Ruff Docstring formatting (#15793)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/nn/modules/block.py

@@ -204,9 +204,7 @@ class C2(nn.Module):
     """CSP Bottleneck with 2 convolutions."""
 
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        """Initializes the CSP Bottleneck with 2 convolutions module with arguments ch_in, ch_out, number, shortcut,
-        groups, expansion.
-        """
+        """Initializes a CSP Bottleneck with 2 convolutions and optional shortcut connection."""
         super().__init__()
         self.c = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, 2 * self.c, 1, 1)
@@ -224,9 +222,7 @@ class C2f(nn.Module):
     """Faster Implementation of CSP Bottleneck with 2 convolutions."""
 
     def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
-        """Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
-        expansion.
-        """
+        """Initializes a CSP bottleneck with 2 convolutions and n Bottleneck blocks for faster processing."""
         super().__init__()
         self.c = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, 2 * self.c, 1, 1)
@@ -335,9 +331,7 @@ class Bottleneck(nn.Module):
     """Standard bottleneck."""
 
     def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
-        """Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, and
-        expansion.
-        """
+        """Initializes a standard bottleneck module with optional shortcut connection and configurable parameters."""
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, k[0], 1)
@@ -345,7 +339,7 @@ class Bottleneck(nn.Module):
         self.add = shortcut and c1 == c2
 
     def forward(self, x):
-        """'forward()' applies the YOLO FPN to input data."""
+        """Applies the YOLO FPN to input data."""
         return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
 
@@ -449,9 +443,7 @@ class C2fAttn(nn.Module):
     """C2f module with an additional attn module."""
 
     def __init__(self, c1, c2, n=1, ec=128, nh=1, gc=512, shortcut=False, g=1, e=0.5):
-        """Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
-        expansion.
-        """
+        """Initializes C2f module with attention mechanism for enhanced feature extraction and processing."""
         super().__init__()
         self.c = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, 2 * self.c, 1, 1)
@@ -521,9 +513,7 @@ class ImagePoolingAttn(nn.Module):
 
 
 class ContrastiveHead(nn.Module):
-    """Contrastive Head for YOLO-World compute the region-text scores according to the similarity between image and text
-    features.
-    """
+    """Implements contrastive learning head for region-text similarity in vision-language models."""
 
     def __init__(self):
         """Initializes ContrastiveHead with specified region-text similarity parameters."""
@@ -569,16 +559,14 @@ class RepBottleneck(Bottleneck):
     """Rep bottleneck."""
 
     def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
-        """Initializes a RepBottleneck module with customizable in/out channels, shortcut option, groups and expansion
-        ratio.
-        """
+        """Initializes a RepBottleneck module with customizable in/out channels, shortcuts, groups and expansion."""
         super().__init__(c1, c2, shortcut, g, k, e)
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = RepConv(c1, c_, k[0], 1)
 
 
 class RepCSP(C3):
-    """Rep CSP Bottleneck with 3 convolutions."""
+    """Repeatable Cross Stage Partial Network (RepCSP) module for efficient feature extraction."""
 
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes RepCSP layer with given channels, repetitions, shortcut, groups and expansion ratio."""

ultralytics/nn/modules/conv.py

@@ -158,9 +158,7 @@ class GhostConv(nn.Module):
     """Ghost Convolution https://github.com/huawei-noah/ghostnet."""
 
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
-        """Initializes the GhostConv object with input channels, output channels, kernel size, stride, groups and
-        activation.
-        """
+        """Initializes Ghost Convolution module with primary and cheap operations for efficient feature learning."""
         super().__init__()
         c_ = c2 // 2  # hidden channels
         self.cv1 = Conv(c1, c_, k, s, None, g, act=act)

ultralytics/nn/modules/head.py

@@ -266,9 +266,7 @@ class Classify(nn.Module):
     """YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
-        """Initializes YOLOv8 classification head with specified input and output channels, kernel size, stride,
-        padding, and groups.
-        """
+        """Initializes YOLOv8 classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
         super().__init__()
         c_ = 1280  # efficientnet_b0 size
         self.conv = Conv(c1, c_, k, s, p, g)
@@ -571,7 +569,7 @@ class RTDETRDecoder(nn.Module):
 
 class v10Detect(Detect):
     """
-    v10 Detection head from https://arxiv.org/pdf/2405.14458
+    v10 Detection head from https://arxiv.org/pdf/2405.14458.
 
     Args:
         nc (int): Number of classes.

ultralytics/nn/modules/transformer.py

@@ -352,7 +352,6 @@ class DeformableTransformerDecoderLayer(nn.Module):
 
     def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None):
         """Perform the forward pass through the entire decoder layer."""
-
         # Self attention
         q = k = self.with_pos_embed(embed, query_pos)
         tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1), attn_mask=attn_mask)[

ultralytics/nn/modules/utils.py

@@ -50,7 +50,6 @@ def multi_scale_deformable_attn_pytorch(
 
     https://github.com/IDEA-Research/detrex/blob/main/detrex/layers/multi_scale_deform_attn.py
     """
-
     bs, _, num_heads, embed_dims = value.shape
     _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
     value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)