[GSK-2571] modified behaviour of tests w.r.t. cropped DL (#26)

* modified behaviour of tests w.r.t. cropped DL

* pdm format

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