You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
2.4 KiB
68 lines
2.4 KiB
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
|