Fix precommit tests

Author: RHeckerIntel

tests/cpp/precommit/test_model_config.cpp

@@ -122,14 +122,14 @@ TEST_P(ClassificationModelParameterizedTestSaveLoad, TestClassificationCorrectne
     auto ov_model = model->getModel();
     ov::serialize(ov_model, TMP_MODEL_FILE);
 
-    auto result = model->infer(image)->topLabels;
+    auto result = model->infer(image)->boxes;
 
     auto model_restored = ClassificationModel::create_model(TMP_MODEL_FILE, {}, preload, "CPU");
     auto result_data = model_restored->infer(image);
-    auto result_restored = result_data->topLabels;
+    auto result_restored = result_data->boxes;
 
-    EXPECT_EQ(result_restored[0].id, result[0].id);
-    EXPECT_EQ(result_restored[0].score, result[0].score);
+    EXPECT_EQ(result_restored[0].labels[0].label.id, result[0].labels[0].label.id);
+    EXPECT_EQ(result_restored[0].labels[0].score, result[0].labels[0].score);
 }
 
 TEST_P(ClassificationModelParameterizedTestSaveLoad, TestClassificationCorrectnessAfterSaveLoadWithAdapter) {
@@ -143,15 +143,15 @@ TEST_P(ClassificationModelParameterizedTestSaveLoad, TestClassificationCorrectne
     auto model = ClassificationModel::create_model(DATA_DIR + "/" + model_path, {}, preload, "CPU");
     auto ov_model = model->getModel();
     ov::serialize(ov_model, TMP_MODEL_FILE);
-    auto result = model->infer(image)->topLabels;
+    auto result = model->infer(image)->boxes;
 
     std::shared_ptr<InferenceAdapter> adapter = std::make_shared<MockAdapter>(TMP_MODEL_FILE);
     auto model_restored = ClassificationModel::create_model(adapter);
     auto result_data = model_restored->infer(image);
-    auto result_restored = result_data->topLabels;
+    auto result_restored = result_data->boxes;
 
-    EXPECT_EQ(result_restored[0].id, result[0].id);
-    EXPECT_EQ(result_restored[0].score, result[0].score);
+    EXPECT_EQ(result_restored[0].labels[0].label.id, result[0].labels[0].label.id);
+    EXPECT_EQ(result_restored[0].labels[0].score, result[0].labels[0].score);
 }
 
 TEST_P(SSDModelParameterizedTest, TestDetectionDefaultConfig) {
@@ -206,23 +206,23 @@ TEST_P(DetectionModelParameterizedTestSaveLoad, TestDetctionCorrectnessAfterSave
     auto ov_model = model->getModel();
     ov::serialize(ov_model, TMP_MODEL_FILE);
 
-    auto result = model->infer(image)->objects;
+    auto result = model->infer(image)->boxes;
 
     image = cv::imread(DATA_DIR + "/" + IMAGE_PATH);
     if (!image.data) {
         throw std::runtime_error{"Failed to read the image"};
     }
     auto model_restored = DetectionModel::create_model(TMP_MODEL_FILE, {}, "", preload, "CPU");
     auto result_data = model_restored->infer(image);
-    auto result_restored = result_data->objects;
+    auto result_restored = result_data->boxes;
 
     ASSERT_EQ(result.size(), result_restored.size());
 
     for (size_t i = 0; i < result.size(); i++) {
-        ASSERT_EQ(result[i].x, result_restored[i].x);
-        ASSERT_EQ(result[i].y, result_restored[i].y);
-        ASSERT_EQ(result[i].width, result_restored[i].width);
-        ASSERT_EQ(result[i].height, result_restored[i].height);
+        ASSERT_EQ(result[i].shape.x, result_restored[i].shape.x);
+        ASSERT_EQ(result[i].shape.y, result_restored[i].shape.y);
+        ASSERT_EQ(result[i].shape.width, result_restored[i].shape.width);
+        ASSERT_EQ(result[i].shape.height, result_restored[i].shape.height);
     }
 }
 
@@ -237,7 +237,7 @@ TEST_P(DetectionModelParameterizedTestSaveLoad, TestDetctionCorrectnessAfterSave
     auto model = DetectionModel::create_model(DATA_DIR + "/" + model_path, {}, "", preload, "CPU");
     auto ov_model = model->getModel();
     ov::serialize(ov_model, TMP_MODEL_FILE);
-    auto result = model->infer(image)->objects;
+    auto result = model->infer(image)->boxes;
 
     image = cv::imread(DATA_DIR + "/" + IMAGE_PATH);
     if (!image.data) {
@@ -247,15 +247,15 @@ TEST_P(DetectionModelParameterizedTestSaveLoad, TestDetctionCorrectnessAfterSave
     std::shared_ptr<InferenceAdapter> adapter = std::make_shared<MockAdapter>(TMP_MODEL_FILE);
     auto model_restored = DetectionModel::create_model(adapter);
     auto result_data = model_restored->infer(image);
-    auto result_restored = result_data->objects;
+    auto result_restored = result_data->boxes;
 
     ASSERT_EQ(result.size(), result_restored.size());
 
     for (size_t i = 0; i < result.size(); i++) {
-        ASSERT_EQ(result[i].x, result_restored[i].x);
-        ASSERT_EQ(result[i].y, result_restored[i].y);
-        ASSERT_EQ(result[i].width, result_restored[i].width);
-        ASSERT_EQ(result[i].height, result_restored[i].height);
+        ASSERT_EQ(result[i].shape.x, result_restored[i].shape.x);
+        ASSERT_EQ(result[i].shape.y, result_restored[i].shape.y);
+        ASSERT_EQ(result[i].shape.width, result_restored[i].shape.width);
+        ASSERT_EQ(result[i].shape.height, result_restored[i].shape.height);
     }
 }
 

tests/cpp/precommit/test_sanity.cpp

@@ -73,17 +73,17 @@ TEST_P(ModelParameterizedTest, SynchronousInference) {
         bool preload = true;
         auto model = DetectionModel::create_model(DATA_DIR + "/" + model_path, {}, "", preload, "CPU");
         auto result = model->infer(image);
-        EXPECT_GT(result->objects.size(), 0);
+        EXPECT_GT(result->boxes.size(), 0);
     } else if ("ClassificationModel" == GetParam().type) {
         bool preload = true;
         auto model = ClassificationModel::create_model(DATA_DIR + "/" + model_path, {}, preload, "CPU");
-        std::unique_ptr<ClassificationResult> result = model->infer(image);
-        ASSERT_GT(result->topLabels.size(), 0);
-        EXPECT_GT(result->topLabels.front().score, 0.0f);
+        auto result = model->infer(image);
+        ASSERT_GT(result->boxes.size(), 0);
+        EXPECT_GT(result->boxes.front().labels.front().score, 0.0f);
     } else if ("SegmentationModel" == GetParam().type) {
         bool preload = true;
         auto model = SegmentationModel::create_model(DATA_DIR + "/" + model_path, {}, preload, "CPU");
-        auto result = model->infer(image)->asRef<ImageResultWithSoftPrediction>();
+        auto result = model->infer(image);
         ASSERT_GT(model->getContours(result).size(), 0);
     }
 }