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import os
from functools import partial
from pathlib import Path
from typing import Tuple, Union
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory
from torchtext.data.datasets_utils import _wrap_split_argument
if is_module_available("torchdata"):
from torchdata.datapipes.iter import FileOpener, IterableWrapper
from torchtext._download_hooks import HttpReader
URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
MD5 = "7c2ac02c03563afcf9b574c7e56c153a"
NUM_LINES = {
"train": 25000,
"test": 25000,
}
MAP_LABELS = {"neg": 1, "pos": 2}
_PATH = "aclImdb_v1.tar.gz"
DATASET_NAME = "IMDB"
def _filepath_fn(root, _=None):
return os.path.join(root, _PATH)
def _decompressed_filepath_fn(root, decompressed_folder, split, labels, _=None):
return os.path.join(root, decompressed_folder, split)
def _filter_fn(filter_imdb_data, split, t):
return filter_imdb_data(split, t[0])
def _path_map_fn(t):
return Path(t[0]).parts[-2], t[1]
def _encode_map_fn(x):
return x[0], x[1].encode()
def _cache_filepath_fn(root, decompressed_folder, split, x):
return os.path.join(root, decompressed_folder, split, x)
def _modify_res(t):
return MAP_LABELS[Path(t[0]).parts[-1]], t[1]
def filter_imdb_data(key, fname):
labels = {"neg", "pos"}
# eg. fname = "aclImdb/train/neg/12416_3.txt"
*_, split, label, file = Path(fname).parts
return key == split and label in labels
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def IMDB(root: str, split: Union[Tuple[str], str]):
"""IMDB 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 http://ai.stanford.edu/~amaas/data/sentiment/
Number of lines per split:
- train: 25000
- test: 25000
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`, `test`)
:returns: DataPipe that yields tuple of label (1 to 2) and text containing the movie review
:rtype: (int, str)
"""
if not is_module_available("torchdata"):
raise ModuleNotFoundError(
"Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data"
)
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 = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
labels = {"neg", "pos"}
decompressed_folder = "aclImdb_v1"
cache_decompressed_dp = cache_compressed_dp.on_disk_cache(
filepath_fn=partial(_decompressed_filepath_fn, root, decompressed_folder, split, labels)
)
cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b")
cache_decompressed_dp = cache_decompressed_dp.load_from_tar()
cache_decompressed_dp = cache_decompressed_dp.filter(partial(_filter_fn, filter_imdb_data, split))
# eg. "aclImdb/train/neg/12416_3.txt" -> "neg"
cache_decompressed_dp = cache_decompressed_dp.map(_path_map_fn)
cache_decompressed_dp = cache_decompressed_dp.readlines(decode=True)
cache_decompressed_dp = cache_decompressed_dp.lines_to_paragraphs() # group by label in cache file
cache_decompressed_dp = cache_decompressed_dp.map(_encode_map_fn)
cache_decompressed_dp = cache_decompressed_dp.end_caching(
mode="wb", filepath_fn=partial(_cache_filepath_fn, root, decompressed_folder, split), skip_read=True
)
data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
# get label from cache file, eg. "aclImdb_v1/train/neg" -> "neg"
return data_dp.readlines().map(_modify_res).shuffle().set_shuffle(False).sharding_filter()
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