[GSK-2571] modified behaviour of tests w.r.t. cropped DL

giskard_vision/landmark_detection/dataloaders/wrappers.py

@@ -47,7 +47,7 @@ def __init__(
         self._margins = margins
 
     @property
-    def name(self):
+    def name(self) -> str:
         """
         Gets the name of the cropped data loader.
 
@@ -56,6 +56,16 @@ def name(self):
         """
         return f"{self._wrapped_dataloader.name} cropped on {self._part.name}"
 
+    @property
+    def facial_part(self) -> FacialPart:
+        """
+        Gets the facial_part used for the copping.
+
+        Returns:
+            FacialPart: The name of the cropped data loader.
+        """
+        return self._part
+
     def get_image(self, idx: int) -> np.ndarray:
         """
         Gets a cropped image based on facial landmarks.

giskard_vision/landmark_detection/tests/base.py

@@ -207,20 +207,23 @@ def run(
         self,
         model: FaceLandmarksModelBase,
         dataloader: DataIteratorBase,
-        facial_part: FacialPart = FacialParts.ENTIRE.value,
+        facial_part: FacialPart = None,
     ) -> TestResult:
         """Run the test on the specified model and dataloader.
         Passes if metric <= threhsold.
 
         Args:
             model (FaceLandmarksModelBase): Model to be evaluated.
             dataloader (DataIteratorBase): Dataloader providing input data.
-            facial_part (FacialPart, optional): Facial part to consider during the evaluation. Defaults to entire face.
+            facial_part (FacialPart, optional): Facial part to consider during the evaluation. Defaults to entire face if dataloader doesn't have facial_part as property.
 
         Returns:
             TestResult: Result of the test.
 
         """
+        facial_part = (
+            getattr(dataloader, "facial_part", FacialParts.ENTIRE.value) if facial_part is None else facial_part
+        )
         ground_truth = dataloader.all_marks
         prediction_result = model.predict(dataloader, facial_part=facial_part)
         metric_value = self.metric.get(prediction_result, ground_truth)
@@ -259,7 +262,7 @@ def run(
         model: FaceLandmarksModelBase,
         dataloader: DataIteratorBase,
         dataloader_ref: DataIteratorBase,
-        facial_part: FacialPart = FacialParts.ENTIRE.value,
+        facial_part: Optional[FacialPart] = None,  # FacialParts.ENTIRE.value,
     ) -> TestResult:
         """Run the differential test on the specified model and dataloaders.
         Defined as metric_diff = (metric_ref-metric)/metric_ref.
@@ -269,12 +272,16 @@ def run(
             model (FaceLandmarksModelBase): Model to be evaluated.
             dataloader (DataIteratorBase):  Main dataloader.
             dataloader_ref (DataIteratorBase): Reference dataloader for comparison.
-            facial_part (FacialPart, optional): Facial part to consider during the evaluation. Defaults to entire face.
+            facial_part (FacialPart, optional): Facial part to consider during the evaluation. Defaults to entire face if dataloader doesn't have facial_part as property.
 
         Returns:
             TestResult: Result of the differential test.
 
         """
+        facial_part = (
+            getattr(dataloader, "facial_part", FacialParts.ENTIRE.value) if facial_part is None else facial_part
+        )
+
         prediction_result = model.predict(dataloader, facial_part=facial_part)
         prediction_result_ref = model.predict(dataloader_ref, facial_part=facial_part)
 

tests/landmark_detection/tests_and_metrics/test_tests.py

@@ -0,0 +1,20 @@
+from giskard_vision.landmark_detection.dataloaders.wrappers import CroppedDataLoader
+from giskard_vision.landmark_detection.marks.facial_parts import FacialParts
+from giskard_vision.landmark_detection.tests.base import Test, TestDiff
+from giskard_vision.landmark_detection.tests.performance import NMEMean
+
+
+def test_tests_on_cropped_dl(opencv_model, dataset_300w):
+    fp = FacialParts.LEFT_HALF.value
+    dl = CroppedDataLoader(dataset_300w, part=fp)
+
+    for test in [Test, TestDiff]:
+        kwargs = {"model": opencv_model, "dataloader": dl, "facial_part": fp}
+        if test == TestDiff:
+            kwargs["dataloader_ref"] = dataset_300w
+
+        test1 = test(metric=NMEMean, threshold=1).run(**kwargs)
+        kwargs.pop("facial_part")
+        test2 = test(metric=NMEMean, threshold=1).run(**kwargs)
+
+        assert test1.metric_value == test2.metric_value