fpn代码
时间: 2023-07-02 20:12:54 浏览: 61
以下是一个简单的 FPN(Feature Pyramid Network)的代码实现:
```python
import torch.nn as nn
import torch.nn.functional as F
class FPN(nn.Module):
def __init__(self, in_channels, out_channels):
super(FPN, self).__init__()
self.conv6 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.conv7 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.conv8 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.lateral3 = nn.Conv2d(in_channels=256, out_channels=out_channels, kernel_size=1, stride=1, padding=0)
self.lateral4 = nn.Conv2d(in_channels=512, out_channels=out_channels, kernel_size=1, stride=1, padding=0)
self.lateral5 = nn.Conv2d(in_channels=1024, out_channels=out_channels, kernel_size=1, stride=1, padding=0)
self.smooth3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.smooth4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.smooth5 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
c3, c4, c5 = x[0], x[1], x[2]
p6 = self.conv6(c5)
p7 = self.conv7(F.relu(p6))
p8 = self.conv8(F.relu(p7))
p5 = self.lateral5(c5)
p4 = F.interpolate(self.lateral4(c4), scale_factor=2, mode="nearest")
p3 = F.interpolate(self.lateral3(c3), scale_factor=4, mode="nearest")
p4 = p4 + p5
p3 = p3 + p4
p3 = self.smooth3(p3)
p4 = self.smooth4(p4)
p5 = self.smooth5(p5)
return [p3, p4, p5, p6, p7, p8]
```
这是一个典型的 FPN 模块,它由三个卷积层和三个横向连接层组成。输入是三个不同尺寸的特征图(c3, c4, c5),输出是六个特征图(p3, p4, p5, p6, p7, p8),其中 p3, p4, p5 是横向连接后的结果,p6, p7, p8 是卷积层产生的结果。这里使用了 relu 和 interpolate 函数。这个代码实现是基于 PyTorch 框架的。
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