diff options
Diffstat (limited to 'tests/use_case/object_detection/InferenceTestYoloFastest.cc')
| -rw-r--r-- | tests/use_case/object_detection/InferenceTestYoloFastest.cc | 108 |
1 files changed, 53 insertions, 55 deletions
diff --git a/tests/use_case/object_detection/InferenceTestYoloFastest.cc b/tests/use_case/object_detection/InferenceTestYoloFastest.cc index f1c3719..b3cf37d 100644 --- a/tests/use_case/object_detection/InferenceTestYoloFastest.cc +++ b/tests/use_case/object_detection/InferenceTestYoloFastest.cc @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com> - * SPDX-License-Identifier: Apache-2.0 + * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates + * <open-source-office@arm.com> 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. @@ -14,58 +14,51 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -#include "log_macros.h" -#include "ImageUtils.hpp" -#include "YoloFastestModel.hpp" -#include "TensorFlowLiteMicro.hpp" +#include "BufAttributes.hpp" #include "DetectorPostProcessing.hpp" +#include "ImageUtils.hpp" #include "InputFiles.hpp" -#include "BufAttributes.hpp" +#include "TensorFlowLiteMicro.hpp" +#include "YoloFastestModel.hpp" +#include "log_macros.h" namespace arm { - namespace app { - static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; - namespace object_detection { - extern uint8_t* GetModelPointer(); - extern size_t GetModelLen(); - } /* namespace object_detection */ - } /* namespace app */ +namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + namespace object_detection { + extern uint8_t* GetModelPointer(); + extern size_t GetModelLen(); + } /* namespace object_detection */ +} /* namespace app */ } /* namespace arm */ #include <catch.hpp> -void GetExpectedResults(std::vector<std::vector<arm::app::object_detection::DetectionResult>> &expected_results) +void GetExpectedResults( + std::vector<std::vector<arm::app::object_detection::DetectionResult>>& expected_results) { /* Img1 0) (0.999246) -> Detection box: {x=89,y=17,w=41,h=56} 1) (0.995367) -> Detection box: {x=27,y=81,w=48,h=53} */ - expected_results.push_back({ - arm::app::object_detection::DetectionResult(0.99,89,17,41,56), - arm::app::object_detection::DetectionResult(0.99,27,81,48,53) - }); + expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 89, 17, 41, 56), + arm::app::object_detection::DetectionResult(0.99, 27, 81, 48, 53)}); /* Img2 0) (0.998107) -> Detection box: {x=87,y=35,w=53,h=64} */ - expected_results.push_back({ - arm::app::object_detection::DetectionResult(0.99,87,35,53,64) - }); + expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 87, 35, 53, 64)}); /* Img3 0) (0.999244) -> Detection box: {x=105,y=73,w=58,h=66} 1) (0.985984) -> Detection box: {x=34,y=40,w=70,h=95} */ - expected_results.push_back({ - arm::app::object_detection::DetectionResult(0.99,105,73,58,66), - arm::app::object_detection::DetectionResult(0.98,34,40,70,95) - }); + expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 105, 73, 58, 66), + arm::app::object_detection::DetectionResult(0.98, 34, 40, 70, 95)}); /* Img4 0) (0.993294) -> Detection box: {x=22,y=43,w=39,h=53} 1) (0.992021) -> Detection box: {x=63,y=60,w=38,h=45} */ - expected_results.push_back({ - arm::app::object_detection::DetectionResult(0.99,22,43,39,53), - arm::app::object_detection::DetectionResult(0.99,63,60,38,45) - }); + expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 22, 43, 39, 53), + arm::app::object_detection::DetectionResult(0.99, 63, 60, 38, 45)}); } bool RunInference(arm::app::Model& model, const uint8_t imageData[]) @@ -73,41 +66,43 @@ bool RunInference(arm::app::Model& model, const uint8_t imageData[]) TfLiteTensor* inputTensor = model.GetInputTensor(0); REQUIRE(inputTensor); - const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? - inputTensor->bytes : IMAGE_DATA_SIZE; + const size_t copySz = + inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE; - arm::app::image::RgbToGrayscale(imageData,inputTensor->data.uint8,copySz); + arm::app::image::RgbToGrayscale(imageData, inputTensor->data.uint8, copySz); - if(model.IsDataSigned()){ + if (model.IsDataSigned()) { arm::app::image::ConvertImgToInt8(inputTensor->data.data, copySz); } return model.RunInference(); } -template<typename T> -void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) { +template <typename T> +void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) +{ std::vector<arm::app::object_detection::DetectionResult> results; - auto image = get_img_array(imageIdx); + auto image = GetImgArray(imageIdx); TfLiteIntArray* inputShape = model.GetInputShape(0); - auto nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx]; - auto nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx]; + auto nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx]; + auto nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx]; REQUIRE(RunInference(model, image)); - std::vector<TfLiteTensor*> output_arr{model.GetOutputTensor(0), model.GetOutputTensor(1)}; - for (size_t i =0; i < output_arr.size(); i++) { + for (size_t i = 0; i < output_arr.size(); i++) { REQUIRE(output_arr[i]); REQUIRE(tflite::GetTensorData<T>(output_arr[i])); } - const arm::app::object_detection::PostProcessParams postProcessParams { - nRows, nCols, arm::app::object_detection::originalImageSize, - arm::app::object_detection::anchor1, arm::app::object_detection::anchor2 - }; + const arm::app::object_detection::PostProcessParams postProcessParams{ + nRows, + nCols, + arm::app::object_detection::originalImageSize, + arm::app::object_detection::anchor1, + arm::app::object_detection::anchor2}; arm::app::DetectorPostProcess postp{output_arr[0], output_arr[1], results, postProcessParams}; postp.DoPostProcess(); @@ -117,18 +112,21 @@ void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T toler /* Validate got the same number of boxes */ REQUIRE(results.size() == expected_results[imageIdx].size()); - - for (int i=0; i < (int)results.size(); i++) { + for (int i = 0; i < (int)results.size(); i++) { /* Validate confidence and box dimensions */ - REQUIRE(std::abs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1); - REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance)); - REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance)); - REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance)); - REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance)); + REQUIRE(std::abs(results[i].m_normalisedVal - + expected_results[imageIdx][i].m_normalisedVal) < 0.1); + REQUIRE(static_cast<int>(results[i].m_x0) == + Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance)); + REQUIRE(static_cast<int>(results[i].m_y0) == + Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance)); + REQUIRE(static_cast<int>(results[i].m_w) == + Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance)); + REQUIRE(static_cast<int>(results[i].m_h) == + Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance)); } } - TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]") { SECTION("Executing inferences sequentially") @@ -142,12 +140,12 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[Yolo arm::app::object_detection::GetModelLen())); REQUIRE(model.IsInited()); - for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) { + for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) { TestInferenceDetectionResults<uint8_t>(i, model, 1); } } - for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) { + for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) { DYNAMIC_SECTION("Executing inference with re-init") { arm::app::YoloFastestModel model{}; |