Source code for torchtext.datasets.iwslt2017
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,
)
URL = "https://fbk.sharepoint.com/sites/MTUnit/_layouts/15/download.aspx?SourceUrl=%2Fsites%2FMTUnit%2FShared%20Documents%2Fwebsites%2FWIT3%2Dlibrary%2F2017%2D01%2Dtrnmted%2Etgz"
_PATH = "2017-01-trnmted.tgz"
MD5 = "aca701032b1c4411afc4d9fa367796ba"
SUPPORTED_DATASETS = {
"valid_test": ["dev2010", "tst2010"],
"language_pair": {
"en": ["nl", "de", "it", "ro"],
"ro": ["de", "en", "nl", "it"],
"de": ["ro", "en", "nl", "it"],
"it": ["en", "nl", "de", "ro"],
"nl": ["de", "en", "it", "ro"],
},
"year": 17,
}
NUM_LINES = {
"train": {
"train": {
("en", "nl"): 237240,
("de", "en"): 206112,
("en", "it"): 231619,
("en", "ro"): 220538,
("de", "ro"): 201455,
("nl", "ro"): 206920,
("it", "ro"): 217551,
("de", "nl"): 213628,
("de", "it"): 205465,
("it", "nl"): 233415,
}
},
"valid": {
"dev2010": {
("en", "nl"): 1003,
("de", "en"): 888,
("en", "it"): 929,
("en", "ro"): 914,
("de", "ro"): 912,
("nl", "ro"): 913,
("it", "ro"): 914,
("de", "nl"): 1001,
("de", "it"): 923,
("it", "nl"): 1001,
},
"tst2010": {
("en", "nl"): 1777,
("de", "en"): 1568,
("en", "it"): 1566,
("en", "ro"): 1678,
("de", "ro"): 1677,
("nl", "ro"): 1680,
("it", "ro"): 1643,
("de", "nl"): 1779,
("de", "it"): 1567,
("it", "nl"): 1669,
},
},
"test": {
"dev2010": {
("en", "nl"): 1003,
("de", "en"): 888,
("en", "it"): 929,
("en", "ro"): 914,
("de", "ro"): 912,
("nl", "ro"): 913,
("it", "ro"): 914,
("de", "nl"): 1001,
("de", "it"): 923,
("it", "nl"): 1001,
},
"tst2010": {
("en", "nl"): 1777,
("de", "en"): 1568,
("en", "it"): 1566,
("en", "ro"): 1678,
("de", "ro"): 1677,
("nl", "ro"): 1680,
("it", "ro"): 1643,
("de", "nl"): 1779,
("de", "it"): 1567,
("it", "nl"): 1669,
},
},
}
DATASET_NAME = "IWSLT2017"
def _return_full_filepath(full_filepath, _=None):
return full_filepath
def _filter_filename_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_filename_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
[docs]@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "valid", "test"))
def IWSLT2017(root=".data", split=("train", "valid", "test"), language_pair=("de", "en")):
"""IWSLT2017 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/2017-01
The available datasets include following:
**Language pairs**:
+-----+-----+-----+-----+-----+-----+
| |"en" |"nl" |"de" |"it" |"ro" |
+-----+-----+-----+-----+-----+-----+
|"en" | | x | x | x | x |
+-----+-----+-----+-----+-----+-----+
|"nl" | x | | x | x | x |
+-----+-----+-----+-----+-----+-----+
|"de" | x | x | | x | x |
+-----+-----+-----+-----+-----+-----+
|"it" | x | x | x | | x |
+-----+-----+-----+-----+-----+-----+
|"ro" | x | x | x | x | |
+-----+-----+-----+-----+-----+-----+
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
:return: DataPipe that yields tuple of source and target sentences
:rtype: (str, str)
Examples:
>>> from torchtext.datasets import IWSLT2017
>>> train_iter, valid_iter, test_iter = IWSLT2017()
>>> 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"
)
from torchdata.datapipes.iter import FileOpener, GDriveReader, HttpReader, IterableWrapper # noqa
valid_set = "dev2010"
test_set = "tst2010"
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],
)
)
(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)
# 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/2017-01-trnmted/texts/.../DeEnItNlRo-DeEnItNlRo.tgz will never be in
# /root/2017-01-trnmted.tgz/texts/.../DeEnItNlRo-DeEnItNlRo.tgz
inner_iwslt_tar = os.path.join(
root,
os.path.splitext(_PATH)[0],
"texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.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()
# As we had filenames duplicated, any trash files in archive can become tgz
def extracted_file_name(inner_iwslt_tar, inner_tar_name):
name = os.path.basename(inner_tar_name)
path = os.path.dirname(inner_iwslt_tar)
return os.path.join(path, name)
cache_decompressed_dp = cache_decompressed_dp.end_caching(
mode="wb", filepath_fn=partial(extracted_file_name, inner_iwslt_tar)
)
# As we corrected path, we need to leave tgz files only now and no dot files
def leave_only_tgz(file_name):
name = os.path.basename(file_name)
_, file_extension = os.path.splitext(file_name)
return file_extension == ".tgz" and name[0] != "."
cache_decompressed_dp = cache_decompressed_dp.filter(leave_only_tgz)
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,
os.path.splitext(_PATH)[0],
"texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo",
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,
os.path.splitext(_PATH)[0],
"texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo",
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()
