Update calibrate tool (#2174)

### Changes

- Add support for models that don't have per-sample metrics.

### Reason for changes

Some models don't have per-sample metrics. An error occurred with these
models.

### Related tickets

N/A

### Tests

<!--- How was the correctness of changes tested and whether new tests
were added -->

tests/openvino/tools/calibrate.py

@@ -125,22 +125,43 @@ class ACValidationFunction:
         "ndcg": "sigmoid_recom_loss",
     }
 
-    def __init__(self, model_evaluator: ModelEvaluator, metric_name: str, requests_number: Optional[int] = None):
+    SPECIAL_METRICS = [
+        "cmc",
+        "reid_map",
+        "pairwise_accuracy_subsets",
+        "pairwise_accuracy",
+        "normalized_embedding_accuracy",
+        "face_recognition_tafa_pair_metric",
+        "localization_recall",
+        "coco_orig_keypoints_precision",
+        "coco_orig_segm_precision",
+        "coco_orig_keypoints_precision",
+        "spearman_correlation_coef",
+        "pearson_correlation_coef",
+    ]
+
+    def __init__(
+        self, model_evaluator: ModelEvaluator, metric_name: str, metric_type: str, requests_number: Optional[int] = None
+    ):
         """
         :param model_evaluator: Model Evaluator.
         :param metric_name: Name of a metric.
+        :param metric_type: Type of a metric.
         :param requests_number: A number of infer requests. If it is `None`,
             the count will be selected automatically.
         """
         self._model_evaluator = model_evaluator
         self._metric_name = metric_name
+        self._metric_type = metric_type
         self._persample_metric_name = self.METRIC_TO_PERSAMPLE_METRIC.get(self._metric_name, self._metric_name)
         registered_metrics = model_evaluator.get_metrics_attributes()
         if self._persample_metric_name not in registered_metrics:
             self._model_evaluator.register_metric(self._persample_metric_name)
         self._requests_number = requests_number
         self._values_for_each_item = []
 
+        self._collect_outputs = self._metric_type in self.SPECIAL_METRICS
+
     def __call__(self, compiled_model: ov.CompiledModel, indices: Optional[Iterable[int]] = None) -> float:
         """
         Calculates metrics for the provided model.
@@ -203,6 +224,11 @@ def _output_callback(self, raw_predictions, **kwargs):
             return
 
         for sample_id, results in metrics_result.items():
+            if self._collect_outputs:
+                output = list(raw_predictions.values())[0]
+                self._values_for_each_item.append({"sample_id": sample_id, "metric_value": output})
+                continue
+
             for metric_result in results:
                 if metric_result.metric_name != self._persample_metric_name:
                     continue
@@ -940,10 +966,11 @@ def quantize_model_with_accuracy_control(
         )
         model_evaluator.load_network([{"model": ov_model}])
 
+    metric_type = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0]["type"]
     metric_name = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0].get("name", None)
     if metric_name is None:
-        metric_name = accuracy_checker_config["models"][0]["datasets"][0]["metrics"][0]["type"]
-    validation_fn = ACValidationFunction(model_evaluator, metric_name)
+        metric_name = metric_type
+    validation_fn = ACValidationFunction(model_evaluator, metric_name, metric_type)
 
     name_to_quantization_impl_map = {
         "pot": pot_quantize_with_native_accuracy_control,