ccc-pytorch-卷积神经网络实战（6）

一、CIFAR10 与 lenet5

第一步：准备数据集
lenet5.py

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transformsdef main():batchsz = 128CIFAR_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), download=True)cifar_train = DataLoader(CIFAR_train, batch_size=batchsz, shuffle=True)CIFAR_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), download=True)cifar_test = DataLoader(CIFAR_test, batch_size=batchsz, shuffle=True)x,label = iter(cifar_train).next()print('x',x.shape,'label:',label.shape)if __name__ =='__main__':main()

第二步：确认Lenet5网络流程结构
main.py

import torch
from torch import nn
from torch.nn import functional as Fclass Lenet5(nn.Module):def __init__(self):super(Lenet5, self).__init__()self.conv_unit = nn.Sequential(# x: [b, 3, 32, 32] => [b, 6, ]nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),nn.AvgPool2d(kernel_size=2, stride=2, padding=0),#nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),nn.MaxPool2d(kernel_size=2, stride=2, padding=0),)self.fc_unit = nn.Sequential(nn.Linear(2,120), # 由输出结果反推（拉直打平）nn.ReLU(),nn.Linear(120,84),nn.ReLU(),nn.Linear(84,10))#[b,3,32,32]tmp = torch.randn(2, 3, 32, 32)out = self.conv_unit(tmp)#[2,16,5,5]   由输出结果得到print('conv out:', out.shape)def main():net = Lenet5()if __name__ == '__main__':main()

第三步：完善lenet5 结构并使用GPU加速
lenet5.py

import torch
from torch import nn
from torch.nn import functional as Fclass Lenet5(nn.Module):def __init__(self):super(Lenet5, self).__init__()self.conv_unit = nn.Sequential(# x: [b, 3, 32, 32] => [b, 6, ]nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),nn.AvgPool2d(kernel_size=2, stride=2, padding=0),#nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),nn.MaxPool2d(kernel_size=2, stride=2, padding=0),)self.fc_unit = nn.Sequential(nn.Linear(16*5*5,120),nn.ReLU(),nn.Linear(120,84),nn.ReLU(),nn.Linear(84,10))#[b,3,32,32]tmp = torch.randn(2, 3, 32, 32)out = self.conv_unit(tmp)#[b,16,5,5]print('conv out:', out.shape)def forward(self,x):batchsz = x.size(0)# [b, 3, 32, 32] => [b, 16, 5, 5]x = self.conv_unit(x)#[b, 16, 5, 5] => [b,16*5*5]x = x.view(batchsz,16*5*5)# [b, 16*5*5] => [b, 10]logits = self.fc_unit(x)pred = F.softmax(logits,dim=1)return logitsdef main():net = Lenet5()tmp = torch.randn(2, 3, 32, 32)out = net(tmp)print('lenet out:', out.shape)if __name__ == '__main__':main()

main.py

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from lenet5 import Lenet5
from    torch import nn, optimdef main():batchsz = 128CIFAR_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_train = DataLoader(CIFAR_train, batch_size=batchsz, shuffle=True)CIFAR_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_test = DataLoader(CIFAR_test, batch_size=batchsz, shuffle=True)x,label = iter(cifar_train).next()print('x',x.shape,'label:',label.shape)device = torch.device('cuda')model = Lenet5().to(device)print(model)if __name__ =='__main__':main()

第四步：计算交叉熵和准确率，完成迭代

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from lenet5 import Lenet5
from    torch import nn, optimdef main():batchsz = 128CIFAR_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_train = DataLoader(CIFAR_train, batch_size=batchsz, shuffle=True)CIFAR_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_test = DataLoader(CIFAR_test, batch_size=batchsz, shuffle=True)x,label = iter(cifar_train).next()print('x',x.shape,'label:',label.shape)device = torch.device('cuda')model = Lenet5().to(device)criteon = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(),lr=1e-3)print(model)for epoch in range(1000):for batchidx, (x,label) in enumerate(cifar_train):# [b, 3, 32, 32]# [b]x,label = x.to(device),label.to(device)logits = model(x)# logits: [b, 10]# label:  [b]loss = criteon(logits,label)# backpropoptimizer.zero_grad()loss.backward()optimizer.step()print(epoch,'loss:',loss.item())model.eval()with torch.no_grad(): #之后代码不需backproptotal_correct = 0total_num = 0for x ,label in cifar_test:# [b, 3, 32, 32]# [b]x,label = x.to(device),label.to(device)logits = model(x)pred = logits.argmax(dim=1)total_correct += torch.eq(pred,label).float().sum()total_num += x.size(0)acc = total_correct / total_numprint(epoch,acc)if __name__ =='__main__':main()

