pytorch fpn实现

以下是使用PyTorch实现FPN的示例代码：

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

class FPN(nn.Module):
    def __init__(self, block, layers):
        super(FPN, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0) 
        self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def _upsample_add(self, x, y):
        _, _, H, W = y.size()
        return F.upsample(x, size=(H, W), mode='bilinear') + y

    def forward(self, x):
        c1 = F.relu(self.bn1(self.conv1(x)))
        c1 = F.max_pool2d(c1, kernel_size=3, stride=2, padding=1)
        c2 = self.layer1(c1)
        c3 = self.layer2(c2)
        c4 = self.layer3(c3)
        c5 = self.layer4(c4)

        p5 = self.toplayer(c5)
        p4 = self._upsample_add(p5, self.latlayer1(c4))
        p3 = self._upsample_add(p4, self.latlayer2(c3))
        p2 = self._upsample_add(p3, self.latlayer3(c2))

        p4 = self.smooth1(p4)
        p3 = self.smooth2(p3)
        p2 = self.smooth3(p2)

        return p2, p3, p4, p5

其中，block参数指的是ResNet中的基本块类型，例如BasicBlock或Bottleneck；layers参数指的是ResNet中每个stage中基本块的数量，例如[3, 4, 6, 3]表示ResNet50。在FPN中，我们首先通过ResNet的前四个stage得到c2、c3、c4和c5特征层，然后通过自下而上的方式将这些特征层融合起来，得到p2、p3、p4和p5特征层。最后，我们对这些特征层进行平滑处理，以便更好地进行目标检测。