - m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)} - { - this->m_mfcc.Init(); - - /* Deduce the data length required for 1 inference from the network parameters. */ - this->m_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride + - (this->m_mfccFrameLength - this->m_mfccFrameStride); - - /* Creating an MFCC feature sliding window for the data required for 1 inference. */ - this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize, - this->m_mfccFrameLength, this->m_mfccFrameStride); - - /* For longer audio clips we choose to move by half the audio window size - * => for a 1 second window size there is an overlap of 0.5 seconds. */ - this->m_audioDataStride = this->m_audioDataWindowSize / 2; - - /* To have the previously calculated features re-usable, stride must be multiple - * of MFCC features window stride. Reduce stride through audio if needed. */ - if (0 != this->m_audioDataStride % this->m_mfccFrameStride) { - this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride; - } - - this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride; - - /* Calculate number of the feature vectors in the window overlap region. - * These feature vectors will be reused.*/ - this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1 - - this->m_numMfccVectorsInAudioStride; - - /* Construct feature calculation function. */ - this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor, - this->m_numReusedMfccVectors); - - if (!this->m_mfccFeatureCalculator) { - printf_err("Feature calculator not initialized."); - } - } - - bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize) - { - UNUSED(inputSize); - if (data == nullptr) { - printf_err("Data pointer is null"); - } - - /* Set the features sliding window to the new address. */ - auto input = static_cast<const int16_t*>(data); - this->m_mfccSlidingWindow.Reset(input); - - /* Cache is only usable if we have more than 1 inference in an audio clip. */ - bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0; - - /* Use a sliding window to calculate MFCC features frame by frame. */ - while (this->m_mfccSlidingWindow.HasNext()) { - const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next(); - - std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow, - mfccWindow + this->m_mfccFrameLength); - - /* Compute features for this window and write them to input tensor. */ - this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(), - useCache, this->m_numMfccVectorsInAudioStride); - } - - debug("Input tensor populated \n"); - - 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)> - KwsPreProcess::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)> - KwsPreProcess::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)> - KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor, - size_t cacheSize, - std::function<std::vector<float>(std::vector<int16_t>&)> compute); - - - std::function<void (std::vector<int16_t>&, int, bool, size_t)> - KwsPreProcess::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 = this->FeatureCalc<int8_t>(inputTensor, - cacheSize, - [=, &mfcc](std::vector<int16_t>& audioDataWindow) { - return mfcc.MfccComputeQuant<int8_t>(audioDataWindow, - quantScale, - quantOffset); - } - ); - break; - } - default: - printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); - } - } else { - mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize, - [&mfcc](std::vector<int16_t>& audioDataWindow) { - return mfcc.MfccCompute(audioDataWindow); } - ); - } - return mfccFeatureCalc; - } - - KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results) - :m_outputTensor{outputTensor}, - m_kwsClassifier{classifier}, - m_labels{labels}, - m_results{results} - {} - - bool KwsPostProcess::DoPostProcess() - { - return this->m_kwsClassifier.GetClassificationResults( - this->m_outputTensor, this->m_results, - this->m_labels, 1, true); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/MainLoop.cc b/source/use_case/kws_asr/src/MainLoop.cc index f1d97a0..2365264 100644 --- a/source/use_case/kws_asr/src/MainLoop.cc +++ b/source/use_case/kws_asr/src/MainLoop.cc @@ -23,7 +23,24 @@ #include "Wav2LetterModel.hpp" /* ASR model class for running inference. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { +namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + + namespace asr { + extern uint8_t* GetModelPointer(); + extern size_t GetModelLen(); + } + + namespace kws { + extern uint8_t* GetModelPointer(); + extern size_t GetModelLen(); + } +} /* namespace app */ +} /* namespace arm */ using KwsClassifier = arm::app::Classifier; @@ -60,14 +77,29 @@ void main_loop() arm::app::Wav2LetterModel asrModel; /* Load the models. */ - if (!kwsModel.Init()) { + if (!kwsModel.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + arm::app::kws::GetModelPointer(), + arm::app::kws::GetModelLen())) { printf_err("Failed to initialise KWS model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (kwsModel.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the KWS model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Initialise the asr model using the same allocator from KWS * to re-use the tensor arena. */ - if (!asrModel.Init(kwsModel.GetAllocator())) { + if (!asrModel.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + arm::app::asr::GetModelPointer(), + arm::app::asr::GetModelLen(), + kwsModel.GetAllocator())) { printf_err("Failed to initialise ASR model\n"); return; } else if (!VerifyTensorDimensions(asrModel)) { @@ -75,6 +107,14 @@ void main_loop() return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (asrModel.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the ASR model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; diff --git a/source/use_case/kws_asr/src/MicroNetKwsModel.cc b/source/use_case/kws_asr/src/MicroNetKwsModel.cc deleted file mode 100644 index 663faa0..0000000 --- a/source/use_case/kws_asr/src/MicroNetKwsModel.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* - * 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 "MicroNetKwsModel.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { -namespace kws { - extern uint8_t* GetModelPointer(); - extern size_t GetModelLen(); -} /* namespace kws */ -} /* namespace app */ -} /* namespace arm */ - -const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::MicroNetKwsModel::EnlistOperations() -{ - this->m_opResolver.AddAveragePool2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddFullyConnected(); - this->m_opResolver.AddRelu(); - this->m_opResolver.AddReshape(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -const uint8_t* arm::app::MicroNetKwsModel::ModelPointer() -{ - return arm::app::kws::GetModelPointer(); -} - -size_t arm::app::MicroNetKwsModel::ModelSize() -{ - return arm::app::kws::GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/OutputDecode.cc b/source/use_case/kws_asr/src/OutputDecode.cc deleted file mode 100644 index 41fbe07..0000000 --- a/source/use_case/kws_asr/src/OutputDecode.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* - * 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 "OutputDecode.hpp" - -namespace arm { -namespace app { -namespace audio { -namespace asr { - - std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults) - { - std::string CleanOutputBuffer; - - for (size_t i = 0; i < vecResults.size(); ++i) /* For all elements in vector. */ - { - while (i+1 < vecResults.size() && - vecResults[i].m_label == vecResults[i+1].m_label) /* While the current element is equal to the next, ignore it and move on. */ - { - ++i; - } - if (vecResults[i].m_label != "$") /* $ is a character used to represent unknown and double characters so should not be in output. */ - { - CleanOutputBuffer += vecResults[i].m_label; /* If the element is different to the next, it will be appended to CleanOutputBuffer. */ - } - } - - return CleanOutputBuffer; /* Return string type containing clean output. */ - } - -} /* namespace asr */ -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc index 01aefae..9427ae0 100644 --- a/source/use_case/kws_asr/src/UseCaseHandler.cc +++ b/source/use_case/kws_asr/src/UseCaseHandler.cc @@ -25,6 +25,7 @@ #include "MicroNetKwsMfcc.hpp" #include "Classifier.hpp" #include "KwsResult.hpp" +#include "Wav2LetterModel.hpp" #include "Wav2LetterMfcc.hpp" #include "Wav2LetterPreprocess.hpp" #include "Wav2LetterPostprocess.hpp" @@ -470,4 +471,4 @@ namespace app { } } /* namespace app */ -} /* namespace arm */
\ No newline at end of file +} /* namespace arm */ diff --git a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc deleted file mode 100644 index f2c50f3..0000000 --- a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc +++ /dev/null @@ -1,141 +0,0 @@ -/* - * 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 "Wav2LetterMfcc.hpp" - -#include "PlatformMath.hpp" -#include "log_macros.h" - -#include <cfloat> - -namespace arm { -namespace app { -namespace audio { - - bool Wav2LetterMFCC::ApplyMelFilterBank( - std::vector<float>& fftVec, - std::vector<std::vector<float>>& melFilterBank, - std::vector<uint32_t>& filterBankFilterFirst, - std::vector<uint32_t>& filterBankFilterLast, - std::vector<float>& melEnergies) - { - const size_t numBanks = melEnergies.size(); - - if (numBanks != filterBankFilterFirst.size() || - numBanks != filterBankFilterLast.size()) { - printf_err("unexpected filter bank lengths\n"); - return false; - } - - for (size_t bin = 0; bin < numBanks; ++bin) { - auto filterBankIter = melFilterBank[bin].begin(); - auto end = melFilterBank[bin].end(); - /* Avoid log of zero at later stages, same value used in librosa. - * The number was used during our default wav2letter model training. */ - float melEnergy = 1e-10; - const uint32_t firstIndex = filterBankFilterFirst[bin]; - const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1); - - for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) { - melEnergy += (*filterBankIter++ * fftVec[i]); - } - - melEnergies[bin] = melEnergy; - } - - return true; - } - - void Wav2LetterMFCC::ConvertToLogarithmicScale( - std::vector<float>& melEnergies) - { - float maxMelEnergy = -FLT_MAX; - - /* Container for natural logarithms of mel energies. */ - std::vector <float> vecLogEnergies(melEnergies.size(), 0.f); - - /* Because we are taking natural logs, we need to multiply by log10(e). - * Also, for wav2letter model, we scale our log10 values by 10. */ - constexpr float multiplier = 10.0 * /* Default scalar. */ - 0.4342944819032518; /* log10f(std::exp(1.0))*/ - - /* Take log of the whole vector. */ - math::MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies); - - /* Scale the log values and get the max. */ - for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin(); - iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) { - - *iterM = *iterL * multiplier; - - /* Save the max mel energy. */ - if (*iterM > maxMelEnergy) { - maxMelEnergy = *iterM; - } - } - - /* Clamp the mel energies. */ - constexpr float maxDb = 80.0; - const float clampLevelLowdB = maxMelEnergy - maxDb; - for (float & melEnergie : melEnergies) { - melEnergie = std::max(melEnergie, clampLevelLowdB); - } - } - - std::vector<float> Wav2LetterMFCC::CreateDCTMatrix( - const int32_t inputLength, - const int32_t coefficientCount) - { - std::vector<float> dctMatix(inputLength * coefficientCount); - - /* Orthonormal normalization. */ - const float normalizerK0 = 2 * math::MathUtils::SqrtF32(1.0f / - static_cast<float>(4*inputLength)); - const float normalizer = 2 * math::MathUtils::SqrtF32(1.0f / - static_cast<float>(2*inputLength)); - - const float angleIncr = M_PI/inputLength; - float angle = angleIncr; /* We start using it at k = 1 loop. */ - - /* First row of DCT will use normalizer K0 */ - for (int32_t n = 0; n < inputLength; ++n) { - dctMatix[n] = normalizerK0 /* cos(0) = 1 */; - } - - /* Second row (index = 1) onwards, we use standard normalizer. */ - for (int32_t k = 1, m = inputLength; k < coefficientCount; ++k, m += inputLength) { - for (int32_t n = 0; n < inputLength; ++n) { - dctMatix[m+n] = normalizer * - math::MathUtils::CosineF32((n + 0.5f) * angle); - } - angle += angleIncr; - } - return dctMatix; - } - - float Wav2LetterMFCC::GetMelFilterBankNormaliser( - const float& leftMel, - const float& rightMel, - const bool useHTKMethod) - { - /* Slaney normalization for mel weights. */ - return (2.0f / (MFCC::InverseMelScale(rightMel, useHTKMethod) - - MFCC::InverseMelScale(leftMel, useHTKMethod))); - } - -} /* namespace audio */ -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/kws_asr/src/Wav2LetterModel.cc b/source/use_case/kws_asr/src/Wav2LetterModel.cc deleted file mode 100644 index 52bd23a..0000000 --- a/source/use_case/kws_asr/src/Wav2LetterModel.cc +++ /dev/null @@ -1,61 +0,0 @@ -/* - * 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 "Wav2LetterModel.