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authorKshitij Sisodia <kshitij.sisodia@arm.com>2022-05-06 09:13:03 +0100
committerKshitij Sisodia <kshitij.sisodia@arm.com>2022-05-06 17:11:41 +0100
commitaa4bcb14d0cbee910331545dd2fc086b58c37170 (patch)
treee67a43a43f61c6f8b6aad19018b0827baf7e31a6 /source/application/api/common
parentfcca863bafd5f33522bc14c23dde4540e264ec94 (diff)
downloadml-embedded-evaluation-kit-aa4bcb14d0cbee910331545dd2fc086b58c37170.tar.gz
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>
Diffstat (limited to 'source/application/api/common')
-rw-r--r--source/application/api/common/CMakeLists.txt59
-rw-r--r--source/application/api/common/include/AudioUtils.hpp172
-rw-r--r--source/application/api/common/include/BaseProcessing.hpp67
-rw-r--r--source/application/api/common/include/ClassificationResult.hpp41
-rw-r--r--source/application/api/common/include/Classifier.hpp89
-rw-r--r--source/application/api/common/include/DataStructures.hpp128
-rw-r--r--source/application/api/common/include/ImageUtils.hpp116
-rw-r--r--source/application/api/common/include/Mfcc.hpp255
-rw-r--r--source/application/api/common/include/Model.hpp152
-rw-r--r--source/application/api/common/include/TensorFlowLiteMicro.hpp91
-rw-r--r--source/application/api/common/source/Classifier.cc169
-rw-r--r--source/application/api/common/source/ImageUtils.cc126
-rw-r--r--source/application/api/common/source/Mfcc.cc353
-rw-r--r--source/application/api/common/source/Model.cc359
-rw-r--r--source/application/api/common/source/TensorFlowLiteMicro.cc46
15 files changed, 2223 insertions, 0 deletions
diff --git a/source/application/api/common/CMakeLists.txt b/source/application/api/common/CMakeLists.txt
new file mode 100644
index 0000000..5078adc
--- /dev/null
+++ b/source/application/api/common/CMakeLists.txt
@@ -0,0 +1,59 @@
+#----------------------------------------------------------------------------
+# 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.
+#----------------------------------------------------------------------------
+
+#########################################################
+# Common utility library used by use case libraries. #
+# NOTE: this library should not depend on HAL. #
+#########################################################
+
+cmake_minimum_required(VERSION 3.15.6)
+
+set(COMMON_UC_UTILS_TARGET common_api)
+project(${COMMON_UC_UTILS_TARGET}
+ DESCRIPTION "Common Utilities library"
+ LANGUAGES CXX)
+
+# Create static library
+add_library(${COMMON_UC_UTILS_TARGET} STATIC)
+
+## Include directories - public
+target_include_directories(${COMMON_UC_UTILS_TARGET}
+ PUBLIC
+ include
+ ${TENSORFLOW_SRC_PATH}/tensorflow/lite/micro/tools/make/downloads/flatbuffers/include)
+
+## Sources
+target_sources(${COMMON_UC_UTILS_TARGET}
+ PRIVATE
+ source/Classifier.cc
+ source/ImageUtils.cc
+ source/Mfcc.cc
+ source/Model.cc
+ source/TensorFlowLiteMicro.cc)
+
+# Link time library targets:
+target_link_libraries(${COMMON_UC_UTILS_TARGET}
+ PUBLIC
+ log # Logging functions
+ arm_math # Math functions
+ tensorflow-lite-micro) # TensorFlow Lite Micro library
+
+# Display status:
+message(STATUS "*******************************************************")
+message(STATUS "Library : " ${COMMON_UC_UTILS_TARGET})
+message(STATUS "CMAKE_SYSTEM_PROCESSOR : " ${CMAKE_SYSTEM_PROCESSOR})
+message(STATUS "*******************************************************")
diff --git a/source/application/api/common/include/AudioUtils.hpp b/source/application/api/common/include/AudioUtils.hpp
new file mode 100644
index 0000000..cbf7bb7
--- /dev/null
+++ b/source/application/api/common/include/AudioUtils.hpp
@@ -0,0 +1,172 @@
+/*
+ * 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 AUDIO_UTILS_HPP
+#define AUDIO_UTILS_HPP
+
+#include <cstddef>
+#include <cstdint>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ template<class T>
+ class SlidingWindow {
+ public:
+
+ /**
+ * @brief Creates the window slider through the given data.
+ *
+ * @param[in] data Pointer to the data to slide through.
+ * @param[in] dataSize Size in T type elements wise.
+ * @param[in] windowSize Sliding window size in T type wise elements.
+ * @param[in] stride Stride size in T type wise elements.
+ */
+ SlidingWindow(T *data, size_t dataSize,
+ size_t windowSize, size_t stride) {
+ m_start = data;
+ m_dataSize = dataSize;
+ m_size = windowSize;
+ m_stride = stride;
+ }
+
+ SlidingWindow() = default;
+
+ ~SlidingWindow() = default;
+
+ /**
+ * @brief Get the next data window.
+ * @return Pointer to the next window, if next window is not available nullptr is returned.
+ */
+ virtual T *Next() {
+ if (HasNext()) {
+ m_count++;
+ return m_start + Index() * m_stride;
+ } else {
+ return nullptr;
+ }
+ }
+
+ /**
+ * @brief Checks if the next data portion is available.
+ * @return true if next data portion is available.
+ */
+ virtual bool HasNext() {
+ return m_size + m_count * m_stride <= m_dataSize;
+ }
+
+ /**
+ * @brief Reset the slider to the initial position.
+ */
+ virtual void Reset() {
+ m_count = 0;
+ }
+
+ /**
+ * @brief Resets the slider to the start of the new data.
+ * New data size MUST be the same as the old one.
+ * @param[in] newStart Pointer to the new data to slide through.
+ */
+ virtual void Reset(T *newStart) {
+ m_start = newStart;
+ Reset();
+ }
+
+ /**
+ * @brief Gets current index of the sliding window.
+ * @return Current position of the sliding window in number of strides.
+ */
+ size_t Index() {
+ return m_count == 0? 0: m_count - 1;
+ }
+
+ /**
+ * @brief Gets the index from the start of the data where the next window will begin.
+ * While Index() returns the index of sliding window itself this function
+ * returns the index of the data element itself.
+ * @return Index from the start of the data where the next sliding window will begin.
+ */
+ virtual uint32_t NextWindowStartIndex() {
+ return m_count == 0? 0: ((m_count) * m_stride);
+ }
+
+ /**
+ * @brief Go to given sliding window index.
+ * @param[in] index New position of the sliding window. If index is invalid
+ * (greater than possible range of strides) then next call to Next() will return nullptr.
+ */
+ void FastForward(size_t index) {
+ m_count = index;
+ }
+
+ /**
+ * @brief Calculates whole number of times the window can stride through the given data.
+ * @return Maximum number of whole strides.
+ */
+ size_t TotalStrides() {
+ if (m_size > m_dataSize) {
+ return 0;
+ }
+ return ((m_dataSize - m_size)/m_stride);
+ }
+
+
+ protected:
+ T *m_start = nullptr;
+ size_t m_dataSize = 0;
+ size_t m_size = 0;
+ size_t m_stride = 0;
+ size_t m_count = 0;
+ };
+
+ /*
+ * Sliding window that will cover the whole length of the input, even if
+ * this means the last window is not a full window length.
