--unsafe-fixes

Author: Borda

.pre-commit-config.yaml

@@ -30,14 +30,11 @@ repos:
     rev: v0.7.0
     hooks:
     -   id: ruff
-        args: ["--fix"]
+        args: ["--fix"
         exclude: |
           (?x)(
               ^versioneer.py|
-              ^monai/_version.py|
-              ^monai/networks/| # avoid typing rewrites
-              ^monai/apps/detection/utils/anchor_utils.py| # avoid typing rewrites
-              ^tests/test_compute_panoptic_quality.py # avoid typing rewrites
+              ^monai/_version.py
           )
 
   - repo: https://github.com/asottile/yesqa

monai/apps/detection/utils/anchor_utils.py

@@ -39,7 +39,7 @@
 
 from __future__ import annotations
 
-from typing import List, Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn
@@ -106,7 +106,7 @@ class AnchorGenerator(nn.Module):
             anchor_generator = AnchorGenerator(sizes, aspect_ratios)
     """
 
-    __annotations__ = {"cell_anchors": List[torch.Tensor]}
+    __annotations__ = {"cell_anchors": list[torch.Tensor]}
 
     def __init__(
         self,
@@ -364,7 +364,7 @@ class AnchorGeneratorWithAnchorShape(AnchorGenerator):
             anchor_generator = AnchorGeneratorWithAnchorShape(feature_map_scales, base_anchor_shapes)
     """
 
-    __annotations__ = {"cell_anchors": List[torch.Tensor]}
+    __annotations__ = {"cell_anchors": list[torch.Tensor]}
 
     def __init__(
         self,

monai/networks/blocks/attention_utils.py

@@ -9,7 +9,6 @@
 
 from __future__ import annotations
 
-from typing import Tuple
 
 import torch
 import torch.nn.functional as F
@@ -50,7 +49,7 @@ def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor
 
 
 def add_decomposed_rel_pos(
-    attn: torch.Tensor, q: torch.Tensor, rel_pos_lst: nn.ParameterList, q_size: Tuple, k_size: Tuple
+    attn: torch.Tensor, q: torch.Tensor, rel_pos_lst: nn.ParameterList, q_size: tuple, k_size: tuple
 ) -> torch.Tensor:
     r"""
     Calculate decomposed Relative Positional Embeddings from mvitv2 implementation:

monai/networks/blocks/crossattention.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-from typing import Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -41,9 +40,9 @@ def __init__(
         save_attn: bool = False,
         causal: bool = False,
         sequence_length: int | None = None,
-        rel_pos_embedding: Optional[str] = None,
-        input_size: Optional[Tuple] = None,
-        attention_dtype: Optional[torch.dtype] = None,
+        rel_pos_embedding: str | None = None,
+        input_size: tuple | None = None,
+        attention_dtype: torch.dtype | None = None,
         use_flash_attention: bool = False,
     ) -> None:
         """
@@ -134,7 +133,7 @@ def __init__(
         )
         self.input_size = input_size
 
-    def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None):
+    def forward(self, x: torch.Tensor, context: torch.Tensor | None = None):
         """
         Args:
             x (torch.Tensor): input tensor. B x (s_dim_1 * ... * s_dim_n) x C

monai/networks/blocks/denseblock.py

@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 import torch.nn as nn

monai/networks/blocks/mlp.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-from typing import Union
 
 import torch.nn as nn
 
@@ -56,8 +55,8 @@ def __init__(
         self.linear2 = nn.Linear(mlp_dim, hidden_size)
         self.fn = get_act_layer(act)
         # Use Union[nn.Dropout, nn.Identity] for type annotations
-        self.drop1: Union[nn.Dropout, nn.Identity]
-        self.drop2: Union[nn.Dropout, nn.Identity]
+        self.drop1: nn.Dropout | nn.Identity
+        self.drop2: nn.Dropout | nn.Identity
 
         dropout_opt = look_up_option(dropout_mode, SUPPORTED_DROPOUT_MODE)
         if dropout_opt == "vit":

monai/networks/blocks/patchembedding.py

@@ -12,7 +12,6 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from typing import Optional
 
