import torch as t import torch.nn as nn import torch.nn.functional as F from torchvision import models # 残差快 残差网络公式 a^[L+2] = g(a^[L]+z^[L+2]) class ResidualBlock(nn.Module): def __init__(self, inchannel, outchannel, stride=1, shortcut=None): #shortcut=None对应图中跨层连接的实线,对应残差网络公式 a^[L+2] = g(a^[L]+z^[L+2])，否则对应当 # 通道数变化后第一个残差块的虚线，此时对应的残差公式为a^[L+2] = g(z^[L+1]+z^[L+2]) nn.Module.__init__(self) self.left = nn.Sequential(#得到z^[L+2] nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False), nn.BatchNorm2d(outchannel), nn.ReLU(inplace= True), nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False), nn.BatchNorm2d(outchannel)) self.right = shortcut#决定是跨层连接的是实线还是虚线 def forward(self, x): out = self.left(x) residual = x if self.right is None else self.right(x) out += residual return F.relu(out) #a^[L+2] = g(a^[L]+z^[L+2]) # ResNet34 class ResNet(nn.Module): def __init__(self, num_classes=1000): nn.Module.__init__(self) # 前几层图像转换(网络输入部分) self.pre = nn.Sequential(#对应图中开始残差处理之前的部分 nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(3, 2, 1) ) # 中间卷积部分 self.layer1 = self._make_layer(64, 64, 3) self.layer2 = self._make_layer(64, 128, 4, stride=2)#stride=2代表每一个残差快的第一个层的2/ self.layer3 = self._make_layer(128, 256, 6, stride=2) self.layer4 = self._make_layer(256, 512, 3, stride=2) # 平均池化 self.avgpool = nn.AvgPool2d(7, stride=1) # 分类用的全连接 self.fc = nn.Linear(512, 1000) def _make_layer(self, inchannel, outchannel, block_num, stride=1): # 使得输入输出通道数调整为一致。比如第二个layer时，第一个残差快输入为64，输出为128 shortcut = nn.Sequential(#对应着每类相同通道数的残差快的第一个跨层直线是虚线 nn.Conv2d(inchannel, outchannel, 1, stride, bias=False), nn.BatchNorm2d(outchannel)) layers = [] layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut)) for i in range(1, block_num): layers.append(ResidualBlock(outchannel, outchannel)) return nn.Sequential(*layers) def forward(self, x): x = self.pre(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1)#torch.Size([1, 512]) return self.fc(x) model = ResNet() input = t.autograd.Variable(t.randn(1, 3, 224, 224)) o = model(input) print(o) model = models.resnet34()#调用工具包实线残差网络 o1 = model(input) print(o1)
output
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