mindspore.dataset.IWSLT2017Dataset

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

IWSLT2017(International Workshop on Spoken Language Translation) dataset.

The generated dataset has two columns: [text, translation] . The tensor of column text and translation are of the string type.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Acceptable usages include 'train', 'valid', 'test' and 'all'. Default: None , all samples.
language_pair (sequence, optional) – List containing src and tgt language, supported values are ('en', 'nl'), ('en', 'de'), ('en', 'it'), ('en', 'ro'), ('nl', 'en'), ('nl', 'de'), ('nl', 'it'), ('nl', 'ro'), ('de', 'en'), ('de', 'nl'), ('de', 'it'), ('de', 'ro'), ('it', 'en'), ('it', 'nl'), ('it', 'de'), ('it', 'ro'), ('ro', 'en'), ('ro', 'nl'), ('ro', 'de'), ('ro', 'it'). Default: None, set to ('de', 'en').
num_samples (int, optional) – Number of samples (rows) to read. Default: None , reads the full dataset.
shuffle (Union[bool, Shuffle], optional) –
Perform reshuffling of the data every epoch. Bool type and Shuffle enum are both supported to pass in. Default: Shuffle.GLOBAL . If shuffle is False , no shuffling will be performed. If shuffle is True , it is equivalent to setting shuffle to mindspore.dataset.Shuffle.GLOBAL . Set the mode of data shuffling by passing in enumeration variables:
- Shuffle.GLOBAL : Shuffle both the files and samples.
- Shuffle.FILES : Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the max sample number of per shard. Used in data parallel training .
shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.
num_parallel_workers (int, optional) – Number of worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.

Raises

RuntimeError – If dataset_dir does not contain data files.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If num_parallel_workers exceeds the max thread numbers.

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> iwslt2017_dataset_dir = "/path/to/iwslt2017_dataset_dir"
>>> dataset = ds.IWSLT2017Dataset(dataset_dir=iwslt2017_dataset_dir, usage='all', language_pair=('de', 'en'))

About IWSLT2017 dataset:

IWSLT is an international oral translation conference, a major annual scientific conference dedicated to all aspects of oral translation. The MT task of the IWSLT evaluation activity constitutes a dataset, which can be publicly obtained through the WIT3 website wit3 . The IWSLT2017 dataset involves German, English, Italian, Dutch, and Romanian. The dataset includes translations in any two different languages.

You can unzip the original IWSLT2017 dataset files into this directory structure and read by MindSpore's API. You need to decompress the dataset package in texts/DeEnItNlRo/DeEnItNlRo directory to get the DeEnItNlRo-DeEnItNlRo subdirectory.

.
└── iwslt2017_dataset_directory
    └── DeEnItNlRo
        └── DeEnItNlRo
            └── DeEnItNlRo-DeEnItNlRo
                ├── IWSLT17.TED.dev2010.de-en.de.xml
                ├── train.tags.de-en.de
                ├── ...

Citation:

@inproceedings{cettoloEtAl:EAMT2012,
Address = {Trento, Italy},
Author = {Mauro Cettolo and Christian Girardi and Marcello Federico},
Booktitle = {Proceedings of the 16$^{th}$ Conference of the European Association for Machine Translation
             (EAMT)},
Date = {28-30},
Month = {May},
Pages = {261--268},
Title = {WIT$^3$: Web Inventory of Transcribed and Translated Talks},
Year = {2012}}

Pre-processing Operation

`mindspore.dataset.Dataset.apply`	Apply a function in this dataset.
`mindspore.dataset.Dataset.concat`	Concatenate the dataset objects in the input list.
`mindspore.dataset.Dataset.filter`	Filter dataset by prediction.
`mindspore.dataset.Dataset.flat_map`	Map func to each row in dataset and flatten the result.
`mindspore.dataset.Dataset.map`	Apply each operation in operations to this dataset.
`mindspore.dataset.Dataset.project`	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
`mindspore.dataset.Dataset.rename`	Rename the columns in input datasets.
`mindspore.dataset.Dataset.repeat`	Repeat this dataset count times.
`mindspore.dataset.Dataset.reset`	Reset the dataset for next epoch.
`mindspore.dataset.Dataset.save`	Save the dynamic data processed by the dataset pipeline in common dataset format.
`mindspore.dataset.Dataset.shuffle`	Shuffle the dataset by creating a cache with the size of buffer_size .
`mindspore.dataset.Dataset.skip`	Skip the first N elements of this dataset.
`mindspore.dataset.Dataset.split`	Split the dataset into smaller, non-overlapping datasets.
`mindspore.dataset.Dataset.take`	Take the first specified number of samples from the dataset.
`mindspore.dataset.Dataset.zip`	Zip the datasets in the sense of input tuple of datasets.

Batch

`mindspore.dataset.Dataset.batch`	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
`mindspore.dataset.Dataset.bucket_batch_by_length`	Bucket elements according to their lengths.
`mindspore.dataset.Dataset.padded_batch`	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

`mindspore.dataset.Dataset.create_dict_iterator`	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Get the mapping dictionary from category names to category indexes.
`mindspore.dataset.Dataset.get_col_names`	Return the names of the columns in dataset.
`mindspore.dataset.Dataset.get_dataset_size`	Return the number of batches in an epoch.
`mindspore.dataset.Dataset.get_repeat_count`	Get the replication times in RepeatDataset.
`mindspore.dataset.Dataset.input_indexs`	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
`mindspore.dataset.Dataset.num_classes`	Get the number of classes in a dataset.
`mindspore.dataset.Dataset.output_shapes`	Get the shapes of output data.
`mindspore.dataset.Dataset.output_types`	Get the types of output data.

Apply Sampler

`mindspore.dataset.MappableDataset.add_sampler`	Add a child sampler for the current dataset.
`mindspore.dataset.MappableDataset.use_sampler`	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

`mindspore.dataset.Dataset.sync_update`	Release a blocking condition and trigger callback with given data.
`mindspore.dataset.Dataset.sync_wait`	Add a blocking condition to the input Dataset and a synchronize action will be applied.
`mindspore.dataset.Dataset.to_json`	Serialize a pipeline into JSON string and dump into file if filename is provided.