Don't rely on outputs order from adapter in cls wrapper (#165)

* Filter cls outputs when using inference adapter

* Add a cls model with unordered outputs to test data

* Fix black

* Fix mean values missing in embedProcessing

Author: sovrasov

model_api/cpp/models/src/classification_model.cpp

@@ -158,6 +158,20 @@ std::vector<std::string> get_non_xai_names(const std::vector<ov::Output<ov::Node
     return outputNames;
 }
 
+std::vector<std::string> get_non_xai_names(const std::vector<std::string>& outputs) {
+    std::vector<std::string> outputNames;
+    outputNames.reserve(std::max(1, int(outputs.size()) - 2));
+    for (const auto& output : outputs) {
+        if (output.find(saliency_map_name) != std::string::npos) {
+            continue;
+        } if (output.find(feature_vector_name) != std::string::npos) {
+            continue;
+        }
+        outputNames.push_back(output);
+    }
+    return outputNames;
+}
+
 void append_xai_names(const std::vector<ov::Output<ov::Node>>& outputs, std::vector<std::string>& outputNames) {
     for (const ov::Output<ov::Node>& output : outputs) {
         if (output.get_names().count(saliency_map_name) > 0) {
@@ -167,6 +181,16 @@ void append_xai_names(const std::vector<ov::Output<ov::Node>>& outputs, std::vec
         }
     }
 }
+
+void append_xai_names(const std::vector<std::string>& outputs, std::vector<std::string>& outputNames) {
+    for (const auto& output : outputs) {
+        if (output.find(saliency_map_name) != std::string::npos) {
+            outputNames.emplace_back(saliency_map_name);
+        } else if (output.find(feature_vector_name) != std::string::npos) {
+            outputNames.push_back(feature_vector_name);
+        }
+    }
+}
 }
 
 void ClassificationModel::init_from_config(const ov::AnyMap& top_priority, const ov::AnyMap& mid_priority) {
@@ -192,6 +216,8 @@ ClassificationModel::ClassificationModel(std::shared_ptr<ov::Model>& model, cons
 
 ClassificationModel::ClassificationModel(std::shared_ptr<InferenceAdapter>& adapter)
         : ImageModel(adapter) {
+    outputNames = get_non_xai_names(adapter->getOutputNames());
+    append_xai_names(adapter->getOutputNames(), outputNames);
     init_from_config(adapter->getModelConfig(), ov::AnyMap{});
 }
 
@@ -408,7 +434,7 @@ void ClassificationModel::prepareInputsOutputs(std::shared_ptr<ov::Model>& model
                                                   inputShape[ov::layout::height_idx(inputLayout)]},
                                         pad_value,
                                         reverse_input_channels,
-                                        {},
+                                        mean_values,
                                         scale_values);
 
         ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);

tests/python/accuracy/prepare_data.py

@@ -109,6 +109,9 @@ async def main():
             download_otx_model(client, otx_models_dir, "anomaly_padim_bottle_mvtec"),
             download_otx_model(client, otx_models_dir, "anomaly_stfpm_bottle_mvtec"),
             download_otx_model(client, otx_models_dir, "deit-tiny"),
+            download_otx_model(
+                client, otx_models_dir, "cls_efficient_b0_shuffled_outputs"
+            ),
         )
 
 

tests/python/accuracy/public_scope.json

@@ -223,6 +223,16 @@
             }
         ]
     },
+    {
+        "name": "otx_models/cls_efficient_b0_shuffled_outputs.xml",
+        "type": "ClassificationModel",
+        "test_data": [
+            {
+                "image": "coco128/images/train2017/000000000471.jpg",
+                "reference": ["3 (Non-Rigid): 0.503, 4 (Circle): 0.943, 5 (Lion): 0.969, 6 (Panda): 0.988, [1,7,7,7], [1,7], [0]"]
+            }
+        ]
+    },
     {
         "name": "otx_models/cls_efficient_v2s_cars.xml",
         "type": "ClassificationModel",