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { -namespace asr { - extern uint8_t* GetModelPointer(); - extern size_t GetModelLen(); -} -} /* namespace app */ -} /* namespace arm */ - -const tflite::MicroOpResolver& arm::app::Wav2LetterModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::Wav2LetterModel::EnlistOperations() -{ - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddLeakyRelu(); - this->m_opResolver.AddSoftmax(); - this->m_opResolver.AddReshape(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -const uint8_t* arm::app::Wav2LetterModel::ModelPointer() -{ - return arm::app::asr::GetModelPointer(); -} - -size_t arm::app::Wav2LetterModel::ModelSize() -{ - return arm::app::asr::GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc deleted file mode 100644 index 42f434e..0000000 --- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "Wav2LetterPostprocess.hpp" - -#include "Wav2LetterModel.hpp" -#include "log_macros.h" - -#include <cmath> - -namespace arm { -namespace app { - - AsrPostProcess::AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier, - const std::vector<std::string>& labels, std::vector<ClassificationResult>& results, - const uint32_t outputContextLen, - const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx - ): - m_classifier(classifier), - m_outputTensor(outputTensor), - m_labels{labels}, - m_results(results), - m_outputContextLen(outputContextLen), - m_countIterations(0), - m_blankTokenIdx(blankTokenIdx), - m_reductionAxisIdx(reductionAxisIdx) - { - this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen); - this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen); - } - - bool AsrPostProcess::DoPostProcess() - { - /* Basic checks. */ - if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) { - return false; - } - - /* Irrespective of tensor type, we use unsigned "byte" */ - auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor); - const uint32_t elemSz = AsrPostProcess::GetTensorElementSize(this->m_outputTensor); - - /* Other sanity checks. */ - if (0 == elemSz) { - printf_err("Tensor type not supported for post processing\n"); - return false; - } else if (elemSz * this->m_totalLen > this->m_outputTensor->bytes) { - printf_err("Insufficient number of tensor bytes\n"); - return false; - } - - /* Which axis do we need to process? */ - switch (this->m_reductionAxisIdx) { - case Wav2LetterModel::ms_outputRowsIdx: - this->EraseSectionsRowWise( - ptrData, elemSz * this->m_outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx], - this->m_lastIteration); - break; - default: - printf_err("Unsupported axis index: %" PRIu32 "\n", this->m_reductionAxisIdx); - return false; - } - this->m_classifier.GetClassificationResults(this->m_outputTensor, - this->m_results, this->m_labels, 1); - - return true; - } - - bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const - { - if (nullptr == tensor) { - return false; - } - - if (static_cast<int>(axisIdx) >= tensor->dims->size) { - printf_err("Invalid axis index: %" PRIu32 "; Max: %d\n", - axisIdx, tensor->dims->size); - return false; - } - - if (static_cast<int>(this->m_totalLen) != - tensor->dims->data[axisIdx]) { - printf_err("Unexpected tensor dimension for axis %d, got %d, \n", - axisIdx, tensor->dims->data[axisIdx]); - return false; - } - - return true; - } - - uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor) - { - switch(tensor->type) { - case kTfLiteUInt8: - case kTfLiteInt8: - return 1; - case kTfLiteInt16: - return 2; - case kTfLiteInt32: - case kTfLiteFloat32: - return 4; - default: - printf_err("Unsupported tensor type %s\n", - TfLiteTypeGetName(tensor->type)); - } - - return 0; - } - - bool AsrPostProcess::EraseSectionsRowWise( - uint8_t* ptrData, - const uint32_t strideSzBytes, - const bool lastIteration) - { - /* In this case, the "zero-ing" is quite simple as the region - * to be zeroed sits in contiguous memory (row-major). */ - const uint32_t eraseLen = strideSzBytes * this->m_outputContextLen; - - /* Erase left context? */ - if (this->m_countIterations > 0) { - /* Set output of each classification window to the blank token. */ - std::memset(ptrData, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - ptrData[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - /* Erase right context? */ - if (false == lastIteration) { - uint8_t* rightCtxPtr = ptrData + (strideSzBytes * (this->m_outputContextLen + this->m_outputInnerLen)); - /* Set output of each classification window to the blank token. */ - std::memset(rightCtxPtr, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - rightCtxPtr[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - if (lastIteration) { - this->m_countIterations = 0; - } else { - ++this->m_countIterations; - } - - return true; - } - - uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model) - { - TfLiteTensor* inputTensor = model.GetInputTensor(0); - const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0); - if (inputRows == 0) { - printf_err("Error getting number of input rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_inputRowsIdx); - } - return inputRows; - } - - uint32_t AsrPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen) - { - const uint32_t outputRows = std::max(outputTensor->dims->data[Wav2LetterModel::ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - } - - /* Watching for underflow. */ - int innerLen = (outputRows - (2 * outputCtxLen)); - - return std::max(innerLen, 0); - } - - uint32_t AsrPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen) - { - const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model); - const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); - constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx; - - /* Check to make sure that the input tensor supports the above - * context and inner lengths. */ - if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) { - printf_err("Input rows not compatible with ctx of %" PRIu32 "\n", - inputCtxLen); - return 0; - } - - TfLiteTensor* outputTensor = model.GetOutputTensor(0); - const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - return 0; - } - - const float inOutRowRatio = static_cast<float>(inputRows) / - static_cast<float>(outputRows); - - return std::round(static_cast<float>(inputCtxLen) / inOutRowRatio); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc deleted file mode 100644 index 92b0631..0000000 --- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "Wav2LetterPreprocess.hpp" - -#include "PlatformMath.hpp" -#include "TensorFlowLiteMicro.hpp" - -#include <algorithm> -#include <cmath> - -namespace arm { -namespace app { - - AsrPreProcess::AsrPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures, - const uint32_t numFeatureFrames, const uint32_t mfccWindowLen, - const uint32_t mfccWindowStride - ): - m_mfcc(numMfccFeatures, mfccWindowLen), - m_inputTensor(inputTensor), - m_mfccBuf(numMfccFeatures, numFeatureFrames), - m_delta1Buf(numMfccFeatures, numFeatureFrames), - m_delta2Buf(numMfccFeatures, numFeatureFrames), - m_mfccWindowLen(mfccWindowLen), - m_mfccWindowStride(mfccWindowStride), - m_numMfccFeats(numMfccFeatures), - m_numFeatureFrames(numFeatureFrames) - { - if (numMfccFeatures > 0 && mfccWindowLen > 0) { - this->m_mfcc.Init(); - } - } - - bool AsrPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen) - { - this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>( - static_cast<const int16_t*>(audioData), audioDataLen, - this->m_mfccWindowLen, this->m_mfccWindowStride); - - uint32_t mfccBufIdx = 0; - - std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f); - std::fill(m_delta1Buf.begin(), m_delta1Buf.end(), 0.f); - std::fill(m_delta2Buf.begin(), m_delta2Buf.end(), 0.f); - - /* While we can slide over the audio. */ - while (this->m_mfccSlidingWindow.HasNext()) { - const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next(); - auto mfccAudioData = std::vector<int16_t>( - mfccWindow, - mfccWindow + this->m_mfccWindowLen); - auto mfcc = this->m_mfcc.MfccCompute(mfccAudioData); - for (size_t i = 0; i < this->m_mfccBuf.size(0); ++i) { - this->m_mfccBuf(i, mfccBufIdx) = mfcc[i]; - } - ++mfccBufIdx; - } - - /* Pad MFCC if needed by adding MFCC for zeros. */ - if (mfccBufIdx != this->m_numFeatureFrames) { - std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_mfccWindowLen, 0); - std::vector<float> mfccZeros = this->m_mfcc.MfccCompute(zerosWindow); - - while (mfccBufIdx != this->m_numFeatureFrames) { - memcpy(&this->m_mfccBuf(0, mfccBufIdx), - mfccZeros.data(), sizeof(float) * m_numMfccFeats); - ++mfccBufIdx; - } - } - - /* Compute first and second order deltas from MFCCs. */ - AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf); - - /* Standardize calculated features. */ - this->Standarize(); - - /* Quantise. */ - QuantParams quantParams = GetTensorQuantParams(this->m_inputTensor); - - if (0 == quantParams.scale) { - printf_err("Quantisation scale can't be 0\n"); - return false; - } - - switch(this->m_inputTensor->type) { - case kTfLiteUInt8: - return this->Quantise<uint8_t>( - tflite::GetTensorData<uint8_t>(this->m_inputTensor), this->m_inputTensor->bytes, - quantParams.scale, quantParams.offset); - case kTfLiteInt8: - return this->Quantise<int8_t>( - tflite::GetTensorData<int8_t>(this->m_inputTensor), this->m_inputTensor->bytes, - quantParams.scale, quantParams.offset); - default: - printf_err("Unsupported tensor type %s\n", - TfLiteTypeGetName(this->m_inputTensor->type)); - } - - return false; - } - - bool AsrPreProcess::ComputeDeltas(Array2d<float>& mfcc, - Array2d<float>& delta1, - Array2d<float>& delta2) - { - const std::vector <float> delta1Coeffs = - {6.66666667e-02, 5.00000000e-02, 3.33333333e-02, - 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, - -3.33333333e-02, -5.00000000e-02, -6.66666667e-02}; - - const std::vector <float> delta2Coeffs = - {0.06060606, 0.01515152, -0.01731602, - -0.03679654, -0.04329004, -0.03679654, - -0.01731602, 0.01515152, 0.06060606}; - - if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) || - mfcc.size(0) == 0 || mfcc.size(1) == 0) { - return false; - } - - /* Get the middle index; coeff vec len should always be odd. */ - const size_t coeffLen = delta1Coeffs.size(); - const size_t fMidIdx = (coeffLen - 1)/2; - const size_t numFeatures = mfcc.size(0); - const size_t numFeatVectors = mfcc.size(1); - - /* Iterate through features in MFCC vector. */ - for (size_t i = 0; i < numFeatures; ++i) { - /* For each feature, iterate through time (t) samples representing feature evolution and - * calculate d/dt and d^2/dt^2, using 1D convolution with differential kernels. - * Convolution padding = valid, result size is `time length - kernel length + 1`. - * The result is padded with 0 from both sides to match the size of initial time samples data. - * - * For the small filter, conv1D implementation as a simple loop is efficient enough. - * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32. - */ - - for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) { - float d1 = 0; - float d2 = 0; - const size_t mfccStIdx = j - fMidIdx; - - for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) { - - d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m]; - d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m]; - } - - delta1(i,j) = d1; - delta2(i,j) = d2; - } - } - - return true; - } - - void AsrPreProcess::StandardizeVecF32(Array2d<float>& vec) - { - auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize()); - auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); - - debug("Mean: %f, Stddev: %f\n", mean, stddev); - if (stddev == 0) { - std::fill(vec.begin(), vec.end(), 0); - } else { - const float stddevInv = 1.f/stddev; - const float normalisedMean = mean/stddev; - - auto NormalisingFunction = [=](float& value) { - value = value * stddevInv - normalisedMean; - }; - std::for_each(vec.begin(), vec.end(), NormalisingFunction); - } - } - - void AsrPreProcess::Standarize() - { - AsrPreProcess::StandardizeVecF32(this->m_mfccBuf); - AsrPreProcess::StandardizeVecF32(this->m_delta1Buf); - AsrPreProcess::StandardizeVecF32(this->m_delta2Buf); - } - - float AsrPreProcess::GetQuantElem( - const float elem, - const float quantScale, - const int quantOffset, - const float minVal, - const float maxVal) - { - float val = std::round((elem/quantScale) + quantOffset); - return std::min<float>(std::max<float>(val, minVal), maxVal); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/kws_asr/usecase.cmake b/source/use_case/kws_asr/usecase.cmake index 40df4d7..59ef450 100644 --- a/source/use_case/kws_asr/usecase.cmake +++ b/source/use_case/kws_asr/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the APIs to use for this use case +list(APPEND ${use_case}_API_LIST "kws" "asr") USER_OPTION(${use_case}_FILE_PATH "Directory with WAV files, or path to a single WAV file, to use in the evaluation application." ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ @@ -145,4 +147,4 @@ generate_audio_code(${${use_case}_FILE_PATH} ${SRC_GEN_DIR} ${INC_GEN_DIR} ${${use_case}_AUDIO_OFFSET} ${${use_case}_AUDIO_DURATION} ${${use_case}_AUDIO_RES_TYPE} - ${${use_case}_AUDIO_MIN_SAMPLES})
