Albumentations in Pytorch: Inconsistent Augmentation for multi-target datasets
I'm using Pytorch and want to perform the data augmentation of my images with Albumentations. My dataset object has two different targets: 'blurry' and 'sharp'. Each instance of both targets needs to have identical changes. When I try to perform the data augmentation with a Dataset object like this:
class ApplyTransform(Dataset):
def __init__(self, dataset, transformation):
    self.dataset = dataset
    self.aug = transformation
     
def __len__(self):
    return (len(self.dataset))

def __getitem__(self, idx):
    sample, target = self.dataset[idx]['blurry'], self.dataset[idx]['sharp']
    transformedImgs = self.aug(image=sample, target_image=target)
    sample_aug, target_aug = transformedImgs[&quot;image&quot;], transformedImgs[&quot;target_image&quot;]
    return {'blurry': sample_aug, 'sharp': target_aug}   

Unfortunately, I receive two images with two different augmentations:

When I try the same without a Dataset object, I receive two images with the identical application of augmentations. Does anybody know how to make it work with a dataset object?
Here is my augmentation pipeline:
augmentation_transform = A.Compose(
        [
        A.Resize(1024,1024, p=1),
        A.HorizontalFlip(p=0.25),
        A.Rotate(limit=(-45, 65)),
        A.VerticalFlip(p=0.24),
        A.RandomContrast(limit=0.3, p=0.15),
        A.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        A.pytorch.transforms.ToTensorV2(always_apply=True, p=1.0)
        ],
        additional_targets={&quot;target_image&quot;: &quot;image&quot;}
        )


You can stack your blurry and sharp images, apply your augmentation then unstack them
