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

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

kornia/contrib/models/rt_detr/post_processor.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Optional, Union, List
+from typing import List, Optional, Union
 
 import torch
 

kornia/core/__init__.py

@@ -32,7 +32,7 @@
  zeros,
  zeros_like,
 )
-from .module import ImageModule, ONNXExportMixin, ImageModuleMixIn
+from .module import ImageModule, ImageModuleMixIn, ONNXExportMixin
 from .tensor_wrapper import TensorWrapper # type: ignore
 
 __all__ = [

kornia/core/module.py

@@ -2,7 +2,7 @@
 import math
 import os
 from functools import wraps
-from typing import Any, Callable, Dict, ClassVar, List, Optional, Tuple, Union
+from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -94,8 +94,7 @@ def to_onnx(
  self._add_metadata(onnx_name)
 
  def _create_dummy_input(self, input_shape: List[int]) -> Union[Tuple[Any, ...], Tensor]:
- return rand(*[
- (self.ONNX_EXPORT_PSEUDO_SHAPE[i] if dim == -1 else dim) for i, dim in enumerate(input_shape)])
+ return rand(*[(self.ONNX_EXPORT_PSEUDO_SHAPE[i] if dim == -1 else dim) for i, dim in enumerate(input_shape)])
 
  def _create_dynamic_axes(self, input_shape: List[int], output_shape: List[int]) -> Dict[str, Dict[int, str]]:
  return {

kornia/models/detector/utils.py

@@ -1,11 +1,11 @@
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
 from kornia.core import Module, ONNXExportMixin, Tensor, rand
-from typing import ClassVar, List, Any, Union, Tuple, Optional
 
 __all__ = ["BoxFiltering"]
 
 
 class BoxFiltering(Module, ONNXExportMixin):
-
  ONNX_DEFAULT_INPUTSHAPE: ClassVar[List[int]] = [-1, -1, 6]
  ONNX_DEFAULT_OUTPUTSHAPE: ClassVar[List[int]] = [-1, -1, 6]
  ONNX_EXPORT_PSEUDO_SHAPE: ClassVar[List[int]] = [5, 20, 6]
@@ -34,14 +34,17 @@ def forward(self, boxes: Tensor, confidence_threshold: Optional[Tensor] = None)
  else:
  filtered_boxes = []
  for i in range(boxes.shape[0]):
- box = boxes[i:i + 1][(boxes[i:i + 1, :, 1] > confidence_threshold).unsqueeze(-1).expand_as(boxes[i:i + 1])]
+ box = boxes[i : i + 1][
+ (boxes[i : i + 1, :, 1] > confidence_threshold).unsqueeze(-1).expand_as(boxes[i : i + 1])
+ ]
  filtered_boxes.append(box.view(1, -1, boxes.shape[-1]))
 
  return filtered_boxes
 
  def _create_dummy_input(self, input_shape: List[int]) -> Union[Tuple[Any, ...], Tensor]:
- pseudo_input = rand(*[
- (self.ONNX_EXPORT_PSEUDO_SHAPE[i] if dim == -1 else dim) for i, dim in enumerate(input_shape)])
+ pseudo_input = rand(
+ *[(self.ONNX_EXPORT_PSEUDO_SHAPE[i] if dim == -1 else dim) for i, dim in enumerate(input_shape)]
+ )
  if self.confidence_threshold is None:
  return pseudo_input, 0.1
  return pseudo_input