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

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

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))