resnet 基于迁移学习对 CIFAR10 数据集的分类

import torch import torch.nn as nn # residual block class BasicBlock(nn.Module): expansion = 1 def __init__(self,in_channel,out_channel,stride=1,downsample=None): super(BasicBlock,self).__init__() self.conv1 = nn.Conv2d(in_channel,out_channel,kernel_size=3,stride=stride,padding=1,bias=False) # 第一层的话，可能会缩小size，这时候 stride = 2 self.bn1 = nn.BatchNorm2d(out_channel) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(out_channel,out_channel,kernel_size=3,stride=1,padding=1,bias=False) self.bn2 = nn.BatchNorm2d(out_channel) self.downsample = downsample def forward(self,x): identity = x if self.downsample is not None: # 有下采样，意味着需要1*1进行降维，同时channel翻倍，residual block虚线部分 identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += identity out = self.relu(out) return out # bottleneck class Bottleneck(nn.Module): expansion = 4 # 卷积核的变化 def __init__(self,in_channel,out_channel,stride=1,downsample=None): super(Bottleneck,self).__init__() # 1*1 降维度 --------> padding默认为 0，size不变,channel被降低 self.conv1 = nn.Conv2d(in_channel,out_channel,kernel_size=1,stride=1,bias=False) self.bn1 = nn.BatchNorm2d(out_channel) # 3*3 卷积 self.conv2 = nn.Conv2d(out_channel,out_channel,kernel_size=3,stride=stride,bias=False) self.bn2 = nn.BatchNorm2d(out_channel) # 1*1 还原维度 --------> padding默认为 0，size不变,channel被还原 self.conv3 = nn.Conv2d(out_channel,out_channel*self.expansion,kernel_size=1,stride=1,bias=False) self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) # other self.relu = nn.ReLU(inplace=True) self.downsample =downsample def forward(self,x): identity = x if self.downsample is not None: identity = self.downsample(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) out += identity out = self.relu(out) return out # resnet class ResNet(nn.Module): def __init__(self,block,block_num,num_classes=1000,include_top=True): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 # max pool 之后的 depth # 网络最开始的部分，输入是RGB图像，经过卷积，图像size减半，通道变为64 self.conv1 = nn.Conv2d(3,self.in_channel,kernel_size=7,stride=2,padding=3,bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3,stride=2,padding=1) # size减半，padding = 1 self.layer1 = self.__make_layer(block,64,block_num[0]) # conv2_x self.layer2 = self.__make_layer(block,128,block_num[1],stride=2) # conv3_x self.layer3 = self.__make_layer(block,256,block_num[2],stride=2) # conv4_X self.layer4 = self.__make_layer(block,512,block_num[3],stride=2) # conv5_x if self.include_top: # 分类部分 self.avgpool = nn.AdaptiveAvgPool2d((1,1)) # out_size = 1*1 self.fc = nn.Linear(512*block.expansion,num_classes) def __make_layer(self,block,channel,block_num,stride=1): downsample =None if stride != 1 or self.in_channel != channel*block.expansion: # shortcut 部分，1*1 进行升维 downsample=nn.Sequential( nn.Conv2d(self.in_channel,channel*block.expansion,kernel_size=1,stride=stride,bias=False), nn.BatchNorm2d(channel*block.expansion) ) layers =[] layers.append(block(self.in_channel, channel, downsample =downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1,block_num): # residual 实线的部分 layers.append(block(self.in_channel,channel)) return nn.Sequential(*layers) def forward(self,x): # resnet 前面的卷积部分 x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) # residual 特征提取层 x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # 分类 if self.include_top: x = self.avgpool(x) x = torch.flatten(x,start_dim=1) x = self.fc(x) return x # 定义网络 def resnet34(num_classes=1000,include_top=True): return ResNet(BasicBlock,[3,4,6,3],num_classes=num_classes,include_top=include_top) def resnet101(num_classes=1000,include_top=True): return ResNet(Bottleneck,[3,4,23,3],num_classes=num_classes,include_top=include_top)