gcn进行图片分类的案例包含代码

以下是使用GCN进行图像分类的示例代码：

首先，需要导入必要的依赖项：

import torch
import torch.nn.functional as F
from torch_geometric.datasets import MNISTSuperpixels
from torch_geometric.nn import GCNConv
from torch_geometric.data import DataLoader

接下来，定义模型：

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(1, 32, cached=False)
        self.conv2 = GCNConv(32, 64, cached=False)
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, 10)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = torch_geometric.nn.global_max_pool(x, batch)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

然后，加载数据集并进行训练：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.NLLLoss()

train_dataset = MNISTSuperpixels(root='./data', train=True)
test_dataset = MNISTSuperpixels(root='./data', train=False)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

def train():
    model.train()
    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data.y)
        loss.backward()
        optimizer.step()

def test(loader):
    model.eval()
    correct = 0
    for data in loader:
        data = data.to(device)
        output = model(data)
        pred = output.max(1)[1]
        correct += pred.eq(data.y).sum().item()
    return correct / len(loader.dataset)

for epoch in range(1, 201):
    train()
    train_acc = test(train_loader)
    test_acc = test(test_loader)
    print(f'Epoch: {epoch}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')

这里使用了MNISTSuperpixels数据集进行训练和测试，但是可以根据实际情况替换为其他图像数据集。