图卷积网络进行骨骼识别代码_PyG训练图卷积网络GCN的代码参考

以下是使用PyG训练图卷积网络GCN进行骨骼识别的代码参考：

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import ModelNet
from torch_geometric.loader import DataLoader

class GCN(torch.nn.Module):
    def __init__(self):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(3, 16)
        self.conv2 = GCNConv(16, 32)
        self.conv3 = GCNConv(32, 64)
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, 10)

    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = F.relu(self.conv3(x, edge_index))
        x = 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)

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

dataset = ModelNet(root='path/to/dataset', name='10')
loader = DataLoader(dataset, batch_size=32, shuffle=True)

def train():
    model.train()
    for data in loader:
        data = data.to(device)
        optimizer.zero_grad()
        out = model(data.x, data.edge_index)
        loss = F.nll_loss(out, data.y)
        loss.backward()
        optimizer.step()

def test(loader):
    model.eval()
    correct = 0
    for data in loader:
        data = data.to(device)
        with torch.no_grad():
            out = model(data.x, data.edge_index)
        pred = out.argmax(dim=1)
        correct += int((pred == data.y).sum())
    return correct / len(loader.dataset)

for epoch in range(1, 201):
    train()
    train_acc = test(loader)
    test_acc = test(loader)
    print('Epoch: {:03d}, Train Acc: {:.4f}, Test Acc: {:.4f}'.format(
        epoch, train_acc, test_acc))

在这个例子中，我们创建了一个包含三个GCNConv层的GCN模型来处理3D点云数据。然后我们使用PyG中的ModelNet数据集进行训练和测试，并使用Adam优化器进行优化。在每个epoch中，我们都会计算训练集和测试集的准确率，并打印出来。

需要注意的是，这里的global_max_pool函数是从torch_geometric.nn中导入的，用于进行全局最大池化操作。这个函数会对每个batch中的节点特征进行最大池化，得到一个大小为（batch_size, num_channels）的特征向量。