\ No newline at end of file + ${${use_case}_AUDIO_MIN_SAMPLES}) diff --git a/source/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp b/source/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp deleted file mode 100644 index cbf0e4e..0000000 --- a/source/use_case/noise_reduction/include/RNNoiseFeatureProcessor.hpp +++ /dev/null @@ -1,341 +0,0 @@ -/* - * Copyright (c) 2021-2022 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. - */ -#ifndef RNNOISE_FEATURE_PROCESSOR_HPP -#define RNNOISE_FEATURE_PROCESSOR_HPP - -#include "PlatformMath.hpp" -#include <cstdint> -#include <vector> -#include <array> -#include <tuple> - -namespace arm { -namespace app { -namespace rnn { - - using vec1D32F = std::vector<float>; - using vec2D32F = std::vector<vec1D32F>; - using arrHp = std::array<float, 2>; - using math::FftInstance; - using math::FftType; - - class FrameFeatures { - public: - bool m_silence{false}; /* If frame contains silence or not. */ - vec1D32F m_featuresVec{}; /* Calculated feature vector to feed to model. */ - vec1D32F m_fftX{}; /* Vector of floats arranged to represent complex numbers. */ - vec1D32F m_fftP{}; /* Vector of floats arranged to represent complex numbers. */ - vec1D32F m_Ex{}; /* Spectral band energy for audio x. */ - vec1D32F m_Ep{}; /* Spectral band energy for pitch p. */ - vec1D32F m_Exp{}; /* Correlated spectral energy between x and p. */ - }; - - /** - * @brief RNNoise pre and post processing class based on the 2018 paper from - * Jan-Marc Valin. Recommended reading: - * - https://jmvalin.ca/demo/rnnoise/ - * - https://arxiv.org/abs/1709.08243 - **/ - class RNNoiseFeatureProcessor { - /* Public interface */ - public: - RNNoiseFeatureProcessor(); - ~RNNoiseFeatureProcessor() = default; - - /** - * @brief Calculates the features from a given audio buffer ready to be sent to RNNoise model. - * @param[in] audioData Pointer to the floating point vector - * with audio data (within the numerical - * limits of int16_t type). - * @param[in] audioLen Number of elements in the audio window. - * @param[out] features FrameFeatures object reference. - **/ - void PreprocessFrame(const float* audioData, - size_t audioLen, - FrameFeatures& features); - - /** - * @brief Use the RNNoise model output gain values with pre-processing features - * to generate audio with noise suppressed. - * @param[in] modelOutput Output gain values from model. - * @param[in] features Calculated features from pre-processing step. - * @param[out] outFrame Output frame to be populated. - **/ - void PostProcessFrame(vec1D32F& modelOutput, FrameFeatures& features, vec1D32F& outFrame); - - - /* Public constants */ - public: - static constexpr uint32_t FRAME_SIZE_SHIFT{2}; - static constexpr uint32_t FRAME_SIZE{512}; - static constexpr uint32_t WINDOW_SIZE{2 * FRAME_SIZE}; - static constexpr uint32_t FREQ_SIZE{FRAME_SIZE + 1}; - - static constexpr uint32_t PITCH_MIN_PERIOD{64}; - static constexpr uint32_t PITCH_MAX_PERIOD{820}; - static constexpr uint32_t PITCH_FRAME_SIZE{1024}; - static constexpr uint32_t PITCH_BUF_SIZE{PITCH_MAX_PERIOD + PITCH_FRAME_SIZE}; - - static constexpr uint32_t NB_BANDS{22}; - static constexpr uint32_t CEPS_MEM{8}; - static constexpr uint32_t NB_DELTA_CEPS{6}; - - static constexpr uint32_t NB_FEATURES{NB_BANDS + 3*NB_DELTA_CEPS + 2}; - - /* Private functions */ - private: - - /** - * @brief Initialises the half window and DCT tables. - */ - void InitTables(); - - /** - * @brief Applies a bi-quadratic filter over the audio window. - * @param[in] bHp Constant coefficient set b (arrHp type). - * @param[in] aHp Constant coefficient set a (arrHp type). - * @param[in,out] memHpX Coefficients populated by this function. - * @param[in,out] audioWindow Floating point vector with audio data. - **/ - void BiQuad( - const arrHp& bHp, - const arrHp& aHp, - arrHp& memHpX, - vec1D32F& audioWindow); - - /** - * @brief Computes features from the "filtered" audio window. - * @param[in] audioWindow Floating point vector with audio data. - * @param[out] features FrameFeatures object reference. - **/ - void ComputeFrameFeatures(vec1D32F& audioWindow, FrameFeatures& features); - - /** - * @brief Runs analysis on the audio buffer. - * @param[in] audioWindow Floating point vector with audio data. - * @param[out] fft Floating point FFT vector containing real and - * imaginary pairs of elements. NOTE: this vector - * does not contain the mirror image (conjugates) - * part of the spectrum. - * @param[out] energy Computed energy for each band in the Bark scale. - * @param[out] analysisMem Buffer sequentially, but partially, - * populated with new audio data. - **/ - void FrameAnalysis( - const vec1D32F& audioWindow, - vec1D32F& fft, - vec1D32F& energy, - vec1D32F& analysisMem); - - /** - * @brief Applies the window function, in-place, over the given - * floating point buffer. - * @param[in,out] x Buffer the window will be applied to. - **/ - void ApplyWindow(vec1D32F& x); - - /** - * @brief Computes the FFT for a given vector. - * @param[in] x Vector to compute the FFT from. - * @param[out] fft Floating point FFT vector containing real and - * imaginary pairs of elements. NOTE: this vector - * does not contain the mirror image (conjugates) - * part of the spectrum. - **/ - void ForwardTransform( - vec1D32F& x, - vec1D32F& fft); - - /** - * @brief Computes band energy for each of the 22 Bark scale bands. - * @param[in] fft_X FFT spectrum (as computed by ForwardTransform). - * @param[out] bandE Vector with 22 elements populated with energy for - * each band. - **/ - void ComputeBandEnergy(const vec1D32F& fft_X, vec1D32F& bandE); - - /** - * @brief Computes band energy correlation. - * @param[in] X FFT vector X. - * @param[in] P FFT vector P. - * @param[out] bandC Vector with 22 elements populated with band energy - * correlation for the two input FFT vectors. - **/ - void ComputeBandCorr(const vec1D32F& X, const vec1D32F& P, vec1D32F& bandC); - - /** - * @brief Performs pitch auto-correlation for a given vector for - * given lag. - * @param[in] x Input vector. - * @param[out] ac Auto-correlation output vector. - * @param[in] lag Lag value. - * @param[in] n Number of elements to consider for correlation - * computation. - **/ - void AutoCorr(const vec1D32F &x, - vec1D32F &ac, - size_t lag, - size_t n); - - /** - * @brief Computes pitch cross-correlation. - * @param[in] x Input vector 1. - * @param[in] y Input vector 2. - * @param[out] xCorr Cross-correlation output vector. - * @param[in] len Number of elements to consider for correlation. - * computation. - * @param[in] maxPitch Maximum pitch. - **/ - void PitchXCorr( - const vec1D32F& x, - const vec1D32F& y, - vec1D32F& xCorr, - size_t len, - size_t maxPitch); - - /** - * @brief Computes "Linear Predictor Coefficients". - * @param[in] ac Correlation vector. - * @param[in] p Number of elements of input vector to consider. - * @param[out] lpc Output coefficients vector. - **/ - void LPC(const vec1D32F& ac, int32_t p, vec1D32F& lpc); - - /** - * @brief Custom FIR implementation. - * @param[in] num FIR coefficient vector. - * @param[in] N Number of elements. - * @param[out] x Vector to be be processed. - **/ - void Fir5(const vec1D32F& num, uint32_t N, vec1D32F& x); - - /** - * @brief Down-sample the pitch buffer. - * @param[in,out] pitchBuf Pitch buffer. - * @param[in] pitchBufSz Buffer size. - **/ - void PitchDownsample(vec1D32F& pitchBuf, size_t pitchBufSz); - - /** - * @brief Pitch search function. - * @param[in] xLP Shifted pitch buffer input. - * @param[in] y Pitch buffer input. - * @param[in] len Length to search for. - * @param[in] maxPitch Maximum pitch. - * @return pitch index. - **/ - int PitchSearch(vec1D32F& xLp, vec1D32F& y, uint32_t len, uint32_t maxPitch); - - /** - * @brief Finds the "best" pitch from the buffer. - * @param[in] xCorr Pitch correlation vector. - * @param[in] y Pitch buffer input. - * @param[in] len Length to search for. - * @param[in] maxPitch Maximum pitch. - * @return pitch array (2 elements). - **/ - arrHp FindBestPitch(vec1D32F& xCorr, vec1D32F& y, uint32_t len, uint32_t maxPitch); - - /** - * @brief Remove pitch period doubling errors. - * @param[in,out] pitchBuf Pitch buffer vector. - * @param[in] maxPeriod Maximum period. - * @param[in] minPeriod Minimum period. - * @param[in] frameSize Frame size. - * @param[in] pitchIdx0_ Pitch index 0. - * @return pitch index. - **/ - int RemoveDoubling( - vec1D32F& pitchBuf, - uint32_t maxPeriod, - uint32_t minPeriod, - uint32_t frameSize, - size_t pitchIdx0_); - - /** - * @brief Computes pitch gain. - * @param[in] xy Single xy cross correlation value. - * @param[in] xx Single xx auto correlation value. - * @param[in] yy Single yy auto correlation value. - * @return Calculated pitch gain. - **/ - float ComputePitchGain(float xy, float xx, float yy); - - /** - * @brief Computes DCT vector from the given input. - * @param[in] input Input vector. - * @param[out] output Output vector with DCT coefficients. - **/ - void DCT(vec1D32F& input, vec1D32F& output); - - /** - * @brief Perform inverse fourier transform on complex spectral vector. - * @param[out] out Output vector. - * @param[in] fftXIn Vector of floats arranged to represent complex numbers interleaved. - **/ - void InverseTransform(vec1D32F& out, vec1D32F& fftXIn); - - /** - * @brief Perform pitch filtering. - * @param[in] features Object with pre-processing calculated frame features. - * @param[in] g Gain values. - **/ - void PitchFilter(FrameFeatures& features, vec1D32F& g); - - /** - * @brief Interpolate the band gain values. - * @param[out] g Gain values. - * @param[in] bandE Vector with 22 elements populated with energy for - * each band. - **/ - void InterpBandGain(vec1D32F& g, vec1D32F& bandE); - - /** - * @brief Create de-noised frame. - * @param[out] outFrame Output vector for storing the created audio frame. - * @param[in] fftY Gain adjusted complex spectral vector. - */ - void FrameSynthesis(vec1D32F& outFrame, vec1D32F& fftY); - - /* Private objects */ - private: - FftInstance m_fftInstReal; /* FFT instance for real numbers */ - FftInstance m_fftInstCmplx; /* FFT instance for complex numbers */ - vec1D32F m_halfWindow; /* Window coefficients */ - vec1D32F m_dctTable; /* DCT table */ - vec1D32F m_analysisMem; /* Buffer used for frame analysis */ - vec2D32F m_cepstralMem; /* Cepstral coefficients */ - size_t m_memId; /* memory ID */ - vec1D32F m_synthesisMem; /* Synthesis mem (used by post-processing) */ - vec1D32F m_pitchBuf; /* Pitch buffer */ - float m_lastGain; /* Last gain calculated */ - int m_lastPeriod; /* Last period calculated */ - arrHp m_memHpX; /* HpX coefficients. */ - vec1D32F m_lastGVec; /* Last gain vector (used by post-processing) */ - - /* Constants */ - const std::array <uint32_t, NB_BANDS> m_eband5ms { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, - 14, 16, 20, 24, 28, 34, 40, 48, 60, 78, 100}; - }; - - -} /* namespace rnn */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* RNNOISE_FEATURE_PROCESSOR_HPP */ diff --git a/source/use_case/noise_reduction/include/RNNoiseModel.hpp b/source/use_case/noise_reduction/include/RNNoiseModel.hpp deleted file mode 100644 index f6e4510..0000000 --- a/source/use_case/noise_reduction/include/RNNoiseModel.hpp +++ /dev/null @@ -1,82 +0,0 @@ -/* - * 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. - */ -#ifndef RNNOISE_MODEL_HPP -#define RNNOISE_MODEL_HPP - -#include "Model.hpp" - -extern const uint32_t g_NumInputFeatures; -extern const uint32_t g_FrameLength; -extern const uint32_t g_FrameStride; - -namespace arm { -namespace app { - - class RNNoiseModel : public Model { - public: - /** - * @brief Runs inference for RNNoise model. - * - * Call CopyGruStates so GRU state outputs are copied to GRU state inputs before the inference run. - * Run ResetGruState() method to set states to zero before starting processing logically related data. - * @return True if inference succeeded, False - otherwise - */ - bool RunInference() override; - - /** - * @brief Sets GRU input states to zeros. - * Call this method before starting processing the new sequence of logically related data. - */ - void ResetGruState(); - - /** - * @brief Copy current GRU output states to input states. - * Call this method before starting processing the next sequence of logically related data. - */ - bool CopyGruStates(); - - /* Which index of model outputs does the main output (gains) come from. */ - const size_t m_indexForModelOutput = 1; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - /* - Each inference after the first needs to copy 3 GRU states from a output index to input index (model dependent): - 0 -> 3, 2 -> 2, 3 -> 1 - */ - const std::vector<std::pair<size_t, size_t>> m_gruStateMap = {{0,3}, {2, 2}, {3, 1}}; - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 15; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* RNNOISE_MODEL_HPP */