+ */
+ template<class T>
+ class FractionalSlidingWindow : public SlidingWindow<T> {
+ public:
+ using SlidingWindow<T>::SlidingWindow;
+
+ /**
+ * @brief Checks if the next data portion is available.
+ * @return true if next data portion is available.
+ */
+ bool HasNext() {
+ return this->m_count < 1 + this->FractionalTotalStrides() && (this->NextWindowStartIndex() < this->m_dataSize);
+ }
+
+ /**
+ * @brief Calculates number of times the window can stride through the given data.
+ * May not be a whole number.
+ * @return Number of strides to cover all data.
+ */
+ float FractionalTotalStrides() {
+ if (this->m_dataSize < this->m_size) {
+ return 0;
+ } else {
+ return ((this->m_dataSize - this->m_size) / static_cast<float>(this->m_stride));
+ }
+ }
+ };
+
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* AUDIO_UTILS_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/BaseProcessing.hpp b/source/application/api/common/include/BaseProcessing.hpp
new file mode 100644
index 0000000..a54dd12
--- /dev/null
+++ b/source/application/api/common/include/BaseProcessing.hpp
@@ -0,0 +1,67 @@
+/*
+ * 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 BASE_PROCESSING_HPP
+#define BASE_PROCESSING_HPP
+
+#include <cstddef>
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Base class exposing pre-processing API.
+ * Use cases should provide their own PreProcessing class that inherits from this one.
+ * All steps required to take raw input data and populate tensors ready for inference
+ * should be handled.
+ */
+ class BasePreProcess {
+
+ public:
+ virtual ~BasePreProcess() = default;
+
+ /**
+ * @brief Should perform pre-processing of 'raw' input 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.
+ **/
+ virtual bool DoPreProcess(const void* input, size_t inputSize) = 0;
+ };
+
+ /**
+ * @brief Base class exposing post-processing API.
+ * Use cases should provide their own PostProcessing class that inherits from this one.
+ * All steps required to take inference output and populate results vectors should be handled.
+ */
+ class BasePostProcess {
+
+ public:
+ virtual ~BasePostProcess() = default;
+
+ /**
+ * @brief Should perform post-processing of the result of inference then populate
+ * populate result data for any later use.
+ * @return true if successful, false otherwise.
+ **/
+ virtual bool DoPostProcess() = 0;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* BASE_PROCESSING_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/ClassificationResult.hpp b/source/application/api/common/include/ClassificationResult.hpp
new file mode 100644
index 0000000..eae28e4
--- /dev/null
+++ b/source/application/api/common/include/ClassificationResult.hpp
@@ -0,0 +1,41 @@
+/*
+ * 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 CLASSIFICATION_RESULT_HPP
+#define CLASSIFICATION_RESULT_HPP
+
+#include <string>
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Class representing a single classification result.
+ */
+ class ClassificationResult {
+ public:
+ double m_normalisedVal = 0.0;
+ std::string m_label;
+ uint32_t m_labelIdx = 0;
+
+ ClassificationResult() = default;
+ ~ClassificationResult() = default;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* CLASSIFICATION_RESULT_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/Classifier.hpp b/source/application/api/common/include/Classifier.hpp
new file mode 100644
index 0000000..d641c22
--- /dev/null
+++ b/source/application/api/common/include/Classifier.hpp
@@ -0,0 +1,89 @@
+/*
+ * 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 CLASSIFIER_HPP
+#define CLASSIFIER_HPP
+
+#include "ClassificationResult.hpp"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <vector>
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Classifier - a helper class to get certain number of top
+ * results from the output vector from a classification NN.
+ **/
+ class Classifier{
+ public:
+ /** @brief Constructor. */
+ Classifier() = default;
+
+ /**
+ * @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. Default is 1.
+ * @param[in] useSoftmax Whether Softmax normalisation should be applied to output. Default is false.
+ * @return true if successful, false otherwise.
+ **/
+
+ virtual bool GetClassificationResults(
+ TfLiteTensor* outputTensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels, uint32_t topNCount,
+ bool use_softmax);
+
+ /**
+ * @brief Populate the elements of the Classification Result object.
+ * @param[in] topNSet Ordered set of top 5 output class scores and labels.
+ * @param[out] vecResults A vector of classification results.
+ * populated by this function.
+ * @param[in] labels Labels vector to match classified classes.
+ **/
+
+ void SetVectorResults(
+ std::set<std::pair<float, uint32_t>>& topNSet,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels);
+
+ private:
+ /**
+ * @brief Utility function that gets the top N classification results from the
+ * output 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] topNCount Number of top classifications to pick.
+ * @param[in] labels Labels vector to match classified classes.
+ * @return true if successful, false otherwise.
+ **/
+
+ bool GetTopNResults(const std::vector<float>& tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount,
+ const std::vector <std::string>& labels);
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* CLASSIFIER_HPP */
diff --git a/source/application/api/common/include/DataStructures.hpp b/source/application/api/common/include/DataStructures.hpp
new file mode 100644
index 0000000..0616839
--- /dev/null
+++ b/source/application/api/common/include/DataStructures.hpp
@@ -0,0 +1,128 @@
+/*
+ * 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 DATA_STRUCTURES_HPP
+#define DATA_STRUCTURES_HPP
+
+#include <iterator>
+
+namespace arm {
+namespace app {
+
+ /**
+ * Class Array2d is a data structure that represents a two dimensional array.
+ * The data is allocated in contiguous memory, arranged row-wise
+ * and individual elements can be accessed with the () operator.
+ * For example a two dimensional array D of size (M, N) can be accessed:
+ *
+ * _|<------------- col size = N -------->|
+ * | D(r=0, c=0) D(r=0, c=1)... D(r=0, c=N)
+ * | D(r=1, c=0) D(r=1, c=1)... D(r=1, c=N)
+ * | ...
+ * row size = M ...
+ * | ...
+ * _ D(r=M, c=0) D(r=M, c=1)... D(r=M, c=N)
+ *
+ */
+ template<typename T>
+ class Array2d {
+ public:
+ /**
+ * @brief Creates the array2d with the given sizes.
+ * @param[in] rows Number of rows.
+ * @param[in] cols Number of columns.
+ */
+ Array2d(unsigned rows, unsigned cols): m_rows(rows), m_cols(cols)
+ {
+ if (rows == 0 || cols == 0) {
+ printf("Array2d constructor has 0 size.\n");
+ m_data = nullptr;
+ return;
+ }
+ m_data = new T[rows * cols];
+ }
+
+ ~Array2d()
+ {
+ delete[] m_data;
+ }
+
+ T& operator() (unsigned int row, unsigned int col)
+ {
+#if defined(DEBUG)
+ if (row >= m_rows || col >= m_cols || m_data == nullptr) {
+ printf_err("Array2d subscript out of bounds.\n");
+ }
+#endif /* defined(DEBUG) */
+ return m_data[m_cols * row + col];
+ }
+
+ T operator() (unsigned int row, unsigned int col) const
+ {
+#if defined(DEBUG)
+ if (row >= m_rows || col >= m_cols || m_data == nullptr) {
+ printf_err("const Array2d subscript out of bounds.\n");
+ }
+#endif /* defined(DEBUG) */
+ return m_data[m_cols * row + col];
+ }
+
+ /**
+ * @brief Gets rows number of the current array2d.
+ * @return Number of rows.
+ */
+ size_t size(size_t dim)
+ {
+ switch (dim)
+ {
+ case 0:
+ return m_rows;
+ case 1:
+ return m_cols;
+ default:
+ return 0;
+ }
+ }
+
+ /**
+ * @brief Gets the array2d total size.
