update

kornia/contrib/models/rt_detr/architecture/hybrid_encoder.py

@@ -153,7 +153,7 @@ def build_2d_sincos_pos_emb(
 
 class TransformerEncoder(nn.Module):
     def __init__(self, encoder_layer: nn.Module, num_layers: int) -> None:
-        super(TransformerEncoder, self).__init__()
+        super().__init__()
         self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(num_layers)])
         self.num_layers = num_layers
 

kornia/contrib/models/rt_detr/architecture/resnet_d.py

@@ -7,7 +7,6 @@
 from __future__ import annotations
 
 from collections import OrderedDict
-from typing import List, Tuple
 
 from torch import nn
 
@@ -104,7 +103,7 @@ def __init__(self, n_blocks: list[int], block: type[BasicBlockD | BottleneckD])
     @staticmethod
     def make_stage(
         in_channels: int, out_channels: int, stride: int, n_blocks: int, block: type[BasicBlockD | BottleneckD]
-    ) -> Tuple[Module, int]:
+    ) -> tuple[Module, int]:
         stage = Block(
             nn.Sequential(
                 block(in_channels, out_channels, stride, False),
@@ -113,7 +112,7 @@ def make_stage(
         )
         return stage, out_channels * block.expansion
 
-    def forward(self, x: Tensor) -> List[Tensor]:
+    def forward(self, x: Tensor) -> list[Tensor]:
         x = self.conv1(x)
         res2 = self.res_layers[0](x)
         res3 = self.res_layers[1](res2)

kornia/contrib/models/rt_detr/model.py

@@ -5,7 +5,7 @@
 import re
 from dataclasses import dataclass
 from enum import Enum
-from typing import Dict, Optional
+from typing import Optional
 
 import torch
 
@@ -194,7 +194,7 @@ def map_name(old_name: str) -> str:
 
             return new_name
 
-        def _state_dict_proc(state_dict: Dict[str, Tensor]) -> Dict[str, Tensor]:
+        def _state_dict_proc(state_dict: dict[str, Tensor]) -> dict[str, Tensor]:
             state_dict = state_dict["ema"]["module"]  # type:ignore
             new_state_dict = {}