Michael Levit | 06fcf75 | 2022-01-12 11:53:46 +0200 | [diff] [blame^] | 1 | /* |
| 2 | * Copyright (c) 2022 Arm Limited. All rights reserved. |
| 3 | * SPDX-License-Identifier: Apache-2.0 |
| 4 | * |
| 5 | * Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | * you may not use this file except in compliance with the License. |
| 7 | * You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | #include "hal.h" |
| 18 | #include "ImageUtils.hpp" |
| 19 | #include "YoloFastestModel.hpp" |
| 20 | #include "TensorFlowLiteMicro.hpp" |
| 21 | #include "DetectorPostProcessing.hpp" |
| 22 | #include "InputFiles.hpp" |
| 23 | #include "UseCaseCommonUtils.hpp" |
| 24 | #include "DetectionUseCaseUtils.hpp" |
| 25 | #include "ExpectedObjectDetectionResults.hpp" |
| 26 | |
| 27 | #include <catch.hpp> |
| 28 | |
| 29 | |
| 30 | bool RunInference(arm::app::Model& model, const uint8_t imageData[]) |
| 31 | { |
| 32 | TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| 33 | REQUIRE(inputTensor); |
| 34 | |
| 35 | const size_t copySz = inputTensor->bytes < (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT) ? |
| 36 | inputTensor->bytes : |
| 37 | (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT); |
| 38 | |
| 39 | arm::app::RgbToGrayscale(imageData,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT); |
| 40 | |
| 41 | if(model.IsDataSigned()){ |
| 42 | convertImgIoInt8(inputTensor->data.data, copySz); |
| 43 | } |
| 44 | |
| 45 | return model.RunInference(); |
| 46 | } |
| 47 | |
| 48 | template<typename T> |
| 49 | void TestInference(int imageIdx, arm::app::Model& model, T tolerance) { |
| 50 | |
| 51 | info("Entering TestInference for image %d \n", imageIdx); |
| 52 | |
| 53 | std::vector<arm::app::DetectionResult> results; |
| 54 | auto image = get_img_array(imageIdx); |
| 55 | |
| 56 | REQUIRE(RunInference(model, image)); |
| 57 | |
| 58 | |
| 59 | TfLiteTensor* output_arr[2] = {nullptr,nullptr}; |
| 60 | output_arr[0] = model.GetOutputTensor(0); |
| 61 | output_arr[1] = model.GetOutputTensor(1); |
| 62 | |
| 63 | for (int i =0; i < 2; i++) { |
| 64 | REQUIRE(output_arr[i]); |
| 65 | REQUIRE(tflite::GetTensorData<T>(output_arr[i])); |
| 66 | } |
| 67 | |
| 68 | RunPostProcessing(NULL,output_arr,results); |
| 69 | |
| 70 | info("Got %ld boxes \n",results.size()); |
| 71 | |
| 72 | std::vector<std::vector<arm::app::DetectionResult>> expected_results; |
| 73 | get_expected_ut_results(expected_results); |
| 74 | |
| 75 | /*validate got the same number of boxes */ |
| 76 | REQUIRE(results.size() == expected_results[imageIdx].size()); |
| 77 | |
| 78 | |
| 79 | for (int i=0; i < (int)results.size(); i++) { |
| 80 | |
| 81 | info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", (int)i, |
| 82 | results[i].m_normalisedVal, "Detection box:", |
| 83 | results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h ); |
| 84 | |
| 85 | /*validate confidence and box dimensions */ |
| 86 | REQUIRE(fabs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1); |
| 87 | REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance)); |
| 88 | REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance)); |
| 89 | REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance)); |
| 90 | REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance)); |
| 91 | } |
| 92 | |
| 93 | |
| 94 | } |
| 95 | |
| 96 | |
| 97 | TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]") |
| 98 | { |
| 99 | SECTION("Executing inferences sequentially") |
| 100 | { |
| 101 | arm::app::YoloFastestModel model{}; |
| 102 | |
| 103 | REQUIRE_FALSE(model.IsInited()); |
| 104 | REQUIRE(model.Init()); |
| 105 | REQUIRE(model.IsInited()); |
| 106 | |
| 107 | for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) { |
| 108 | TestInference<uint8_t>(i, model, 1); |
| 109 | } |
| 110 | } |
| 111 | |
| 112 | for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) { |
| 113 | DYNAMIC_SECTION("Executing inference with re-init") |
| 114 | { |
| 115 | arm::app::YoloFastestModel model{}; |
| 116 | |
| 117 | REQUIRE_FALSE(model.IsInited()); |
| 118 | REQUIRE(model.Init()); |
| 119 | REQUIRE(model.IsInited()); |
| 120 | |
| 121 | TestInference<uint8_t>(i, model, 1); |
| 122 | } |
| 123 | } |
| 124 | } |