Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/nn/modules/conv.py

@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-Convolution modules
-"""
+"""Convolution modules."""
 
 import math
 
@@ -69,7 +67,9 @@ class Conv2(Conv):
 
 
 class LightConv(nn.Module):
-    """Light convolution with args(ch_in, ch_out, kernel).
+    """
+    Light convolution with args(ch_in, ch_out, kernel).
+
     https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
     """
 
@@ -148,12 +148,15 @@ class GhostConv(nn.Module):
 
 class RepConv(nn.Module):
     """
-    RepConv is a basic rep-style block, including training and deploy status. This module is used in RT-DETR.
+    RepConv is a basic rep-style block, including training and deploy status.
+
+    This module is used in RT-DETR.
     Based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
     """
     default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
+        """Initializes Light Convolution layer with inputs, outputs & optional activation function."""
         super().__init__()
         assert k == 3 and p == 1
         self.g = g
@@ -166,27 +169,30 @@ class RepConv(nn.Module):
         self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)
 
     def forward_fuse(self, x):
-        """Forward process"""
+        """Forward process."""
         return self.act(self.conv(x))
 
     def forward(self, x):
-        """Forward process"""
+        """Forward process."""
         id_out = 0 if self.bn is None else self.bn(x)
         return self.act(self.conv1(x) + self.conv2(x) + id_out)
 
     def get_equivalent_kernel_bias(self):
+        """Returns equivalent kernel and bias by adding 3x3 kernel, 1x1 kernel and identity kernel with their biases."""
         kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
         kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
         kernelid, biasid = self._fuse_bn_tensor(self.bn)
         return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
 
     def _pad_1x1_to_3x3_tensor(self, kernel1x1):
+        """Pads a 1x1 tensor to a 3x3 tensor."""
         if kernel1x1 is None:
             return 0
         else:
             return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
 
     def _fuse_bn_tensor(self, branch):
+        """Generates appropriate kernels and biases for convolution by fusing branches of the neural network."""
         if branch is None:
             return 0, 0
         if isinstance(branch, Conv):
@@ -214,6 +220,7 @@ class RepConv(nn.Module):
         return kernel * t, beta - running_mean * gamma / std
 
     def fuse_convs(self):
+        """Combines two convolution layers into a single layer and removes unused attributes from the class."""
         if hasattr(self, 'conv'):
             return
         kernel, bias = self.get_equivalent_kernel_bias()
@@ -243,12 +250,14 @@ class ChannelAttention(nn.Module):
     """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
 
     def __init__(self, channels: int) -> None:
+        """Initializes the class and sets the basic configurations and instance variables required."""
         super().__init__()
         self.pool = nn.AdaptiveAvgPool2d(1)
         self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
         self.act = nn.Sigmoid()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""
         return x * self.act(self.fc(self.pool(x)))