diff options
Diffstat (limited to 'tests/use_case/object_detection/InferenceTestYoloFastest.cc')
| -rw-r--r-- | tests/use_case/object_detection/InferenceTestYoloFastest.cc | 101 |
1 files changed, 64 insertions, 37 deletions
diff --git a/tests/use_case/object_detection/InferenceTestYoloFastest.cc b/tests/use_case/object_detection/InferenceTestYoloFastest.cc index e6ae573..e5a5efe 100644 --- a/tests/use_case/object_detection/InferenceTestYoloFastest.cc +++ b/tests/use_case/object_detection/InferenceTestYoloFastest.cc @@ -21,22 +21,52 @@ #include "DetectorPostProcessing.hpp" #include "InputFiles.hpp" #include "UseCaseCommonUtils.hpp" -#include "DetectionUseCaseUtils.hpp" -#include "ExpectedObjectDetectionResults.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 < (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT) ? - inputTensor->bytes : - (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT); + const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? + inputTensor->bytes : IMAGE_DATA_SIZE; - arm::app::RgbToGrayscale(imageData,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT); + image::RgbToGrayscale(imageData,inputTensor->data.uint8,copySz); if(model.IsDataSigned()){ convertImgIoInt8(inputTensor->data.data, copySz); @@ -46,51 +76,48 @@ bool RunInference(arm::app::Model& model, const uint8_t imageData[]) } template<typename T> -void TestInference(int imageIdx, arm::app::Model& model, T tolerance) { - - info("Entering TestInference for image %d \n", imageIdx); +void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) { - std::vector<arm::app::DetectionResult> results; + 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)); - TfLiteTensor* output_arr[2] = {nullptr,nullptr}; - output_arr[0] = model.GetOutputTensor(0); - output_arr[1] = model.GetOutputTensor(1); - - for (int i =0; i < 2; i++) { - REQUIRE(output_arr[i]); + 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])); } - RunPostProcessing(NULL,output_arr,results); - - info("Got %ld boxes \n",results.size()); - - std::vector<std::vector<arm::app::DetectionResult>> expected_results; - get_expected_ut_results(expected_results); - - /*validate got the same number of boxes */ + arm::app::object_detection::DetectorPostprocessing postp; + postp.RunPostProcessing( + nullptr, + 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++) { - - info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", (int)i, - results[i].m_normalisedVal, "Detection box:", - results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h ); - /*validate confidence and box dimensions */ - REQUIRE(fabs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1); + + 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)); } - - } @@ -105,7 +132,7 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[Yolo REQUIRE(model.IsInited()); for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) { - TestInference<uint8_t>(i, model, 1); + TestInferenceDetectionResults<uint8_t>(i, model, 1); } } @@ -118,7 +145,7 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[Yolo REQUIRE(model.Init()); REQUIRE(model.IsInited()); - TestInference<uint8_t>(i, model, 1); + TestInferenceDetectionResults<uint8_t>(i, model, 1); } } } |