From 8b94f853b7f8605848bb5c1843e55b2e476ce0e8 Mon Sep 17 00:00:00 2001
From: Christoffer <christofferedlund@hotmail.com>
Date: Mon, 23 Oct 2023 13:11:37 +0200
Subject: [PATCH] black

---
 darwin/torch/transforms.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/darwin/torch/transforms.py b/darwin/torch/transforms.py
index 458a4454c..8285638ff 100644
--- a/darwin/torch/transforms.py
+++ b/darwin/torch/transforms.py
@@ -143,7 +143,9 @@ class ColorJitter(transforms.ColorJitter):
     def __call__(
         self, image: PILImage.Image, target: Optional[TargetType] = None
     ) -> Union[PILImage.Image, Tuple[PILImage.Image, TargetType]]:
-        transform = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
+        transform = self.get_params(
+            self.brightness, self.contrast, self.saturation, self.hue
+        )
         image = transform(image)
         if target is None:
             return image
@@ -198,7 +200,9 @@ class ConvertPolygonsToInstanceMasks(object):
     Converts given polygon to an ``InstanceMask``.
     """
 
-    def __call__(self, image: PILImage.Image, target: TargetType) -> Tuple[PILImage.Image, TargetType]:
+    def __call__(
+        self, image: PILImage.Image, target: TargetType
+    ) -> Tuple[PILImage.Image, TargetType]:
         w, h = image.size
 
         image_id = target["image_id"]
@@ -255,7 +259,9 @@ class ConvertPolygonsToSemanticMask(object):
     Converts given polygon to an ``SemanticMask``.
     """
 
-    def __call__(self, image: PILImage.Image, target: TargetType) -> Tuple[PILImage.Image, TargetType]:
+    def __call__(
+        self, image: PILImage.Image, target: TargetType
+    ) -> Tuple[PILImage.Image, TargetType]:
         w, h = image.size
         image_id = target["image_id"]
         image_id = torch.tensor([image_id])
@@ -282,7 +288,9 @@ class ConvertPolygonToMask(object):
     Converts given polygon to a ``Mask``.
     """
 
-    def __call__(self, image: PILImage.Image, annotation: Dict[str, Any]) -> Tuple[PILImage.Image, PILImage.Image]:
+    def __call__(
+        self, image: PILImage.Image, annotation: Dict[str, Any]
+    ) -> Tuple[PILImage.Image, PILImage.Image]:
         w, h = image.size
         segmentations = [obj["segmentation"] for obj in annotation]
         cats = [obj["category_id"] for obj in annotation]
@@ -368,7 +376,9 @@ def _post_process(self, albumentation_output: dict, annotation: dict) -> tuple:
         if bboxes is not None:
             output_annotation["boxes"] = torch.tensor(bboxes)
             if "area" in annotation and "masks" not in albumentation_output:
-                output_annotation["area"] = output_annotation["boxes"][:, 2] * output_annotation["boxes"][:, 3]
+                output_annotation["area"] = (
+                    output_annotation["boxes"][:, 2] * output_annotation["boxes"][:, 3]
+                )
 
         labels = albumentation_output.get("labels")
         if labels is not None:
@@ -381,7 +391,9 @@ def _post_process(self, albumentation_output: dict, annotation: dict) -> tuple:
             else:
                 output_annotation["masks"] = torch.stack(masks)
             if "area" in annotation:
-                output_annotation["area"] = torch.sum(output_annotation["masks"], dim=[1, 2])
+                output_annotation["area"] = torch.sum(
+                    output_annotation["masks"], dim=[1, 2]
+                )
 
         # Copy other metadata from original annotation
         for key, value in annotation.items():