这是DGCHead的代码，我想让你在这段代码里加入正余弦位置编码：class DualGCNHead(nn.Module): def __init__(self, inplanes, interplanes, num_classes): super(DualGCNHead, self).__init__() self.conva = nn.Sequential(nn.Conv2d(inplanes, interplanes, 3, padding=1, bias=False), BatchNorm2d(interplanes), nn.ReLU(interplanes)) self.dualgcn = DualGCN(interplanes) self.convb = nn.Sequential(nn.Conv2d(interplanes, interplanes, 3, padding=1, bias=False), BatchNorm2d(interplanes), nn.ReLU(interplanes)) self.bottleneck = nn.Sequential( nn.Conv2d(inplanes + interplanes, interplanes, kernel_size=3, padding=1, dilation=1, bias=False), BatchNorm2d(interplanes), nn.ReLU(interplanes), nn.Conv2d(interplanes, num_classes, kernel_size=1, stride=1, padding=0, bias=True) ###################### 512-->256 ) def forward(self, x): output = self.conva(x) output = self.dualgcn(output) output = self.convb(output) output = self.bottleneck(torch.cat([x, output], 1)) return output

Sure! Here is the modified code with added sine and cosine positional encoding:

import math

class DualGCNHead(nn.Module):
    def __init__(self, inplanes, interplanes, num_classes, height, width):
        super(DualGCNHead, self).__init__()
        self.conva = nn.Sequential(nn.Conv2d(inplanes, interplanes, 3, padding=1, bias=False),
                                   BatchNorm2d(interplanes),
                                   nn.ReLU(interplanes))
        self.dualgcn = DualGCN(interplanes)
        self.convb = nn.Sequential(nn.Conv2d(interplanes, interplanes, 3, padding=1, bias=False),
                                   BatchNorm2d(interplanes),
                                   nn.ReLU(interplanes))
        self.height = height
        self.width = width
        self.pos_encoding = self.get_positional_encoding(interplanes, height, width)
        self.bottleneck = nn.Sequential(
            nn.Conv2d(inplanes + interplanes*2, interplanes, kernel_size=3, padding=1, dilation=1, bias=False),
            BatchNorm2d(interplanes),
            nn.ReLU(interplanes),
            nn.Conv2d(interplanes, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
        )

    def forward(self, x):
        output = self.conva(x)
        output = self.dualgcn(output)
        output = self.convb(output)
        output = torch.cat([x, output, self.pos_encoding], 1)
        output = self.bottleneck(output)
        return output
    
    def get_positional_encoding(self, d_model, height, width):
        pe_h = torch.zeros(height, d_model)
        pe_w = torch.zeros(width, d_model)
        for pos in range(height):
            for i in range(0, d_model, 2):
                pe_h[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
                pe_h[pos, i+1] = math.cos(pos / (10000 ** ((2 * (i+1))/d_model)))
        for pos in range(width):
            for i in range(0, d_model, 2):
                pe_w[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
                pe_w[pos, i+1] = math.cos(pos / (10000 ** ((2 * (i+1))/d_model)))
        pos_encoding = torch.cat([pe_h.unsqueeze(0).repeat(width, 1, 1).transpose(0, 1).unsqueeze(0), 
                                  pe_w.unsqueeze(0).repeat(height, 1, 1).unsqueeze(0)], dim=0)
        return pos_encoding

The `get_positional_encoding` function generates the sine and cosine positional encoding based on the input dimension, height, and width. The encoding is then concatenated with the output of the dual GCN layer before passing through the bottleneck layer.