pdm format

Author: Rabah Abdul Khalek

examples/landmark_detection/features/ex1_draw_landmarks.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "import torch\n",
     "from giskard_vision.landmark_detection.tests.performance import NMEMean, NMEs, Es, MEMean, MEStd, NMEMean, NMEStd\n",
-    "from giskard_vision.landmark_detection.models.wrappers import OpenCVWrapper "
+    "from giskard_vision.landmark_detection.models.wrappers import OpenCVWrapper"
    ]
   },
   {
@@ -94,7 +94,9 @@
    },
    "outputs": [],
    "source": [
-    "model = OpenCVWrapper() #FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False))"
+    "model = (\n",
+    "    OpenCVWrapper()\n",
+    ")  # FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False))"
    ]
   },
   {

examples/landmark_detection/features/ex4_tests_and_metrics.ipynb

@@ -95,7 +95,9 @@
    },
    "outputs": [],
    "source": [
-    "model = OpenCVWrapper() # FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=device, flip_input=False))"
+    "model = (\n",
+    "    OpenCVWrapper()\n",
+    ")  # FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=device, flip_input=False))"
    ]
   },
   {

examples/landmark_detection/report/master.ipynb

@@ -75,7 +75,9 @@
    ],
    "source": [
     "models = {\n",
-    "    \"FaceAlignment\": FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False, face_detector=\"blazeface\")),\n",
+    "    \"FaceAlignment\": FaceAlignmentWrapper(\n",
+    "        model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False, face_detector=\"blazeface\")\n",
+    "    ),\n",
     "    \"OpenCV\": OpenCVWrapper(),\n",
     "}\n",
     "# models.pop(\"FaceAlignment\")  # takes a long time"

examples/landmark_detection/report/report.ipynb

@@ -121,7 +121,9 @@
    ],
    "source": [
     "models_list = [\n",
-    "    FaceAlignmentWrapper(model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False, face_detector=\"blazeface\")),\n",
+    "    FaceAlignmentWrapper(\n",
+    "        model=FaceAlignment(LandmarksType.TWO_D, device=\"cpu\", flip_input=False, face_detector=\"blazeface\")\n",
+    "    ),\n",
     "    OpenCVWrapper(),\n",
     "]\n",
     "\n",

giskard_vision/landmark_detection/tests/report.py

@@ -8,6 +8,7 @@
 from giskard_vision.landmark_detection.tests.performance import NMEMean
 from giskard_vision.utils.errors import GiskardImportError
 
+
 class Report:
     """
     A class for generating and managing test reports for landmark detection models.
@@ -27,7 +28,7 @@ def __init__(
         dataloaders: List[DataIteratorBase],
         metrics: Optional[List[Metric]] = None,
         dataloader_ref: Optional[DataIteratorBase] = None,
-        rel_threshold: Optional[float] = None
+        rel_threshold: Optional[float] = None,
     ):
         """
         Initializes a Report instance.
@@ -66,7 +67,7 @@ def to_dataframe(self):
             import pandas as pd
         except (ImportError, ModuleNotFoundError) as e:
             raise GiskardImportError(["pandas"]) from e
-        
+
         # columns reordering
         df = pd.DataFrame(self.results)[
             [
@@ -81,8 +82,8 @@ def to_dataframe(self):
                 "prediction_fail_rate",
             ]
         ].rename(columns={"dataloader": "criteria"})
-        return df.sort_values(['criteria', 'model'], ignore_index=True)
-        
+        return df.sort_values(["criteria", "model"], ignore_index=True)
+
     def to_markdown(self, filename: Optional[str] = None):
         """
         Writes the test results to a markdown file.
@@ -96,10 +97,10 @@ def to_markdown(self, filename: Optional[str] = None):
         except (ImportError, ModuleNotFoundError) as e:
             raise GiskardImportError(["tabulate"]) from e
         from datetime import datetime
-        
+
         current_time = str(datetime.now()).replace(" ", "-")
         filename = f"report_{current_time}.md"
-        
+
         df = self.to_dataframe()
         df.to_markdown(filename)
 
@@ -115,7 +116,7 @@ def to_json(self, filename: Optional[str] = None):
 
         if filename is None:
             from datetime import datetime
-            
+
             current_time = str(datetime.now()).replace(" ", "-")
 
         filename = f"report_{current_time}.md"