\ No newline at end of file diff --git a/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp b/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp deleted file mode 100644 index 15e62d9..0000000 --- a/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp +++ /dev/null @@ -1,113 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef RNNOISE_PROCESSING_HPP -#define RNNOISE_PROCESSING_HPP - -#include "BaseProcessing.hpp" -#include "Model.hpp" -#include "RNNoiseFeatureProcessor.hpp" - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Noise Reduction use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class RNNoisePreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in/out] featureProcessor RNNoise specific feature extractor object. - * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing. - * - **/ - explicit RNNoisePreProcess(TfLiteTensor* inputTensor, - std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, - std::shared_ptr<rnn::FrameFeatures> frameFeatures); - - /** - * @brief Should perform pre-processing of 'raw' input audio data and load it into - * TFLite Micro input tensors ready for inference - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - private: - TfLiteTensor* m_inputTensor; /* Model input tensor. */ - std::shared_ptr<rnn::RNNoiseFeatureProcessor> m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */ - std::shared_ptr<rnn::FrameFeatures> m_frameFeatures; /* RNNoise features shared between pre & post-processing. */ - rnn::vec1D32F m_audioFrame; /* Audio frame cast to FP32 */ - - /** - * @brief Quantize the given features and populate the input Tensor. - * @param[in] inputFeatures Vector of floating point features to quantize. - * @param[in] quantScale Quantization scale for the inputTensor. - * @param[in] quantOffset Quantization offset for the inputTensor. - * @param[in,out] inputTensor TFLite micro tensor to populate. - **/ - static void QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures, - float quantScale, int quantOffset, - TfLiteTensor* inputTensor); - }; - - /** - * @brief Post-processing class for Noise Reduction use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class RNNoisePostProcess : public BasePostProcess { - - public: - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[out] denoisedAudioFrame Vector to store the final denoised audio frame. - * @param[in/out] featureProcessor RNNoise specific feature extractor object. - * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing. - **/ - RNNoisePostProcess(TfLiteTensor* outputTensor, - std::vector<int16_t>& denoisedAudioFrame, - std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, - std::shared_ptr<rnn::FrameFeatures> frameFeatures); - - /** - * @brief Should perform post-processing of the result of inference then - * populate result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - - private: - TfLiteTensor* m_outputTensor; /* Model output tensor. */ - std::vector<int16_t>& m_denoisedAudioFrame; /* Vector to store the final denoised frame. */ - rnn::vec1D32F m_denoisedAudioFrameFloat; /* Internal vector to store the final denoised frame (FP32). */ - std::shared_ptr<rnn::RNNoiseFeatureProcessor> m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */ - std::shared_ptr<rnn::FrameFeatures> m_frameFeatures; /* RNNoise features shared between pre & post-processing. */ - std::vector<float> m_modelOutputFloat; /* Internal vector to store de-quantized model output. */ - - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* RNNOISE_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/noise_reduction/src/MainLoop.cc b/source/use_case/noise_reduction/src/MainLoop.cc index fd72127..4c74a48 100644 --- a/source/use_case/noise_reduction/src/MainLoop.cc +++ b/source/use_case/noise_reduction/src/MainLoop.cc @@ -18,7 +18,17 @@ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ #include "RNNoiseModel.hpp" /* Model class for running inference. */ #include "InputFiles.hpp" /* For input audio clips. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); enum opcodes { @@ -62,10 +72,22 @@ void main_loop() constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false; /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("Failed to initialise model\n"); return; } + +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; @@ -124,4 +146,4 @@ void main_loop() } } while (executionSuccessful && bUseMenu); info("Main loop terminated.\n"); -}
\ No newline at end of file +} diff --git a/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc b/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc deleted file mode 100644 index 036894c..0000000 --- a/source/use_case/noise_reduction/src/RNNoiseFeatureProcessor.cc +++ /dev/null @@ -1,892 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "RNNoiseFeatureProcessor.hpp" -#include "log_macros.h" - -#include <algorithm> -#include <cmath> -#include <cstring> - -namespace arm { -namespace app { -namespace rnn { - -#define VERIFY(x) \ -do { \ - if (!(x)) { \ - printf_err("Assert failed:" #x "\n"); \ - exit(1); \ - } \ -} while(0) - -RNNoiseFeatureProcessor::RNNoiseFeatureProcessor() : - m_halfWindow(FRAME_SIZE, 0), - m_dctTable(NB_BANDS * NB_BANDS), - m_analysisMem(FRAME_SIZE, 0), - m_cepstralMem(CEPS_MEM, vec1D32F(NB_BANDS, 0)), - m_memId{0}, - m_synthesisMem(FRAME_SIZE, 0), - m_pitchBuf(PITCH_BUF_SIZE, 0), - m_lastGain{0.0}, - m_lastPeriod{0}, - m_memHpX{}, - m_lastGVec(NB_BANDS, 0) -{ - constexpr uint32_t numFFt = 2 * FRAME_SIZE; - static_assert(numFFt != 0, "Num FFT can't be 0"); - - math::MathUtils::FftInitF32(numFFt, this->m_fftInstReal, FftType::real); - math::MathUtils::FftInitF32(numFFt, this->m_fftInstCmplx, FftType::complex); - this->InitTables(); -} - -void RNNoiseFeatureProcessor::PreprocessFrame(const float* audioData, - const size_t audioLen, - FrameFeatures& features) -{ - /* Note audioWindow is modified in place */ - const arrHp aHp {-1.99599, 0.99600 }; - const arrHp bHp {-2.00000, 1.00000 }; - - vec1D32F audioWindow{audioData, audioData + audioLen}; - - this->BiQuad(bHp, aHp, this->m_memHpX, audioWindow); - this->ComputeFrameFeatures(audioWindow, features); -} - -void RNNoiseFeatureProcessor::PostProcessFrame(vec1D32F& modelOutput, FrameFeatures& features, vec1D32F& outFrame) -{ - std::vector<float> outputBands = modelOutput; - std::vector<float> gain(FREQ_SIZE, 0); - - if (!features.m_silence) { - PitchFilter(features, outputBands); - for (size_t i = 0; i < NB_BANDS; i++) { - float alpha = .6f; - outputBands[i] = std::max(outputBands[i], alpha * m_lastGVec[i]); - m_lastGVec[i] = outputBands[i]; - } - InterpBandGain(gain, outputBands); - for (size_t i = 0; i < FREQ_SIZE; i++) { - features.m_fftX[2 * i] *= gain[i]; /* Real. */ - features.m_fftX[2 * i + 1] *= gain[i]; /*imaginary. */ - - } - - } - - FrameSynthesis(outFrame, features.m_fftX); -} - -void RNNoiseFeatureProcessor::InitTables() -{ - constexpr float pi = M_PI; - constexpr float halfPi = M_PI / 2; - constexpr float halfPiOverFrameSz = halfPi/FRAME_SIZE; - - for (uint32_t i = 0; i < FRAME_SIZE; i++) { - const float sinVal = math::MathUtils::SineF32(halfPiOverFrameSz * (i + 0.5f)); - m_halfWindow[i] = math::MathUtils::SineF32(halfPi * sinVal * sinVal); - } - - for (uint32_t i = 0; i < NB_BANDS; i++) { - for (uint32_t j = 0; j < NB_BANDS; j++) { - m_dctTable[i * NB_BANDS + j] = math::MathUtils::CosineF32((i + 0.5f) * j * pi / NB_BANDS); - } - m_dctTable[i * NB_BANDS] *= math::MathUtils::SqrtF32(0.5f); - } -} - -void RNNoiseFeatureProcessor::BiQuad( - const arrHp& bHp, - const arrHp& aHp, - arrHp& memHpX, - vec1D32F& audioWindow) -{ - for (float& audioElement : audioWindow) { - const auto xi = audioElement; - const auto yi = audioElement + memHpX[0]; - memHpX[0] = memHpX[1] + (bHp[0] * xi - aHp[0] * yi); - memHpX[1] = (bHp[1] * xi - aHp[1] * yi); - audioElement = yi; - } -} - -void RNNoiseFeatureProcessor::ComputeFrameFeatures(vec1D32F& audioWindow, - FrameFeatures& features) -{ - this->FrameAnalysis(audioWindow, - features.m_fftX, - features.m_Ex, - this->m_analysisMem); - - float energy = 0.0; - - vec1D32F Ly(NB_BANDS, 0); - vec1D32F p(WINDOW_SIZE, 0); - vec1D32F pitchBuf(PITCH_BUF_SIZE >> 1, 0); - - VERIFY(PITCH_BUF_SIZE >= this->m_pitchBuf.size()); - std::copy_n(this->m_pitchBuf.begin() + FRAME_SIZE, - PITCH_BUF_SIZE - FRAME_SIZE, - this->m_pitchBuf.begin()); - - VERIFY(FRAME_SIZE <= audioWindow.size() && PITCH_BUF_SIZE > FRAME_SIZE); - std::copy_n(audioWindow.begin(), - FRAME_SIZE, - this->m_pitchBuf.begin() + PITCH_BUF_SIZE - FRAME_SIZE); - - this->PitchDownsample(pitchBuf, PITCH_BUF_SIZE); - - VERIFY(pitchBuf.size() > PITCH_MAX_PERIOD/2); - vec1D32F xLp(pitchBuf.size() - PITCH_MAX_PERIOD/2, 0); - std::copy_n(pitchBuf.begin() + PITCH_MAX_PERIOD/2, xLp.size(), xLp.begin()); - - int pitchIdx = this->PitchSearch(xLp, pitchBuf, - PITCH_FRAME_SIZE, (PITCH_MAX_PERIOD - (3*PITCH_MIN_PERIOD))); - - pitchIdx = this->RemoveDoubling( - pitchBuf, - PITCH_MAX_PERIOD, - PITCH_MIN_PERIOD, - PITCH_FRAME_SIZE, - PITCH_MAX_PERIOD - pitchIdx); - - size_t stIdx = PITCH_BUF_SIZE - WINDOW_SIZE - pitchIdx; - VERIFY((static_cast<int>(PITCH_BUF_SIZE) - static_cast<int>(WINDOW_SIZE) - pitchIdx) >= 0); - std::copy_n(this->m_pitchBuf.begin() + stIdx, WINDOW_SIZE, p.begin()); - - this->ApplyWindow(p); - this->ForwardTransform(p, features.m_fftP); - this->ComputeBandEnergy(features.m_fftP, features.m_Ep); - this->ComputeBandCorr(features.m_fftX, features.m_fftP, features.m_Exp); - - for (uint32_t i = 0 ; i < NB_BANDS; ++i) { - features.m_Exp[i] /= math::MathUtils::SqrtF32( - 0.001f + features.m_Ex[i] * features.m_Ep[i]); - } - - vec1D32F dctVec(NB_BANDS, 0); - this->DCT(features.m_Exp, dctVec); - - features.m_featuresVec = vec1D32F (NB_FEATURES, 0); - for (uint32_t i = 0; i < NB_DELTA_CEPS; ++i) { - features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS + i] = dctVec[i]; - } - - features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS] -= 1.3; - features.m_featuresVec[NB_BANDS + 2*NB_DELTA_CEPS + 1] -= 0.9; - features.m_featuresVec[NB_BANDS + 3*NB_DELTA_CEPS] = 0.01 * (static_cast<int>(pitchIdx) - 300); - - float logMax = -2.f; - float follow = -2.f; - for (uint32_t i = 0; i < NB_BANDS; ++i) { - Ly[i] = log10f(1e-2f + features.m_Ex[i]); - Ly[i] = std::max<float>(logMax - 7, std::max<float>(follow - 1.5, Ly[i])); - logMax = std::max<float>(logMax, Ly[i]); - follow = std::max<float>(follow - 1.5, Ly[i]); - energy += features.m_Ex[i]; - } - - /* If there's no audio avoid messing up the state. */ - features.m_silence = true; - if (energy < 0.04) { - return; - } else { - features.m_silence = false; - } - - this->DCT(Ly, features.m_featuresVec); - features.m_featuresVec[0] -= 12.0; - features.m_featuresVec[1] -= 4.0; - - VERIFY(CEPS_MEM > 2); - uint32_t stIdx1 = this->m_memId < 1 ? CEPS_MEM + this->m_memId - 1 : this->m_memId - 1; - uint32_t stIdx2 = this->m_memId < 2 ? CEPS_MEM + this->m_memId - 2 : this->m_memId - 2; - VERIFY(stIdx1 < this->m_cepstralMem.size()); - VERIFY(stIdx2 < this->m_cepstralMem.size()); - auto ceps1 = this->m_cepstralMem[stIdx1]; - auto ceps2 = this->m_cepstralMem[stIdx2]; - - /* Ceps 0 */ - for (uint32_t i = 0; i < NB_BANDS; ++i) { - this->m_cepstralMem[this->m_memId][i] = features.m_featuresVec[i]; - } - - for (uint32_t i = 0; i < NB_DELTA_CEPS; ++i) { - features.m_featuresVec[i] = this->m_cepstralMem[this->m_memId][i] + ceps1[i] + ceps2[i]; - features.m_featuresVec[NB_BANDS + i] = this->m_cepstralMem[this->m_memId][i] - ceps2[i]; - features.m_featuresVec[NB_BANDS + NB_DELTA_CEPS + i] = - this->m_cepstralMem[this->m_memId][i] - 2 * ceps1[i] + ceps2[i]; - } - - /* Spectral variability features. */ - this->m_memId += 1; - if (this->m_memId == CEPS_MEM) { - this->m_memId = 0; - } - - float specVariability = 0.f; - - VERIFY(this->m_cepstralMem.size() >= CEPS_MEM); - for (size_t i = 0; i < CEPS_MEM; ++i) { - float minDist = 1e15; - for (size_t j = 0; j < CEPS_MEM; ++j) { - float dist = 0.f; - for (size_t k = 0; k < NB_BANDS; ++k) { - VERIFY(this->m_cepstralMem[i].size() >= NB_BANDS); - auto tmp = this->m_cepstralMem[i][k] - this->m_cepstralMem[j][k]; - dist += tmp * tmp; - } - - if (j != i) { - minDist = std::min<float>(minDist, dist); - } - } - specVariability += minDist; - } - - VERIFY(features.m_featuresVec.size() >= NB_BANDS + 3 * NB_DELTA_CEPS + 1); - features.m_featuresVec[NB_BANDS + 3 * NB_DELTA_CEPS + 1] = specVariability / CEPS_MEM - 2.1; -} - -void RNNoiseFeatureProcessor::FrameAnalysis( - const vec1D32F& audioWindow, - vec1D32F& fft, - vec1D32F& energy, - vec1D32F& analysisMem) -{ - vec1D32F x(WINDOW_SIZE, 0); - - /* Move old audio down and populate end with latest audio window. */ - VERIFY(x.size() >= FRAME_SIZE && analysisMem.size() >= FRAME_SIZE); - VERIFY(audioWindow.size() >= FRAME_SIZE); - - std::copy_n(analysisMem.begin(), FRAME_SIZE, x.begin()); - std::copy_n(audioWindow.begin(), x.size() - FRAME_SIZE, x.begin() + FRAME_SIZE); - std::copy_n(audioWindow.begin(), FRAME_SIZE, analysisMem.begin()); - - this->ApplyWindow(x); - - /* Calculate FFT. */ - ForwardTransform(x, fft); - - /* Compute band energy. */ - ComputeBandEnergy(fft, energy); -} - -void RNNoiseFeatureProcessor::ApplyWindow(vec1D32F& x) -{ - if (WINDOW_SIZE != x.size()) { - printf_err("Invalid size for vector to be windowed\n"); - return; - } - - VERIFY(this->m_halfWindow.size() >= FRAME_SIZE); - - /* Multiply input by sinusoidal function. */ - for (size_t i = 0; i < FRAME_SIZE; i++) { - x[i] *= this->m_halfWindow[i]; - x[WINDOW_SIZE - 1 - i] *= this->m_halfWindow[i]; - } -} - -void RNNoiseFeatureProcessor::ForwardTransform( - vec1D32F& x, - vec1D32F& fft) -{ - /* The input vector can be modified by the fft function. */ - fft.reserve(x.size() + 2); - fft.resize(x.size() + 2, 0); - math::MathUtils::FftF32(x, fft, this->m_fftInstReal); - - /* Normalise. */ - for (auto& f : fft) { - f /= this->m_fftInstReal.m_fftLen; - } - - /* Place the last freq element correctly */ - fft[fft.size()-2] = fft[1]; - fft[1] = 0; - - /* NOTE: We don't truncate out FFT vector as it already contains only the - * first half of the FFT's. The conjugates are not present. */ -} - -void RNNoiseFeatureProcessor::ComputeBandEnergy(const vec1D32F& fftX, vec1D32F& bandE) -{ - bandE = vec1D32F(NB_BANDS, 0); - - VERIFY(this->m_eband5ms.size() >= NB_BANDS); - for (uint32_t i = 0; i < NB_BANDS - 1; i++) { - const auto bandSize = (this->m_eband5ms[i + 1] - this->m_eband5ms[i]) - << FRAME_SIZE_SHIFT; - - for (uint32_t j = 0; j < bandSize; j++) { - const auto frac = static_cast<float>(j) / bandSize; - const auto idx = (this->m_eband5ms[i] << FRAME_SIZE_SHIFT) + j; - - auto tmp = fftX[2 * idx] * fftX[2 * idx]; /* Real part */ - tmp += fftX[2 * idx + 1] * fftX[2 * idx + 1]; /* Imaginary part */ - - bandE[i] += (1 - frac) * tmp; - bandE[i + 1] += frac * tmp; - } - } - bandE[0] *= 2; - bandE[NB_BANDS - 1] *= 2; -} - -void RNNoiseFeatureProcessor::ComputeBandCorr(const vec1D32F& X, const vec1D32F& P, vec1D32F& bandC) -{ - bandC = vec1D32F(NB_BANDS, 0); - VERIFY(this->m_eband5ms.size() >= NB_BANDS); - - for (uint32_t i = 0; i < NB_BANDS - 1; i++) { - const auto bandSize = (this->m_eband5ms[i + 1] - this->m_eband5ms[i]) << FRAME_SIZE_SHIFT; - - for (uint32_t j = 0; j < bandSize; j++) { - const auto frac = static_cast<float>(j) / bandSize; - const auto idx = (this->m_eband5ms[i] << FRAME_SIZE_SHIFT) + j; - - auto tmp = X[2 * idx] * P[2 * idx]; /* Real part */ - tmp += X[2 * idx + 1] * P[2 * idx + 1]; /* Imaginary part */ - - bandC[i] += (1 - frac) * tmp; - bandC[i + 1] += frac * tmp; - } - } - bandC[0] *= 2; - bandC[NB_BANDS - 1] *= 2; -} - -void RNNoiseFeatureProcessor::DCT(vec1D32F& input, vec1D32F& output) -{ - VERIFY(this->m_dctTable.size() >= NB_BANDS * NB_BANDS); - for (uint32_t i = 0; i < NB_BANDS; ++i) { - float sum = 0; - - for (uint32_t j = 0, k = 0; j < NB_BANDS; ++j, k += NB_BANDS) { - sum += input[j] * this->m_dctTable[k + i]; - } - output[i] = sum * math::MathUtils::SqrtF32(2.0/22); - } -} - -void RNNoiseFeatureProcessor::PitchDownsample(vec1D32F& pitchBuf, size_t pitchBufSz) { - for (size_t i = 1; i < (pitchBufSz >> 1); ++i) { - pitchBuf[i] = 0.5 * ( - 0.5 * (this->m_pitchBuf[2 * i - 1] + this->m_pitchBuf[2 * i + 1]) - + this->m_pitchBuf[2 * i]); - } - - pitchBuf[0] = 0.5*(0.5*(this->m_pitchBuf[1]) + this->m_pitchBuf[0]); - - vec1D32F ac(5, 0); - size_t numLags = 4; - - this->AutoCorr(pitchBuf, ac, numLags, pitchBufSz >> 1); - - /* Noise floor -40db */ - ac[0] *= 1.0001; - - /* Lag windowing. */ - for (size_t i = 1; i < numLags + 1; ++i) { - ac[i] -= ac[i] * (0.008 * i) * (0.008 * i); - } - - vec1D32F lpc(numLags, 0); - this->LPC(ac, numLags, lpc); - - float tmp = 1.0; - for (size_t i = 0; i < numLags; ++i) { - tmp = 0.9f * tmp; - lpc[i] = lpc[i] * tmp; - } - - vec1D32F lpc2(numLags + 1, 0); - float c1 = 0.8; - - /* Add a zero. */ - lpc2[0] = lpc[0] + 0.8; - lpc2[1] = lpc[1] + (c1 * lpc[0]); - lpc2[2] = lpc[2] + (c1 * lpc[1]); - lpc2[3] = lpc[3] + (c1 * lpc[2]); - lpc2[4] = (c1 * lpc[3]); - - this->Fir5(lpc2, pitchBufSz >> 1, pitchBuf); -} - -int RNNoiseFeatureProcessor::PitchSearch(vec1D32F& xLp, vec1D32F& y, uint32_t len, uint32_t maxPitch) { - uint32_t lag = len + maxPitch; - vec1D32F xLp4(len >> 2, 0); - vec1D32F yLp4(lag >> 2, 0); - vec1D32F xCorr(maxPitch >> 1, 0); - - /* Downsample by 2 again. */ - for (size_t j = 0; j < (len >> 2); ++j) { - xLp4[j] = xLp[2*j]; - } - for (size_t j = 0; j < (lag >> 2); ++j) { - yLp4[j] = y[2*j]; - } - - this->PitchXCorr(xLp4, yLp4, xCorr, len >> 2, maxPitch >> 2); - - /* Coarse search with 4x decimation. */ - arrHp bestPitch = this->FindBestPitch(xCorr, yLp4, len >> 2, maxPitch >> 2); - - /* Finer search with 2x decimation. */ - const int maxIdx = (maxPitch >> 1); - for (int i = 0; i < maxIdx; ++i) { - xCorr[i] = 0; - if (std::abs(i - 2*bestPitch[0]) > 2 and std::abs(i - 2*bestPitch[1]) > 2) { - continue; - } - float sum = 0; - for (size_t j = 0; j < len >> 1; ++j) { - sum += xLp[j] * y[i+j]; - } - - xCorr[i] = std::max(-1.0f, sum); - } - - bestPitch = this->FindBestPitch(xCorr, y, len >> 1, maxPitch >> 1); - - int offset; - /* Refine by pseudo-interpolation. */ - if ( 0 < bestPitch[0] && bestPitch[0] < ((maxPitch >> 1) - 1)) { - float a = xCorr[bestPitch[0] - 1]; - float b = xCorr[bestPitch[0]]; - float c = xCorr[bestPitch[0] + 1]; - - if ( (c-a) > 0.7*(b-a) ) { - offset = 1; - } else if ( (a-c) > 0.7*(b-c) ) { - offset = -1; - } else { - offset = 0; - } - } else { - offset = 0; - } - - return 2*bestPitch[0] - offset; -} - -arrHp RNNoiseFeatureProcessor::FindBestPitch(vec1D32F& xCorr, vec1D32F& y, uint32_t len, uint32_t maxPitch) -{ - float Syy = 1; - arrHp bestNum {-1, -1}; - arrHp bestDen {0, 0}; - arrHp bestPitch {0, 1}; - - for (size_t j = 0; j < len; ++j) { - Syy += (y[j] * y[j]); - } - - for (size_t i = 0; i < maxPitch; ++i ) { - if (xCorr[i] > 0) { - float xCorr16 = xCorr[i] * 1e-12f; /* Avoid problems when squaring. */ - - float num = xCorr16 * xCorr16; - if (num*bestDen[1] > bestNum[1]*Syy) { - if (num*bestDen[0] > bestNum[0]*Syy) { - bestNum[1] = bestNum[0]; - bestDen[1] = bestDen[0]; - bestPitch[1] = bestPitch[0]; - bestNum[0] = num; - bestDen[0] = Syy; - bestPitch[0] = i; - } else { - bestNum[1] = num; - bestDen[1] = Syy; - bestPitch[1] = i; - } - } - } - - Syy += (y[i+len]*y[i+len]) - (y[i]*y[i]); - Syy = std::max(1.0f, Syy); - } - - return bestPitch; -} - -int RNNoiseFeatureProcessor::RemoveDoubling( - vec1D32F& pitchBuf, - uint32_t maxPeriod, - uint32_t minPeriod, - uint32_t frameSize, - size_t pitchIdx0_) -{ - constexpr std::array<size_t, 16> secondCheck {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2}; - uint32_t minPeriod0 = minPeriod; - float lastPeriod = static_cast<float>(this->m_lastPeriod)/2; - float lastGain = static_cast<float>(this->m_lastGain); - - maxPeriod /= 2; - minPeriod /= 2; - pitchIdx0_ /= 2; - frameSize /= 2; - uint32_t xStart = maxPeriod; - - if (pitchIdx0_ >= maxPeriod) { - pitchIdx0_ = maxPeriod - 1; - } - - size_t pitchIdx = pitchIdx0_; - const size_t pitchIdx0 = pitchIdx0_; - - float xx = 0; - for ( size_t i = xStart; i < xStart+frameSize; ++i) { - xx += (pitchBuf[i] * pitchBuf[i]); - } - - float xy = 0; - for ( size_t i = xStart; i < xStart+frameSize; ++i) { - xy += (pitchBuf[i] * pitchBuf[i-pitchIdx0]); - } - - vec1D32F yyLookup (maxPeriod+1, 0); - yyLookup[0] = xx; - float yy = xx; - - for ( size_t i = 1; i < yyLookup.size(); ++i) { - yy = yy + (pitchBuf[xStart-i] * pitchBuf[xStart-i]) - - (pitchBuf[xStart+frameSize-i] * pitchBuf[xStart+frameSize-i]); - yyLookup[i] = std::max(0.0f, yy); - } - - yy = yyLookup[pitchIdx0]; - float bestXy = xy; - float bestYy = yy; - - float g = this->ComputePitchGain(xy, xx, yy); - float g0 = g; - - /* Look for any pitch at pitchIndex/k. */ - for ( size_t k = 2; k < 16; ++k) { - size_t pitchIdx1 = (2*pitchIdx0+k) / (2*k); - if (pitchIdx1 < minPeriod) { - break; - } - - size_t pitchIdx1b; - /* Look for another strong correlation at T1b. */ - if (k == 2) { - if ((pitchIdx1 + pitchIdx0) > maxPeriod) { - pitchIdx1b = pitchIdx0; - } else { - pitchIdx1b = pitchIdx0 + pitchIdx1; - } - } else { - pitchIdx1b = (2*(secondCheck[k])*pitchIdx0 + k) / (2*k); - } - - xy = 0; - for ( size_t i = xStart; i < xStart+frameSize; ++i) { - xy += (pitchBuf[i] * pitchBuf[i-pitchIdx1]); - } - - float xy2 = 0; - for ( size_t i = xStart; i < xStart+frameSize; ++i) { - xy2 += (pitchBuf[i] * pitchBuf[i-pitchIdx1b]); - } - xy = 0.5f * (xy + xy2); - VERIFY(pitchIdx1b < maxPeriod+1); - yy = 0.5f * (yyLookup[pitchIdx1] + yyLookup[pitchIdx1b]); - - float g1 = this->ComputePitchGain(xy, xx, yy); - - float cont; - if (std::abs(pitchIdx1-lastPeriod) <= 1) { - cont = lastGain; - } else if (std::abs(pitchIdx1-lastPeriod) <= 2 and 5*k*k < pitchIdx0) { - cont = 0.5f*lastGain; - } else { - cont = 0.0f; - } - - float thresh = std::max(0.3, 0.7*g0-cont); - - /* Bias against very high pitch (very short period) to avoid false-positives - * due to short-term correlation */ - if (pitchIdx1 < 3*minPeriod) { - thresh = std::max(0.4, 0.85*g0-cont); - } else if (pitchIdx1 < 2*minPeriod) { - thresh = std::max(0.5, 0.9*g0-cont); - } - if (g1 > thresh) { - bestXy = xy; - bestYy = yy; - pitchIdx = pitchIdx1; - g = g1; - } - } - - bestXy = std::max(0.0f, bestXy); - float pg; - if (bestYy <= bestXy) { - pg = 1.0; - } else { - pg = bestXy/(bestYy+1); - } - - std::array<float, 3> xCorr {0}; - for ( size_t k = 0; k < 3; ++k ) { - for ( size_t i = xStart; i < xStart+frameSize; ++i) { - xCorr[k] += (pitchBuf[i] * pitchBuf[i-(pitchIdx+k-1)]); - } - } - - size_t offset; - if ((xCorr[2]-xCorr[0]) > 0.7*(xCorr[1]-xCorr[0])) { - offset = 1; - } else if ((xCorr[0]-xCorr[2]) > 0.7*(xCorr[1]-xCorr[2])) { - offset = -1; - } else { - offset = 0; - } - - if (pg > g) { - pg = g; - } - - pitchIdx0_ = 2*pitchIdx + offset; - - if (pitchIdx0_ < minPeriod0) { - pitchIdx0_ = minPeriod0; - } - - this->m_lastPeriod = pitchIdx0_; - this->m_lastGain = pg; - - return this->m_lastPeriod; -} - -float RNNoiseFeatureProcessor::ComputePitchGain(float xy, float xx, float yy) -{ - return xy / math::MathUtils::SqrtF32(1+xx*yy); -} - -void RNNoiseFeatureProcessor::AutoCorr( - const vec1D32F& x, - vec1D32F& ac, - size_t lag, - size_t n) -{ - if (n < lag) { - printf_err("Invalid parameters for AutoCorr\n"); - return; - } - - auto fastN = n - lag; - - /* Auto-correlation - can be done by PlatformMath functions */ - this->PitchXCorr(x, x, ac, fastN, lag + 1); - - /* Modify auto-correlation by summing with auto-correlation for different lags. */ - for (size_t k = 0; k < lag + 1; k++) { - float d = 0; - for (size_t i = k + fastN; i < n; i++) { - d += x[i] * x[i - k]; - } - ac[k] += d; - } -} - - -void RNNoiseFeatureProcessor::PitchXCorr( - const vec1D32F& x, - const vec1D32F& y, - vec1D32F& xCorr, - size_t len, - size_t maxPitch) -{ - for (size_t i = 0; i < maxPitch; i++) { - float sum = 0; - for (size_t j = 0; j < len; j++) { - sum += x[j] * y[i + j]; - } - xCorr[i] = sum; - } -} - -/* Linear predictor coefficients */ -void RNNoiseFeatureProcessor::LPC( - const vec1D32F& correlation, - int32_t p, - vec1D32F& lpc) -{ - auto error = correlation[0]; - - if (error != 0) { - for (int i = 0; i < p; i++) { - - /* Sum up this iteration's reflection coefficient */ - float rr = 0; - for (int j = 0; j < i; j++) { - rr += lpc[j] * correlation[i - j]; - } - - rr += correlation[i + 1]; - auto r = -rr / error; - - /* Update LP coefficients and total error */ - lpc[i] = r; - for (int j = 0; j < ((i + 1) >> 1); j++) { - auto tmp1 = lpc[j]; - auto