Traktor/myenv/Lib/site-packages/torchvision/datasets/fakedata.py

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2024-05-26 05:12:46 +02:00
from typing import Any, Callable, Optional, Tuple
import torch
from .. import transforms
from .vision import VisionDataset
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 a 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().__init__(transform=transform, 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(f"{self.__class__.__name__} index out of range")
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