使用pytorch实现gcn图像分类代码

下面是一个使用 PyTorch 实现 GCN 进行图像分类的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class GCN(nn.Module):
    def __init__(self, in_channels, out_channels, use_bias=True):
        super(GCN, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.use_bias = use_bias
        self.weight = nn.Parameter(torch.Tensor(in_channels, out_channels))
        if self.use_bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.weight)
        if self.use_bias:
            nn.init.zeros_(self.bias)

    def forward(self, x, adj):
        h = torch.matmul(x, self.weight)
        h = torch.matmul(adj, h)
        if self.use_bias:
            h = h + self.bias
        return h

class GCNClassifier(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers, use_bias=True):
        super(GCNClassifier, self).__init__()
        self.layers = nn.ModuleList()
        self.layers.append(GCN(in_channels, hidden_channels, use_bias))
        for _ in range(num_layers - 2):
            self.layers.append(GCN(hidden_channels, hidden_channels, use_bias))
        self.layers.append(GCN(hidden_channels, out_channels, use_bias))

    def forward(self, x, adj):
        for layer in self.layers:
            x = F.relu(layer(x, adj))
        return x

model = GCNClassifier(in_channels=128, hidden_channels=64, out_channels=10, num_layers=4)

在这个示例代码中，我们首先定义了一个 GCN 类，它实现了图卷积操作；接着我们定义了 GCNClassifier 类，它使用了多个 GCN 层，并通过最后一层输出图像的分类