faster r-cnn Python代码

以下是使用PyTorch实现Faster R-CNN的Python代码示例：

import torch
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 定义Faster R-CNN模型
class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()
        self.features = models.vgg16(pretrained=True).features
        self.roi_pooling = nn.AdaptiveMaxPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 21)
        )
        self.bbox = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 84)
        )

    def forward(self, x, proposals):
        x = self.features(x)
        rois = []
        for i in range(proposals.shape[0]):
            roi = proposals[i]
            x1, y1, x2, y2 = roi.tolist()
            roi_feature = x[:, :, int(y1):int(y2), int(x1):int(x2)]
            roi_feature = self.roi_pooling(roi_feature)
            rois.append(roi_feature)
        rois = torch.stack(rois, dim=0)
        rois = rois.view(-1, 512 * 7 * 7)
        cls_score = self.classifier(rois)
        bbox_pred = self.bbox(rois)
        return cls_score, bbox_pred

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = datasets.CocoDetection(root='./data', annFile='./annotations/instances_train2017.json', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# 定义优化器和损失函数
model = FasterRCNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(10):
    for i, (images, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        cls_score, bbox_pred = model(images, targets)
        loss_cls = criterion(cls_score, targets)
        loss_bbox = criterion(bbox_pred, targets)
        loss = loss_cls + loss_bbox
        loss.backward()
        optimizer.step()
        print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_loader), loss.item()))

请注意，这只是一个简单的示例，实际使用时需要根据数据集和模型的特点进行相应的修改。