Source code for torchtext.datasets.iwslt2016
import os
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_clean_files,
_create_dataset_directory,
_generate_iwslt_files_for_lang_and_split,
)
if is_module_available("torchdata"):
from torchdata.datapipes.iter import FileOpener, GDriveReader, IterableWrapper
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"
# 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=lambda x: full_filepath
)
cache_inner_decompressed_dp = FileOpener(
cache_inner_decompressed_dp, mode="b"
).read_from_tar()
cache_inner_decompressed_dp = cache_inner_decompressed_dp.filter(
lambda x: os.path.basename(uncleaned_filename) in x[0]
)
cache_inner_decompressed_dp = cache_inner_decompressed_dp.map(
lambda x: _clean_files(full_filepath, x[0], x[1])
)
cache_inner_decompressed_dp = cache_inner_decompressed_dp.end_caching(
mode="wb", same_filepath_fn=True
)
return cache_inner_decompressed_dp
[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
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=lambda x: os.path.join(root, _PATH),
hash_dict={os.path.join(root, _PATH): 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=lambda x: inner_iwslt_tar
)
cache_decompressed_dp = (
FileOpener(cache_decompressed_dp, mode="b")
.read_from_tar()
.filter(lambda x: os.path.basename(inner_iwslt_tar) in x[0])
)
cache_decompressed_dp = cache_decompressed_dp.end_caching(
mode="wb", same_filepath_fn=True
)
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, 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, full_tgt_filepath, uncleaned_tgt_filename
)
# TODO: read in text mode with utf-8 encoding, see: https://github.com/pytorch/pytorch/issues/72713
tgt_data_dp = FileOpener(cache_inner_tgt_decompressed_dp, mode="b")
src_data_dp = FileOpener(cache_inner_src_decompressed_dp, mode="b")
src_lines = src_data_dp.readlines(return_path=False, strip_newline=False, decode=True)
tgt_lines = tgt_data_dp.readlines(return_path=False, strip_newline=False, decode=True)
return src_lines.zip(tgt_lines)