注意事项：

• 之所以在 测试时 添加 model.eval()是因为eval()时，BN会使用之前计算好的值，并且停止使用DropOut。保证用全部训练的均值和方差

二、CIFAR10 与 ResNet

第一步：构建ResNet18的网络结构
ResNet.py

import torch
from torch import  nn
from torch.nn import functional as Fclass ResBlk(nn.Module):def __init__(self,ch_in,ch_out,stride=1):super(ResBlk,self).__init__()self.conv1 = nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=stride,padding=1)self.bn1 = nn.BatchNorm2d(ch_out)self.conv2 = nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1)self.bn2 = nn.BatchNorm2d(ch_out)self.extra = nn.Sequential()if ch_out != ch_in:self.extra = nn.Sequential(nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=stride),nn.BatchNorm2d(ch_out))def forward(self, x):out = F.relu(self.bn1(self.conv1(x)))out = self.bn2(self.conv2(out))#[b, ch_in, h, w] = > [b, ch_out, h, w]out = self.extra(x) + outout = F.relu((out))return outclass ResNet18(nn.Module):def __init__(self):super(ResNet18, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(3,64,kernel_size=3,stride=3,padding=0),nn.BatchNorm2d(64))# followed 4 blocks# [b, 64, h, w] => [b, 128, h ,w]self.blk1 = ResBlk(64,128)# [b, 128, h, w] => [b, 256, h ,w]self.blk2 = ResBlk(128,256)# [b, 256, h, w] => [b, 512, h ,w]self.blk3 = ResBlk(256,512)# [b, 512, h, w] => [b, 1024, h ,w]self.blk4 = ResBlk(512,512)self.outlayer = nn.Linear(512*1*1,10)def forward(self,x):x = F.relu(self.conv1(x))x = self.blk1(x)x = self.blk2(x)x = self.blk3(x)x = self.blk4(x)print('after conv:', x.shape)# [b, 512, h, w] => [b, 512, 1, 1]x = F.adaptive_avg_pool2d(x, [1, 1])print('after pool:', x.shape)x = x.view(x.size(0), -1)x = self.outlayer(x)return xdef main():blk = ResBlk(64,128,stride=2)tmp = torch.randn(2,64,32,32)out = blk(tmp)print('block:',out.shape)x = torch.randn(2,3,32,32)model  = ResNet18()out = model(x)print('resnet:',out.shape)if __name__ == '__main__':main()

第二步：代入第一个项目的main函数中即可
main.py

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from resnet import ResNet18
from    torch import nn, optimdef main():batchsz = 128CIFAR_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_train = DataLoader(CIFAR_train, batch_size=batchsz, shuffle=True)CIFAR_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_test = DataLoader(CIFAR_test, batch_size=batchsz, shuffle=True)x,label = iter(cifar_train).next()print('x',x.shape,'label:',label.shape)device = torch.device('cuda')model = ResNet18().to(device)criteon = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(),lr=1e-3)print(model)for epoch in range(1000):for batchidx, (x,label) in enumerate(cifar_train):# [b, 3, 32, 32]# [b]x,label = x.to(device),label.to(device)logits = model(x)# logits: [b, 10]# label:  [b]loss = criteon(logits,label)# backpropoptimizer.zero_grad()loss.backward()optimizer.step()print(epoch,'loss:',loss.item())model.eval()with torch.no_grad(): #之后代码不需backproptotal_correct = 0total_num = 0for x ,label in cifar_test:# [b, 3, 32, 32]# [b]x,label = x.to(device),label.to(device)logits = model(x)pred = logits.argmax(dim=1)total_correct += torch.eq(pred,label).float().sum()total_num += x.size(0)acc = total_correct / total_numprint(epoch,acc)if __name__ =='__main__':main()

网络结构如下：

迭代准确率和交叉熵计算如下：

其他需要注意的地方：

• 并不是ResNet的paper中流程完全相同，但是十分类似
• 可以对数据进行数据增强和归一化等操作进一步提升效果