Residual block和layer出现了多次，故将Residual block实现为一个子Module，layer实现为子函数
结合使用了nn.Module、nn.functional，尽量使用nn.Sequential
每个Residual block都有一个shortcut, 如果其和主干卷积网络的输入输出通道不一致或步长不为1时, 需要有专门单元将二者转成一致才可以相加
本程序为ResNet实现，实际使用可直接调用torchvision.medels接口，其实现了大部分model
import torch as t
from torch import nn
from torch.nn import functional as F
# 本程序为ResNet实现，实际使用可直接调用torchvision接口，其实现了大部分model
# 使用方式：
# from torchvision import models
# model = models.resnet34()
class ResidualBlock(nn.Module):
"""
实现子module：ResidualBlock
用子module来实现residual block，在_make_layer()中调用
"""
def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
# 继承时需要使用 super(FooChild,self) ,
# 首先找到 FooChild 的父类（就是类 FooParent），然后把类 FooChild 的对象转换为类 FooParent 的对象
super(ResidualBlock, self).__init__()
self.left = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False), # 只在第一个卷积层进行通道变换
nn.BatchNorm2d(outchannel),
nn.ReLU(inplace=True),
nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
nn.BatchNorm2d(outchannel))
self.right = shortcut
def forward(self, x):
out = self.left(x) # x 经过网络分支
residual = x if self.right is None else self.right(x)
out += residual
return F.relu(out)
class ResNet(nn.Module):
'''
实现主module：ResNet34
ResNet34 包含多个layer，每个layer又包含多个residual block
用_make_layer函数来实现layer，用子module来实现residual block
'''
def __init__(self, num_classes=1000): # 定义了分类数
super(ResNet, self).__init__()
""" 前几层网络 """
self.pre = nn.Sequential(
nn.Conv2d(3, 64, 7, 2, 3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 1))
""" 重复的layer，分别有3、4、6、3个Residual block"""
self.layer1 = self._make_layer(64, 64, 3, 1)
self.layer2 = self._make_layer(64, 128, 4, 2)
self.layer3 = self._make_layer(128, 256, 5, 2)
self.layer4 = self._make_layer(256, 512, 3, 2)
""" 分类用的全连接层 """
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, inchannel, outchannel, block_num, stride=1):
"""
构建 layer， 包含多个Residual block
"""
shortcut = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 1, stride, bias=False), # kernel size=1
nn.BatchNorm2d(outchannel))
layers = []
# 第一层个Residual Block，需要改变通道数, 使用1*1的卷积核来进行通道数改变
layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
# 剩下的Residual Block
for i in range(1, block_num):
layers.append(ResidualBlock(outchannel, outchannel))
return nn.Sequential(*layers)
def forward(self, x):
"""
网络结构构建
"""
x = self.pre(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = F.avg_pool2d(x, 7)
x = x.view(x.size(0), -1)
return self.fc(x)
model = ResNet()
input = t.randn(1,3,224,224)
print(input)
o = model(input)
print(o)