+ */
+ size_t totalSize()
+ {
+ return m_rows * m_cols;
+ }
+
+ /**
+ * array2d iterator.
+ */
+ using iterator=T*;
+ using const_iterator=T const*;
+
+ iterator begin() { return m_data; }
+ iterator end() { return m_data + totalSize(); }
+ const_iterator begin() const { return m_data; }
+ const_iterator end() const { return m_data + totalSize(); };
+
+ private:
+ size_t m_rows;
+ size_t m_cols;
+ T* m_data;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* DATA_STRUCTURES_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/ImageUtils.hpp b/source/application/api/common/include/ImageUtils.hpp
new file mode 100644
index 0000000..a8c7650
--- /dev/null
+++ b/source/application/api/common/include/ImageUtils.hpp
@@ -0,0 +1,116 @@
+/*
+ * 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 IMAGE_UTILS_HPP
+#define IMAGE_UTILS_HPP
+
+#include <cstddef>
+#include <cstdint>
+#include <forward_list>
+#include <vector>
+
+/* Helper macro to convert RGB888 to RGB565 format. */
+#define RGB888_TO_RGB565(R8,G8,B8) ((((R8>>3) & 0x1F) << 11) | \
+ (((G8>>2) & 0x3F) << 5) | \
+ ((B8>>3) & 0x1F))
+
+constexpr uint16_t COLOR_BLACK = 0;
+constexpr uint16_t COLOR_GREEN = RGB888_TO_RGB565( 0, 255, 0); // 2016;
+constexpr uint16_t COLOR_YELLOW = RGB888_TO_RGB565(255, 255, 0); // 65504;
+
+
+namespace arm {
+namespace app {
+namespace image {
+
+ /**
+ * Contains the x,y co-ordinates of a box centre along with the box width and height.
+ */
+ struct Box {
+ float x;
+ float y;
+ float w;
+ float h;
+ };
+
+ struct Detection {
+ Box bbox;
+ std::vector<float> prob;
+ float objectness;
+ };
+
+ /**
+ * @brief Calculate the 1D overlap.
+ * @param[in] x1Center First center point.
+ * @param[in] width1 First width.
+ * @param[in] x2Center Second center point.
+ * @param[in] width2 Second width.
+ * @return The overlap between the two lines.
+ **/
+ float Calculate1DOverlap(float x1Center, float width1, float x2Center, float width2);
+
+ /**
+ * @brief Calculate the intersection between the two given boxes.
+ * @param[in] box1 First box.
+ * @param[in] box2 Second box.
+ * @return The intersection value.
+ **/
+ float CalculateBoxIntersect(Box& box1, Box& box2);
+
+ /**
+ * @brief Calculate the union between the two given boxes.
+ * @param[in] box1 First box.
+ * @param[in] box2 Second box.
+ * @return The two given boxes union value.
+ **/
+ float CalculateBoxUnion(Box& box1, Box& box2);
+
+ /**
+ * @brief Calculate the intersection over union between the two given boxes.
+ * @param[in] box1 First box.
+ * @param[in] box2 Second box.
+ * @return The intersection over union value.
+ **/
+ float CalculateBoxIOU(Box& box1, Box& box2);
+
+ /**
+ * @brief Calculate the Non-Maxima suppression on the given detection boxes.
+ * @param[in] detections List of Detection boxes.
+ * @param[in] classes Number of classes.
+ * @param[in] iouThreshold Intersection over union threshold.
+ **/
+ void CalculateNMS(std::forward_list<Detection>& detections, int classes, float iouThreshold);
+
+ /**
+ * @brief Helper function to convert a UINT8 image to INT8 format.
+ * @param[in,out] data Pointer to the data start.
+ * @param[in] kMaxImageSize Total number of pixels in the image.
+ **/
+ void ConvertImgToInt8(void* data, size_t kMaxImageSize);
+
+ /**
+ * @brief Converts RGB image to grayscale.
+ * @param[in] srcPtr Pointer to RGB source image.
+ * @param[out] dstPtr Pointer to grayscale destination image.
+ * @param[in] imgSz Destination image size.
+ **/
+ void RgbToGrayscale(const uint8_t* srcPtr, uint8_t* dstPtr, size_t dstImgSz);
+
+} /* namespace image */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* IMAGE_UTILS_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/Mfcc.hpp b/source/application/api/common/include/Mfcc.hpp
new file mode 100644
index 0000000..86330ca
--- /dev/null
+++ b/source/application/api/common/include/Mfcc.hpp
@@ -0,0 +1,255 @@
+/*
+ * 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 MFCC_HPP
+#define MFCC_HPP
+
+#include "PlatformMath.hpp"
+
+#include <vector>
+#include <cstdint>
+#include <cmath>
+#include <limits>
+#include <string>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ /* MFCC's consolidated parameters. */
+ class MfccParams {
+ public:
+ float m_samplingFreq;
+ uint32_t m_numFbankBins;
+ float m_melLoFreq;
+ float m_melHiFreq;
+ uint32_t m_numMfccFeatures;
+ uint32_t m_frameLen;
+ uint32_t m_frameLenPadded;
+ bool m_useHtkMethod;
+
+ /** @brief Constructor */
+ MfccParams(float samplingFreq, uint32_t numFbankBins,
+ float melLoFreq, float melHiFreq,
+ uint32_t numMfccFeats, uint32_t frameLen,
+ bool useHtkMethod);
+
+ MfccParams() = delete;
+
+ ~MfccParams() = default;
+
+ /** @brief Log parameters */
+ void Log() const;
+ };
+
+ /**
+ * @brief Class for MFCC 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 MFCC {
+ public:
+ /**
+ * @brief Constructor
+ * @param[in] params MFCC parameters
+ */
+ explicit MFCC(const MfccParams& params);
+
+ MFCC() = delete;
+
+ ~MFCC() = default;
+
+ /**
+ * @brief Extract MFCC features for one single small frame of
+ * audio data e.g. 640 samples.
+ * @param[in] audioData Vector of audio samples to calculate
+ * features for.
+ * @return Vector of extracted MFCC features.
+ **/
+ std::vector<float> MfccCompute(const std::vector<int16_t>& audioData);
+
+ /** @brief Initialise. */
+ void Init();
+
+ /**
+ * @brief Extract MFCC 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.
+ * @return Vector of extracted quantised MFCC features.
+ **/
+ template<typename T>
+ std::vector<T> MfccComputeQuant(const std::vector<int16_t>& audioData,
+ const float quantScale,
+ const int quantOffset)
+ {
+ this->MfccComputePreFeature(audioData);
+ float minVal = std::numeric_limits<T>::min();
+ float maxVal = std::numeric_limits<T>::max();
+
+ std::vector<T> mfccOut(this->m_params.m_numMfccFeatures);
+ const size_t numFbankBins = this->m_params.m_numFbankBins;
+
+ /* Take DCT. Uses matrix mul. */
+ for (size_t i = 0, j = 0; i < mfccOut.size(); ++i, j += numFbankBins) {
+ float sum = 0;
+ for (size_t k = 0; k < numFbankBins; ++k) {
+ sum += this->m_dctMatrix[j + k] * this->m_melEnergies[k];
+ }
+ /* Quantize to T. */
+ sum = std::round((sum / quantScale) + quantOffset);
+ mfccOut[i] = static_cast<T>(std::min<float>(std::max<float>(sum, minVal), maxVal));
+ }
+
+ return mfccOut;
+ }
+
+ /* 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(float freq,
+ 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(float melFreq,
+ 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 Create a matrix used to calculate Discrete Cosine
+ * Transform.
