盤點目標檢測中的特征融合技巧（根據(jù)YOLO v4總結(jié)）

import torch.nn as nnimport torch.nn.functional as Fimport math
class Bottleneck(nn.Module):    expansion = 4    def __init__(self, in_planes, planes, stride=1, downsample=None):        super(Bottleneck, self).__init__()        self.bottleneck = nn.Sequential(                nn.Conv2d(in_planes, planes, 1, bias=False),                nn.BatchNorm2d(planes),                nn.ReLU(inplace=True),                nn.Conv2d(planes, planes, 3, stride, 1, bias=False),                nn.BatchNorm2d(planes),                nn.ReLU(inplace=True),                nn.Conv2d(planes, self.expansion * planes, 1, bias=False),                nn.BatchNorm2d(self.expansion * planes),            )        self.relu = nn.ReLU(inplace=True)        self.downsample = downsample    def forward(self, x):        identity = x        out = self.bottleneck(x)        if self.downsample is not None:            identity = self.downsample(x)        out += identity        out = self.relu(out)        return out
class FPN(nn.Module):    def __init__(self, layers):        super(FPN, self).__init__()        self.inplanes = 64        self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)        self.bn1 = nn.BatchNorm2d(64)        self.relu = nn.ReLU(inplace=True)        self.maxpool = nn.MaxPool2d(3, 2, 1)
        self.layer1 = self._make_layer(64, layers[0])        self.layer2 = self._make_layer(128, layers[1], 2)        self.layer3 = self._make_layer(256, layers[2], 2)        self.layer4 = self._make_layer(512, layers[3], 2)        self.toplayer = nn.Conv2d(2048, 256, 1, 1, 0)
        self.smooth1 = nn.Conv2d(256, 256, 3, 1, 1)        self.smooth2 = nn.Conv2d(256, 256, 3, 1, 1)        self.smooth3 = nn.Conv2d(256, 256, 3, 1, 1)
        self.latlayer1 = nn.Conv2d(1024, 256, 1, 1, 0)        self.latlayer2 = nn.Conv2d( 512, 256, 1, 1, 0)        self.latlayer3 = nn.Conv2d( 256, 256, 1, 1, 0)
    def _make_layer(self, planes, blocks, stride=1):        downsample  = None        if stride != 1 or self.inplanes != Bottleneck.expansion * planes:            downsample  = nn.Sequential(                nn.Conv2d(self.inplanes, Bottleneck.expansion * planes, 1, stride, bias=False),                nn.BatchNorm2d(Bottleneck.expansion * planes)            )        layers = []        layers.append(Bottleneck(self.inplanes, planes, stride, downsample))        self.inplanes = planes * Bottleneck.expansion        for i in range(1, blocks):            layers.append(Bottleneck(self.inplanes, planes))        return nn.Sequential(*layers)
    def _upsample_add(self, x, y):        _,_,H,W = y.shape        return F.upsample(x, size=(H,W), mode='bilinear') + y
    def forward(self, x):
        c1 = self.maxpool(self.relu(self.bn1(self.conv1(x))))        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