Fix ruff style

segmentation_models_pytorch/encoders/timm_universal.py

@@ -1,29 +1,29 @@
 """
-TimmUniversalEncoder provides a unified feature extraction interface built on the 
-`timm` library, supporting both traditional-style (e.g., ResNet) and transformer-style 
+TimmUniversalEncoder provides a unified feature extraction interface built on the
+`timm` library, supporting both traditional-style (e.g., ResNet) and transformer-style
 models (e.g., Swin Transformer, ConvNeXt).
 
-This encoder produces consistent multi-level feature maps for semantic segmentation tasks. 
-It allows configuring the number of feature extraction stages (`depth`) and adjusting 
+This encoder produces consistent multi-level feature maps for semantic segmentation tasks.
+It allows configuring the number of feature extraction stages (`depth`) and adjusting
 `output_stride` when supported.
 
 Key Features:
 - Flexible model selection using `timm.create_model`.
-- Unified multi-level output across different model hierarchies.  
+- Unified multi-level output across different model hierarchies.
 - Automatic alignment for inconsistent feature scales:
-  - Transformer-style models (start at 1/4 scale): Insert dummy features for 1/2 scale.  
-  - VGG-style models (include scale-1 features): Align outputs for compatibility.  
+  - Transformer-style models (start at 1/4 scale): Insert dummy features for 1/2 scale.
+  - VGG-style models (include scale-1 features): Align outputs for compatibility.
 - Easy access to feature scale information via the `reduction` property.
 
 Feature Scale Differences:
-- Traditional-style models (e.g., ResNet): Scales at 1/2, 1/4, 1/8, 1/16, 1/32.  
-- Transformer-style models (e.g., Swin Transformer): Start at 1/4 scale, skip 1/2 scale.  
+- Traditional-style models (e.g., ResNet): Scales at 1/2, 1/4, 1/8, 1/16, 1/32.
+- Transformer-style models (e.g., Swin Transformer): Start at 1/4 scale, skip 1/2 scale.
 - VGG-style models: Include scale-1 features (input resolution).
 
 Notes:
-- `output_stride` is unsupported in some models, especially transformer-based architectures. 
-- Special handling for models like TResNet and DLA to ensure correct feature indexing.  
-- VGG-style models use `_is_skip_first` to align scale-1 features with standard outputs.
+- `output_stride` is unsupported in some models, especially transformer-based architectures.
+- Special handling for models like TResNet and DLA to ensure correct feature indexing.
+- VGG-style models use `_is_vgg_style` to align scale-1 features with standard outputs.
 """
 
 from typing import Any
@@ -35,7 +35,7 @@
 
 class TimmUniversalEncoder(nn.Module):
     """
-    A universal encoder leveraging the `timm` library for feature extraction from 
+    A universal encoder leveraging the `timm` library for feature extraction from
     various model architectures, including traditional-style and transformer-style models.
 
     Features:
@@ -92,15 +92,15 @@ def __init__(
         if reduction_scales == [2 ** (i + 2) for i in range(encoder_stage)]:
             # Transformer-style downsampling: scales (4, 8, 16, 32)
             self._is_transformer_style = True
-            self._is_skip_first = False
+            self._is_vgg_style = False
         elif reduction_scales == [2 ** (i + 1) for i in range(encoder_stage)]:
             # Traditional-style downsampling: scales (2, 4, 8, 16, 32)
             self._is_transformer_style = False
-            self._is_skip_first = False
-        elif reduction_scales == [2 ** i for i in range(encoder_stage)]:
-            # Models including scale 1: scales (1, 2, 4, 8, 16, 32)
+            self._is_vgg_style = False
+        elif reduction_scales == [2**i for i in range(encoder_stage)]:
+            # Vgg-style models including scale 1: scales (1, 2, 4, 8, 16, 32)
             self._is_transformer_style = False
-            self._is_skip_first = True
+            self._is_vgg_style = True
         else:
             raise ValueError("Unsupported model downsampling pattern.")
 
@@ -125,14 +125,14 @@ def __init__(
             if "dla" in name:
                 # For 'dla' models, out_indices starts at 0 and matches the input size.
                 common_kwargs["out_indices"] = tuple(range(1, depth + 1))
-            if self._is_skip_first:
+            if self._is_vgg_style:
                 common_kwargs["out_indices"] = tuple(range(depth + 1))
 
             self.model = timm.create_model(
                 name, **_merge_kwargs_no_duplicates(common_kwargs, kwargs)
             )
 
-            if self._is_skip_first:
+            if self._is_vgg_style:
                 self._out_channels = self.model.feature_info.channels()
             else:
                 self._out_channels = [in_channels] + self.model.feature_info.channels()
@@ -164,9 +164,9 @@ def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
             B, _, H, W = x.shape
             dummy = torch.empty([B, 0, H // 2, W // 2], dtype=x.dtype, device=x.device)
             features = [dummy] + features
-    
-        # Add input tensor as scale 1 feature if `self._is_skip_first` is False
-        if not self._is_skip_first:
+
+        # Add input tensor as scale 1 feature if `self._is_vgg_style` is False
+        if not self._is_vgg_style:
             features = [x] + features
 
         return features