给出完整的用于3d图像分类的3d resnet18网络的python代码，包括数据处理（分成训练、验证、测试三部分）、训练以及训练结果评估（输出训练每个epoch的loss、acc，并进行prc、roc、混淆矩阵评估）

为了提供一个完整的3D ResNet-18网络应用于3D图像分类的Python代码示例，我们将使用PyTorch库。首先，确保已经安装了torch、torchvision和其他必要的库。下面是一个简化的示例，包含数据预处理、模型训练、验证和评估的部分内容：

# 导入所需库
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, datasets, models
from sklearn.metrics import precision_recall_curve, roc_auc_score, confusion_matrix

# 数据预处理
transform = transforms.Compose([
    transforms.Resize((64, 64)),  # 调整输入尺寸
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # 归一化
])

# 定义数据集类
class CustomDataset(Dataset):
    def __init__(self, data_dir, transform=None, train=True):
        self.data_dir = data_dir
        self.transform = transform
        self.train_data = ...  # 加载训练数据
        self.test_data = ...  # 加载测试数据 (类似train_data)

    def __len__(self):
        return len(self.train_data) if train else len(self.test_data)

    def __getitem__(self, idx):
        img_path = ...
        label = ...
        sample = {'image': img_path, 'label': label}
        if self.transform:
            sample['image'] = self.transform(sample['image'])
        return sample

# 创建数据加载器
batch_size = 32
train_dataset = CustomDataset('path_to_train_data', transform=transform, train=True)
val_dataset = CustomDataset('path_to_val_data', transform=transform, train=False)
test_dataset = CustomDataset('path_to_test_data', transform=transform, train=False)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

# 使用3D ResNet-18模型
model = models.resnet3d.resnet18(pretrained=False, num_classes=len(train_dataset.classes))  # 更改最后一层为你的类别数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练循环
for epoch in range(num_epochs):
    model.train()
    train_loss = 0
    correct = 0
    for i, (images, labels) in enumerate(train_loader):
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        correct += (predicted == labels).sum().item()

    train_acc = correct / len(train_dataset)
    print(f"Epoch {epoch + 1}/{num_epochs}, Training Loss: {train_loss / len(train_loader):.4f}, Train Acc: {train_acc:.4f}")

    # 验证和评估
    model.eval()
    val_loss = 0
    with torch.no_grad():
        for images, labels in val_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            val_loss += loss.item()

    val_acc = evaluate_model_on_loader(model, val_loader)
    print(f"Epoch {epoch + 1}/{num_epochs}, Validation Loss: {val_loss / len(val_loader):.4f}, Val Acc: {val_acc:.4f}")

    # 可视化PRC和ROC曲线，计算AUC
    prc_auc, _ = calculate_PR_and_ROC(model, test_loader, train_dataset.classes)
    print(f"Epoch {epoch + 1}, PRC AUC: {prc_auc:.4f}")

    # 混淆矩阵
    cm = confusion_matrix(y_true=test_labels, y_pred=predicted)
    plot_confusion_matrix(cm, classes=train_dataset.classes)

# 函数定义（省略）
def evaluate_model_on_loader(model, loader):
    correct = 0
    total = 0
    for images, labels in loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    return correct / total

def calculate_PR_and_ROC(model, loader, classes):
    y_true, y_pred = [], []
    for images, labels in loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, preds = torch.max(outputs.data, 1)
        y_true.extend(labels.tolist())
        y_pred.extend(preds.tolist())

    prc_auc = precision_recall_curve(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_pred)
    return prc_auc[-1], roc_auc

# 其他函数（如混淆矩阵绘制等）

注意：这个代码示例并不完整，你需要填充`...`处的数据加载和数据结构，以及一些辅助函数的定义（如evaluate_model_on_loader、calculate_PR_and_ROC）。此外，`confusion_matrix`和`plot_confusion_matrix`需要额外的库（例如matplotlib和sklearn.metrics），这里仅作为指导思想。实际运行时，请根据项目需求调整和完成剩余部分。