transformer目标检测代码

您好！下面是一个使用Transformer模型进行目标检测的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.models import transformer

# 构建模型
model = transformer.__dict__['transformer'](pretrained=True)
model.fc = nn.Linear(model.fc.in_features, num_classes) # 替换最后一层全连接层

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

# 加载和预处理数据
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

train_dataset = torchvision.datasets.CocoDetection(root='path/to/data', annFile='path/to/annotations.json', transform=transform)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

# 训练模型
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for images, labels in train_dataloader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1} Loss: {running_loss / len(train_dataloader)}")

# 测试模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_dataloader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(f"Accuracy: {100 * correct / total}%")

请注意，这只是一个示例代码，您需要根据您的具体数据集和需求进行相应的修改。希望对您有帮助！如果您有任何其他问题，请随时提问。