给我一个GAMI-Net的代码

GAMI-Net (Group Attention and Multi-scale Interaction Network)是一种用于图像分割任务的深度学习模型，它结合了注意力机制和多尺度特征融合。由于具体的代码实现通常非常复杂，这里我将提供一个简化的伪代码示例，实际应用中你需要使用深度学习框架如PyTorch或TensorFlow来编写。

# 首先，导入必要的库
import torch
import torch.nn as nn

class GAMIBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(GAMIBlock, self).__init__()
        self.group_attention = GroupAttention(in_channels)
        self.multi_scale_interation = MultiScaleInteraction(out_channels)

    def forward(self, x):
        # 进行组内注意力操作
        group_output = self.group_attention(x)
        
        # 接下来进行多尺度交互
        multi_scale_output = self.multi_scale_interation(group_output)
        
        return multi_scale_output

class GAMI_Net(nn.Module):
    def __init__(self, num_classes, backbone='resnet50'):
        super(GAMI_Net, self).__init__()
        self.backbone = get_pretrained_backbone(backbone)
        self.gami_blocks = nn.Sequential(*[GAMIBlock(backbone.out_channels, backbone.out_channels) for _ in range(num_layers)])
        self.classifier = nn.Conv2d(backbone.out_channels, num_classes, kernel_size=1)

    def forward(self, x):
        features = self.backbone(x)
        high_level_features = features[-1]
        for block in self.gami_blocks:
            high_level_features = block(high_level_features)
        output = self.classifier(high_level_features)
        return output

# 注意这里的get_pretrained_backbone函数需要替换为实际的预训练网络获取函数

这个代码只是一个基础框架，并未包含所有细节，例如损失函数、优化器等。在实际项目中，还需要根据需求添加数据加载、训练循环以及可能的预处理步骤。