FIX: remove irrelevant information from report + display original and…

… modified image
Giskard-AI · Aug 13, 2024 · 6575de5 · 6575de5
1 parent b9ab6b8
commit 6575de5
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Showing 3 changed files with 18 additions and 10 deletions.
diff --git a/giskard_vision/core/detectors/base.py b/giskard_vision/core/detectors/base.py
@@ -36,15 +36,16 @@ class ScanResult:
     relative_delta: float
     issue_group: Optional[IssueGroup] = None
 
-    def get_meta_required(self) -> dict:
+    def get_meta_required(self, add_slice_size=True) -> dict:
         # Get the meta required by the original scan API
         deviation = f"{self.relative_delta * 100:+.2f}% than global"
+        extra_meta = dict(slice_size=self.slice_size) if add_slice_size else dict()
         return {
             "metric": self.metric_name,
             "metric_value": self.metric_value,
             "metric_reference_value": self.metric_reference_value,
             "deviation": deviation,
-            "slice_size": self.slice_size,
+            **extra_meta,
         }
 
 
@@ -64,6 +65,8 @@ class DetectorVisionBase(DetectorSpecsBase):
             evaluation results for the scan.
     """
 
+    slicing: bool = True
+
     def run(
         self,
         model: Any,
@@ -115,16 +118,20 @@ def get_issues(
 
         for result in results:
             if result.issue_level in issue_levels:
+                issue_attribute = (
+                    dict(slicing_fn=result.name, meta=result.get_meta_required())
+                    if self.slicing
+                    else dict(transformation_fn=result.name, meta=result.get_meta_required(False))
+                )
                 issues.append(
                     Issue(
                         model,
                         dataset,
                         level=result.issue_level,
-                        slicing_fn=result.name,
                         group=result.issue_group if result.issue_group else self.issue_group,
-                        meta=result.get_meta_required(),
                         scan_examples=ImagesScanExamples(result.filename_examples, embed=embed),
                         display_footer_info=False,
+                        **issue_attribute,
                     )
                 )
 

diff --git a/giskard_vision/core/detectors/perturbation.py b/giskard_vision/core/detectors/perturbation.py
@@ -6,7 +6,6 @@
 
 import cv2
 
-from giskard_vision.core.dataloaders.wrappers import FilteredDataLoader
 from giskard_vision.core.detectors.base import DetectorVisionBase, ScanResult
 from giskard_vision.core.issues import Robustness
 from giskard_vision.core.tests.base import TestDiffBase
@@ -29,6 +28,7 @@ class PerturbationBaseDetector(DetectorVisionBase):
     """
 
     issue_group = Robustness
+    slicing = False
 
     def set_specs_from_model_type(self, model_type):
         module = import_module(f"giskard_vision.{model_type}.detectors.specs")
@@ -64,10 +64,9 @@ def get_results(self, model: Any, dataset: Any) -> Sequence[ScanResult]:
 
             index_worst = 0 if test_result.indexes_examples is None else test_result.indexes_examples[0]
 
-            if isinstance(dl, FilteredDataLoader):
-                filename_example_dataloader_ref = str(Path() / "examples_images" / f"{dataset.name}_{index_worst}.png")
-                cv2.imwrite(filename_example_dataloader_ref, dataset[index_worst][0][0])
-                filename_examples.append(filename_example_dataloader_ref)
+            filename_example_dataloader_ref = str(Path() / "examples_images" / f"{dataset.name}_{index_worst}.png")
+            cv2.imwrite(filename_example_dataloader_ref, dataset[index_worst][0][0])
+            filename_examples.append(filename_example_dataloader_ref)
 
             filename_example_dataloader = str(Path() / "examples_images" / f"{dl.name}_{index_worst}.png")
             cv2.imwrite(filename_example_dataloader, dl[index_worst][0][0])

diff --git a/giskard_vision/core/detectors/transformation_color_detector.py b/giskard_vision/core/detectors/transformation_color_detector.py
@@ -4,7 +4,9 @@
 from .perturbation import PerturbationBaseDetector
 
 
-@maybe_detector("coloring", tags=["vision", "robustness", "image_classification", "landmark", "object_detection"])
+@maybe_detector(
+    "coloring", tags=["vision", "robustness", "image_classification", "landmark", "object_detection", "coloring"]
+)
 class TransformationColorDetector(PerturbationBaseDetector):
     """
     Detector that evaluates models performance depending on images in grayscale