support for array prediction format

image_prediction/classifier/classifier.py

@@ -23,18 +23,28 @@ class Classifier:
         self.__estimator_adapter = estimator_adapter
         self._classes = classes
 
-    def __validate_prediction_format(self, prediction):
+    def __validate_dict_prediction_format(self, prediction):
         if not max(prediction.keys) <= len(self._classes):
             raise UnexpectedPredictionFormat(f"Received prediction in an unexpected format: {prediction}")
 
+    def __validate_array_prediction_format(self, prediction):
+        if not len(prediction) == len(self._classes):
+            raise UnexpectedPredictionFormat(
+                f"Received fewer probabilities ({len(prediction)}) than classes were specified ({len(self._classes)}."
+            )
+
     def __format_prediction(self, prediction):
         if isinstance(prediction, int):
             return self._classes[prediction]
 
         elif isinstance(prediction, dict):
-            self.__validate_prediction_format(prediction)
+            self.__validate_dict_prediction_format(prediction)
             return {self._classes[cls_idx] for cls_idx, prob in prediction.items()}
 
+        elif isinstance(prediction, np.ndarray):
+            self.__validate_array_prediction_format(prediction)
+            return dict(zip(self._classes, prediction))
+
         else:
             return prediction
 

image_prediction/estimator/adapter/adapter.py

@@ -3,7 +3,4 @@ class EstimatorAdapter:
         self.estimator = estimator
 
     def predict(self, batch):
-        return self.estimator.predict(batch)
+        return self.estimator(batch)
-
-    def predict_proba(self, batch):
-        return self.estimator.predict_proba(batch)

image_prediction/exceptions.py

@@ -19,4 +19,4 @@ class UnexpectedPredictionFormat(ValueError):
 
 
 class IncorrectInstantiation(RuntimeError):
-    pass
+    pass

image_prediction/extractor_classifier/extractor_classifier.py

@@ -21,7 +21,7 @@ class ExtractorClassifier:
             return []
 
         predictions = self.classifier(images)
-        responses = ({"label": lbl, **mdt} for lbl, mdt in zip(predictions, metadata))
+        responses = ({"prediction": prd, **mdt} for prd, mdt in zip(predictions, metadata))
         return responses
 
     def __call__(self, obj) -> Iterable[ImageMetadataPair]:

image_prediction/redai_adapter/model.py

@@ -6,11 +6,14 @@ class PredictionModelHandle:
 
     def __init__(self, model_handle):
         self.__model_handle = model_handle
+        self.__predict = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict)
+        self.__predict_proba = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict_proba)
 
     def predict(self, *args, **kwargs):
-        predict = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict)
+        return self.__predict(*args, **kwargs)
-        return predict(*args, **kwargs)
 
     def predict_proba(self, *args, **kwargs):
-        predict = rcompose(self.__model_handle.prep_images, self.__model_handle.model.predict_proba)
+        return self.__predict_proba(*args, **kwargs)
-        return predict(*args, **kwargs)
+
+    def __call__(self, *args, **kwargs):
+        return self.predict_proba(*args, **kwargs)

test/unit_tests/conftest.py

@@ -52,6 +52,9 @@ class EstimatorMock:
     def predict(batch):
         return [None for _ in batch]
 
+    def __call__(self, batch):
+        return self.predict(batch)
+
 
 @pytest.fixture
 def estimator_adapter(estimator_type, keras_model, output_batch_generator, monkeypatch):

test/unit_tests/extractor_classifier_test.py

@@ -11,5 +11,5 @@ from image_prediction.extractor_classifier.extractor_classifier import Extractor
 def test_extractor_classifier(image_extractor, image_classifier, images, expected_predictions):
     extractor_classifier = ExtractorClassifier(image_extractor, image_classifier)
     results = extractor_classifier(images)
-    labels = list(map(itemgetter("label"), results))
+    labels = list(map(itemgetter("prediction"), results))
     assert labels == expected_predictions