pyProject/test_pytorch_cuda.py

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

# 检查是否有可用的GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 定义数据预处理步骤
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载MNIST训练数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 加载MNIST测试数据集
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

# 定义卷积神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 创建模型实例并将其移动到设备（GPU或CPU）
model = Net()
model.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
def train_model():
    model.train()
    for epoch in range(20):
        running_loss = 0.0
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.to(device), target.to(device)

            optimizer.zero_grad()

            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            if batch_idx % 100 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch + 1, batch_idx * 64, len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), running_loss / (batch_idx + 1)))

        print('====> Epoch: {} Average Loss: {:.4f}'.format(epoch + 1, running_loss / len(train_loader)))

# 测试模型
def test_model():
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)

            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

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

    print('Test set: Average loss: {:.4f}, Accuracy: {:.2f}%'.format(test_loss, 100 * accuracy))

if __name__ == "__main__":
    train_model()
    test_model()