Differences with torchvision.datasets.CelebA

torchvision.datasets.CelebA

class torchvision.datasets.CelebA(
    root: str,
    split: str = 'train',
    target_type: Union[List[str], str] = 'attr',
    transform: Optional[Callable] = None,
    target_transform: Optional[Callable] = None,
    download: bool = False)

For more information, see torchvision.datasets.CelebA.

mindspore.dataset.CelebADataset

class mindspore.dataset.CelebADataset(
    dataset_dir,
    num_parallel_workers=None,
    shuffle=None,
    usage='all',
    sampler=None,
    decode=False,
    extensions=None,
    num_samples=None,
    num_shards=None,
    shard_id=None,
    cache=None,
    decrypt=None)

For more information, see mindspore.dataset.CelebADataset.

Differences

PyTorch: Read the CelebA (CelebFaces Attributes) dataset. API integrates the transformation operations for image and label.

MindSpore: Read the CelebA (CelebFaces Attributes) dataset. Downloading dataset from web is not supported. Transforms for image and label depends on mindshare.dataset.map operation.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter1	root	dataset_dir	-
	Parameter2	split	usage	-
	Parameter3	target_type	-	-
	Parameter4	transform	-	Supported by mindspore.dataset.map operation
	Parameter5	target_transform	-	Supported by mindspore.dataset.map operation
	Parameter6	download	-	Not supported by MindSpore
	Parameter7	-	num_parallel_workers	Number of worker threads to read the data
	Parameter8	-	shuffle	Whether to perform shuffle on the dataset
	Parameter9	-	sampler	Object used to choose samples from the dataset
	Parameter10	-	decode	Whether to decode the images after reading
	Parameter11	-	extensions	List of file extensions to be included in the dataset
	Parameter12	-	num_samples	The number of images to be included in the dataset
	Parameter13	-	num_shards	Number of shards that the dataset will be divided into
	Parameter14	-	shard_id	The shard ID within num_shards
	Parameter15	-	cache	Use tensor caching service to speed up dataset processing
	Parameter16	-	decrypt	Image decryption function

Code Example

# PyTorch
import torchvision.transforms as T
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

root = "/path/to/dataset_directory/"
dataset = datasets.CelebA(root, split='train', target_type="attr", transform=T.ToTensor(), download=True)
dataloader = DataLoader(dataset)

# MindSpore
import mindspore.dataset as ds
import mindspore.dataset.vision as vision

# Download CelebA dataset files, unzip the img_align_celeba.zip and put list_attr_celeba.txt together like
# .
# └── /path/to/dataset_directory/
#      ├── list_attr_celeba.txt
#      ├── 000001.jpg
#      ├── 000002.jpg
#      ├── 000003.jpg
#      ├── ...
root = "/path/to/dataset_directory/"
ms_dataloader = ds.CelebADataset(root, usage='train', decode=True)
ms_dataloader = ms_dataloader.map(vision.ToTensor(), ["image"])