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Source code for torchtext.datasets.iwslt2016

import os
from functools import partial

from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
    _clean_files,
    _create_dataset_directory,
    _generate_iwslt_files_for_lang_and_split,
    _wrap_split_argument,
)

if is_module_available("torchdata"):
    from torchdata.datapipes.iter import FileOpener, IterableWrapper
    from torchtext._download_hooks import GDriveReader

URL = "https://drive.google.com/uc?id=1l5y6Giag9aRPwGtuZHswh3w5v3qEz8D8"

_PATH = "2016-01.tgz"

MD5 = "c393ed3fc2a1b0f004b3331043f615ae"

SUPPORTED_DATASETS = {
    "valid_test": ["dev2010", "tst2010", "tst2011", "tst2012", "tst2013", "tst2014"],
    "language_pair": {
        "en": ["ar", "de", "fr", "cs"],
        "ar": ["en"],
        "fr": ["en"],
        "de": ["en"],
        "cs": ["en"],
    },
    "year": 16,
}

NUM_LINES = {
    "train": {
        "train": {
            ("ar", "en"): 224126,
            ("de", "en"): 196884,
            ("en", "fr"): 220400,
            ("cs", "en"): 114390,
        }
    },
    "valid": {
        "dev2010": {
            ("ar", "en"): 887,
            ("de", "en"): 887,
            ("en", "fr"): 887,
            ("cs", "en"): 480,
        },
        "tst2010": {
            ("ar", "en"): 1569,
            ("de", "en"): 1565,
            ("en", "fr"): 1664,
            ("cs", "en"): 1511,
        },
        "tst2011": {
            ("ar", "en"): 1199,
            ("de", "en"): 1433,
            ("en", "fr"): 818,
            ("cs", "en"): 1013,
        },
        "tst2012": {
            ("ar", "en"): 1702,
            ("de", "en"): 1700,
            ("en", "fr"): 1124,
            ("cs", "en"): 1385,
        },
        "tst2013": {
            ("ar", "en"): 1169,
            ("de", "en"): 993,
            ("en", "fr"): 1026,
            ("cs", "en"): 1327,
        },
        "tst2014": {("ar", "en"): 1107, ("de", "en"): 1305, ("en", "fr"): 1305},
    },
    "test": {
        "dev2010": {
            ("ar", "en"): 887,
            ("de", "en"): 887,
            ("en", "fr"): 887,
            ("cs", "en"): 480,
        },
        "tst2010": {
            ("ar", "en"): 1569,
            ("de", "en"): 1565,
            ("en", "fr"): 1664,
            ("cs", "en"): 1511,
        },
        "tst2011": {
            ("ar", "en"): 1199,
            ("de", "en"): 1433,
            ("en", "fr"): 818,
            ("cs", "en"): 1013,
        },
        "tst2012": {
            ("ar", "en"): 1702,
            ("de", "en"): 1700,
            ("en", "fr"): 1124,
            ("cs", "en"): 1385,
        },
        "tst2013": {
            ("ar", "en"): 1169,
            ("de", "en"): 993,
            ("en", "fr"): 1026,
            ("cs", "en"): 1327,
        },
        "tst2014": {("ar", "en"): 1107, ("de", "en"): 1305, ("en", "fr"): 1305},
    },
}

SET_NOT_EXISTS = {
    ("en", "ar"): [],
    ("en", "de"): [],
    ("en", "fr"): [],
    ("en", "cs"): ["tst2014"],
    ("ar", "en"): [],
    ("fr", "en"): [],
    ("de", "en"): [],
    ("cs", "en"): ["tst2014"],
}

DATASET_NAME = "IWSLT2016"


def _return_full_filepath(full_filepath, _=None):
    return full_filepath


def _filter_file_name_fn(uncleaned_filename, x):
    return os.path.basename(uncleaned_filename) in x[0]


def _clean_files_wrapper(full_filepath, x):
    return _clean_files(full_filepath, x[0], x[1])


