update typings

doctr/models/detection/differentiable_binarization/pytorch.py

@@ -207,7 +207,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(prob_map: torch.Tensor) -> List[Dict[str, Any]]:
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
                 return [
                     dict(zip(self.class_names, preds))
                     for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())

doctr/models/detection/fast/pytorch.py

@@ -198,7 +198,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(prob_map: torch.Tensor) -> List[Dict[str, Any]]:
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
                 return [
                     dict(zip(self.class_names, preds))
                     for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())

doctr/models/detection/linknet/pytorch.py

@@ -185,7 +185,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(prob_map: torch.Tensor) -> List[Dict[str, Any]]:
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
                 return [
                     dict(zip(self.class_names, preds))
                     for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())

doctr/models/recognition/crnn/pytorch.py

@@ -215,7 +215,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(logits: torch.Tensor) -> List[Tuple[str, float]]:
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
                 return self.postprocessor(logits)
 
             # Post-process boxes

doctr/models/recognition/master/pytorch.py

@@ -211,7 +211,7 @@ def forward(
         if return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(logits: torch.Tensor) -> List[Tuple[str, float]]:
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
                 return self.postprocessor(logits)
 
             # Post-process boxes

doctr/models/recognition/parseq/pytorch.py

@@ -374,7 +374,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(logits: torch.Tensor) -> List[Tuple[str, float]]:
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
                 return self.postprocessor(logits)
 
             # Post-process boxes

doctr/models/recognition/sar/pytorch.py

@@ -264,7 +264,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(decoded_features: torch.Tensor) -> List[Tuple[str, float]]:
+            def _postprocess(decoded_features: torch.Tensor) -> list[tuple[str, float]]:
                 return self.postprocessor(decoded_features)
 
             # Post-process boxes

doctr/models/recognition/vitstr/pytorch.py

@@ -109,7 +109,7 @@ def forward(
         if target is None or return_preds:
             # Disable for torch.compile compatibility
             @torch.compiler.disable  # type: ignore[attr-defined]
-            def _postprocess(decoded_features: torch.Tensor) -> List[Tuple[str, float]]:
+            def _postprocess(decoded_features: torch.Tensor) -> list[tuple[str, float]]:
                 return self.postprocessor(decoded_features)
 
             # Post-process boxes