Bump ruff from 0.8.6 to 0.9.0 in /requirements

pyproject.toml

@@ -43,7 +43,7 @@ test = [
     'pytest',
     'pytest-cov',
     'pytest-xdist',
-    'ruff',
+    'ruff>=0.9',
 ]
 
 [project.urls]

requirements/test.txt

@@ -2,4 +2,4 @@ packaging==24.2
 pytest==8.3.4
 pytest-xdist==3.6.1
 pytest-cov==6.0.0
-ruff==0.8.6
+ruff==0.9.1

segmentation_models_pytorch/decoders/unetplusplus/decoder.py

@@ -119,7 +119,7 @@ def __init__(
                 blocks[f"x_{depth_idx}_{layer_idx}"] = DecoderBlock(
                     in_ch, skip_ch, out_ch, **kwargs
                 )
-        blocks[f"x_{0}_{len(self.in_channels)-1}"] = DecoderBlock(
+        blocks[f"x_{0}_{len(self.in_channels) - 1}"] = DecoderBlock(
             self.in_channels[-1], 0, self.out_channels[-1], **kwargs
         )
         self.blocks = nn.ModuleDict(blocks)
@@ -148,8 +148,8 @@ def forward(self, *features):
                     )
                     dense_x[f"x_{depth_idx}_{dense_l_i}"] = self.blocks[
                         f"x_{depth_idx}_{dense_l_i}"
-                    ](dense_x[f"x_{depth_idx}_{dense_l_i-1}"], cat_features)
+                    ](dense_x[f"x_{depth_idx}_{dense_l_i - 1}"], cat_features)
         dense_x[f"x_{0}_{self.depth}"] = self.blocks[f"x_{0}_{self.depth}"](
-            dense_x[f"x_{0}_{self.depth-1}"]
+            dense_x[f"x_{0}_{self.depth - 1}"]
         )
         return dense_x[f"x_{0}_{self.depth}"]

segmentation_models_pytorch/encoders/mix_transformer.py

@@ -82,9 +82,9 @@ def __init__(
         sr_ratio=1,
     ):
         super().__init__()
-        assert (
-            dim % num_heads == 0
-        ), f"dim {dim} should be divided by num_heads {num_heads}."
+        assert dim % num_heads == 0, (
+            f"dim {dim} should be divided by num_heads {num_heads}."
+        )
 
         self.dim = dim
         self.num_heads = num_heads

segmentation_models_pytorch/encoders/mobileone.py

@@ -381,9 +381,7 @@
         for ix, stride in enumerate(strides):
             use_se = False
             if num_se_blocks > num_blocks:
-                raise ValueError(
-                    "Number of SE blocks cannot " "exceed number of layers."
-                )
+                raise ValueError("Number of SE blocks cannot exceed number of layers.")
             if ix >= (num_blocks - num_se_blocks):
                 use_se = True
 

segmentation_models_pytorch/losses/dice.py

@@ -44,9 +44,9 @@
         super(DiceLoss, self).__init__()
         self.mode = mode
         if classes is not None:
-            assert (
-                mode != BINARY_MODE
-            ), "Masking classes is not supported with mode=binary"
+            assert mode != BINARY_MODE, (
+                "Masking classes is not supported with mode=binary"
+            )
             classes = to_tensor(classes, dtype=torch.long)
 
         self.classes = classes

segmentation_models_pytorch/losses/jaccard.py

@@ -43,9 +43,9 @@
 
         self.mode = mode
         if classes is not None:
-            assert (
-                mode != BINARY_MODE
-            ), "Masking classes is not supported with mode=binary"
+            assert mode != BINARY_MODE, (
+                "Masking classes is not supported with mode=binary"
+            )
             classes = to_tensor(classes, dtype=torch.long)
 
         self.classes = classes

tests/encoders/base.py

@@ -127,12 +127,12 @@ def test_depth(self):
                 self.assertEqual(
                     height_strides,
                     self.output_strides[: depth + 1],
-                    f"Encoder `{encoder_name}` should have output strides {self.output_strides[:depth + 1]}, but has {height_strides}",
+                    f"Encoder `{encoder_name}` should have output strides {self.output_strides[: depth + 1]}, but has {height_strides}",
                 )
                 self.assertEqual(
                     width_strides,
                     self.output_strides[: depth + 1],
-                    f"Encoder `{encoder_name}` should have output strides {self.output_strides[:depth + 1]}, but has {width_strides}",
+                    f"Encoder `{encoder_name}` should have output strides {self.output_strides[: depth + 1]}, but has {width_strides}",
                 )
 
                 # check encoder output stride property