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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
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commit 776ca86369
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60 changed files with 241 additions and 309 deletions
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28
ultralytics/nn/modules/block.py
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@ -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."""