 import numpy as np
 import torch
@@ -54,7 +53,7 @@ def __init__(
         pos_embed_type: str = "learnable",
         dropout_rate: float = 0.0,
         spatial_dims: int = 3,
-        pos_embed_kwargs: Optional[dict] = None,
+        pos_embed_kwargs: dict | None = None,
     ) -> None:
         """
         Args:

monai/networks/blocks/pos_embed_utils.py

@@ -13,7 +13,6 @@
 
 import collections.abc
 from itertools import repeat
-from typing import List, Union
 
 import torch
 import torch.nn as nn
@@ -33,7 +32,7 @@ def parse(x):
 
 
 def build_fourier_position_embedding(
-    grid_size: Union[int, List[int]], embed_dim: int, spatial_dims: int = 3, scales: Union[float, List[float]] = 1.0
+    grid_size: int | list[int], embed_dim: int, spatial_dims: int = 3, scales: float | list[float] = 1.0
 ) -> torch.nn.Parameter:
     """
     Builds a (Anistropic) Fourier feature position embedding based on the given grid size, embed dimension,
@@ -86,7 +85,7 @@ def build_fourier_position_embedding(
 
 
 def build_sincos_position_embedding(
-    grid_size: Union[int, List[int]], embed_dim: int, spatial_dims: int = 3, temperature: float = 10000.0
+    grid_size: int | list[int], embed_dim: int, spatial_dims: int = 3, temperature: float = 10000.0
 ) -> torch.nn.Parameter:
     """
     Builds a sin-cos position embedding based on the given grid size, embed dimension, spatial dimensions, and temperature.

monai/networks/blocks/rel_pos_embedding.py

@@ -9,7 +9,7 @@
 
 from __future__ import annotations
 
-from typing import Iterable, Tuple
+from collections.abc import Iterable
 
 import torch
 from torch import nn
@@ -19,7 +19,7 @@
 
 
 class DecomposedRelativePosEmbedding(nn.Module):
-    def __init__(self, s_input_dims: Tuple[int, int] | Tuple[int, int, int], c_dim: int, num_heads: int) -> None:
+    def __init__(self, s_input_dims: tuple[int, int] | tuple[int, int, int], c_dim: int, num_heads: int) -> None:
         """
         Args:
             s_input_dims (Tuple): input spatial dimension. (H, W) or (H, W, D)

monai/networks/blocks/selfattention.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-from typing import Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -41,7 +40,7 @@ def __init__(
         causal: bool = False,
         sequence_length: int | None = None,
         rel_pos_embedding: str | None = None,
-        input_size: Tuple | None = None,
+        input_size: tuple | None = None,
         attention_dtype: torch.dtype | None = None,
         include_fc: bool = True,
         use_combined_linear: bool = True,
@@ -101,16 +100,16 @@ def __init__(
 
         self.num_heads = num_heads
         self.hidden_input_size = hidden_input_size if hidden_input_size else hidden_size
-        self.out_proj: Union[nn.Linear, nn.Identity]
+        self.out_proj: nn.Linear | nn.Identity
         if include_fc:
             self.out_proj = nn.Linear(self.inner_dim, self.hidden_input_size)
         else:
             self.out_proj = nn.Identity()
 
-        self.qkv: Union[nn.Linear, nn.Identity]
-        self.to_q: Union[nn.Linear, nn.Identity]
-        self.to_k: Union[nn.Linear, nn.Identity]
-        self.to_v: Union[nn.Linear, nn.Identity]
+        self.qkv: nn.Linear | nn.Identity
+        self.to_q: nn.Linear | nn.Identity
+        self.to_k: nn.Linear | nn.Identity
+        self.to_v: nn.Linear | nn.Identity
 
         if use_combined_linear:
             self.qkv = nn.Linear(self.hidden_input_size, self.inner_dim * 3, bias=qkv_bias)
@@ -153,7 +152,7 @@ def __init__(
         )
         self.input_size = input_size
 
-    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
+    def forward(self, x, attn_mask: torch.Tensor | None = None):
         """
         Args:
             x (torch.Tensor): input tensor. B x (s_dim_1 * ... * s_dim_n) x C