+ * @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.
+ */
+ virtual std::vector<float> CreateDCTMatrix(
+ int32_t inputLength,
+ int32_t coefficientCount);
+
+ /**
+ * @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 Value to use for normalizing.
+ */
+ virtual float GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ bool useHTKMethod);
+
+ private:
+ MfccParams 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<float> m_dctMatrix;
+ 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 and the DCT matrix. **/
+ void InitMelFilterBank();
+
+ /**
+ * @brief Signals whether the instance of MFCC has had its
+ * required buffers initialised.
+ * @return true if initialised, false otherwise.
+ **/
+ bool IsMelFilterBankInited() const;
+
+ /**
+ * @brief Create mel filter banks for MFCC calculation.
+ * @return 2D vector of floats.
+ **/
+ std::vector<std::vector<float>> CreateMelFilterBank();
+
+ /**
+ * @brief Computes and populates internal memeber buffers used
+ * in MFCC feature calculation
+ * @param[in] audioData 1D vector of 16-bit audio data.
+ */
+ void MfccComputePreFeature(const std::vector<int16_t>& audioData);
+
+ /** @brief Computes the magnitude from an interleaved complex array. */
+ void ConvertToPowerSpectrum();
+
+ };
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* MFCC_HPP */ \ No newline at end of file
diff --git a/source/application/api/common/include/Model.hpp b/source/application/api/common/include/Model.hpp
new file mode 100644
index 0000000..df1b259
--- /dev/null
+++ b/source/application/api/common/include/Model.hpp
@@ -0,0 +1,152 @@
+/*
+ * 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 MODEL_HPP
+#define MODEL_HPP
+
+#include "TensorFlowLiteMicro.hpp"
+
+#include <cstdint>
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief NN model class wrapping the underlying TensorFlow-Lite-Micro API.
+ */
+ class Model {
+ public:
+ /** @brief Constructor. */
+ Model();
+
+ /** @brief Destructor. */
+ ~Model();
+
+ /** @brief Gets the pointer to the model's input tensor at given input index. */
+ TfLiteTensor* GetInputTensor(size_t index) const;
+
+ /** @brief Gets the pointer to the model's output tensor at given output index. */
+ TfLiteTensor* GetOutputTensor(size_t index) const;
+
+ /** @brief Gets the model's data type. */
+ TfLiteType GetType() const;
+
+ /** @brief Gets the pointer to the model's input shape. */
+ TfLiteIntArray* GetInputShape(size_t index) const;
+
+ /** @brief Gets the pointer to the model's output shape at given output index. */
+ TfLiteIntArray* GetOutputShape(size_t index) const;
+
+ /** @brief Gets the number of input tensors the model has. */
+ size_t GetNumInputs() const;
+
+ /** @brief Gets the number of output tensors the model has. */
+ size_t GetNumOutputs() const;
+
+ /** @brief Logs the tensor information to stdout. */
+ void LogTensorInfo(TfLiteTensor* tensor);
+
+ /** @brief Logs the interpreter information to stdout. */
+ void LogInterpreterInfo();
+
+ /** @brief Initialise the model class object.
+ * @param[in] tensorArenaAddress Pointer to the tensor arena buffer.
+ * @param[in] tensorArenaAddress Size of the tensor arena buffer in bytes.
+ * @param[in] nnModelAddr Pointer to the model.
+ * @param[in] nnModelSize Size of the model in bytes, if known.
+ * @param[in] allocator Optional: a pre-initialised micro allocator pointer,
+ * if available. If supplied, this allocator will be used
+ * to create the interpreter instance.
+ * @return true if initialisation succeeds, false otherwise.
+ **/
+ bool Init(uint8_t* tensorArenaAddr,
+ uint32_t tensorArenaSize,
+ uint8_t* nnModelAddr,
+ uint32_t nnModelSize,
+ tflite::MicroAllocator* allocator = nullptr);
+
+ /**
+ * @brief Gets the allocator pointer for this instance.
+ * @return Pointer to a tflite::MicroAllocator object, if
+ * available; nullptr otherwise.
+ **/
+ tflite::MicroAllocator* GetAllocator();
+
+ /** @brief Checks if this object has been initialised. */
+ bool IsInited() const;
+
+ /** @brief Checks if the model uses signed data. */
+ bool IsDataSigned() const;
+
+ /** @brief Checks if the model uses Ethos-U operator */
+ bool ContainsEthosUOperator() const;
+
+ /** @brief Runs the inference (invokes the interpreter). */
+ virtual bool RunInference();
+
+ /** @brief Model information handler common to all models.
+ * @return true or false based on execution success.
+ **/
+ bool ShowModelInfoHandler();
+
+ /** @brief Gets a pointer to the tensor arena. */
+ uint8_t* GetTensorArena();
+
+ protected:
+ /** @brief Gets the pointer to the NN model data array.
+ * @return Pointer of uint8_t type.
+ **/
+ const uint8_t* ModelPointer();
+
+ /** @brief Gets the model size.
+ * @return size_t, size in bytes.
+ **/
+ uint32_t ModelSize();
+
+ /**
+ * @brief Gets the op resolver for the model instance.
+ * @return const reference to a tflite::MicroOpResolver object.
+ **/
+ virtual const tflite::MicroOpResolver& GetOpResolver() = 0;
+
+ /**
+ * @brief Add all the operators required for the given model.
+ * Implementation of this should come from the use case.
+ * @return true is ops are successfully added, false otherwise.
+ **/
+ virtual bool EnlistOperations() = 0;
+
+ /** @brief Gets the total size of tensor arena available for use. */
+ size_t GetActivationBufferSize();
+
+ private:
+ tflite::ErrorReporter* m_pErrorReporter = nullptr; /* Pointer to the error reporter. */
+ const tflite::Model* m_pModel = nullptr; /* Tflite model pointer. */
+ tflite::MicroInterpreter* m_pInterpreter = nullptr; /* Tflite interpreter. */
+ tflite::MicroAllocator* m_pAllocator = nullptr; /* Tflite micro allocator. */
+ bool m_inited = false; /* Indicates whether this object has been initialised. */
+ uint8_t* m_modelAddr = nullptr; /* Model address */
+ uint32_t m_modelSize = 0; /* Model size */
+
+ std::vector<TfLiteTensor*> m_input = {}; /* Model's input tensor pointers. */
+ std::vector<TfLiteTensor*> m_output = {}; /* Model's output tensor pointers. */
+ TfLiteType m_type = kTfLiteNoType;/* Model's data type. */
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* MODEL_HPP */
diff --git a/source/application/api/common/include/TensorFlowLiteMicro.hpp b/source/application/api/common/include/TensorFlowLiteMicro.hpp
new file mode 100644
index 0000000..f6639fd
--- /dev/null
+++ b/source/application/api/common/include/TensorFlowLiteMicro.hpp
@@ -0,0 +1,91 @@
+/*
+ * 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 TENSORFLOW_LITE_MICRO_LOCAL_HPP
+#define TENSORFLOW_LITE_MICRO_LOCAL_HPP
+
+/* We include all our TensorFlow Lite Micro headers here */
+
+/**
+ * TensorFlow Lite Micro sources can generate a lot of warnings from the usage
+ * of a single macro (TF_LITE_REMOVE_VIRTUAL_DELETE). Suppress the known ones
+ * here to prevent them from masking warnings that might be generated by our
+ * application sources.
