PyTorch 实现 ResNet34 分类（数据cifar10）「终于解决」

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       又到整理的时候了，这次参考torchvision里面的resnet34源代码，自己修改了一下，实现cifar10数据集的分类任务。

       其实网络上已经有很多优秀的源代码了，没必要再写，如果执意要说个理由的话，就当是自己的笔记了哈哈，方便以后使用可以快速查阅。没别的，菜鸟就应该多积累。

ResNet34大体结构：

图片：来自《深度学习框架PyTorch:入门与实践》

PyTorch 使用 torchvision 自带的 CIFAR10 数据实现。

运行环境：pytorch 0.4.0 CPU版、Python 3.6、Windows 7

import torchvision as tv
import torchvision.transforms as transforms
from torch import nn
import torch as t
from torch import optim
from torch.nn import functional as F
t.set_num_threads(8)


class ResidualBlock(nn.Module):

    # 实现子module: Residual Block

    def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
        super(ResidualBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
            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)
        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
    # 用子module来实现residual block，用_make_layer函数来实现layer

    def __init__(self, num_classes=1000):
        super(ResNet, self).__init__()
        # 前几层图像转换
        self.pre = nn.Sequential(
            nn.Conv2d(3, 16, 3, 1, 1, bias=False),
            nn.BatchNorm2d(16),
            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(16, 16, 3)
        self.layer2 = self._make_layer(16, 32, 4, stride=1)
        self.layer3 = self._make_layer(32, 64, 6, stride=1)
        self.layer4 = self._make_layer(64, 64, 3, stride=1)
        self.fc = nn.Linear(256, num_classes)  # 分类用的全连接

    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), nn.BatchNorm2d(outchannel))
        layers = []
        layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
        for i in range(1, block_num):
            layers.append(ResidualBlock(outchannel, outchannel))
        return nn.Sequential(*layers)

    def forward(self, x):
        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)


def getData():  # 定义对数据的预处理
    transform = transforms.Compose([
        transforms.Resize(40),
        transforms.RandomHorizontalFlip(),
        transforms.RandomCrop(32),
        transforms.ToTensor()])
    trainset = tv.datasets.CIFAR10(root='./data/', train=True,  download=True, transform=transform)  # 训练集
    trainloader = t.utils.data.DataLoader(trainset, batch_size=4, shuffle=True)

    testset = tv.datasets.CIFAR10('./data/', train=False, download=True, transform=transform)  # 测试集
    testloader = t.utils.data.DataLoader(testset, batch_size=4, shuffle=False)
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    return trainloader, testloader, classes


def trainModel():  # 训练模型
    trainloader, testloader, _ = getData()  # 获取数据
    net = ResNet(10)
    print(net)
    criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 定义优化器

    for epoch in range(1):
        for step, (tx, ty) in enumerate(trainloader, 0):
            optimizer.zero_grad()  # 梯度清零
            py = net(tx)  # forward + backward
            loss = criterion(py, ty)
            loss.backward()
            optimizer.step()  # 更新参数
            if step % 10 == 9:  # 每2000个batch打印一下训练状态
                acc = testNet(net, testloader)
                print('Epoch:', epoch, '|Step:', step, '|train loss:%.4f' % loss.item(), '|test accuracy:%.4f' % acc)

    print('Finished Training')
    return net


def testNet(net, testloader):  # 获取在测试集上的准确率
    correct, total = .0, .0
    for x, y in testloader:
        net.eval()
        py = net(x)
        _, predicted = t.max(py, 1)  # 获取分类结果
        total += y.size(0)  # 记录总个数
        correct += (predicted == y).sum()  # 记录分类正确的个数
    return float(correct) / total


if __name__ == '__main__':
    trainModel()

欢迎指正哦