Fix OTE SDK test

external/mmdetection/detection_tasks/apis/detection/openvino_task.py

@@ -146,7 +146,10 @@ def post_process(self, prediction: Dict[str, np.ndarray], metadata: Dict[str, An
             detections = self.convert2array(detections)
         if not isinstance(detections, np.ndarray) and isinstance(self, OpenVINODetectionInferencer):
             detections = np.array(detections)
-        return self.converter.convert_to_annotation(detections, metadata)
+        if isinstance(self.converter, MaskToAnnotationConverter):
+            return self.converter.convert_to_annotation(detections, metadata)
+        detections[:, 2:] /= np.tile(metadata["original_shape"][1::-1], 2)
+        return self.converter.convert_to_annotation(detections)
 
     @check_input_parameters_type()
     def predict(self, image: np.ndarray) -> Tuple[AnnotationSceneEntity, np.ndarray, np.ndarray]:
@@ -255,9 +258,9 @@ def predict_tile(self,
         labels = labels[keep]
         scores = scores[keep]
 
-        metadata["original_shape"] = original_shape
         detections = np.concatenate((labels[:, np.newaxis], scores[:, np.newaxis], boxes), axis=-1)
-        detections = self.converter.convert_to_annotation(detections, metadata)
+        detections[:, 2:] /= np.tile(original_shape[1::-1], 2)
+        detections = self.converter.convert_to_annotation(detections)
         return detections
 
 

ote_sdk/ote_sdk/entities/model.py

@@ -411,14 +411,12 @@ def exportable_code_adapter(self) -> Optional[ExportableCodeAdapter]:
         """
         return self.__exportable_code_adapter
 
-    def get_data(self, key: str) -> Optional[bytes]:
+    def get_data(self, key: str) -> bytes:
         """
         Fetches byte data for a certain model.
         :param key: key to fetch data for
         :return:
         """
-        if key not in self.__model_adapters:
-            return None
         return self.__model_adapters[key].data
 
     def set_data(self, key: str, data: Union[bytes, IDataSource], skip_deletion=False):

ote_sdk/ote_sdk/usecases/exportable_code/prediction_to_annotation_converter.py

@@ -7,7 +7,7 @@
 #
 
 import abc
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
@@ -54,11 +54,13 @@ class DetectionToAnnotationConverter(IPredictionToAnnotationConverter):
     Converts Object Detections to Annotations
     """
 
-    def __init__(self, labels: LabelSchemaEntity):
-        self.label_map = dict(enumerate(labels.get_labels(include_empty=False)))
+    def __init__(self, labels: Union[LabelSchemaEntity, List]):
+        if isinstance(labels, LabelSchemaEntity):
+            labels = labels.get_labels(include_empty=False)
+        self.label_map = dict(enumerate(labels))
 
     def convert_to_annotation(
-        self, predictions: np.ndarray, metadata: Dict[str, Any]
+        self, predictions: np.ndarray, metadata: Optional[Dict] = None
     ) -> AnnotationSceneEntity:
         """
         Converts a set of predictions into an AnnotationScene object
@@ -78,7 +80,7 @@ def convert_to_annotation(
         :returns AnnotationScene: AnnotationScene Object containing the boxes
                                   obtained from the prediction
         """
-        annotations = self.__convert_to_annotations(predictions, metadata)
+        annotations = self.__convert_to_annotations(predictions)
         # media_identifier = ImageIdentifier(image_id=ID())
         annotation_scene = AnnotationSceneEntity(
             id=ID(),
@@ -90,9 +92,7 @@ def convert_to_annotation(
 
         return annotation_scene
 
-    def __convert_to_annotations(
-        self, predictions: np.ndarray, metadata: Dict[str, Any]
-    ) -> List[Annotation]:
+    def __convert_to_annotations(self, predictions: np.ndarray) -> List[Annotation]:
         """
         Converts a list of Detections to OTE SDK Annotation objects
 
@@ -115,7 +115,6 @@ def __convert_to_annotations(
                 f"got {predictions.shape}"
             )
 
-        image_size = metadata["original_shape"][1::-1]
         for prediction in predictions:
             if prediction.shape == (7,):
                 # Some OpenVINO models use an output shape of [7,]
@@ -125,7 +124,7 @@ def __convert_to_annotations(
             label = int(prediction[0])
             confidence = prediction[1]
             scored_label = ScoredLabel(self.label_map[label], confidence)
-            coords = prediction[2:] / np.tile(image_size, 2)
+            coords = prediction[2:]
             annotations.append(
                 Annotation(
                     Rectangle(coords[0], coords[1], coords[2], coords[3]),