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 |
|
Parameter5 |
target_transform |
- |
Supported by |
|
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"])