给出gpu上训练 CIFAR10 ，计算ACC PRECISION F1 RECALL AUC 及每一类ROC曲线的 pytorch 代码

好的，以下是给出GPU上训练CIFAR10并计算指标的PyTorch代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc

# 定义网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 5)
        self.fc1 = nn.Linear(64 * 5 * 5, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 5 * 5)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载数据集并进行数据增强
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 将模型移动到GPU上进行训练
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)

# 训练模型
for epoch in range(20):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 在测试集上进行预测并计算各项指标
y_true = []
y_pred = []
y_score = []
net.eval()
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        y_true += labels.cpu().numpy().tolist()
        y_pred += predicted.cpu().numpy().tolist()
        y_score += nn.functional.softmax(outputs, dim=1).cpu().numpy().tolist()

acc = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='macro')
recall = recall_score(y_true, y_pred, average='macro')
f1 = f1_score(y_true, y_pred, average='macro')

fpr = {}
tpr = {}
roc_auc = {}
for i in range(10):
    fpr[i], tpr[i], _ = roc_curve([1 if j == i else 0 for j in y_true], [score[i] for score in y_score])
    roc_auc[i] = auc(fpr[i], tpr[i])

print("Accuracy:", acc)
print("Precision:", precision)
print("Recall:", recall)
print("F1 score:", f1)
for i in range(10):
    print("ROC AUC of class", i, ":", roc_auc[i])

这段代码会输出ACC PRECISION F1 RECALL AUC以及每一类ROC曲线的值。