recursive-fpn pytorch代码

以下是一个简单的基于PyTorch实现的Recursive FPN代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class RecursiveFPN(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(RecursiveFPN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(x1)
        if x.shape[2] > 2:
            x3 = self.pool(x2)
            x3 = self.forward(x3)
            x3_upsampled = F.interpolate(x3, size=x2.shape[2:], mode='nearest')
            x2 = x2 + x3_upsampled
        return x2

这是一个简单的递归FPN模块，采用两层卷积层和一个最大池化层。如果输入张量的高度或宽度大于2，递归地对下采样后的张量进行处理，并将其上采样与当前层的张量相加。