diff options
Diffstat (limited to 'source/application/api/use_case/object_detection')
8 files changed, 679 insertions, 0 deletions
diff --git a/source/application/api/use_case/object_detection/CMakeLists.txt b/source/application/api/use_case/object_detection/CMakeLists.txt new file mode 100644 index 0000000..797ff55 --- /dev/null +++ b/source/application/api/use_case/object_detection/CMakeLists.txt @@ -0,0 +1,40 @@ +#---------------------------------------------------------------------------- +# 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. +#---------------------------------------------------------------------------- +######################################################### +# OBJECT DETECTION API library # +######################################################### +cmake_minimum_required(VERSION 3.15.6) + +set(OBJECT_DETECTION_API_TARGET object_detection_api) +project(${OBJECT_DETECTION_API_TARGET} + DESCRIPTION "Object detection use case API library" + LANGUAGES C CXX) + +# Create static library +add_library(${OBJECT_DETECTION_API_TARGET} STATIC + src/DetectorPreProcessing.cc + src/DetectorPostProcessing.cc + src/YoloFastestModel.cc) + +target_include_directories(${OBJECT_DETECTION_API_TARGET} PUBLIC include) + +target_link_libraries(${OBJECT_DETECTION_API_TARGET} PUBLIC common_api) + +message(STATUS "*******************************************************") +message(STATUS "Library : " ${OBJECT_DETECTION_API_TARGET}) +message(STATUS "CMAKE_SYSTEM_PROCESSOR : " ${CMAKE_SYSTEM_PROCESSOR}) +message(STATUS "*******************************************************") diff --git a/source/application/api/use_case/object_detection/include/DetectionResult.hpp b/source/application/api/use_case/object_detection/include/DetectionResult.hpp new file mode 100644 index 0000000..aa74d90 --- /dev/null +++ b/source/application/api/use_case/object_detection/include/DetectionResult.hpp @@ -0,0 +1,61 @@ +/* + * 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/application/api/use_case/object_detection/include/DetectorPostProcessing.hpp b/source/application/api/use_case/object_detection/include/DetectorPostProcessing.hpp new file mode 100644 index 0000000..30bc123 --- /dev/null +++ b/source/application/api/use_case/object_detection/include/DetectorPostProcessing.hpp @@ -0,0 +1,125 @@ +/* + * 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 "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/application/api/use_case/object_detection/include/DetectorPreProcessing.hpp b/source/application/api/use_case/object_detection/include/DetectorPreProcessing.hpp new file mode 100644 index 0000000..4936048 --- /dev/null +++ b/source/application/api/use_case/object_detection/include/DetectorPreProcessing.hpp @@ -0,0 +1,60 @@ +/* + * 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/application/api/use_case/object_detection/include/YoloFastestModel.hpp b/source/application/api/use_case/object_detection/include/YoloFastestModel.hpp new file mode 100644 index 0000000..4c64433 --- /dev/null +++ b/source/application/api/use_case/object_detection/include/YoloFastestModel.hpp @@ -0,0 +1,56 @@ +/* + * 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; + + 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/application/api/use_case/object_detection/src/DetectorPostProcessing.cc b/source/application/api/use_case/object_detection/src/DetectorPostProcessing.cc new file mode 100644 index 0000000..fb1606a --- /dev/null +++ b/source/application/api/use_case/object_detection/src/DetectorPostProcessing.cc @@ -0,0 +1,240 @@ +/* + * 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/application/api/use_case/object_detection/src/DetectorPreProcessing.cc b/source/application/api/use_case/object_detection/src/DetectorPreProcessing.cc new file mode 100644 index 0000000..7212046 --- /dev/null +++ b/source/application/api/use_case/object_detection/src/DetectorPreProcessing.cc @@ -0,0 +1,52 @@ +/* + * 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/application/api/use_case/object_detection/src/YoloFastestModel.cc b/source/application/api/use_case/object_detection/src/YoloFastestModel.cc new file mode 100644 index 0000000..e293181 --- /dev/null +++ b/source/application/api/use_case/object_detection/src/YoloFastestModel.cc @@ -0,0 +1,45 @@ +/* + * 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 (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; + } + return true; +} |