用pytorch写sd点云的深度学习代码

下面是一个简单的用PyTorch写SD点云深度学习代码的例子：

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import numpy as np

# 定义数据集
class SDDataset(Dataset):
    def __init__(self, data_path):
        self.data = np.load(data_path)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        return self.data[index]

# 定义模型
class SDNet(nn.Module):
    def __init__(self):
        super(SDNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 6 * 6, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool3(x)
        x = x.view(-1, 64 * 6 * 6)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 训练代码
def train(model, train_loader, criterion, optimizer, device):
    model.train()
    train_loss = 0
    for batch_idx, data in enumerate(train_loader):
        inputs = data[:, :, :, :3]
        targets = data[:, :, :, 3]

        inputs = inputs.to(device)
        targets = targets.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()

    return train_loss / len(train_loader)

# 测试代码
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data in test_loader:
            inputs = data[:, :, :, :3]
            targets = data[:, :, :, 3]

            inputs = inputs.to(device)
            targets = targets.to(device)

            outputs = model(inputs)
            test_loss += criterion(outputs, targets).item()

            preds = outputs.argmax(dim=1)
            correct += preds.eq(targets.view_as(preds)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = correct / len(test_loader.dataset)

    return test_loss, accuracy

# 主函数
def main():
    # 设置超参数
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    epochs = 10
    batch_size = 64
    learning_rate = 0.001
    data_path = "sd_data.npy"

    # 加载数据集
    dataset = SDDataset(data_path)
    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [8000, 2000])
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

    # 初始化模型、损失函数和优化器
    model = SDNet().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    # 训练和测试模型
    for epoch in range(1, epochs + 1):
        train_loss = train(model, train_loader, criterion, optimizer, device)
        test_loss, accuracy = test(model, test_loader, criterion, device)
        print("Epoch {}: Train Loss: {:.4f}, Test Loss: {:.4f}, Accuracy: {:.2f}%".format(epoch, train_loss, test_loss, accuracy * 100))

if __name__ == '__main__':
    main()

上述代码中，我们定义了一个 `SDDataset` 类来加载 SD 点云数据集，定义了一个 `SDNet` 类来实现 SD 点云的深度学习模型。我们使用 PyTorch 自带的 DataLoader 类来加载训练和测试数据集，并使用 Adam 优化器来训练模型。最后，我们使用 `train` 函数来训练模型，使用 `test` 函数来测试模型，并在主函数中执行训练和测试过程。