GhostConv() bug fix

[uyzhang]

The parameters of Conv function:

def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): 

But in ghost conv:

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
        super(GhostConv, self).__init__()
        c_ = c2 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, k, s, g, act)
        self.cv2 = Conv(c_, c_, 5, 1, c_, act)

The parameter p is ignored.

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Optimization of GhostConv layer initialization in YOLOv5's experimental model structures.

📊 Key Changes

• Modified the GhostConv class constructors for cv1 and cv2 methods.
• Adjusted the arguments passed to Conv function, specifically the groups (g) parameter.

🎯 Purpose & Impact

• 🛠️ The update corrects the parameter passing by explicitly assigning None where a default value is expected, enhancing code readability and maintainability.
• ✨ This change may improve model training stability and future proof the code by adhering to expected function signatures.
• 🔍 Users can expect cleaner code inside the GhostConv class, possibly leading to fewer bugs and a small enhancement in performance or stability.

[github-actions]

Hello @LeoYZhang, thank you for submitting a PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

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[glenn-jocher]

@LeoYZhang that's a serious bug indeed, good catch. I must have modified Conv() after I stopped experimenting with GhostConv().

Thank you for your contributions!