68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
from typing import Any, Callable, Optional, Tuple
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import torch
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from .. import transforms
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from .vision import VisionDataset
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class FakeData(VisionDataset):
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"""A fake dataset that returns randomly generated images and returns them as PIL images
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Args:
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size (int, optional): Size of the dataset. Default: 1000 images
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image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224)
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num_classes(int, optional): Number of classes in the dataset. Default: 10
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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random_offset (int): Offsets the index-based random seed used to
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generate each image. Default: 0
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"""
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def __init__(
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self,
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size: int = 1000,
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image_size: Tuple[int, int, int] = (3, 224, 224),
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num_classes: int = 10,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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random_offset: int = 0,
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) -> None:
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super().__init__(transform=transform, target_transform=target_transform)
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self.size = size
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self.num_classes = num_classes
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self.image_size = image_size
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self.random_offset = random_offset
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is class_index of the target class.
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"""
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# create random image that is consistent with the index id
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if index >= len(self):
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raise IndexError(f"{self.__class__.__name__} index out of range")
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rng_state = torch.get_rng_state()
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torch.manual_seed(index + self.random_offset)
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img = torch.randn(*self.image_size)
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target = torch.randint(0, self.num_classes, size=(1,), dtype=torch.long)[0]
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torch.set_rng_state(rng_state)
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# convert to PIL Image
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img = transforms.ToPILImage()(img)
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target.item()
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def __len__(self) -> int:
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return self.size
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