FPN
bottom up + top down.
参考:https://github.com/luliyucoordinate/FPN_pytorch/blob/master/fpn.py
import torch.nn as nn import torch.nn.functional as F import math __all__=['FPN'] class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_planes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out 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.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # Bottom-up layers 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) # Top layer self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0) # Reduce channels # Smooth layers 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) # Lateral layers 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) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != block.expansion * planes: downsample = nn.Sequential( nn.Conv2d(self.inplanes, block.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(block.expansion * planes) ) 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): # Bottom-up x = self.conv1(x) x = self.bn1(x) x = self.relu(x) c1 = self.maxpool(x) c2 = self.layer1(c1) c3 = self.layer2(c2) c4 = self.layer3(c3) c5 = self.layer4(c4) # Top-down 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)) # Smooth p4 = self.smooth1(p4) p3 = self.smooth2(p3) p2 = self.smooth3(p2) return p2, p3, p4, p5 def FPN101(): return FPN(Bottleneck, [2,2,2,2])
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