实现Cifar10分类--ResNet

构建ResNet网络ResNet.py

from torch import nn

# ResNet基本块
def conv3x3(in_channels,out_channnels,stride=1):
    return nn.Conv2d(
        in_channels,
        out_channnels,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=False
    )

# Residual Block
class ResidualBlock(nn.Module):
    def __init__(self,in_channels,out_channels,stride=1,downsample=None):
        super(ResidualBlock, self).__init__()
        self.layer = nn.Sequential(
            conv3x3(in_channels, out_channels, stride),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            conv3x3(out_channels, out_channels),
            nn.BatchNorm2d(out_channels)
        )
        self.downsample = downsample #下采样
        self.relu = nn.ReLU(inplace=True)
    def forward(self,x):
        residual = x
        out = self.layer(x)
        if self.downsample:#下采样
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out

# define ResNet
class ResNet(nn.Module):
    def __init__(self,block,layers,num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 16
        self.conv = conv3x3(3,16)
        self.bn = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(block,16,layers[0])
        self.layer2 = self.make_layer(block, 32, layers[0],2)
        self.layer3 = self.make_layer(block, 64, layers[1], 2)
        self.avg_pool = nn.AvgPool2d(8)
        self.fc = nn.Linear(64,num_classes)

    def make_layer(self,block,out_channels,blocks,stride=1):
        downsample =  None
        # 使dropcut和主线直接相加
        if (stride != 1) or (self.in_channels != out_channels):
            downsample = nn.Sequential(
                conv3x3(self.in_channels,out_channels,stride=stride),
                nn.BatchNorm2d(out_channels)
            )
        layers = []
        layers.append(
            block(self.in_channels,out_channels,stride,downsample)
        )
        self.in_channels = out_channels

        for i in range(1,blocks):
            layers.append(block(out_channels,out_channels))
        return nn.Sequential(*layers)

    def forward(self,x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0),-1)
        out = self.fc(out)
        return out

main.py主函数

import torch
from torch import nn,optim
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
import ResNet

# Hyper-parameters
num_classes = 10
batch_size = 100
learning_rate = 0.001
num_epochs = 80

def main():
    batchsz = 32
    cifar_train = datasets.CIFAR10('cifar',True,transform=transforms.Compose([
        transforms.Resize((32,32)),
        transforms.ToTensor()
    ]),download=True) #一次加载一张
    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)  # 一次加载一张
    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)

    device = torch.device('cuda')
    model = ResNet.ResNet(ResNet.ResidualBlock,[2,2,2,2],num_classes=num_classes).to(device)
    criteon = nn.CrossEntropyLoss().to(device)
    optimizer = optim.Adam(model.parameters(),lr=learning_rate)

    # train
    for epoch in range(1000):
        model.train()
        for batchidx,(x,label) in enumerate(cifar_train):
            # [b,3,32,32] => [b]
            x,label = x.to(device),label.to(device)
            # logits:[b,10]
            # label:[b]
            # loss: tensor scalar
            logits = model(x)
            loss = criteon(logits,label)
            #backprop
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        model.eval()
        with torch.no_grad():
            # test
            total_correct = 0
            total_num = 0
            for x,label in cifar_test:
                x, label = x.to(device), label.to(device)
                # [b,10]
                logits = model(x)
                # [b]
                pred = logits.argmax(dim=1)
                # [b] vs [b] => scalar tensor
                total_correct += torch.eq(pred,label).float().sum().item()
                total_num += x.size(0)
            acc = total_correct/total_num
            print('epoch:{:d}, loss:{:.4f}, acc:{:.2f}%'.format(epoch,loss.item(),acc*100))

if __name__ == '__main__':
    main()