torch cifar10 labels 标签类别

Torch CIFAR10是一个常用的图像分类数据集，包含10个类别：airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck。这些类别标签在Torch CIFAR10中用数字0到9表示，分别对应上述类别。这些类别标签可以帮助我们对CIFAR10数据集进行分类任务的训练和评估。

相关问题

pytorch卷积神经网络cifar10

PyTorch是一个广泛应用于深度学习领域的开源机器学习库。

卷积神经网络（Convolutional Neural Network, CNN）是一种常用于图像识别和处理的机器学习模型。

CIFAR-10是一个常用的图像数据集，由10个类别的60000个32x32彩色图像组成，用于训练和测试图像分类模型。

在PyTorch中，我们可以使用内置的torchvision库来加载和预处理CIFAR-10数据集。首先，我们需要导入必要的库：

import torch
import torchvision
import torchvision.transforms as transforms

然后，我们可以定义一个函数来加载和预处理CIFAR-10数据集：

def load_cifar10():
    # 定义变换
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    # 加载训练集
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                            download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                              shuffle=True, num_workers=2)

    # 加载测试集
    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                             shuffle=False, num_workers=2)

    # 类别标签
    classes = ('plane', 'car', 'bird', 'cat',
               'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    
    return trainloader, testloader, classes

接下来，我们可以定义一个卷积神经网络模型。在PyTorch中，我们可以通过继承`nn.Module`类来定义自己的模型。以下是一个简单的示例：

import torch.nn as nn


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        # 定义卷积层和全连接层
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 创建模型实例
net = Net()

现在我们可以加载数据集并训练这个模型：

import torch.optim as optim


trainloader, testloader, classes = load_cifar10()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 输入数据
        inputs, labels = data
        
        # 梯度置零
        optimizer.zero_grad()
        
        # 正向传播、反向传播、优化
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        # 统计损失值
        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

最后，我们可以在测试集上验证模型的准确率：

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %.2f %%' % (
    100 * correct / total))

总结起来，使用PyTorch实现CIFAR-10图像分类任务需要加载和预处理数据集、定义卷积神经网络模型、训练模型、验证模型准确率等步骤。以上是一个简单的示例，可根据需求进行修改和优化。

torchvision.datasets.CIFAR10源码

以下是torchvision.datasets.CIFAR10的源码：

import torch.utils.data as data
from PIL import Image
import os
import os.path
import numpy as np
import pickle


class CIFAR10(data.Dataset):
    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

    Args:
        root (string): Root directory of dataset where directory
            ``cifar-10-batches-py`` exists or will be downloaded to if download is set to True.
        train (bool, optional): If True, creates dataset from training set, otherwise
            creates from test set.
        transform (callable, optional): A function/transform that takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.

    Returns:
        tuple: (image, target) where target is index of the target class.
    """

    base_folder = 'cifar-10-batches-py'
    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
    filename = "cifar-10-python.tar.gz"
    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
    train_list = [
        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
        ['data_batch_3', '54ebc095f3ab1f03828d0aae7e51cd9d'],
        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
    ]

    test_list = [
        ['test_batch', '40351d587109b95175f43aff81a1287e'],
    ]

    def __init__(self, root, train=True,
                 transform=None, target_transform=None,
                 download=False):
        self.root = os.path.expanduser(root)
        self.transform = transform
        self.target_transform = target_transform
        self.train = train  # training set or test set

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError('Dataset not found or corrupted.' +
                               ' You can use download=True to download it')

        if self.train:
            downloaded_list = self.train_list
        else:
            downloaded_list = self.test_list

        self.data = []
        self.targets = []

        # now load the picked numpy arrays
        for file_name, checksum in downloaded_list:
            file_path = os.path.join(self.root, self.base_folder, file_name)
            with open(file_path, 'rb') as f:
                if 'meta' in file_name:
                    data_dict = pickle.load(f, encoding='latin1')
                    self.classes = data_dict['label_names']
                else:
                    data_dict = pickle.load(f, encoding='latin1')
                    self.data.append(data_dict['data'])
                    self.targets.extend(data_dict['labels'])

        self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img, target = self.data[index], self.targets[index]

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img)

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def __len__(self):
        return len(self.data)

    def _check_integrity(self):
        root = self.root
        for fentry in (self.train_list + self.test_list):
            filename, md5 = fentry[0], fentry[1]
            fpath = os.path.join(root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True

    def download(self):
        import tarfile

        if self._check_integrity():
            print('Files already downloaded and verified')
            return

        download_url(self.url, self.root, self.filename, self.tgz_md5)

        # extract file
        with tarfile.open(os.path.join(self.root, self.filename), "r:gz") as tar:
            tar.extractall(path=self.root)

        print('Done!')


class CIFAR100(CIFAR10):
    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

    This is a subclass of the `CIFAR10` Dataset.
    """

    base_folder = 'cifar-100-python'
    url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
    filename = "cifar-100-python.tar.gz"
    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
    train_list = [
        ['train', '16019d7e3df5f24257cddd939b257f8d'],
    ]

    test_list = [
        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
    ]

    def __init__(self, root, train=True,
                 transform=None, target_transform=None,
                 download=False):
        super(CIFAR100, self).__init__(root, train=train,
                                       transform=transform,
                                       target_transform=target_transform,
                                       download=download)

该代码定义了CIFAR10和CIFAR100数据集的类，这些数据集是用于图像分类任务的标准数据集之一。每个数据集都有一个训练集和一个测试集，每个图像都有一个标签，表示它所属的类别。

这些类继承自torch.utils.data.Dataset类，并实现了__getitem__和__len__方法。它们还提供了下载和检查数据集完整性的方法。

在初始化阶段，数据集从pickle文件中加载数据和标签，并将其存储在self.data和self.targets中。__getitem__方法返回图像和标签的元组，__len__方法返回数据集中图像的数量。