Is there any way to increase dataset size using image augmentation in pytorch, like making copies of same images with variations like cropping or other techniques that are available in torchvision transforms.   I used the code mentioned below, but I want to oversample the dataset and check how that affects the models performance.
transform = {
'train':

transforms.Compose([

    transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),

    transforms.RandomRotation(degrees=15),

    transforms.ColorJitter(),

    transforms.RandomHorizontalFlip(),

    transforms.CenterCrop(size=224),  # Image net standards

    transforms.ToTensor(),

    transforms.Normalize([0.485, 0.456, 0.406],

                         [0.229, 0.224, 0.225])  # Imagenet standards

]),

'val':

transforms.Compose([

    transforms.Resize(size=256),

    transforms.CenterCrop(size=224),

    transforms.ToTensor(),

    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

]),

'test':

transforms.Compose([

    transforms.Resize(size=256),

    transforms.CenterCrop(size=224),

    transforms.ToTensor(),

    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

]),

}
Thankyou