+ */
+#if defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050)
+ #pragma clang diagnostic push
+ #pragma clang diagnostic ignored "-Wunused-parameter"
+ #include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+ #include "tensorflow/lite/micro/micro_interpreter.h"
+ #include "tensorflow/lite/micro/micro_error_reporter.h"
+ #include "tensorflow/lite/micro/all_ops_resolver.h"
+ #pragma clang diagnostic pop
+#elif defined(__GNUC__)
+ #pragma GCC diagnostic push
+ #pragma GCC diagnostic ignored "-Wunused-parameter"
+ #include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+ #include "tensorflow/lite/micro/micro_interpreter.h"
+ #include "tensorflow/lite/micro/micro_error_reporter.h"
+ #include "tensorflow/lite/micro/all_ops_resolver.h"
+ #pragma GCC diagnostic pop
+#else
+ #include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+ #include "tensorflow/lite/micro/micro_interpreter.h"
+ #include "tensorflow/lite/micro/micro_error_reporter.h"
+ #include "tensorflow/lite/micro/all_ops_resolver.h"
+#endif
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/schema/schema_utils.h"
+
+#if defined (TESTS)
+ #include "tensorflow/lite/micro/test_helpers.h"
+#endif /* defined (TESTS) */
+
+namespace arm {
+namespace app {
+
+ /** Struct for quantization parameters. */
+ struct QuantParams {
+ float scale = 1.0;
+ int offset = 0;
+ };
+
+ /**
+ * @brief Gets the quantization parameters from a tensor
+ * @param[in] tensor pointer to the tensor.
+ * @return QuantParams object.
+ */
+ QuantParams GetTensorQuantParams(TfLiteTensor* tensor);
+
+ /**
+ * @brief String logging functionality expected to be defined
+ * by TensorFlow Lite Micro's error reporter.
+ * @param[in] s Pointer to the string.
+ */
+ extern "C" void DebugLog(const char* s);
+
+} /* namespace app */
+} /* namespace arm */
+
+/**
+ * @brief Prints the tensor flow version in use to stdout.
+ */
+void PrintTensorFlowVersion();
+
+#endif /* TENSORFLOW_LITE_MICRO_LOCAL_HPP */
diff --git a/source/application/api/common/source/Classifier.cc b/source/application/api/common/source/Classifier.cc
new file mode 100644
index 0000000..6fabebe
--- /dev/null
+++ b/source/application/api/common/source/Classifier.cc
@@ -0,0 +1,169 @@
+/*
+ * 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 "Classifier.hpp"
+
+#include "TensorFlowLiteMicro.hpp"
+#include "PlatformMath.hpp"
+#include "log_macros.h"
+
+#include <vector>
+#include <string>
+#include <set>
+#include <cstdint>
+#include <cinttypes>
+
+
+namespace arm {
+namespace app {
+
+ void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels)
+ {
+
+ /* Reset the iterator to the largest element - use reverse iterator. */
+
+ auto topNIter = topNSet.rbegin();
+ for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
+ vecResults[i].m_normalisedVal = topNIter->first;
+ vecResults[i].m_label = labels[topNIter->second];
+ vecResults[i].m_labelIdx = topNIter->second;
+ }
+ }
+
+ bool Classifier::GetTopNResults(const std::vector<float>& tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount,
+ const std::vector <std::string>& labels)
+ {
+
+ std::set<std::pair<float , uint32_t>> sortedSet;
+
+ /* NOTE: inputVec's size verification against labels should be
+ * checked by the calling/public function. */
+
+ /* Set initial elements. */
+ for (uint32_t i = 0; i < topNCount; ++i) {
+ sortedSet.insert({tensor[i], i});
+ }
+
+ /* Initialise iterator. */
+ auto setFwdIter = sortedSet.begin();
+
+ /* Scan through the rest of elements with compare operations. */
+ for (uint32_t i = topNCount; i < labels.size(); ++i) {
+ if (setFwdIter->first < tensor[i]) {
+ sortedSet.erase(*setFwdIter);
+ sortedSet.insert({tensor[i], i});
+ setFwdIter = sortedSet.begin();
+ }
+ }
+
+ /* Final results' container. */
+ vecResults = std::vector<ClassificationResult>(topNCount);
+ SetVectorResults(sortedSet, vecResults, labels);
+
+ return true;
+ }
+
+ bool Classifier::GetClassificationResults(
+ TfLiteTensor* outputTensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels,
+ uint32_t topNCount,
+ bool useSoftmax)
+ {
+ if (outputTensor == nullptr) {
+ printf_err("Output vector is null pointer.\n");
+ return false;
+ }
+
+ uint32_t totalOutputSize = 1;
+ for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++) {
+ totalOutputSize *= outputTensor->dims->data[inputDim];
+ }
+
+ /* Sanity checks. */
+ if (totalOutputSize < topNCount) {
+ printf_err("Output vector is smaller than %" PRIu32 "\n", topNCount);
+ return false;
+ } else if (totalOutputSize != labels.size()) {
+ printf_err("Output size doesn't match the labels' size\n");
+ return false;
+ } else if (topNCount == 0) {
+ printf_err("Top N results cannot be zero\n");
+ return false;
+ }
+
+ bool resultState;
+ vecResults.clear();
+
+ /* De-Quantize Output Tensor */
+ QuantParams quantParams = GetTensorQuantParams(outputTensor);
+
+ /* Floating point tensor data to be populated
+ * NOTE: The assumption here is that the output tensor size isn't too
+ * big and therefore, there's neglibible impact on heap usage. */
+ std::vector<float> tensorData(totalOutputSize);
+
+ /* Populate the floating point buffer */
+ switch (outputTensor->type) {
+ case kTfLiteUInt8: {
+ uint8_t *tensor_buffer = tflite::GetTensorData<uint8_t>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = quantParams.scale *
+ (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+ }
+ break;
+ }
+ case kTfLiteInt8: {
+ int8_t *tensor_buffer = tflite::GetTensorData<int8_t>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = quantParams.scale *
+ (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+ }
+ break;
+ }
+ case kTfLiteFloat32: {
+ float *tensor_buffer = tflite::GetTensorData<float>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = tensor_buffer[i];
+ }
+ break;
+ }
+ default:
+ printf_err("Tensor type %s not supported by classifier\n",
+ TfLiteTypeGetName(outputTensor->type));
+ return false;
+ }
+
+ if (useSoftmax) {
+ math::MathUtils::SoftmaxF32(tensorData);
+ }
+
+ /* Get the top N results. */
+ resultState = GetTopNResults(tensorData, vecResults, topNCount, labels);
+
+ if (!resultState) {
+ printf_err("Failed to get top N results set\n");
+ return false;
+ }
+
+ return true;
+ }
+} /* namespace app */
+} /* namespace arm */ \ No newline at end of file
diff --git a/source/application/api/common/source/ImageUtils.cc b/source/application/api/common/source/ImageUtils.cc
new file mode 100644
index 0000000..31b9493
--- /dev/null
+++ b/source/application/api/common/source/ImageUtils.cc
@@ -0,0 +1,126 @@
+/*
+ * 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 "ImageUtils.hpp"
+
+#include <limits>
+
+namespace arm {
+namespace app {
+namespace image {
+
+ float Calculate1DOverlap(float x1Center, float width1, float x2Center, float width2)
+ {
+ float left_1 = x1Center - width1/2;
+ float left_2 = x2Center - width2/2;
+ float leftest = left_1 > left_2 ? left_1 : left_2;
+
+ float right_1 = x1Center + width1/2;
+ float right_2 = x2Center + width2/2;
+ float rightest = right_1 < right_2 ? right_1 : right_2;
+
+ return rightest - leftest;
+ }
+
+ float CalculateBoxIntersect(Box& box1, Box& box2)
+ {
+ float width = Calculate1DOverlap(box1.x, box1.w, box2.x, box2.w);
+ if (width < 0) {
+ return 0;
+ }
+ float height = Calculate1DOverlap(box1.y, box1.h, box2.y, box2.h);
