Implement all missing docstrings (#5298)

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

ultralytics/nn/modules/conv.py

@@ -88,6 +88,7 @@ class DWConv(Conv):
     """Depth-wise convolution."""
 
     def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
+        """Initialize Depth-wise convolution with given parameters."""
         super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
 
 
@@ -95,6 +96,7 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
     """Depth-wise transpose convolution."""
 
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
+        """Initialize DWConvTranspose2d class with given parameters."""
         super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
 
 
@@ -121,12 +123,18 @@ class ConvTranspose(nn.Module):
 class Focus(nn.Module):
     """Focus wh information into c-space."""
 
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
+        """Initializes Focus object with user defined channel, convolution, padding, group and activation values."""
         super().__init__()
         self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
         # self.contract = Contract(gain=2)
 
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+    def forward(self, x):
+        """
+        Applies convolution to concatenated tensor and returns the output.
+
+        Input shape is (b,c,w,h) and output shape is (b,4c,w/2,h/2).
+        """
         return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
         # return self.conv(self.contract(x))
 
@@ -134,7 +142,10 @@ class Focus(nn.Module):
 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):  # ch_in, ch_out, kernel, stride, groups
+    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.
+        """
         super().__init__()
         c_ = c2 // 2  # hidden channels
         self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
@@ -280,7 +291,8 @@ class SpatialAttention(nn.Module):
 class CBAM(nn.Module):
     """Convolutional Block Attention Module."""
 
-    def __init__(self, c1, kernel_size=7):  # ch_in, kernels
+    def __init__(self, c1, kernel_size=7):
+        """Initialize CBAM with given input channel (c1) and kernel size."""
         super().__init__()
         self.channel_attention = ChannelAttention(c1)
         self.spatial_attention = SpatialAttention(kernel_size)