在已经有训练集测试集和训练集的bounding_boxes写一个详细的汽车检测代码

好的，以下是一个基于PyTorch框架的汽车检测代码示例，可以根据你的具体数据集进行修改：

import torch
import torch.nn as nn
import torchvision
import numpy as np
import cv2

# 定义模型
class CarDetector(nn.Module):
    def __init__(self):
        super(CarDetector, self).__init__()
        self.features = torchvision.models.resnet18(pretrained=True)
        self.features.fc = nn.Linear(512, 2 * 4) # 2个坐标，每个坐标4个值

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 2, 4)
        return x

# 定义数据加载函数
class CarDataset(torch.utils.data.Dataset):
    def __init__(self, image_paths, boxes):
        self.image_paths = image_paths
        self.boxes = boxes

    def __getitem__(self, idx):
        image_path = self.image_paths[idx]
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        box = self.boxes[idx]
        x1, y1, x2, y2 = box
        image = image[y1:y2, x1:x2, :]
        image = cv2.resize(image, (224, 224))
        image = image.transpose((2, 0, 1))
        image = torch.from_numpy(image).float()
        return image, torch.Tensor(box)

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

# 定义训练函数
def train(model, dataloader, criterion, optimizer):
    model.train()
    total_loss = 0.
    for images, targets in dataloader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(dataloader)

# 定义测试函数
def test(model, dataloader, criterion):
    model.eval()
    total_loss = 0.
    with torch.no_grad():
        for images, targets in dataloader:
            outputs = model(images)
            loss = criterion(outputs, targets)
            total_loss += loss.item()
    return total_loss / len(dataloader)

# 加载数据
train_image_paths = [...] # 训练集图片路径列表
train_boxes = [...] # 训练集标注框列表
test_image_paths = [...] # 测试集图片路径列表
test_boxes = [...] # 测试集标注框列表
train_dataset = CarDataset(train_image_paths, train_boxes)
test_dataset = CarDataset(test_image_paths, test_boxes)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=True)

# 定义模型、损失函数和优化器
model = CarDetector()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    train_loss = train(model, train_loader, criterion, optimizer)
    test_loss = test(model, test_loader, criterion)
    print('epoch %d, train loss: %.4f, test loss: %.4f' % (epoch+1, train_loss, test_loss))

注意，这只是一个简单的示例代码。如果你的数据集比较复杂，还需要进行更多的数据预处理和数据增强，以及调整模型的参数和架构，来提高模型的准确性和鲁棒性。