+ if (height < 0) {
+ return 0;
+ }
+
+ float total_area = width*height;
+ return total_area;
+ }
+
+ float CalculateBoxUnion(Box& box1, Box& box2)
+ {
+ float boxes_intersection = CalculateBoxIntersect(box1, box2);
+ float boxes_union = box1.w * box1.h + box2.w * box2.h - boxes_intersection;
+ return boxes_union;
+ }
+
+ float CalculateBoxIOU(Box& box1, Box& box2)
+ {
+ float boxes_intersection = CalculateBoxIntersect(box1, box2);
+ if (boxes_intersection == 0) {
+ return 0;
+ }
+
+ float boxes_union = CalculateBoxUnion(box1, box2);
+ if (boxes_union == 0) {
+ return 0;
+ }
+
+ return boxes_intersection / boxes_union;
+ }
+
+ void CalculateNMS(std::forward_list<Detection>& detections, int classes, float iouThreshold)
+ {
+ int idxClass{0};
+ auto CompareProbs = [idxClass](Detection& prob1, Detection& prob2) {
+ return prob1.prob[idxClass] > prob2.prob[idxClass];
+ };
+
+ for (idxClass = 0; idxClass < classes; ++idxClass) {
+ detections.sort(CompareProbs);
+
+ for (auto it=detections.begin(); it != detections.end(); ++it) {
+ if (it->prob[idxClass] == 0) continue;
+ for (auto itc=std::next(it, 1); itc != detections.end(); ++itc) {
+ if (itc->prob[idxClass] == 0) {
+ continue;
+ }
+ if (CalculateBoxIOU(it->bbox, itc->bbox) > iouThreshold) {
+ itc->prob[idxClass] = 0;
+ }
+ }
+ }
+ }
+ }
+
+ void ConvertImgToInt8(void* data, const size_t kMaxImageSize)
+ {
+ auto* tmp_req_data = static_cast<uint8_t*>(data);
+ auto* tmp_signed_req_data = static_cast<int8_t*>(data);
+
+ for (size_t i = 0; i < kMaxImageSize; i++) {
+ tmp_signed_req_data[i] = (int8_t) (
+ (int32_t) (tmp_req_data[i]) - 128);
+ }
+ }
+
+ void RgbToGrayscale(const uint8_t* srcPtr, uint8_t* dstPtr, const size_t dstImgSz)
+ {
+ const float R = 0.299;
+ const float G = 0.587;
+ const float B = 0.114;
+ for (size_t i = 0; i < dstImgSz; ++i, srcPtr += 3) {
+ uint32_t int_gray = R * (*srcPtr) +
+ G * (*(srcPtr + 1)) +
+ B * (*(srcPtr + 2));
+ *dstPtr++ = int_gray <= std::numeric_limits<uint8_t>::max() ?
+ int_gray : std::numeric_limits<uint8_t>::max();
+ }
+ }
+
+} /* namespace image */
+} /* namespace app */
+} /* namespace arm */ \ No newline at end of file
diff --git a/source/application/api/common/source/Mfcc.cc b/source/application/api/common/source/Mfcc.cc
new file mode 100644
index 0000000..3bf5eb3
--- /dev/null
+++ b/source/application/api/common/source/Mfcc.cc
@@ -0,0 +1,353 @@
+/*
+ * 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 "Mfcc.hpp"
+#include "PlatformMath.hpp"
+#include "log_macros.h"
+
+#include <cfloat>
+#include <cinttypes>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ MfccParams::MfccParams(
+ const float samplingFreq,
+ const uint32_t numFbankBins,
+ const float melLoFreq,
+ const float melHiFreq,
+ const uint32_t numMfccFeats,
+ const uint32_t frameLen,
+ const bool useHtkMethod):
+ m_samplingFreq(samplingFreq),
+ m_numFbankBins(numFbankBins),
+ m_melLoFreq(melLoFreq),
+ m_melHiFreq(melHiFreq),
+ m_numMfccFeatures(numMfccFeats),
+ m_frameLen(frameLen),
+
+ /* Smallest power of 2 >= frame length. */
+ m_frameLenPadded(pow(2, ceil((log(frameLen)/log(2))))),
+ m_useHtkMethod(useHtkMethod)
+ {}
+
+ void MfccParams::Log() const
+ {
+ debug("MFCC parameters:\n");
+ debug("\t Sampling frequency: %f\n", this->m_samplingFreq);
+ debug("\t Number of filter banks: %" PRIu32 "\n", this->m_numFbankBins);
+ debug("\t Mel frequency limit (low): %f\n", this->m_melLoFreq);
+ debug("\t Mel frequency limit (high): %f\n", this->m_melHiFreq);
+ debug("\t Number of MFCC features: %" PRIu32 "\n", this->m_numMfccFeatures);
+ debug("\t Frame length: %" PRIu32 "\n", this->m_frameLen);
+ debug("\t Padded frame length: %" PRIu32 "\n", this->m_frameLenPadded);
+ debug("\t Using HTK for Mel scale: %s\n", this->m_useHtkMethod ? "yes" : "no");
+ }
+
+ MFCC::MFCC(const MfccParams& 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);
+ this->m_params.Log();
+ }
+
+ void MFCC::Init()
+ {
+ this->InitMelFilterBank();
+ }
+
+ float MFCC::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 MFCC::InverseMelScale(const float melFreq, const bool useHTKMethod)
+ {
+ if (useHTKMethod) {
+ return 700.0f * (expf (melFreq / 1127.0f) - 1.0f);
+ } else {
+ /* Slaney formula for mel scale. */
+ float freq = ms_freqStep * melFreq;
+
+ if (melFreq >= ms_minLogMel) {
+ freq = ms_minLogHz * expf(ms_logStep * (melFreq - ms_minLogMel));
+ }
+ return freq;
+ }
+ }
+
+
+ bool MFCC::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<uint32_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 MFCC::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
+ {
+ for (float& melEnergy : melEnergies) {
+ melEnergy = logf(melEnergy);
+ }
+ }
+
+ void MFCC::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;
+ }
+
+ std::vector<float> MFCC::CreateDCTMatrix(
+ const int32_t inputLength,
+ const int32_t coefficientCount)
+ {
+ std::vector<float> dctMatix(inputLength * coefficientCount);
+
+ const float normalizer = math::MathUtils::SqrtF32(2.0f/inputLength);
+ const float angleIncr = M_PI/inputLength;
+ float angle = 0;
+
+ for (int32_t k = 0, m = 0; 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 MFCC::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 MFCC::InitMelFilterBank()
+ {
+ if (!this->IsMelFilterBankInited()) {
+ this->m_melFilterBank = this->CreateMelFilterBank();
+ this->m_dctMatrix = this->CreateDCTMatrix(
+ this->m_params.m_numFbankBins,
+ this->m_params.m_numMfccFeatures);
+ this->m_filterBankInitialised = true;
+ }
+ }
+
+ bool MFCC::IsMelFilterBankInited() const
+ {
+ return this->m_filterBankInitialised;
+ }
+
+ void MFCC::MfccComputePreFeature(const std::vector<int16_t>& audioData)
+ {
+ this->InitMelFilterBank();
+
+ /* TensorFlow way of normalizing .wav data to (-1, 1). */
+ constexpr float normaliser = 1.0/(1u<<15u);
+ 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);
+ }
+
+ std::vector<float> MFCC::MfccCompute(const std::vector<int16_t>& audioData)
+ {
+ this->MfccComputePreFeature(audioData);
+
+ std::vector<float> mfccOut(this->m_params.m_numMfccFeatures);
+
+ float * ptrMel = this->m_melEnergies.data();
+ float * ptrDct = this->m_dctMatrix.data();
+ float * ptrMfcc = mfccOut.data();
+
+ /* Take DCT. Uses matrix mul. */
+ for (size_t i = 0, j = 0; i < mfccOut.size();
+ ++i, j += this->m_params.m_numFbankBins) {
+ *ptrMfcc++ = math::MathUtils::DotProductF32(
+ ptrDct + j,
+ ptrMel,
+ this->m_params.m_numFbankBins);
+ }
+ return mfccOut;
+ }
+
+ std::vector<std::vector<float>> MFCC::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 = MFCC::MelScale(this->m_params.m_melLoFreq,
+ this->m_params.m_useHtkMethod);
+ float melHighFreq = MFCC::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 = MFCC::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/application/api/common/source/Model.cc b/source/application/api/common/source/Model.cc
new file mode 100644
index 0000000..f1ac91d
--- /dev/null
+++ b/source/application/api/common/source/Model.cc
@@ -0,0 +1,359 @@
+/*
+ * 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 "Model.hpp"
+#include "log_macros.h"
+
+#include <cinttypes>
+
+/* Initialise the model */
+arm::app::Model::~Model()
+{
+ delete this->m_pInterpreter;
+ /**
+ * No clean-up function available for allocator in TensorFlow Lite Micro yet.
