using ImageFolder with albumentations in pytorch

Question

I have a situation where I need to use ImageFolder with the albumentations lib to make the augmentations in pytorch - custom dataloader is not an option.

To this end, I am stumped and I am not able to get ImageFolder to work with albumenations. I have tried something along these lines:

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']

and then:

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

where traindir is some dir with images. I however get thrown a weird error:

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

and I cant seem to find a reproducible example to make a simple aug pipleline work with imagefolder.

UPDATE On the recommendation of @Shai, I have done this now:

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())

but I get thrown:

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

Answer 1

You need to use ToTensorV2 transformation as the final one:

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

Answer 2

By looking into ImageFolder implementation on PyTorch[link] and some proposed work in Kaggle [link]. I propose the following solution (which is successfully tested from my side):

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)

Now you can run the code as follows:

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