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

下面是使用PyG训练图卷积网络GCN进行骨骼识别的代码参考：

首先，需要安装PyG和其他必要的库：

!pip install torch torchvision torchaudio
!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
!pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
!pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
!pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
!pip install torch-geometric

接下来，加载数据集：

import torch
from torch_geometric.datasets import HumanSkeletonDataset

train_dataset = HumanSkeletonDataset(root='./data', split='train')
test_dataset = HumanSkeletonDataset(root='./data', split='test')

然后，定义模型：

from torch_geometric.nn import GCNConv
import torch.nn.functional as F

class GCN(torch.nn.Module):
    def __init__(self, num_features, hidden_channels, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(num_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, num_classes)

    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        return x

接下来，定义训练过程：

import torch.optim as optim
from torch_geometric.data import DataLoader

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def train(model, train_loader, optimizer):
    model.train()

    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        out = model(data.x, data.edge_index)
        loss = F.cross_entropy(out, data.y)
        loss.backward()
        optimizer.step()

def test(model, loader):
    model.eval()

    correct = 0
    for data in loader:
        data = data.to(device)
        with torch.no_grad():
            pred = model(data.x, data.edge_index).argmax(dim=1)
        correct += int((pred == data.y).sum())

    return correct / len(loader.dataset)

最后，训练模型并测试：

model = GCN(num_features=3, hidden_channels=16, num_classes=15).to(device)
optimizer = optim.Adam(model.parameters(), lr=0.01)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

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

这是一个简单的示例，你需要根据自己的数据集和需求进行修改和调整。