Source code for

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 import _create_dataset_directory
from import _wrap_split_argument

URL = ""

MD5 = "7c2ac02c03563afcf9b574c7e56c153a"

    "train": 25000,
    "test": 25000,

MAP_LABELS = {"neg": 1, "pos": 2}

_PATH = "aclImdb_v1.tar.gz"


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

[docs]@_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 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" ) from torchdata.datapipes.iter import FileOpener, GDriveReader, HttpReader, IterableWrapper # noqa 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 = 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 = 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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