| /* |
| * 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 "log_macros.h" |
| #include "ImageUtils.hpp" |
| #include "YoloFastestModel.hpp" |
| #include "TensorFlowLiteMicro.hpp" |
| #include "DetectorPostProcessing.hpp" |
| #include "InputFiles.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| |
| #include <catch.hpp> |
| |
| 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) |
| }); |
| /* 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) |
| }); |
| /* 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) |
| }); |
| /* 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) |
| }); |
| } |
| |
| 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; |
| |
| arm::app::image::RgbToGrayscale(imageData,inputTensor->data.uint8,copySz); |
| |
| 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) { |
| |
| std::vector<arm::app::object_detection::DetectionResult> results; |
| auto image = get_img_array(imageIdx); |
| |
| TfLiteIntArray* inputShape = model.GetInputShape(0); |
| 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++) { |
| REQUIRE(output_arr[i]); |
| REQUIRE(tflite::GetTensorData<T>(output_arr[i])); |
| } |
| |
| arm::app::object_detection::DetectorPostprocessing postp; |
| postp.RunPostProcessing( |
| nRows, |
| nCols, |
| output_arr[0], |
| output_arr[1], |
| results); |
| |
| std::vector<std::vector<arm::app::object_detection::DetectionResult>> expected_results; |
| GetExpectedResults(expected_results); |
| |
| /* Validate got the same number of boxes */ |
| REQUIRE(results.size() == expected_results[imageIdx].size()); |
| |
| |
| 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)); |
| } |
| } |
| |
| |
| TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]") |
| { |
| SECTION("Executing inferences sequentially") |
| { |
| arm::app::YoloFastestModel model{}; |
| |
| REQUIRE_FALSE(model.IsInited()); |
| REQUIRE(model.Init()); |
| REQUIRE(model.IsInited()); |
| |
| 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) { |
| DYNAMIC_SECTION("Executing inference with re-init") |
| { |
| arm::app::YoloFastestModel model{}; |
| |
| REQUIRE_FALSE(model.IsInited()); |
| REQUIRE(model.Init()); |
| REQUIRE(model.IsInited()); |
| |
| TestInferenceDetectionResults<uint8_t>(i, model, 1); |
| } |
| } |
| } |