+ **/
+}
+
+arm::app::Model::Model() :
+ m_inited (false),
+ m_type(kTfLiteNoType)
+{
+ this->m_pErrorReporter = tflite::GetMicroErrorReporter();
+}
+
+bool arm::app::Model::Init(uint8_t* tensorArenaAddr,
+ uint32_t tensorArenaSize,
+ uint8_t* nnModelAddr,
+ uint32_t nnModelSize,
+ tflite::MicroAllocator* allocator)
+{
+ /* Following tf lite micro example:
+ * Map the model into a usable data structure. This doesn't involve any
+ * copying or parsing, it's a very lightweight operation. */
+ debug("loading model from @ 0x%p\n", nnModelAddr);
+ debug("model size: %" PRIu32 " bytes.\n", nnModelSize);
+
+ this->m_pModel = ::tflite::GetModel(nnModelAddr);
+
+ if (this->m_pModel->version() != TFLITE_SCHEMA_VERSION) {
+ this->m_pErrorReporter->Report(
+ "[ERROR] model's schema version %d is not equal "
+ "to supported version %d.",
+ this->m_pModel->version(), TFLITE_SCHEMA_VERSION);
+ return false;
+ }
+
+ this->m_modelAddr = nnModelAddr;
+ this->m_modelSize = nnModelSize;
+
+ /* Pull in only the operation implementations we need.
+ * This relies on a complete list of all the ops needed by this graph.
+ * An easier approach is to just use the AllOpsResolver, but this will
+ * incur some penalty in code space for op implementations that are not
+ * needed by this graph.
+ * static ::tflite::ops::micro::AllOpsResolver resolver; */
+ /* NOLINTNEXTLINE(runtime-global-variables) */
+ debug("loading op resolver\n");
+
+ this->EnlistOperations();
+
+ /* Create allocator instance, if it doesn't exist */
+ this->m_pAllocator = allocator;
+ if (!this->m_pAllocator) {
+ /* Create an allocator instance */
+ info("Creating allocator using tensor arena at 0x%p\n", tensorArenaAddr);
+
+ this->m_pAllocator = tflite::MicroAllocator::Create(
+ tensorArenaAddr,
+ tensorArenaSize,
+ this->m_pErrorReporter);
+
+ if (!this->m_pAllocator) {
+ printf_err("Failed to create allocator\n");
+ return false;
+ }
+ debug("Created new allocator @ 0x%p\n", this->m_pAllocator);
+ } else {
+ debug("Using existing allocator @ 0x%p\n", this->m_pAllocator);
+ }
+
+ this->m_pInterpreter = new ::tflite::MicroInterpreter(
+ this->m_pModel, this->GetOpResolver(),
+ this->m_pAllocator, this->m_pErrorReporter);
+
+ if (!this->m_pInterpreter) {
+ printf_err("Failed to allocate interpreter\n");
+ return false;
+ }
+
+ /* Allocate memory from the tensor_arena for the model's tensors. */
+ info("Allocating tensors\n");
+ TfLiteStatus allocate_status = this->m_pInterpreter->AllocateTensors();
+
+ if (allocate_status != kTfLiteOk) {
+ printf_err("tensor allocation failed!\n");
+ delete this->m_pInterpreter;
+ return false;
+ }
+
+ /* Get information about the memory area to use for the model's input. */
+ this->m_input.resize(this->GetNumInputs());
+ for (size_t inIndex = 0; inIndex < this->GetNumInputs(); inIndex++)
+ this->m_input[inIndex] = this->m_pInterpreter->input(inIndex);
+
+ this->m_output.resize(this->GetNumOutputs());
+ for (size_t outIndex = 0; outIndex < this->GetNumOutputs(); outIndex++)
+ this->m_output[outIndex] = this->m_pInterpreter->output(outIndex);
+
+ if (this->m_input.empty() || this->m_output.empty()) {
+ printf_err("failed to get tensors\n");
+ return false;
+ } else {
+ this->m_type = this->m_input[0]->type; /* Input 0 should be the main input */
+
+ /* Clear the input & output tensors */
+ for (size_t inIndex = 0; inIndex < this->GetNumInputs(); inIndex++) {
+ std::memset(this->m_input[inIndex]->data.data, 0, this->m_input[inIndex]->bytes);
+ }
+ for (size_t outIndex = 0; outIndex < this->GetNumOutputs(); outIndex++) {
+ std::memset(this->m_output[outIndex]->data.data, 0, this->m_output[outIndex]->bytes);
+ }
+
+ this->LogInterpreterInfo();
+ }
+
+ this->m_inited = true;
+ return true;
+}
+
+tflite::MicroAllocator* arm::app::Model::GetAllocator()
+{
+ if (this->IsInited()) {
+ return this->m_pAllocator;
+ }
+ return nullptr;
+}
+
+void arm::app::Model::LogTensorInfo(TfLiteTensor* tensor)
+{
+ if (!tensor) {
+ printf_err("Invalid tensor\n");
+ assert(tensor);
+ return;
+ }
+
+ debug("\ttensor is assigned to 0x%p\n", tensor);
+ info("\ttensor type is %s\n", TfLiteTypeGetName(tensor->type));
+ info("\ttensor occupies %zu bytes with dimensions\n",
+ tensor->bytes);
+ for (int i = 0 ; i < tensor->dims->size; ++i) {
+ info ("\t\t%d: %3d\n", i, tensor->dims->data[i]);
+ }
+
+ TfLiteQuantization quant = tensor->quantization;
+ if (kTfLiteAffineQuantization == quant.type) {
+ auto* quantParams = (TfLiteAffineQuantization*)quant.params;
+ info("Quant dimension: %" PRIi32 "\n", quantParams->quantized_dimension);
+ for (int i = 0; i < quantParams->scale->size; ++i) {
+ info("Scale[%d] = %f\n", i, quantParams->scale->data[i]);
+ }
+ for (int i = 0; i < quantParams->zero_point->size; ++i) {
+ info("ZeroPoint[%d] = %d\n", i, quantParams->zero_point->data[i]);
+ }
+ }
+}
+
+void arm::app::Model::LogInterpreterInfo()
+{
+ if (!this->m_pInterpreter) {
+ printf_err("Invalid interpreter\n");
+ return;
+ }
+
+ info("Model INPUT tensors: \n");
+ for (auto input : this->m_input) {
+ this->LogTensorInfo(input);
+ }
+
+ info("Model OUTPUT tensors: \n");
+ for (auto output : this->m_output) {
+ this->LogTensorInfo(output);
+ }
+
+ info("Activation buffer (a.k.a tensor arena) size used: %zu\n",
+ this->m_pInterpreter->arena_used_bytes());
