Python 没有目标的验证数据

我有一个图像的验证数据集，可以通过我的CNN模型进行分类.我想用pytorch加载这些图像.torchvision.datasets.ImageFolder()函数不起作用，因为没有目标，因为数据集是未分类的.我假设我需要编写一个自定义的数据集类，我稍后会放入torch.utils.data.DataLoader().我已经在网上搜索过了，但我还是不太明白这门课应该是什么样子的.

我试过这个

import torch
from torch.utils.data import Dataset
from torchvision.io import read_image
import os


class Dset(Dataset):
    def __init__(self, dir: str, transform=None) -> None:
        self.transform = transform
        self.images = os.listdir(dir)
        self.dir = dir
    
    def __getitem__(self, index: int) -> torch.Tensor:
        image = read_image(f'{self.dir}/{self.images[index]}')
        if self.transform is not None:
            image = self.transform(image)
        return image

    def __len__(self) -> int:
        return len(self.images)

但在这个单元格之后(所有图像都在.data/中)

from torchvision import transforms

batch_size = 64
transform = transforms.Compose([transforms.Grayscale(), transforms.ToTensor()])
data = Dset('data', transform=transform)
trainloader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)

images, labels = iter(trainloader)

我遇到这个错误:TypeError: Input image tensor permitted channel values are [1, 3], but found 4

Update个

import torch
from torch.utils.data import Dataset
from torchvision.io import read_image, ImageReadMode
import os


class Dset(Dataset):
    def __init__(self, dir: str, transform=None) -> None:
        self.transform = transform
        self.images = os.listdir(dir)
        self.dir = dir
    
    def __getitem__(self, index: int) -> torch.Tensor:
        image = read_image(f'{self.dir}/{self.images[index]}', mode=ImageReadMode.RGB)
        if self.transform is not None:
            image = self.transform(image)
        return image

    def __len__(self) -> int:
        return len(self.images)

该错误是由图像中的Alpha通道引起的.

在解决这个问题之后，我遇到了这个:TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>

Update 2

from torchvision import transforms

batch_size = 64
transform = transforms.Compose(
[transforms.ToPILImage(), transforms.Resize((512, 512)),
transforms.Grayscale(), transforms.ToTensor()]
)
data = Dset('data', transform=transform)
trainloader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)

images = iter(trainloader)[0]

torchvision.transforms.ToTensor仅将PIL图像或numpy.ndarray转换为张量.

最后一行结果:TypeError: '_SingleProcessDataLoaderIter' object is not subscriptable