请编写基于pythorch的maskrcnn网络

以下是基于PyTorch的Mask R-CNN网络的示例代码：

import torch
import torchvision

# 定义模型
class MaskRCNN(torch.nn.Module):
    def __init__(self, num_classes):
        super(MaskRCNN, self).__init__()
        
        # 加载预训练的ResNet50模型
        self.backbone = torchvision.models.resnet50(pretrained=True)
        
        # 修改ResNet50最后一层为适合目标检测的卷积层
        in_features = self.backbone.fc.in_features
        self.backbone.fc = torch.nn.Conv2d(in_features, in_features, kernel_size=1)
        
        # 定义RPN网络
        self.rpn = torchvision.models.detection.rpn.RegionProposalNetwork(
            in_channels=in_features,
            out_channels=256,
            anchor_generator=torchvision.models.detection.anchor_utils.AnchorGenerator(
                sizes=((32, 64, 128, 256, 512),),
                aspect_ratios=((0.5, 1.0, 2.0),)
            ),
            box_coder=torchvision.models.detection.rpn.BoxCoder(
                torchvision.models.detection.transforms.BboxTransform(
                    mean=[0, 0, 0, 0], std=[1, 1, 1, 1]
                )
            ),
            head=torchvision.models.detection.rpn.RPNHead(
                in_channels=256,
                num_anchors=3
            )
        )
        
        # 定义RoI头网络
        self.roi_heads = torchvision.models.detection.maskrcnn.MaskRCNNHeads(
            in_channels=in_features,
            num_classes=num_classes,
            box_predictor=torchvision.models.detection.maskrcnn.MaskRCNNPredictor(
                in_channels=in_features,
                num_classes=num_classes
            )
        )
        
    def forward(self, x, targets=None):
        # 特征提取
        features = self.backbone(x)
        
        # RPN网络
        proposals, proposal_losses = self.rpn(images, features, targets)
        
        # RoI头网络
        detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
        
        # 如果是训练模式，计算总损失
        if self.training:
            loss_dict = {}
            loss_dict.update(proposal_losses)
            loss_dict.update(detector_losses)
            
            return loss_dict
        
        # 如果是推理模式，返回检测结果
        return detections

此代码是一个简单的Mask R-CNN网络，包括ResNet50作为骨干网络、RPN网络、RoI头网络。你可以根据自己的需求修改它来更好地满足你的目标检测任务。