Differences with torchtext.datasets.IWSLT2017

torchtext.datasets.IWSLT2017

class torchtext.datasets.IWSLT2017(
    root: str = '.data',
    split: Union[List[str], str] = ('train', 'valid', 'test'),
    language_pair: Sequence = ('de', 'en'))

For more information, see torchtext.datasets.IWSLT2017.

mindspore.dataset.IWSLT2017Dataset

class mindspore.dataset.IWSLT2017Dataset(
    dataset_dir,
    usage=None,
    language_pair=None,
    num_samples=None,
    num_parallel_workers=None,
    shuffle=Shuffle.GLOBAL,
    num_shards=None,
    shard_id=None,
    cache=None)

For more information, see mindspore.dataset.IWSLT2017Dataset.

Differences

PyTorch: Read the IWSLT2017 dataset.

MindSpore: Read the IWSLT2017 dataset. Download dataset from web is not supported.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter1	root	dataset_dir	-
	Parameter2	split	usage	-
	Parameter3	language_pair	language_pair	-
	Parameter4	-	num_samples	The number of images to be included in the dataset
	Parameter5	-	num_parallel_workers	Number of worker threads to read the data
	Parameter6	-	shuffle	Whether to perform shuffle on the dataset
	Parameter7	-	num_shards	Number of shards that the dataset will be divided into
	Parameter8	-	shard_id	The shard ID within num_shards
	Parameter9	-	cache	Use tensor caching service to speed up dataset processing

Code Example

# PyTorch
import torchtext.datasets as datasets

root = "/path/to/dataset_root/"
train_iter, valid_iter, test_iter = datasets.IWSLT2017(root, split=('train', 'valid', 'test'))
data = next(iter(train_iter))

# MindSpore
import mindspore.dataset as ds

# Download IWSLT2017 dataset files, unzip into the following structure
# .
# └── /path/to/dataset_directory/
#      └── DeEnItNlRo
#          └── DeEnItNlRo
#              └── DeEnItNlRo-DeEnItNlRo
#                  ├── IWSLT17.TED.dev2010.de-en.de.xml
#                  ├── train.tags.de-en.de
#                  ├── ...
root = "/path/to/dataset_directory/"
dataset = ds.IWSLT2017Dataset(root, usage='all')
data = next(iter(dataset))