'train': datasets.ImageFolder(root=train_directory, transform=image_transforms['train'])

这是一个用于加载图像数据集的代码片段，使用了PyTorch的datasets模块中的ImageFolder类。它从指定的文件夹路径train_directory中读取图像数据，并应用了名为image_transforms的字典中的“train”变换操作。这个字典包含了不同的变换操作，比如随机裁剪、缩放等，用于数据增强和预处理。最终，这个代码片段返回一个ImageFolder对象，该对象包含了训练数据集的图像及其对应的标签信息。

相关问题

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。

我文件夹里有5个子文件夹。是全部数据集。如何结合ImageFolder重写Datasets类

首先，为了将5个子文件夹作为数据集处理，并结合`torchvision.datasets.ImageFolder`的功能，你需要创建一个自定义的`Datasets`类。`ImageFolder`是一个现成的工具，它已经实现了按照目录结构自动识别图片并将其划分为训练、验证和测试集等功能。

下面是如何基于`ImageFolder`重写`Datasets`类的一个简化示例：

from torch.utils.data import Dataset
from torchvision.datasets import ImageFolder
from torchvision import transforms

class CustomImageDataset(Dataset):
    def __init__(self, root_dir, train=True, transform=None):
        # 将根目录改为包含5个子文件夹的路径
        self.root_dir = root_dir
        self.image_folder = ImageFolder(root=self.root_dir, transform=transform)
        
        # 如果train参数未指定，默认假设所有子文件夹都是训练数据
        if train is True:
            self.subfolders = ['subfolder1', 'subfolder2', 'subfolder3', 'subfolder4', 'subfolder5']
        else:
            raise ValueError("Please specify whether it's for training or testing, as only one subfolder should be used for validation/test")

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

    def __getitem__(self, idx):
        path, target = self.image_folder[idx]
        return {'image': path, 'label': target}

# 使用自定义数据集
train_dataset = CustomImageDataset(root_dir='your_directory_path', train=True, transform=transforms.ToTensor())

在这个例子中，`CustomImageDataset`类继承了`Dataset`，并且在初始化时指定了根目录。你可以根据需要调整`subfolders`列表以指定训练、验证或测试的数据子文件夹。`__getitem__`方法返回的是每张图像及其对应的标签。

请注意，由于没有具体说明是用于训练还是评估，这个示例默认所有子文件夹都用于训练。如果你想要区分训练集和验证集，可以在构造函数中添加额外条件，并从单个子文件夹中加载验证数据。