Differences with torchvision.datasets.Cityscapes

torchvision.datasets.Cityscapes

class torchvision.datasets.Cityscapes(
    root: str,
    split: str,
    mode: str,
    target_type: str or list,
    transform: Optional[Callable] = None,
    target_transform: Optional[Callable] = None,
    transforms: Optional[Callable] = None
    )

For more information, see torchvision.datasets.Cityscapes.

mindspore.dataset.CityscapesDataset

class mindspore.dataset.CityscapesDataset(
    dataset_dir,
    usage='train',
    quality_mode='fine',
    task='instance',
    num_samples=None,
    num_parallel_workers=None,
    shuffle=None,
    decode=False,
    sampler=None,
    num_shards=None,
    shard_id=None,
    cache=None
    )

For more information, see mindspore.dataset.CityscapesDataset.

Differences

PyTorch: Read the Cityscapes dataset.

MindSpore: Read the Cityscapes dataset. Downloading dataset from web is not supported.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter1	root	dataset_dir	-
	Parameter2	split	usage	-
	Parameter3	mode	quality_mode	-
	Parameter4	target_type	task	-
	Parameter5	transform	-	Supported by mindspore.dataset.map operation
	Parameter6	target_transform	-	Supported by mindspore.dataset.map operation
	Parameter7	transforms	-	Supported by mindspore.dataset.map operation
	Parameter8	-	num_samples	The number of images to be included in the dataset.
	Parameter9	-	num_parallel_workers	Number of worker threads to read the data
	Parameter10	-	shuffle	Whether to perform shuffle on the dataset
	Parameter11	-	decode	Decode the images after reading
	Parameter12	-	sampler	Object used to choose samples from 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

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.Cityscapes(root, split='train', mode='fine', target_type='semantic')
dataloader = DataLoader(dataset)

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

# Download the dataset files, unzip into the following structure
# .
# └── "/path/to/dataset_directory"
#      ├── leftImg8bit
#      |    ├── train
#      |    |    ├── aachen
#      |    |    |    ├── aachen_000000_000019_leftImg8bit.png
#      |    |    |    ├── aachen_000001_000019_leftImg8bit.png
#      |    |    |    ├── ...
#      |    |    ├── bochum
#      |    |    |    ├── ...
#      |    |    ├── ...
#      |    ├── test
#      |    |    ├── ...
#      |    ├── val
#      |    |    ├── ...
#      └── gtFine
#           ├── train
#           |    ├── aachen
#           |    |    ├── aachen_000000_000019_gtFine_color.png
#           |    |    ├── aachen_000000_000019_gtFine_instanceIds.png
#           |    |    ├── aachen_000000_000019_gtFine_labelIds.png
#           |    |    ├── aachen_000000_000019_gtFine_polygons.json
#           |    |    ├── aachen_000001_000019_gtFine_color.png
#           |    |    ├── aachen_000001_000019_gtFine_instanceIds.png
#           |    |    ├── aachen_000001_000019_gtFine_labelIds.png
#           |    |    ├── aachen_000001_000019_gtFine_polygons.json
#           |    |    ├── ...
#           |    ├── bochum
#           |    |    ├── ...
#           |    ├── ...
#           ├── test
#           |    ├── ...
#           └── val
#                ├── ...

root = "/path/to/dataset_directory/"
ms_dataloader = ds.CityscapesDataset(root, usage='train')
ms_dataloader = ms_dataloader.map(vision.RandomCrop((28, 28)), ["image"])