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

这里提供一个基于 Pytorch Geometric（PyG）的图卷积网络（GCN）进行骨骼识别的代码参考，供您参考：

import torch
from torch_geometric.data import DataLoader
from torch_geometric.datasets import Human36M
from torch_geometric.nn import GCNConv
from torch_geometric.utils import degree

# 加载数据集
train_dataset = Human36M('~/data/Human36M')
test_dataset = Human36M('~/data/Human36M', train=False)

# 构建数据加载器
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义模型
class GCN(torch.nn.Module):
    def __init__(self):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(54, 128)
        self.conv2 = GCNConv(128, 256)
        self.conv3 = GCNConv(256, 512)
        self.fc1 = torch.nn.Linear(512, 256)
        self.fc2 = torch.nn.Linear(256, 17 * 3)

    def forward(self, x, edge_index):
        # 第一层GCN
        x = self.conv1(x, edge_index)
        x = x.relu()
        x = x.dropout()

        # 第二层GCN
        x = self.conv2(x, edge_index)
        x = x.relu()
        x = x.dropout()

        # 第三层GCN
        x = self.conv3(x, edge_index)
        x = x.relu()
        x = x.dropout()

        # 全连接层
        x = x.mean(dim=0)
        x = self.fc1(x)
        x = x.relu()
        x = self.fc2(x)

        return x.view(-1, 17, 3)

# 实例化模型并定义损失函数和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GCN().to(device)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(50):
    model.train()
    train_loss = 0.0
    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        pred = model(data.x, data.edge_index)
        loss = criterion(pred, data.y)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.num_graphs

    # 测试模型
    model.eval()
    test_loss = 0.0
    with torch.no_grad():
        for data in test_loader:
            data = data.to(device)
            pred = model(data.x, data.edge_index)
            loss = criterion(pred, data.y)
            test_loss += loss.item() * data.num_graphs

    print('Epoch {:03d}, Train Loss: {:.4f}, Test Loss: {:.4f}'.format(
        epoch, train_loss / len(train_dataset), test_loss / len(test_dataset)))

这里的数据集是人体骨骼数据集 Human3.6M，每个图表示一个人的骨骼结构，节点表示关节，边表示骨骼。

模型采用了3层 GCN，最后通过全连接层输出每个关节的3D坐标。损失函数为 MSE，优化器为 Adam。

在训练过程中，每个图的损失值都会被累加并除以数据集大小得到平均损失值。同时，每个 epoch 结束后会输出训练集和测试集的平均损失值。