tmp2 = lpc[i - 1 - j]; - lpc[j] = tmp1 + (r * tmp2); - lpc[i - 1 - j] = tmp2 + (r * tmp1); - } - - error = error - (r * r * error); - - /* Bail out once we get 30dB gain */ - if (error < (0.001 * correlation[0])) { - break; - } - } - } -} - -void RNNoiseFeatureProcessor::Fir5( - const vec1D32F &num, - uint32_t N, - vec1D32F &x) -{ - auto num0 = num[0]; - auto num1 = num[1]; - auto num2 = num[2]; - auto num3 = num[3]; - auto num4 = num[4]; - auto mem0 = 0; - auto mem1 = 0; - auto mem2 = 0; - auto mem3 = 0; - auto mem4 = 0; - for (uint32_t i = 0; i < N; i++) - { - auto sum_ = x[i] + (num0 * mem0) + (num1 * mem1) + - (num2 * mem2) + (num3 * mem3) + (num4 * mem4); - mem4 = mem3; - mem3 = mem2; - mem2 = mem1; - mem1 = mem0; - mem0 = x[i]; - x[i] = sum_; - } -} - -void RNNoiseFeatureProcessor::PitchFilter(FrameFeatures &features, vec1D32F &gain) { - std::vector<float> r(NB_BANDS, 0); - std::vector<float> rf(FREQ_SIZE, 0); - std::vector<float> newE(NB_BANDS); - - for (size_t i = 0; i < NB_BANDS; i++) { - if (features.m_Exp[i] > gain[i]) { - r[i] = 1; - } else { - - - r[i] = std::pow(features.m_Exp[i], 2) * (1 - std::pow(gain[i], 2)) / - (.001 + std::pow(gain[i], 2) * (1 - std::pow(features.m_Exp[i], 2))); - } - - - r[i] = math::MathUtils::SqrtF32(std::min(1.0f, std::max(0.0f, r[i]))); - r[i] *= math::MathUtils::SqrtF32(features.m_Ex[i] / (1e-8f + features.m_Ep[i])); - } - - InterpBandGain(rf, r); - for (size_t i = 0; i < FREQ_SIZE - 1; i++) { - features.m_fftX[2 * i] += rf[i] * features.m_fftP[2 * i]; /* Real. */ - features.m_fftX[2 * i + 1] += rf[i] * features.m_fftP[2 * i + 1]; /* Imaginary. */ - - } - ComputeBandEnergy(features.m_fftX, newE); - std::vector<float> norm(NB_BANDS); - std::vector<float> normf(FRAME_SIZE, 0); - for (size_t i = 0; i < NB_BANDS; i++) { - norm[i] = math::MathUtils::SqrtF32(features.m_Ex[i] / (1e-8f + newE[i])); - } - - InterpBandGain(normf, norm); - for (size_t i = 0; i < FREQ_SIZE - 1; i++) { - features.m_fftX[2 * i] *= normf[i]; /* Real. */ - features.m_fftX[2 * i + 1] *= normf[i]; /* Imaginary. */ - - } -} - -void RNNoiseFeatureProcessor::FrameSynthesis(vec1D32F& outFrame, vec1D32F& fftY) { - std::vector<float> x(WINDOW_SIZE, 0); - InverseTransform(x, fftY); - ApplyWindow(x); - for (size_t i = 0; i < FRAME_SIZE; i++) { - outFrame[i] = x[i] + m_synthesisMem[i]; - } - memcpy((m_synthesisMem.data()), &x[FRAME_SIZE], FRAME_SIZE*sizeof(float)); -} - -void RNNoiseFeatureProcessor::InterpBandGain(vec1D32F& g, vec1D32F& bandE) { - for (size_t i = 0; i < NB_BANDS - 1; i++) { - int bandSize = (m_eband5ms[i + 1] - m_eband5ms[i]) << FRAME_SIZE_SHIFT; - for (int j = 0; j < bandSize; j++) { - float frac = static_cast<float>(j) / bandSize; - g[(m_eband5ms[i] << FRAME_SIZE_SHIFT) + j] = (1 - frac) * bandE[i] + frac * bandE[i + 1]; - } - } -} - -void RNNoiseFeatureProcessor::InverseTransform(vec1D32F& out, vec1D32F& fftXIn) { - - std::vector<float> x(WINDOW_SIZE * 2); /* This is complex. */ - vec1D32F newFFT; /* This is complex. */ - - size_t i; - for (i = 0; i < FREQ_SIZE * 2; i++) { - x[i] = fftXIn[i]; - } - for (i = FREQ_SIZE; i < WINDOW_SIZE; i++) { - x[2 * i] = x[2 * (WINDOW_SIZE - i)]; /* Real. */ - x[2 * i + 1] = -x[2 * (WINDOW_SIZE - i) + 1]; /* Imaginary. */ - } - - constexpr uint32_t numFFt = 2 * FRAME_SIZE; - static_assert(numFFt != 0, "numFFt cannot be 0!"); - - vec1D32F fftOut = vec1D32F(x.size(), 0); - math::MathUtils::FftF32(x,fftOut, m_fftInstCmplx); - - /* Normalize. */ - for (auto &f: fftOut) { - f /= numFFt; - } - - out[0] = WINDOW_SIZE * fftOut[0]; /* Real. */ - for (i = 1; i < WINDOW_SIZE; i++) { - out[i] = WINDOW_SIZE * fftOut[(WINDOW_SIZE * 2) - (2 * i)]; /* Real. */ - } -} - - -} /* namespace rnn */ -} /* namespace app */ -} /* namspace arm */ diff --git a/source/use_case/noise_reduction/src/RNNoiseModel.cc b/source/use_case/noise_reduction/src/RNNoiseModel.cc deleted file mode 100644 index 244fa1a..0000000 --- a/source/use_case/noise_reduction/src/RNNoiseModel.cc +++ /dev/null @@ -1,110 +0,0 @@ -/* - * 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 "RNNoiseModel.hpp" -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::RNNoiseModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::RNNoiseModel::EnlistOperations() -{ - this->m_opResolver.AddUnpack(); - this->m_opResolver.AddFullyConnected(); - this->m_opResolver.AddSplit(); - this->m_opResolver.AddSplitV(); - this->m_opResolver.AddAdd(); - this->m_opResolver.AddLogistic(); - this->m_opResolver.AddMul(); - this->m_opResolver.AddSub(); - this->m_opResolver.AddTanh(); - this->m_opResolver.AddPack(); - this->m_opResolver.AddReshape(); - this->m_opResolver.AddQuantize(); - this->m_opResolver.AddConcatenation(); - this->m_opResolver.AddRelu(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::RNNoiseModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::RNNoiseModel::ModelSize() -{ - return GetModelLen(); -} - -bool arm::app::RNNoiseModel::RunInference() -{ - return Model::RunInference(); -} - -void arm::app::RNNoiseModel::ResetGruState() -{ - for (auto& stateMapping: this->m_gruStateMap) { - TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.second); - auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor); - /* Initial value of states is 0, but this is affected by quantization zero point. */ - auto quantParams = arm::app::GetTensorQuantParams(inputGruStateTensor); - memset(inputGruState, quantParams.offset, inputGruStateTensor->bytes); - } -} - -bool arm::app::RNNoiseModel::CopyGruStates() -{ - std::vector<std::pair<size_t, std::vector<int8_t>>> tempOutGruStates; - /* Saving output states before copying them to input states to avoid output states modification in the tensor. - * tflu shares input and output tensors memory, thus writing to input tensor can change output tensor values. */ - for (auto& stateMapping: this->m_gruStateMap) { - TfLiteTensor* outputGruStateTensor = this->GetOutputTensor(stateMapping.first); - std::vector<int8_t> tempOutGruState(outputGruStateTensor->bytes); - auto* outGruState = tflite::GetTensorData<int8_t>(outputGruStateTensor); - memcpy(tempOutGruState.data(), outGruState, outputGruStateTensor->bytes); - /* Index of the input tensor and the data to copy. */ - tempOutGruStates.emplace_back(stateMapping.second, std::move(tempOutGruState)); - } - /* Updating input GRU states with saved GRU output states. */ - for (auto& stateMapping: tempOutGruStates) { - auto outputGruStateTensorData = stateMapping.second; - TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.first); - if (outputGruStateTensorData.size() != inputGruStateTensor->bytes) { - printf_err("Unexpected number of bytes for GRU state mapping. Input = %zuz, output = %zuz.\n", - inputGruStateTensor->bytes, - outputGruStateTensorData.size()); - return false; - } - auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor); - auto* outGruState = outputGruStateTensorData.data(); - memcpy(inputGruState, outGruState, inputGruStateTensor->bytes); - } - return true; -}
\ No newline at end of file diff --git a/source/use_case/noise_reduction/src/RNNoiseProcessing.cc b/source/use_case/noise_reduction/src/RNNoiseProcessing.cc deleted file mode 100644 index f6a3ec4..0000000 --- a/source/use_case/noise_reduction/src/RNNoiseProcessing.cc +++ /dev/null @@ -1,100 +0,0 @@ -/* - * Copyright (c) 2022 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 "RNNoiseProcessing.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - RNNoisePreProcess::RNNoisePreProcess(TfLiteTensor* inputTensor, - std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, std::shared_ptr<rnn::FrameFeatures> frameFeatures) - : m_inputTensor{inputTensor}, - m_featureProcessor{featureProcessor}, - m_frameFeatures{frameFeatures} - {} - - bool RNNoisePreProcess::DoPreProcess(const void* data, size_t inputSize) - { - if (data == nullptr) { - printf_err("Data pointer is null"); - return false; - } - - auto input = static_cast<const int16_t*>(data); - this->m_audioFrame = rnn::vec1D32F(input, input + inputSize); - m_featureProcessor->PreprocessFrame(this->m_audioFrame.data(), inputSize, *this->m_frameFeatures); - - QuantizeAndPopulateInput(this->m_frameFeatures->m_featuresVec, - this->m_inputTensor->params.scale, this->m_inputTensor->params.zero_point, - this->m_inputTensor); - - debug("Input tensor populated \n"); - - return true; - } - - void RNNoisePreProcess::QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures, - const float quantScale, const int quantOffset, - TfLiteTensor* inputTensor) - { - const float minVal = std::numeric_limits<int8_t>::min(); - const float maxVal = std::numeric_limits<int8_t>::max(); - - auto* inputTensorData = tflite::GetTensorData<int8_t>(inputTensor); - - for (size_t i=0; i < inputFeatures.size(); ++i) { - float quantValue = ((inputFeatures[i] / quantScale) + quantOffset); - inputTensorData[i] = static_cast<int8_t>(std::min<float>(std::max<float>(quantValue, minVal), maxVal)); - } - } - - RNNoisePostProcess::RNNoisePostProcess(TfLiteTensor* outputTensor, - std::vector<int16_t>& denoisedAudioFrame, - std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, - std::shared_ptr<rnn::FrameFeatures> frameFeatures) - : m_outputTensor{outputTensor}, - m_denoisedAudioFrame{denoisedAudioFrame}, - m_featureProcessor{featureProcessor}, - m_frameFeatures{frameFeatures} - { - this->m_denoisedAudioFrameFloat.reserve(denoisedAudioFrame.size()); - this->m_modelOutputFloat.resize(outputTensor->bytes); - } - - bool RNNoisePostProcess::DoPostProcess() - { - const auto* outputData = tflite::GetTensorData<int8_t>(this->m_outputTensor); - auto outputQuantParams = GetTensorQuantParams(this->m_outputTensor); - - for (size_t i = 0; i < this->m_outputTensor->bytes; ++i) { - this->m_modelOutputFloat[i] = (static_cast<float>(outputData[i]) - outputQuantParams.offset) - * outputQuantParams.scale; - } - - this->m_featureProcessor->PostProcessFrame(this->m_modelOutputFloat, - *this->m_frameFeatures, this->m_denoisedAudioFrameFloat); - - for (size_t i = 0; i < this->m_denoisedAudioFrame.size(); ++i) { - this->m_denoisedAudioFrame[i] = static_cast<int16_t>( - std::roundf(this->m_denoisedAudioFrameFloat[i])); - } - - return true; - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/noise_reduction/usecase.cmake b/source/use_case/noise_reduction/usecase.cmake index 8dfde58..0cd0761 100644 --- a/source/use_case/noise_reduction/usecase.cmake +++ b/source/use_case/noise_reduction/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "noise_reduction") USER_OPTION(${use_case}_ACTIVATION_BUF_SZ "Activation buffer size for the chosen model" 0x00200000 diff --git a/source/use_case/object_detection/include/DetectionResult.hpp b/source/use_case/object_detection/include/DetectionResult.hpp deleted file mode 100644 index aa74d90..0000000 --- a/source/use_case/object_detection/include/DetectionResult.hpp +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef DETECTION_RESULT_HPP -#define DETECTION_RESULT_HPP - - -namespace arm { -namespace app { -namespace object_detection { - - /** - * @brief Class representing a single detection result. - */ - class DetectionResult { - public: - /** - * @brief Constructor - * @param[in] normalisedVal Result normalized value - * @param[in] x0 Top corner x starting point - * @param[in] y0 Top corner y starting point - * @param[in] w Detection result width - * @param[in] h Detection result height - **/ - DetectionResult(double normalisedVal,int x0,int y0, int w,int h) : - m_normalisedVal(normalisedVal), - m_x0(x0), - m_y0(y0), - m_w(w), - m_h(h) - { - } - - DetectionResult() = default; - ~DetectionResult() = default; - - double m_normalisedVal{0.0}; - int m_x0{0}; - int m_y0{0}; - int m_w{0}; - int m_h{0}; - }; - -} /* namespace object_detection */ -} /* namespace app */ -} /* namespace arm */ - -#endif /* DETECTION_RESULT_HPP */ diff --git a/source/use_case/object_detection/include/DetectorPostProcessing.hpp b/source/use_case/object_detection/include/DetectorPostProcessing.hpp deleted file mode 100644 index b3ddb2c..0000000 --- a/source/use_case/object_detection/include/DetectorPostProcessing.hpp +++ /dev/null @@ -1,126 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef DETECTOR_POST_PROCESSING_HPP -#define DETECTOR_POST_PROCESSING_HPP - -#include "UseCaseCommonUtils.hpp" -#include "ImageUtils.hpp" -#include "DetectionResult.hpp" -#include "YoloFastestModel.hpp" -#include "BaseProcessing.hpp" - -#include <forward_list> - -namespace arm { -namespace app { - -namespace object_detection { - - struct Branch { - int resolution; - int numBox; - const float* anchor; - int8_t* modelOutput; - float scale; - int zeroPoint; - size_t size; - }; - - struct Network { - int inputWidth; - int