mindspore.dataset.Multi30kDataset

class mindspore.dataset.Multi30kDataset(dataset_dir, usage=None, language_pair=None, num_samples=None, num_parallel_workers=None, shuffle=None, num_shards=None, shard_id=None, cache=None)[source]

Multi30k dataset.

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

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Acceptable usages include 'train', 'test', 'valid' or 'all'. Default: None , will read all samples.
language_pair (Sequence[str, str], optional) – Acceptable language_pair include ['en', 'de'], ['de', 'en']. Default: None , means ['en', 'de'].
num_samples (int, optional) – The number of images to be included in the dataset. Default: None , will read all samples.
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() .
shuffle (Union[bool, Shuffle], optional) –
Whether to shuffle the dataset. Default: None , means mindspore.dataset.Shuffle.GLOBAL . If False is provided, no shuffling will be performed. If True is provided, it is the same as setting to mindspore.dataset.Shuffle.GLOBAL . If Shuffle is provided, the effect is as follows:
- 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.
shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.
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.
ValueError – If usage is not 'train', 'test', 'valid' or 'all'.
TypeError – If language_pair is not of type Sequence[str, str].
RuntimeError – If num_samples is less than 0.
RuntimeError – If num_parallel_workers exceeds the max thread numbers.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> multi30k_dataset_dir = "/path/to/multi30k_dataset_directory"
>>> data = ds.Multi30kDataset(dataset_dir=multi30k_dataset_dir, usage='all', language_pair=['de', 'en'])

About Multi30k dataset:

Multi30K is a multilingual dataset that features approximately 31,000 standardized images described in multiple languages. The images are sourced from Flickr and each image comes with sentence descripitions in both English and German, as well as descriptions in other languages. Multi30k is used primarily for training and testing in tasks such as image captioning, machine translation, and visual question answering.

You can unzip the dataset files into the following directory structure and read by MindSpore’s API.

└── multi30k_dataset_directory
      ├── training
      │    ├── train.de
      │    └── train.en
      ├── validation
      │    ├── val.de
      │    └── val.en
      └── mmt16_task1_test
           ├── val.de
           └── val.en

Citation:

@article{elliott-EtAl:2016:VL16,
author    = {{Elliott}, D. and {Frank}, S. and {Sima'an}, K. and {Specia}, L.},
title     = {Multi30K: Multilingual English-German Image Descriptions},
booktitle = {Proceedings of the 5th Workshop on Vision and Language},
year      = {2016},
pages     = {70--74},
year      = 2016
}

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`	Takes at most given numbers of elements 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.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Return the class index.
`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.device_que`	Return a transferred Dataset that transfers data through a device.
`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.