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/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 @@
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/