[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

kornia/contrib/models/rt_detr/model.py

@@ -213,7 +213,7 @@ def _state_dict_proc(state_dict: dict[str, Tensor]) -> dict[str, Tensor]:
 
         model.load_state_dict(_state_dict_proc(state_dict))
         return model
-    
+
     @staticmethod
     def from_name(model_name: str, num_classes: int = 80) -> RTDETR:
         """Load model without pretrained weights.
@@ -234,7 +234,7 @@ def from_name(model_name: str, num_classes: int = 80) -> RTDETR:
             model = RTDETR.from_config(RTDETRConfig(RTDETRModelType.resnet101d, num_classes))
         else:
             raise ValueError
-        
+
         return model
 
     def forward(self, images: Tensor) -> tuple[Tensor, Tensor]:

kornia/contrib/object_detection.py

@@ -168,22 +168,22 @@ def forward(self, images: list[Tensor]) -> list[Tensor]:
         return detections
 
     def draw(self, images: list[Tensor], output_type: str = "torch") -> list[Tensor] | Image.Image:  # type: ignore
-        """Very simple drawing. Needs to be more fancy later.
+        """Very simple drawing.
+
+        Needs to be more fancy later.
         """
         detections = self.forward(images)
         output = []
         for image, detection in zip(images, detections):
             out_img = image[None].clone()
             for out in detection:
                 out_img = draw_rectangle(
-                    out_img,
-                    torch.Tensor([[[out[-4], out[-3], out[-4] + out[-2], out[-3] + out[-1]]]])
+                    out_img, torch.Tensor([[[out[-4], out[-3], out[-4] + out[-2], out[-3] + out[-1]]]])
                 )
             if output_type == "torch":
                 output.append(out_img)
             elif output_type == "pil":
-                output.append(Image.fromarray(
-                    (out_img[0] * 255).permute(1, 2, 0).numpy().astype(np.uint8)))  # type: ignore
+                output.append(Image.fromarray((out_img[0] * 255).permute(1, 2, 0).numpy().astype(np.uint8)))  # type: ignore
         return output
 
     def compile(

kornia/models/detector/rtdetr.py

@@ -1,39 +1,32 @@
-from typing import Optional
 import warnings
+from typing import Optional
 
-from kornia.core import Module
-from kornia.contrib.models.rt_detr.model import RTDETR, RTDETRConfig
 from kornia.contrib.models.rt_detr import DETRPostProcessor
-from kornia.contrib.object_detection import ResizePreProcessor, ObjectDetector
+from kornia.contrib.models.rt_detr.model import RTDETR, RTDETRConfig
+from kornia.contrib.object_detection import ObjectDetector, ResizePreProcessor
 
 
 class RTDETRDetectorBuilder:
-
     @staticmethod
     def build(
         model_name: Optional[str] = None,
         config: Optional[RTDETRConfig] = None,
         pretrained: bool = True,
         image_size: int = 640,
-        confidence_threshold: float = 0.5
+        confidence_threshold: float = 0.5,
     ) -> ObjectDetector:
-        if (model_name is not None and config is not None):
+        if model_name is not None and config is not None:
             raise ValueError("Either `model_name` or `config` should be `None`.")
-        
+
         if model_name is None and config is None:
             warnings.warn("No `model_name` or `config` found. Will build `rtdetr_r18vd`.")
             model_name = "rtdetr_r18vd"
-            
+
         if config is not None:
             model = RTDETR.from_config(config)
+        elif pretrained:
+            model = RTDETR.from_pretrained(model_name)
         else:
-            if pretrained:
-                model = RTDETR.from_pretrained(model_name)
-            else:
-                model = RTDETR.from_name(model_name)
-        
-        return ObjectDetector(
-            model,
-            ResizePreProcessor(image_size),
-            DETRPostProcessor(confidence_threshold)
-        )
+            model = RTDETR.from_name(model_name)
+
+        return ObjectDetector(model, ResizePreProcessor(image_size), DETRPostProcessor(confidence_threshold))