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
D:\anaconda\anaconda\pythonw.exe D:/Code/Python/pytorch入门与实践/第四章_神经网络工具箱nn/搭建ResNet.py
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Process finished with exit code 0View Code
作者：你的雷哥
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