MLECO-2873: Object detection usecase follow-up
Change-Id: Ic14e93a50fb7b3f3cfd9497bac1280794cc0fc15
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
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 @@
}
template<typename T>
-void TestInference(int imageIdx, arm::app::Model& model, T tolerance) {
+void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) {
- info("Entering TestInference for image %d \n", imageIdx);
-
- 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 */
- 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 );
+ arm::app::object_detection::DetectorPostprocessing postp;
+ postp.RunPostProcessing(
+ nullptr,
+ nRows,
+ nCols,
+ output_arr[0],
+ output_arr[1],
+ results);
- /*validate confidence and box dimensions */
- REQUIRE(fabs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1);
+ 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));
}
-
-
}
@@ -105,7 +132,7 @@
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 @@
REQUIRE(model.Init());
REQUIRE(model.IsInited());
- TestInference<uint8_t>(i, model, 1);
+ TestInferenceDetectionResults<uint8_t>(i, model, 1);
}
}
}