Update tests

Author: sovrasov

src/cpp/src/tasks/anomaly.cpp

@@ -7,7 +7,7 @@
 ov::AnyMap Anomaly::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap& input_config) {
     if (utils::model_has_embedded_processing(ov_model)) {
         std::cout << "model already was serialized" << std::endl;
-        return {};
+        return input_config;
     }
 
     auto input = ov_model->inputs().front();

src/cpp/src/tasks/classification.cpp

@@ -85,7 +85,7 @@ std::vector<size_t> get_non_xai_output_indices(const std::vector<ov::Output<ov::
 ov::AnyMap Classification::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap& input_config) {
     if (utils::model_has_embedded_processing(ov_model)) {
         std::cout << "model already was serialized" << std::endl;
-        return {};
+        return input_config;
     }
     // --------------------------- Configure input & output -------------------------------------------------
     // --------------------------- Prepare input  ------------------------------------------------------

src/cpp/src/tasks/detection/ssd.cpp

@@ -70,7 +70,7 @@ std::map<std::string, ov::Tensor> SSD::preprocess(cv::Mat image) {
 ov::AnyMap SSD::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap& input_config) {
     if (utils::model_has_embedded_processing(ov_model)) {
         std::cout << "model already was serialized" << std::endl;
-        return {};
+        return input_config;
     }
     auto output_mode = ov_model->outputs().size() > 1 ? SSDOutputMode::multi : SSDOutputMode::single;
 
@@ -89,7 +89,7 @@ ov::AnyMap SSD::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap
     auto input_shape = ov::Shape{shape[ov::layout::width_idx(layout)], shape[ov::layout::height_idx(layout)]};
     uint8_t pad_value = 0;
 
-    auto config = ov_model->has_rt_info("model_info") ? ov_model->get_rt_info<ov::AnyMap>("model_info") : ov::AnyMap{};
+    auto config(input_config);
 
     std::vector<float> scale_values;
     std::vector<float> mean_values;

src/cpp/src/tasks/instance_segmentation.cpp

@@ -124,7 +124,7 @@ cv::Mat segm_postprocess(const SegmentedObject& box, const cv::Mat& unpadded, in
 ov::AnyMap InstanceSegmentation::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap& input_config) {
     if (utils::model_has_embedded_processing(ov_model)) {
         std::cout << "model already was serialized" << std::endl;
-        return {};
+        return input_config;
     }
     if (ov_model->inputs().size() != 1) {
         throw std::logic_error("MaskRCNNModel model wrapper supports topologies with only 1 input");

src/cpp/src/tasks/semantic_segmentation.cpp

@@ -39,7 +39,7 @@ SemanticSegmentation SemanticSegmentation::load(const std::string& model_path) {
 ov::AnyMap SemanticSegmentation::serialize(std::shared_ptr<ov::Model>& ov_model, const ov::AnyMap& input_config) {
     if (utils::model_has_embedded_processing(ov_model)) {
         std::cout << "model already was serialized" << std::endl;
-        return {};
+        return input_config;
     }
     if (ov_model->inputs().size() != 1) {
         throw std::logic_error("Segmentation model wrapper supports topologies with only 1 input");

tests/cpp/test_accuracy.cpp

@@ -146,6 +146,11 @@ TEST_P(ModelParameterizedTest, SerializedAccuracyTest) {
 
     const std::string& basename = data.name.substr(data.name.find_last_of("/\\") + 1);
     auto model_path = DATA_DIR + "/serialized/" + basename;
+
+    if (model_path.find(".onnx") != std::string::npos) {
+        GTEST_SKIP() << "ONNX models are not serializable";
+    }
+
     if (data.type == "DetectionModel") {
         auto use_tiling = !data.input_res.empty();
         auto model = DetectionModel::load(model_path, {{"tiling", use_tiling}});
@@ -207,6 +212,10 @@ TEST_P(ModelParameterizedTest, AccuracyTestBatch) {
     const std::string& basename = data.name.substr(data.name.find_last_of("/\\") + 1);
     auto model_path = DATA_DIR + "/serialized/" + basename;
 
+    if (model_path.find(".onnx") != std::string::npos) {
+        GTEST_SKIP() << "ONNX models are not serializable";
+    }
+
     if (data.type == "DetectionModel") {
         auto use_tiling = !data.input_res.empty();
         auto model = DetectionModel::load(model_path, {{"tiling", use_tiling}});

tests/python/accuracy/public_scope.json

@@ -23,6 +23,18 @@
       }
     ]
   },
+  {
+    "name": "otx_models/Lite-hrnet-s_mod2.onnx",
+    "type": "SegmentationModel",
+    "test_data": [
+      {
+        "image": "coco128/images/train2017/000000000074.jpg",
+        "reference": [
+          "0: 0.563, 1: 0.437, [426,640,2], [0], [0]; object: 0.520, 26, object: 0.530, 42, object: 0.501, 4, object: 0.507, 27, object: 0.503, 8, object: 0.502, 6, object: 0.505, 18, object: 0.504, 13, object: 0.524, 87, object: 0.521, 89, object: 0.757, 2706, "
+        ]
+      }
+    ]
+  },
   {
     "name": "otx_models/segmentation_model_with_xai_head.xml",
     "type": "SegmentationModel",
@@ -47,6 +59,18 @@
       }
     ]
   },
+  {
+    "name": "otx_models/is_efficientnetb2b_maskrcnn_coco_reduced_onnx.onnx",
+    "type": "MaskRCNNModel",
+    "test_data": [
+      {
+        "image": "coco128/images/train2017/000000000074.jpg",
+        "reference": [
+          "458, 106, 495, 150, 1 (person): 0.818, 852, RotatedRect: 478.119 130.332 28.677 46.408 46.637; 0, 30, 178, 323, 2 (bicycle): 0.753, 26728, RotatedRect: 79.739 177.262 251.785 156.656 87.397; 0; [0]; person: 0.818, 139; bicycle: 0.753, 622; "
+        ]
+      }
+    ]
+  },
   {
     "name": "otx_models/is_resnet50_maskrcnn_coco_reduced.xml",
     "type": "MaskRCNNModel",
@@ -71,6 +95,18 @@
       }
     ]
   },
+  {
+    "name": "otx_models/det_mobilenetv2_atss_bccd_onnx.onnx",
+    "type": "DetectionModel",
+    "test_data": [
+      {
+        "image": "BloodImage_00007.jpg",
+        "reference": [
+          "494, 159, 637, 308, 2 (WBC): 0.697; 28, 139, 135, 228, 1 (RBC): 0.628; 535, 375, 638, 479, 1 (RBC): 0.524; 513, 8, 633, 152, 1 (RBC): 0.430; 21, 291, 143, 399, 1 (RBC): 0.422; 196, 86, 410, 286, 1 (RBC): 0.422; [0]; [0]"
+        ]
+      }
+    ]
+  },
   {
     "name": "otx_models/detection_model_with_xai_head.xml",
     "type": "DetectionModel",
@@ -142,6 +178,16 @@
       }
     ]
   },
+  {
+    "name": "otx_models/cls_mobilenetv3_large_cars.onnx",
+    "type": "ClassificationModel",
+    "test_data": [
+      {
+        "image": "coco128/images/train2017/000000000471.jpg",
+        "reference": ["105 (194): 0.456, [0], [0], [196]"]
+      }
+    ]
+  },
   {
     "name": "otx_models/cls_efficient_b0_cars.xml",
     "type": "ClassificationModel",