Source code for torchvision.datasets.fakedata

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

[docs]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 datset. 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 def __len__(self) -> int: return self.size


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources