ResNet18-CIFAR10-使用Pytorch和CIFAR10数据集训练ResNet18

import torch import torch.nn as nn from torch.nn import functional as F class ResBlock(nn.Module): def __init__(self,ch_in,ch_out,stride=2): super(ResBlock,self).__init__() self.conv1 = nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=stride,padding=1) # ! (h-3+2)/2 + 1 = h/2 图像尺寸减半 self.bn1 = nn.BatchNorm2d(ch_out) self.conv2 = nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1) # ! h-3+2*1+1=h 图像尺寸没变化 self.bn2 = nn.BatchNorm2d(ch_out) self.extra = nn.Sequential( nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=stride), # ! 这句话是针对原图像尺寸写的，要进行element wise add # ! 因此图像尺寸也必须减半，(h-1)/2+1=h/2 图像尺寸减半 nn.BatchNorm2d(ch_out) ) def forward(self,x): out = x x = torch.relu(self.bn1(self.conv1(x))) x = self.bn2(self.conv2(x)) # short cut # ! element wise add [b,ch_in,h,w] [b,ch_out,h,w] 必须当ch_in = ch_out时才能进行相加 out = x + self.extra(out) # todo self.extra强制把输出通道变成一致 return out class ResNet18(nn.Module): def __init__(self): super(ResNet18,self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1), # ! 图像尺寸不变 nn.BatchNorm2d(64) ) # 4个ResBlock # [b,64,h,w] --> [b,128,h,w] self.block1 = ResBlock(64,128) # [b,128,h,w] --> [b,256,h,w] self.block2 = ResBlock(128,256) # [b,256,h,w] --> [b,512,h,w] self.block3 = ResBlock(256,512) # [b,512,h,w] --> [b,512,h,w] self.block4 = ResBlock(512,512) self.outlayer = nn.Linear(512,10) def forward(self,x): x = torch.relu(self.conv1(x)) # [b,64,h,w] --> [b,1024,h,w] x = self.block1(x) x = self.block2(x) x = self.block3(x) x = self.block4(x) # print("after conv:",x.shape) #[b,512,h,w] --> [b,512,1,1] x = F.adaptive_avg_pool2d(x,[1,1]) #flatten x = x.view(x.shape[0],-1) x = self.outlayer(x) return x # def main(): # blk = ResBlock(3,64,stride=2) # tmp = torch.randn(2,3,32,32) # out = blk(tmp) # print(out.shape) # x = torch.randn(2,3,32,32) # model = ResNet18() # out = model(x) # print(out.shape) # if __name__ == '__main__': # main()