# TODO: migrate this to dataset_utils.py once torchdata is a hard dependency to
# avoid additional conditional imports.
def _filter_clean_cache(cache_decompressed_dp, full_filepath, uncleaned_filename):

    cache_inner_decompressed_dp = cache_decompressed_dp.on_disk_cache(
        filepath_fn=partial(_return_full_filepath, full_filepath)
    )
    cache_inner_decompressed_dp = cache_inner_decompressed_dp.open_files(mode="b").load_from_tar()
    cache_inner_decompressed_dp = cache_inner_decompressed_dp.filter(partial(_filter_file_name_fn, uncleaned_filename))
    cache_inner_decompressed_dp = cache_inner_decompressed_dp.map(partial(_clean_files_wrapper, full_filepath))
    cache_inner_decompressed_dp = cache_inner_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
    return cache_inner_decompressed_dp


def _filepath_fn(root, _=None):
    return os.path.join(root, _PATH)


def _inner_iwslt_tar_filepath_fn(inner_iwslt_tar, _=None):
    return inner_iwslt_tar


def _filter_fn(inner_iwslt_tar, x):
    return os.path.basename(inner_iwslt_tar) in x[0]


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "valid", "test")) def IWSLT2016( root=".data", split=("train", "valid", "test"), language_pair=("de", "en"), valid_set="tst2013", test_set="tst2014", ): """IWSLT2016 dataset .. warning:: using datapipes is still currently subject to a few caveats. if you wish to use this dataset with shuffling, multi-processing, or distributed learning, please see :ref:`this note <datapipes_warnings>` for further instructions. For additional details refer to https://wit3.fbk.eu/2016-01 The available datasets include following: **Language pairs**: +-----+-----+-----+-----+-----+-----+ | |"en" |"fr" |"de" |"cs" |"ar" | +-----+-----+-----+-----+-----+-----+ |"en" | | x | x | x | x | +-----+-----+-----+-----+-----+-----+ |"fr" | x | | | | | +-----+-----+-----+-----+-----+-----+ |"de" | x | | | | | +-----+-----+-----+-----+-----+-----+ |"cs" | x | | | | | +-----+-----+-----+-----+-----+-----+ |"ar" | x | | | | | +-----+-----+-----+-----+-----+-----+ **valid/test sets**: ["dev2010", "tst2010", "tst2011", "tst2012", "tst2013", "tst2014"] Args: root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache') split: split or splits to be returned. Can be a string or tuple of strings. Default: (‘train’, ‘valid’, ‘test’) language_pair: tuple or list containing src and tgt language valid_set: a string to identify validation set. test_set: a string to identify test set. :return: DataPipe that yields tuple of source and target sentences :rtype: (str, str) Examples: >>> from torchtext.datasets import IWSLT2016 >>> train_iter, valid_iter, test_iter = IWSLT2016() >>> src_sentence, tgt_sentence = next(iter(train_iter)) """ if not is_module_available("torchdata"): raise ModuleNotFoundError( "Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data" ) if not isinstance(language_pair, list) and not isinstance(language_pair, tuple): raise ValueError("language_pair must be list or tuple but got {} instead".format(type(language_pair))) assert len(language_pair) == 2, "language_pair must contain only 2 elements: src and tgt language respectively" src_language, tgt_language = language_pair[0], language_pair[1] if src_language not in SUPPORTED_DATASETS["language_pair"]: raise ValueError( "src_language '{}' is not valid. Supported source languages are {}".format( src_language, list(SUPPORTED_DATASETS["language_pair"]) ) ) if tgt_language not in SUPPORTED_DATASETS["language_pair"][src_language]: raise ValueError( "tgt_language '{}' is not valid for give src_language '{}'. Supported target language are {}".format( tgt_language, src_language, SUPPORTED_DATASETS["language_pair"][src_language], ) ) if valid_set not in SUPPORTED_DATASETS["valid_test"] or valid_set in SET_NOT_EXISTS[language_pair]: raise ValueError( "valid_set '{}' is not valid for given language pair {}. Supported validation sets are {}".format( valid_set, language_pair, [s for s in SUPPORTED_DATASETS["valid_test"] if s not in SET_NOT_EXISTS[language_pair]], ) ) if test_set not in SUPPORTED_DATASETS["valid_test"] or test_set in SET_NOT_EXISTS[language_pair]: raise ValueError( "test_set '{}' is not valid for give language pair {}. Supported test sets are {}".format( valid_set, language_pair, [s for s in SUPPORTED_DATASETS["valid_test"] if s not in SET_NOT_EXISTS[language_pair]], ) ) (file_path_by_lang_and_split, uncleaned_filenames_by_lang_and_split,) = _generate_iwslt_files_for_lang_and_split( SUPPORTED_DATASETS["year"], src_language, tgt_language, valid_set, test_set ) url_dp = IterableWrapper([URL]) cache_compressed_dp = url_dp.on_disk_cache( filepath_fn=partial(_filepath_fn, root), hash_dict={_filepath_fn(root): MD5}, hash_type="md5", ) cache_compressed_dp = GDriveReader(cache_compressed_dp) cache_compressed_dp = cache_compressed_dp.end_caching(mode="wb", same_filepath_fn=True) languages = "-".join([src_language, tgt_language]) # We create the whole filepath here, but only check for the literal filename in the filter # because we're lazily extracting from the outer tarfile. Thus, # /root/2016-01/texts/.../src-tgt.tgz will never be in /root/2016-01.tgz/texts/.../src-tgt.tgz inner_iwslt_tar = ( os.path.join( root, os.path.splitext(_PATH)[0], "texts", src_language, tgt_language, languages, ) + ".tgz" ) cache_decompressed_dp = cache_compressed_dp.on_disk_cache( filepath_fn=partial(_inner_iwslt_tar_filepath_fn, inner_iwslt_tar) ) cache_decompressed_dp = cache_decompressed_dp.open_files(mode="b").load_from_tar() cache_decompressed_dp = cache_decompressed_dp.filter(partial(_filter_fn, inner_iwslt_tar)) cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True) cache_decompressed_dp_1, cache_decompressed_dp_2 = cache_decompressed_dp.fork(num_instances=2) src_filename = file_path_by_lang_and_split[src_language][split] uncleaned_src_filename = uncleaned_filenames_by_lang_and_split[src_language][split] # We create the whole filepath here, but only check for the literal filename in the filter # because we're lazily extracting from the outer tarfile. full_src_filepath = os.path.join(root, "2016-01/texts/", src_language, tgt_language, languages, src_filename) cache_inner_src_decompressed_dp = _filter_clean_cache( cache_decompressed_dp_1, full_src_filepath, uncleaned_src_filename ) tgt_filename = file_path_by_lang_and_split[tgt_language][split] uncleaned_tgt_filename = uncleaned_filenames_by_lang_and_split[tgt_language][split] # We create the whole filepath here, but only check for the literal filename in the filter # because we're lazily extracting from the outer tarfile. full_tgt_filepath = os.path.join(root, "2016-01/texts/", src_language, tgt_language, languages, tgt_filename) cache_inner_tgt_decompressed_dp = _filter_clean_cache( cache_decompressed_dp_2, full_tgt_filepath, uncleaned_tgt_filename ) tgt_data_dp = FileOpener(cache_inner_tgt_decompressed_dp, encoding="utf-8") src_data_dp = FileOpener(cache_inner_src_decompressed_dp, encoding="utf-8") src_lines = src_data_dp.readlines(return_path=False, strip_newline=False) tgt_lines = tgt_data_dp.readlines(return_path=False, strip_newline=False) return src_lines.zip(tgt_lines).shuffle().set_shuffle(False).sharding_filter()

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