+
+ /* We expect there to be only one subgraph. */
+ const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0);
+ info("Number of operators: %" PRIu32 "\n", nOperators);
+
+ const tflite::SubGraph* subgraph = this->m_pModel->subgraphs()->Get(0);
+
+ auto* opcodes = this->m_pModel->operator_codes();
+
+ /* For each operator, display registration information. */
+ for (size_t i = 0 ; i < nOperators; ++i) {
+ const tflite::Operator* op = subgraph->operators()->Get(i);
+ const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index());
+ const TfLiteRegistration* reg = nullptr;
+
+ tflite::GetRegistrationFromOpCode(opcode, this->GetOpResolver(),
+ this->m_pErrorReporter, &reg);
+ std::string opName;
+
+ if (reg) {
+ if (tflite::BuiltinOperator_CUSTOM == reg->builtin_code) {
+ opName = std::string(reg->custom_name);
+ } else {
+ opName = std::string(EnumNameBuiltinOperator(
+ tflite::BuiltinOperator(reg->builtin_code)));
+ }
+ }
+ info("\tOperator %zu: %s\n", i, opName.c_str());
+ }
+}
+
+bool arm::app::Model::IsInited() const
+{
+ return this->m_inited;
+}
+
+bool arm::app::Model::IsDataSigned() const
+{
+ return this->GetType() == kTfLiteInt8;
+}
+
+bool arm::app::Model::ContainsEthosUOperator() const
+{
+ /* We expect there to be only one subgraph. */
+ const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0);
+ const tflite::SubGraph* subgraph = this->m_pModel->subgraphs()->Get(0);
+ const auto* opcodes = this->m_pModel->operator_codes();
+
+ /* check for custom operators */
+ for (size_t i = 0; (i < nOperators); ++i)
+ {
+ const tflite::Operator* op = subgraph->operators()->Get(i);
+ const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index());
+
+ auto builtin_code = tflite::GetBuiltinCode(opcode);
+ if ((builtin_code == tflite::BuiltinOperator_CUSTOM) &&
+ ( nullptr != opcode->custom_code()) &&
+ ( "ethos-u" == std::string(opcode->custom_code()->c_str())))
+ {
+ return true;
+ }
+ }
+ return false;
+}
+
+bool arm::app::Model::RunInference()
+{
+ bool inference_state = false;
+ if (this->m_pModel && this->m_pInterpreter) {
+ if (kTfLiteOk != this->m_pInterpreter->Invoke()) {
+ printf_err("Invoke failed.\n");
+ } else {
+ inference_state = true;
+ }
+ } else {
+ printf_err("Error: No interpreter!\n");
+ }
+ return inference_state;
+}
+
+TfLiteTensor* arm::app::Model::GetInputTensor(size_t index) const
+{
+ if (index < this->GetNumInputs()) {
+ return this->m_input.at(index);
+ }
+ return nullptr;
+}
+
+TfLiteTensor* arm::app::Model::GetOutputTensor(size_t index) const
+{
+ if (index < this->GetNumOutputs()) {
+ return this->m_output.at(index);
+ }
+ return nullptr;
+}
+
+size_t arm::app::Model::GetNumInputs() const
+{
+ if (this->m_pModel && this->m_pInterpreter) {
+ return this->m_pInterpreter->inputs_size();
+ }
+ return 0;
+}
+
+size_t arm::app::Model::GetNumOutputs() const
+{
+ if (this->m_pModel && this->m_pInterpreter) {
+ return this->m_pInterpreter->outputs_size();
+ }
+ return 0;
+}
+
+
+TfLiteType arm::app::Model::GetType() const
+{
+ return this->m_type;
+}
+
+TfLiteIntArray* arm::app::Model::GetInputShape(size_t index) const
+{
+ if (index < this->GetNumInputs()) {
+ return this->m_input.at(index)->dims;
+ }
+ return nullptr;
+}
+
+TfLiteIntArray* arm::app::Model::GetOutputShape(size_t index) const
+{
+ if (index < this->GetNumOutputs()) {
+ return this->m_output.at(index)->dims;
+ }
+ return nullptr;
+}
+
+bool arm::app::Model::ShowModelInfoHandler()
+{
+ if (!this->IsInited()) {
+ printf_err("Model is not initialised! Terminating processing.\n");
+ return false;
+ }
+
+ PrintTensorFlowVersion();
+ info("Model address: 0x%p", this->ModelPointer());
+ info("Model size: %" PRIu32 " bytes.", this->ModelSize());
+ info("Model info:\n");
+ this->LogInterpreterInfo();
+
+ info("The model is optimised for Ethos-U NPU: %s.\n", this->ContainsEthosUOperator()? "yes": "no");
+
+ return true;
+}
+
+const uint8_t* arm::app::Model::ModelPointer()
+{
+ return this->m_modelAddr;
+}
+
+uint32_t arm::app::Model::ModelSize()
+{
+ return this->m_modelSize;
+}
diff --git a/source/application/api/common/source/TensorFlowLiteMicro.cc b/source/application/api/common/source/TensorFlowLiteMicro.cc
new file mode 100644
index 0000000..8738e5c
--- /dev/null
+++ b/source/application/api/common/source/TensorFlowLiteMicro.cc
@@ -0,0 +1,46 @@
+/*
+ * 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 "TensorFlowLiteMicro.hpp"
+
+void PrintTensorFlowVersion()
+{}
+
+arm::app::QuantParams arm::app::GetTensorQuantParams(TfLiteTensor* tensor)
+{
+ arm::app::QuantParams params;
+ if (kTfLiteAffineQuantization == tensor->quantization.type) {
+ auto* quantParams = (TfLiteAffineQuantization*) (tensor->quantization.params);
+ if (quantParams && 0 == quantParams->quantized_dimension) {
+ if (quantParams->scale->size) {
+ params.scale = quantParams->scale->data[0];
+ }
+ if (quantParams->zero_point->size) {
+ params.offset = quantParams->zero_point->data[0];
+ }
+ } else if (tensor->params.scale != 0.0) {
+ /* Legacy tensorflow quantisation parameters */
+ params.scale = tensor->params.scale;
+ params.offset = tensor->params.zero_point;
+ }
+ }
+ return params;
+}
+
+extern "C" void DebugLog(const char* s)
+{
+ puts(s);
+}
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-13 05:59:12 +0000