inputHeight; - int numClasses; - std::vector<Branch> branches; - int topN; - }; - -} /* namespace object_detection */ - - /** - * @brief Post-processing class for Object Detection use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class DetectorPostProcess : public BasePostProcess { - public: - /** - * @brief Constructor. - * @param[in] outputTensor0 Pointer to the TFLite Micro output Tensor at index 0. - * @param[in] outputTensor1 Pointer to the TFLite Micro output Tensor at index 1. - * @param[out] results Vector of detected results. - * @param[in] inputImgRows Number of rows in the input image. - * @param[in] inputImgCols Number of columns in the input image. - * @param[in] threshold Post-processing threshold. - * @param[in] nms Non-maximum Suppression threshold. - * @param[in] numClasses Number of classes. - * @param[in] topN Top N for each class. - **/ - explicit DetectorPostProcess(TfLiteTensor* outputTensor0, - TfLiteTensor* outputTensor1, - std::vector<object_detection::DetectionResult>& results, - int inputImgRows, - int inputImgCols, - float threshold = 0.5f, - float nms = 0.45f, - int numClasses = 1, - int topN = 0); - - /** - * @brief Should perform YOLO post-processing of the result of inference then - * populate Detection result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - - private: - TfLiteTensor* m_outputTensor0; /* Output tensor index 0 */ - TfLiteTensor* m_outputTensor1; /* Output tensor index 1 */ - std::vector<object_detection::DetectionResult>& m_results; /* Single inference results. */ - int m_inputImgRows; /* Number of rows for model input. */ - int m_inputImgCols; /* Number of cols for model input. */ - float m_threshold; /* Post-processing threshold. */ - float m_nms; /* NMS threshold. */ - int m_numClasses; /* Number of classes. */ - int m_topN; /* TopN. */ - object_detection::Network m_net; /* YOLO network object. */ - - /** - * @brief Insert the given Detection in the list. - * @param[in] detections List of detections. - * @param[in] det Detection to be inserted. - **/ - void InsertTopNDetections(std::forward_list<image::Detection>& detections, image::Detection& det); - - /** - * @brief Given a Network calculate the detection boxes. - * @param[in] net Network. - * @param[in] imageWidth Original image width. - * @param[in] imageHeight Original image height. - * @param[in] threshold Detections threshold. - * @param[out] detections Detection boxes. - **/ - void GetNetworkBoxes(object_detection::Network& net, - int imageWidth, - int imageHeight, - float threshold, - std::forward_list<image::Detection>& detections); - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* DETECTOR_POST_PROCESSING_HPP */ diff --git a/source/use_case/object_detection/include/DetectorPreProcessing.hpp b/source/use_case/object_detection/include/DetectorPreProcessing.hpp deleted file mode 100644 index 4936048..0000000 --- a/source/use_case/object_detection/include/DetectorPreProcessing.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef DETECTOR_PRE_PROCESSING_HPP -#define DETECTOR_PRE_PROCESSING_HPP - -#include "BaseProcessing.hpp" -#include "Classifier.hpp" - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Object detection use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class DetectorPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] rgb2Gray Convert image from 3 channel RGB to 1 channel grayscale. - * @param[in] convertToInt8 Convert the image from uint8 to int8 range. - **/ - explicit DetectorPreProcess(TfLiteTensor* inputTensor, bool rgb2Gray, bool convertToInt8); - - /** - * @brief Should perform pre-processing of 'raw' input image data and load it into - * TFLite Micro input tensor ready for inference - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - private: - TfLiteTensor* m_inputTensor; - bool m_rgb2Gray; - bool m_convertToInt8; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* DETECTOR_PRE_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/object_detection/include/YoloFastestModel.hpp b/source/use_case/object_detection/include/YoloFastestModel.hpp deleted file mode 100644 index 2986a58..0000000 --- a/source/use_case/object_detection/include/YoloFastestModel.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef YOLO_FASTEST_MODEL_HPP -#define YOLO_FASTEST_MODEL_HPP - -#include "Model.hpp" - -extern const int originalImageSize; -extern const int channelsImageDisplayed; -extern const float anchor1[]; -extern const float anchor2[]; - -namespace arm { -namespace app { - - class YoloFastestModel : public Model { - - public: - /* Indices for the expected model - based on input tensor shape */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_inputChannelsIdx = 3; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 8; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* YOLO_FASTEST_MODEL_HPP */ diff --git a/source/use_case/object_detection/src/DetectorPostProcessing.cc b/source/use_case/object_detection/src/DetectorPostProcessing.cc deleted file mode 100644 index fb1606a..0000000 --- a/source/use_case/object_detection/src/DetectorPostProcessing.cc +++ /dev/null @@ -1,240 +0,0 @@ -/* - * Copyright (c) 2022 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 "DetectorPostProcessing.hpp" -#include "PlatformMath.hpp" - -#include <cmath> - -namespace arm { -namespace app { - - DetectorPostProcess::DetectorPostProcess( - TfLiteTensor* modelOutput0, - TfLiteTensor* modelOutput1, - std::vector<object_detection::DetectionResult>& results, - int inputImgRows, - int inputImgCols, - const float threshold, - const float nms, - int numClasses, - int topN) - : m_outputTensor0{modelOutput0}, - m_outputTensor1{modelOutput1}, - m_results{results}, - m_inputImgRows{inputImgRows}, - m_inputImgCols{inputImgCols}, - m_threshold(threshold), - m_nms(nms), - m_numClasses(numClasses), - m_topN(topN) -{ - /* Init PostProcessing */ - this->m_net = - object_detection::Network { - .inputWidth = inputImgCols, - .inputHeight = inputImgRows, - .numClasses = numClasses, - .branches = { - object_detection::Branch { - .resolution = inputImgCols/32, - .numBox = 3, - .anchor = anchor1, - .modelOutput = this->m_outputTensor0->data.int8, - .scale = (static_cast<TfLiteAffineQuantization*>( - this->m_outputTensor0->quantization.params))->scale->data[0], - .zeroPoint = (static_cast<TfLiteAffineQuantization*>( - this->m_outputTensor0->quantization.params))->zero_point->data[0], - .size = this->m_outputTensor0->bytes - }, - object_detection::Branch { - .resolution = inputImgCols/16, - .numBox = 3, - .anchor = anchor2, - .modelOutput = this->m_outputTensor1->data.int8, - .scale = (static_cast<TfLiteAffineQuantization*>( - this->m_outputTensor1->quantization.params))->scale->data[0], - .zeroPoint = (static_cast<TfLiteAffineQuantization*>( - this->m_outputTensor1->quantization.params))->zero_point->data[0], - .size = this->m_outputTensor1->bytes - } - }, - .topN = m_topN - }; - /* End init */ -} - -bool DetectorPostProcess::DoPostProcess() -{ - /* Start postprocessing */ - int originalImageWidth = originalImageSize; - int originalImageHeight = originalImageSize; - - std::forward_list<image::Detection> detections; - GetNetworkBoxes(this->m_net, originalImageWidth, originalImageHeight, m_threshold, detections); - - /* Do nms */ - CalculateNMS(detections, this->m_net.numClasses, m_nms); - - for (auto& it: detections) { - float xMin = it.bbox.x - it.bbox.w / 2.0f; - float xMax = it.bbox.x + it.bbox.w / 2.0f; - float yMin = it.bbox.y - it.bbox.h / 2.0f; - float yMax = it.bbox.y + it.bbox.h / 2.0f; - - if (xMin < 0) { - xMin = 0; - } - if (yMin < 0) { - yMin = 0; - } - if (xMax > originalImageWidth) { - xMax = originalImageWidth; - } - if (yMax > originalImageHeight) { - yMax = originalImageHeight; - } - - float boxX = xMin; - float boxY = yMin; - float boxWidth = xMax - xMin; - float boxHeight = yMax - yMin; - - for (int j = 0; j < this->m_net.numClasses; ++j) { - if (it.prob[j] > 0) { - - object_detection::DetectionResult tmpResult = {}; - tmpResult.m_normalisedVal = it.prob[j]; - tmpResult.m_x0 = boxX; - tmpResult.m_y0 = boxY; - tmpResult.m_w = boxWidth; - tmpResult.m_h = boxHeight; - - this->m_results.push_back(tmpResult); - } - } - } - return true; -} - -void DetectorPostProcess::InsertTopNDetections(std::forward_list<image::Detection>& detections, image::Detection& det) -{ - std::forward_list<image::Detection>::iterator it; - std::forward_list<image::Detection>::iterator last_it; - for ( it = detections.begin(); it != detections.end(); ++it ) { - if(it->objectness > det.objectness) - break; - last_it = it; - } - if(it != detections.begin()) { - detections.emplace_after(last_it, det); - detections.pop_front(); - } -} - -void DetectorPostProcess::GetNetworkBoxes( - object_detection::Network& net, - int imageWidth, - int imageHeight, - float threshold, - std::forward_list<image::Detection>& detections) -{ - int numClasses = net.numClasses; - int num = 0; - auto det_objectness_comparator = [](image::Detection& pa, image::Detection& pb) { - return pa.objectness < pb.objectness; - }; - for (size_t i = 0; i < net.branches.size(); ++i) { - int height = net.branches[i].resolution; - int width = net.branches[i].resolution; - int channel = net.branches[i].numBox*(5+numClasses); - - for (int h = 0; h < net.branches[i].resolution; h++) { - for (int w = 0; w < net.branches[i].resolution; w++) { - for (int anc = 0; anc < net.branches[i].numBox; anc++) { - - /* Objectness score */ - int bbox_obj_offset = h * width * channel + w * channel + anc * (numClasses + 5) + 4; - float objectness = math::MathUtils::SigmoidF32( - (static_cast<float>(net.branches[i].modelOutput[bbox_obj_offset]) - - net.branches[i].zeroPoint - ) * net.branches[i].scale); - - if(objectness > threshold) { - image::Detection det; - det.objectness = objectness; - /* Get bbox prediction data for each anchor, each feature point */ - int bbox_x_offset = bbox_obj_offset -4; - int bbox_y_offset = bbox_x_offset + 1; - int bbox_w_offset = bbox_x_offset + 2; - int bbox_h_offset = bbox_x_offset + 3; - int bbox_scores_offset = bbox_x_offset + 5; - - det.bbox.x = (static_cast<float>(net.branches[i].modelOutput[bbox_x_offset]) - - net.branches[i].zeroPoint) * net.branches[i].scale; - det.bbox.y = (static_cast<float>(net.branches[i].modelOutput[bbox_y_offset]) - - net.branches[i].zeroPoint) * net.branches[i].scale; - det.bbox.w = (static_cast<float>(net.branches[i].modelOutput[bbox_w_offset]) - - net.branches[i].zeroPoint) * net.branches[i].scale; - det.bbox.h = (static_cast<float>(net.branches[i].modelOutput[bbox_h_offset]) - - net.branches[i].zeroPoint) * net.branches[i].scale; - - float bbox_x, bbox_y; - - /* Eliminate grid sensitivity trick involved in YOLOv4 */ - bbox_x = math::MathUtils::SigmoidF32(det.bbox.x); - bbox_y = math::MathUtils::SigmoidF32(det.bbox.y); - det.bbox.x = (bbox_x + w) / width; - det.bbox.y = (bbox_y + h) / height; - - det.bbox.w = std::exp(det.bbox.w) * net.branches[i].anchor[anc*2] / net.inputWidth; - det.bbox.h = std::exp(det.bbox.h) * net.branches[i].anchor[anc*2+1] / net.inputHeight; - - for (int s = 0; s < numClasses; s++) { - float sig = math::MathUtils::SigmoidF32( - (static_cast<float>(net.branches[i].modelOutput[bbox_scores_offset + s]) - - net.branches[i].zeroPoint) * net.branches[i].scale - ) * objectness; - det.prob.emplace_back((sig > threshold) ? sig : 0); - } - - /* Correct_YOLO_boxes */ - det.bbox.x *= imageWidth; - det.bbox.w *= imageWidth; - det.bbox.y *= imageHeight; - det.bbox.h *= imageHeight; - - if (num < net.topN || net.topN <=0) { - detections.emplace_front(det); - num += 1; - } else if (num == net.topN) { - detections.sort(det_objectness_comparator); - InsertTopNDetections(detections,det); - num += 1; - } else { - InsertTopNDetections(detections,det); - } - } - } - } - } - } - if(num > net.topN) - num -=1; -} - -} /* namespace app */ -} /* namespace arm */ diff --git a/source/use_case/object_detection/src/DetectorPreProcessing.cc b/source/use_case/object_detection/src/DetectorPreProcessing.cc deleted file mode 100644 index 7212046..0000000 --- a/source/use_case/object_detection/src/DetectorPreProcessing.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2022 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 "DetectorPreProcessing.hpp" -#include "ImageUtils.