Source code for torchvision.datasets.fakedata

import torch
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from .. import transforms


class FakeData(VisionDataset):
    """A fake dataset that returns randomly generated images and returns them as PIL images

    Args:
        size (int, optional): Size of the dataset. Default: 1000 images
        image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224)
        num_classes(int, optional): Number of classes in the dataset. Default: 10
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        random_offset (int): Offsets the index-based random seed used to
            generate each image. Default: 0

    """

    def __init__(
            self,
            size: int = 1000,
            image_size: Tuple[int, int, int] = (3, 224, 224),
            num_classes: int = 10,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            random_offset: int = 0,
    ) -> None:
        super(FakeData, self).__init__(None, transform=transform,  # type: ignore[arg-type]
                                       target_transform=target_transform)
        self.size = size
        self.num_classes = num_classes
        self.image_size = image_size
        self.random_offset = random_offset

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

        Returns:
            tuple: (image, target) where target is class_index of the target class.
        """
        # create random image that is consistent with the index id
        if index >= len(self):
            raise IndexError("{} index out of range".format(self.__class__.__name__))
        rng_state = torch.get_rng_state()
        torch.manual_seed(index + self.random_offset)
        img = torch.randn(*self.image_size)
        target = torch.randint(0, self.num_classes, size=(1,), dtype=torch.long)[0]
        torch.set_rng_state(rng_state)

        # convert to PIL Image
        img = transforms.ToPILImage()(img)
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target.item()

    def __len__(self) -> int:
        return self.size