请求尽快回复，最好是扣扣回复，打扰你了你了

这个代码跑不通（格式如此，其实只想请你回复博主你好，你那里有HorBlock模块，以及RepLKDeXt模块这两个模块的结构框图吗？，我已经加群了因为时间紧所以就在这里提问了）

import torch
import torch.nn as nn
def get_dwconv(c, k=3, s=True):
    if s:
        return nn.Sequential(
            nn.Conv2d(c, c, k, stride=2, padding=k//2, groups=c, bias=False),
            nn.BatchNorm2d(c),
            nn.Conv2d(c, c, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(c),
            nn.ReLU(inplace=True)
        )
    else:
        return nn.Sequential(
            nn.Conv2d(c, c, k, stride=1, padding=k//2, groups=c, bias=False),
            nn.BatchNorm2d(c),
            nn.Conv2d(c, c, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(c),
            nn.ReLU(inplace=True)
        )
class gnconv(nn.Module):
    def __init__(self, dim, order=5, gflayer=None, h=14, w=8, s=1.0):
        super(gnconv, self).__init__()
        self.order = order
        self.dims = [dim // 2 ** i for i in range(order)]
        self.dims.reverse()
        self.proj_in = nn.Conv2d(dim, 2 * dim, 1)
        if gflayer is None:
            self.dwconv = get_dwconv(sum(self.dims), 7, True)
        else:
            self.dwconv = gflayer(sum(self.dims), h=h, w=w)
        self.proj_out = nn.Conv2d(dim, dim, 1)
        self.pws = nn.ModuleList(
            [nn.Conv2d(self.dims[i], self.dims[i+1], 1) for i in range(order-1)]
        )
        self.scale = s
 
    def forward(self, x, mask=None, dummy=False):
        fused_x = self.proj_in(x) 
        pwa, abc = torch.split(fused_x, (self.dims[0], sum(self.dims)), dim=1)
        abc = self.dwconv(abc)
        abc = torch.cat(torch.split(abc, self.dims[1:], dim=1), dim=1)
        abc = self.proj_out(abc)
        x = pwa + self.scale * abc
        return x

请复制一下 代码出来，别发图片

参考GPT和自己的思路，你看看我修改的行不行，

import torch
import torch.nn as nn

def get_dwconv(c, k=3, s=True):
    if s:
        return nn.Sequential(
            nn.Conv2d(c, c, k, stride=2, padding=k//2, groups=c, bias=False),
            nn.BatchNorm2d(c),
            nn.Conv2d(c, c, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(c),
            nn.ReLU(inplace=True)
        )
    else:
        return nn.Sequential(
            nn.Conv2d(c, c, k, stride=1, padding=k//2, groups=c, bias=False),
            nn.BatchNorm2d(c),
            nn.Conv2d(c, c, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(c),
            nn.ReLU(inplace=True)
        )

class gnconv(nn.Module):
    def __init__(self, dim, order=5, gflayer=None, h=14, w=8, s=1.0):
        super(gnconv, self).__init__()
        self.order = order
        self.dims = [dim // 2 ** i for i in range(order)]
        self.dims.reverse()
        self.proj_in = nn.Conv2d(dim, 2 * dim, 1)
        if gflayer is None:
            self.dwconv = get_dwconv(sum(self.dims), 7, True)
        else:
            self.dwconv = gflayer(sum(self.dims), h=h, w=w)
        self.proj_out = nn.Conv2d(dim, dim, 1)
        self.pws = nn.ModuleList(
            [nn.Conv2d(self.dims[i], self.dims[i+1], 1) for i in range(order-1)]
        )
        self.scale = s

    def forward(self, x, mask=None, dummy=False):
        fused_x = self.proj_in(x) # dummy
        pwa, abc = torch.split(fused_x, (self.dims[0], sum(self.dims)), dim=1)
        return pwa, abc