在 pytorch 中将 ImageFolder 与相册一起使用

Question

我遇到一种情况，我需要将 ImageFolder 与相册库一起使用以在 pytorch 中进行扩充 - 自定义数据加载器不是一个选项。

为此，我很困惑，我无法让 ImageFolder 处理相册。我已经尝试过这些方法：

class Transforms:
    def __init__(self, transforms: A.Compose):
        self.transforms = transforms

    def __call__(self, img, *args, **kwargs):
        return self.transforms(image=np.array(img))['image']

然后：

    trainset = datasets.ImageFolder(traindir,transform=Transforms(transforms=A.Resize(32 , 32)))

其中 traindir 是一些带有图像的目录。然而，我得到了一个奇怪的错误：

RuntimeError: Given groups=1, weight of size [16, 3, 3, 3], expected input[1024, 32, 32, 3] to have 3 channels, but got 32 channels instead

而且我似乎无法找到一个可重现的示例来使简单的 aug pipleline 与 imagefolder 一起工作。

更新在@Shai的推荐下，我现在这样做了：

class Transforms:
    def __init__(self):
        self.transforms = A.Compose([A.Resize(224,224),ToTensorV2()])

    def __call__(self, img, *args, **kwargs):
        return self.transforms(image=np.array(img))['image']

trainset = datasets.ImageFolder(traindir,transform=Transforms())

但我被抛出：

    self.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same

Answer 1

您需要使用 ToTensorV2 转换作为最后一个：

trainset = datasets.ImageFolder(traindir,transform=Transforms(transforms=A.Compose([A.Resize(32 , 32), ToTensorV2()]))

Answer 2

通过研究 PyTorch 上的 ImageFolder 实现[link] and some proposed work in Kaggle [link]。我提出以下解决方案（已从我这边成功测试）：

import numpy as np
from typing import Any, Callable, Optional, Tuple
from torchvision.datasets.folder import DatasetFolder, default_loader, IMG_EXTENSIONS
class CustomImageFolder(DatasetFolder):
def __init__(
    self,
    root: str,
    transform: Optional[Callable] = None,
    target_transform: Optional[Callable] = None,
    loader: Callable[[str], Any] = default_loader,
    is_valid_file: Optional[Callable[[str], bool]] = None,
):
    super().__init__(
        root,
        loader,
        IMG_EXTENSIONS if is_valid_file is None else None,
        transform=transform,
        target_transform=target_transform,
        is_valid_file=is_valid_file,
    )
    self.imgs = self.samples

def __getitem__(self, index: int) -> Tuple[Any, Any]:
    """
    Args:
        index (int): Index

    Returns:
        tuple: (sample, target) where target is class_index of the target class.
    """
    path, target = self.samples[index]
    sample = self.loader(path)
    if self.transform is not None:
        try:
            sample = self.transform(sample)
        except Exception:
            sample = self.transform(image=np.array(sample))["image"]
    if self.target_transform is not None:
        target = self.target_transform(target)

    return sample, target

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

现在可以运行代码如下：

trainset = CustomImageFolder(traindir,transform=Transforms(transforms=A.Resize(32 , 32)))

在 pytorch 中将 ImageFolder 与相册一起使用

using ImageFolder with albumentations in pytorch

conv-neural-network

pytorch

albumentations