MLECO-3183: Refactoring application sources
Platform agnostic application sources are moved into application
api module with their own independent CMake projects.
Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.
Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
diff --git a/source/application/api/use_case/ad/include/AdProcessing.hpp b/source/application/api/use_case/ad/include/AdProcessing.hpp
new file mode 100644
index 0000000..abee75e
--- /dev/null
+++ b/source/application/api/use_case/ad/include/AdProcessing.hpp
@@ -0,0 +1,231 @@
+/*
+ * 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 "TensorFlowLiteMicro.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 */