我想把yolov5中的原有卷积层替换成门控卷积层，代码有什么变化呢

将YoloV5中的卷积层替换为门控卷积层需要进行以下变化：

1.导入门控卷积层代码库

from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
import torch.nn.functional as F

class GatedConv2d(_ConvNd):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        super(GatedConv2d, self).__init__(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            False, _pair(0), groups, bias)
        self.weight_i = nn.Parameter(torch.Tensor(out_channels, in_channels, *kernel_size))
        self.weight_f = nn.Parameter(torch.Tensor(out_channels, in_channels, *kernel_size))
        self.weight_c = nn.Parameter(torch.Tensor(out_channels, in_channels, *kernel_size))
        self.weight_o = nn.Parameter(torch.Tensor(out_channels, in_channels, *kernel_size))
        self.bias_i = nn.Parameter(torch.Tensor(out_channels))
        self.bias_f = nn.Parameter(torch.Tensor(out_channels))
        self.bias_c = nn.Parameter(torch.Tensor(out_channels))
        self.bias_o = nn.Parameter(torch.Tensor(out_channels))
        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1. / math.sqrt(n)
        self.weight_i.data.uniform_(-stdv, stdv)
        self.weight_f.data.uniform_(-stdv, stdv)
        self.weight_c.data.uniform_(-stdv, stdv)
        self.weight_o.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias_i.data.uniform_(-stdv, stdv)
            self.bias_f.data.uniform_(-stdv, stdv)
            self.bias_c.data.uniform_(-stdv, stdv)
            self.bias_o.data.uniform_(-stdv, stdv)

    def forward(self, input):
        i = F.conv2d(input, self.weight_i, self.bias_i, self.stride,
                     self.padding, self.dilation, self.groups)
        f = F.conv2d(input, self.weight_f, self.bias_f, self.stride,
                     self.padding, self.dilation, self.groups)
        o = F.conv2d(input, self.weight_o, self.bias_o, self.stride,
                     self.padding, self.dilation, self.groups)
        c = F.conv2d(input, self.weight_c, self.bias_c, self.stride,
                     self.padding, self.dilation, self.groups)
        g = F.sigmoid(f)
        c = F.tanh(c)
        x = g * c
        h = F.relu(i + x)
        return o * h

2.将YoloV5中的卷积层替换为门控卷积层

在YoloV5的网络定义中，将原有的卷积层替换为门控卷积层即可。

例如，将YoloV5中的卷积层定义

class Conv(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act else nn.Identity()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

替换为门控卷积层定义

class Conv(nn.Module):
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        super(Conv, self).__init__()
        self.conv = GatedConv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act else nn.Identity()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

这样就完成了将YoloV5中的卷积层替换为门控卷积层的操作。