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - DetectorPreProcess::DetectorPreProcess(TfLiteTensor* inputTensor, bool rgb2Gray, bool convertToInt8) - : m_inputTensor{inputTensor}, - m_rgb2Gray{rgb2Gray}, - m_convertToInt8{convertToInt8} - {} - - bool DetectorPreProcess::DoPreProcess(const void* data, size_t inputSize) { - if (data == nullptr) { - printf_err("Data pointer is null"); - } - - auto input = static_cast<const uint8_t*>(data); - - if (this->m_rgb2Gray) { - image::RgbToGrayscale(input, this->m_inputTensor->data.uint8, this->m_inputTensor->bytes); - } else { - std::memcpy(this->m_inputTensor->data.data, input, inputSize); - } - debug("Input tensor populated \n"); - - if (this->m_convertToInt8) { - image::ConvertImgToInt8(this->m_inputTensor->data.data, this->m_inputTensor->bytes); - } - - return true; - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/object_detection/src/MainLoop.cc b/source/use_case/object_detection/src/MainLoop.cc index 4291164..d119501 100644 --- a/source/use_case/object_detection/src/MainLoop.cc +++ b/source/use_case/object_detection/src/MainLoop.cc @@ -19,7 +19,17 @@ #include "YoloFastestModel.hpp" /* Model class for running inference. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); static void DisplayDetectionMenu() { @@ -40,11 +50,22 @@ void main_loop() arm::app::YoloFastestModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("Failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; diff --git a/source/use_case/object_detection/src/YoloFastestModel.cc b/source/use_case/object_detection/src/YoloFastestModel.cc deleted file mode 100644 index b1fd776..0000000 --- a/source/use_case/object_detection/src/YoloFastestModel.cc +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2022 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 "YoloFastestModel.hpp" - -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::YoloFastestModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::YoloFastestModel::EnlistOperations() -{ - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddAdd(); - this->m_opResolver.AddResizeNearestNeighbor(); - /*These are needed for UT to work, not needed on FVP */ - this->m_opResolver.AddPad(); - this->m_opResolver.AddMaxPool2D(); - this->m_opResolver.AddConcatenation(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::YoloFastestModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::YoloFastestModel::ModelSize() -{ - return GetModelLen(); -} diff --git a/source/use_case/object_detection/usecase.cmake b/source/use_case/object_detection/usecase.cmake index 42c4f2c..850e7fc 100644 --- a/source/use_case/object_detection/usecase.cmake +++ b/source/use_case/object_detection/usecase.cmake @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "object_detection") USER_OPTION(${use_case}_FILE_PATH "Directory with custom image files to use, or path to a single image, in the evaluation application" ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ diff --git a/source/use_case/vww/include/VisualWakeWordModel.hpp b/source/use_case/vww/include/VisualWakeWordModel.hpp deleted file mode 100644 index 1ed9202..0000000 --- a/source/use_case/vww/include/VisualWakeWordModel.hpp +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2021 - 2022 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. - */ -#ifndef VISUAL_WAKE_WORD_MODEL_HPP -#define VISUAL_WAKE_WORD_MODEL_HPP - -#include "Model.hpp" - -namespace arm { -namespace app { - - class VisualWakeWordModel : public Model { - - public: - /* Indices for the expected model - based on input tensor shape */ - static constexpr uint32_t ms_inputRowsIdx = 1; - static constexpr uint32_t ms_inputColsIdx = 2; - static constexpr uint32_t ms_inputChannelsIdx = 3; - - protected: - /** @brief Gets the reference to op resolver interface class. */ - const tflite::MicroOpResolver& GetOpResolver() override; - - /** @brief Adds operations to the op resolver instance. */ - bool EnlistOperations() override; - - const uint8_t* ModelPointer() override; - - size_t ModelSize() override; - private: - /* Maximum number of individual operations that can be enlisted. */ - static constexpr int ms_maxOpCnt = 7; - - /* A mutable op resolver instance. */ - tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* VISUAL_WAKE_WORD_MODEL_HPP */ diff --git a/source/use_case/vww/include/VisualWakeWordProcessing.hpp b/source/use_case/vww/include/VisualWakeWordProcessing.hpp deleted file mode 100644 index f9f9d72..0000000 --- a/source/use_case/vww/include/VisualWakeWordProcessing.hpp +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2022 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. - */ -#ifndef VWW_PROCESSING_HPP -#define VWW_PROCESSING_HPP - -#include "BaseProcessing.hpp" -#include "Model.hpp" -#include "Classifier.hpp" - -namespace arm { -namespace app { - - /** - * @brief Pre-processing class for Visual Wake Word use case. - * Implements methods declared by BasePreProcess and anything else needed - * to populate input tensors ready for inference. - */ - class VisualWakeWordPreProcess : public BasePreProcess { - - public: - /** - * @brief Constructor - * @param[in] inputTensor Pointer to the TFLite Micro input Tensor. - * @param[in] rgb2Gray Convert image from 3 channel RGB to 1 channel grayscale. - **/ - explicit VisualWakeWordPreProcess(TfLiteTensor* inputTensor, bool rgb2Gray=true); - - /** - * @brief Should perform pre-processing of 'raw' input image data and load it into - * TFLite Micro input tensors ready for inference - * @param[in] input Pointer to the data that pre-processing will work on. - * @param[in] inputSize Size of the input data. - * @return true if successful, false otherwise. - **/ - bool DoPreProcess(const void* input, size_t inputSize) override; - - private: - TfLiteTensor* m_inputTensor; - bool m_rgb2Gray; - }; - - /** - * @brief Post-processing class for Visual Wake Word use case. - * Implements methods declared by BasePostProcess and anything else needed - * to populate result vector. - */ - class VisualWakeWordPostProcess : public BasePostProcess { - - private: - TfLiteTensor* m_outputTensor; - Classifier& m_vwwClassifier; - const std::vector<std::string>& m_labels; - std::vector<ClassificationResult>& m_results; - - public: - /** - * @brief Constructor - * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. - * @param[in] classifier Classifier object used to get top N results from classification. - * @param[in] model Pointer to the VWW classification Model object. - * @param[in] labels Vector of string labels to identify each output of the model. - * @param[out] results Vector of classification results to store decoded outputs. - **/ - VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, - std::vector<ClassificationResult>& results); - - /** - * @brief Should perform post-processing of the result of inference then - * populate classification result data for any later use. - * @return true if successful, false otherwise. - **/ - bool DoPostProcess() override; - }; - -} /* namespace app */ -} /* namespace arm */ - -#endif /* VWW_PROCESSING_HPP */
\ No newline at end of file diff --git a/source/use_case/vww/src/MainLoop.cc b/source/use_case/vww/src/MainLoop.cc index 041ea18..2161b0a 100644 --- a/source/use_case/vww/src/MainLoop.cc +++ b/source/use_case/vww/src/MainLoop.cc @@ -21,7 +21,17 @@ #include "VisualWakeWordModel.hpp" /* Model class for running inference. */ #include "UseCaseHandler.hpp" /* Handlers for different user options. */ #include "UseCaseCommonUtils.hpp" /* Utils functions. */ -#include "log_macros.h" +#include "log_macros.h" /* Logging functions */ +#include "BufAttributes.hpp" /* Buffer attributes to be applied */ + +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); using ViusalWakeWordClassifier = arm::app::Classifier; @@ -30,11 +40,22 @@ void main_loop() arm::app::VisualWakeWordModel model; /* Model wrapper object. */ /* Load the model. */ - if (!model.Init()) { + if (!model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())) { printf_err("Failed to initialise model\n"); return; } +#if !defined(ARM_NPU) + /* If it is not a NPU build check if the model contains a NPU operator */ + if (model.ContainsEthosUOperator()) { + printf_err("No driver support for Ethos-U operator found in the model.\n"); + return; + } +#endif /* ARM_NPU */ + /* Instantiate application context. */ arm::app::ApplicationContext caseContext; @@ -55,7 +76,7 @@ void main_loop() constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false; do { int menuOption = common::MENU_OPT_RUN_INF_NEXT; - if (bUseMenu) { + if (bUseMenu) { DisplayCommonMenu(); menuOption = arm::app::ReadUserInputAsInt(); printf("\n"); diff --git a/source/use_case/vww/src/VisualWakeWordModel.cc b/source/use_case/vww/src/VisualWakeWordModel.cc deleted file mode 100644 index 59beccc..0000000 --- a/source/use_case/vww/src/VisualWakeWordModel.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * 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 "VisualWakeWordModel.hpp" -#include "log_macros.h" - -const tflite::MicroOpResolver& arm::app::VisualWakeWordModel::GetOpResolver() -{ - return this->m_opResolver; -} - -bool arm::app::VisualWakeWordModel::EnlistOperations() -{ - this->m_opResolver.AddDepthwiseConv2D(); - this->m_opResolver.AddConv2D(); - this->m_opResolver.AddAveragePool2D(); - this->m_opResolver.AddReshape(); - this->m_opResolver.AddPad(); - this->m_opResolver.AddAdd(); - -#if defined(ARM_NPU) - if (kTfLiteOk == this->m_opResolver.AddEthosU()) { - info("Added %s support to op resolver\n", - tflite::GetString_ETHOSU()); - } else { - printf_err("Failed to add Arm NPU support to op resolver."); - return false; - } -#endif /* ARM_NPU */ - return true; -} - -extern uint8_t* GetModelPointer(); -const uint8_t* arm::app::VisualWakeWordModel::ModelPointer() -{ - return GetModelPointer(); -} - -extern size_t GetModelLen(); -size_t arm::app::VisualWakeWordModel::ModelSize() -{ - return GetModelLen(); -}
\ No newline at end of file diff --git a/source/use_case/vww/src/VisualWakeWordProcessing.cc b/source/use_case/vww/src/VisualWakeWordProcessing.cc deleted file mode 100644 index 4ae8a54..0000000 --- a/source/use_case/vww/src/VisualWakeWordProcessing.cc +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright (c) 2022 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 "VisualWakeWordProcessing.hpp" - -#include "ImageUtils.hpp" -#include "VisualWakeWordModel.hpp" -#include "log_macros.h" - -namespace arm { -namespace app { - - VisualWakeWordPreProcess::VisualWakeWordPreProcess(TfLiteTensor* inputTensor, bool rgb2Gray) - :m_inputTensor{inputTensor}, - m_rgb2Gray{rgb2Gray} - {} - - bool VisualWakeWordPreProcess::DoPreProcess(const void* data, size_t inputSize) - { - if (data == nullptr) { - printf_err("Data pointer is null"); - } - - auto input = static_cast<const uint8_t*>(data); - - uint8_t* unsignedDstPtr = this->m_inputTensor->data.uint8; - - if (this->m_rgb2Gray) { - image::RgbToGrayscale(input, unsignedDstPtr, inputSize); - } else { - std::memcpy(unsignedDstPtr, input, inputSize); - } - - /* VWW model pre-processing is image conversion from uint8 to [0,1] float values, - * then quantize them with input quantization info. */ - QuantParams inQuantParams = GetTensorQuantParams(this->m_inputTensor); - - int8_t* signedDstPtr = this->m_inputTensor->data.int8; - for (size_t i = 0; i < this->m_inputTensor->bytes; i++) { - auto i_data_int8 = static_cast<int8_t>( - ((static_cast<float>(unsignedDstPtr[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset - ); - signedDstPtr[i] = std::min<int8_t>(INT8_MAX, std::max<int8_t>(i_data_int8, INT8_MIN)); - } - - debug("Input tensor populated \n"); - - return true; - } - - VisualWakeWordPostProcess::VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier, - const std::vector<std::string>& labels, std::vector<ClassificationResult>& results) - :m_outputTensor{outputTensor}, - m_vwwClassifier{classifier}, - m_labels{labels}, - m_results{results} - {} - - bool VisualWakeWordPostProcess::DoPostProcess() - { - return this->m_vwwClassifier.GetClassificationResults( - this->m_outputTensor, this->m_results, - this->m_labels, 1, true); - } - -} /* namespace app */ -} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/vww/usecase.cmake b/source/use_case/vww/usecase.cmake index 8bf55fc..f6a3efe 100644 --- a/source/use_case/vww/usecase.cmake +++ b/source/use_case/vww/usecase.cmake @@ -1,3 +1,4 @@ +#---------------------------------------------------------------------------- # Copyright (c) 2021 Arm Limited. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # @@ -12,7 +13,10 @@ # 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. +#---------------------------------------------------------------------------- +# Append the API to use for this use case +list(APPEND ${use_case}_API_LIST "vww") USER_OPTION(${use_case}_FILE_PATH "Directory with custom image files, or path to a single image file, to use in the evaluation application" ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ |