# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ """ from __future__ import print_function import os import six import tarfile import numpy as np from collections import defaultdict import paddle from paddle.io import Dataset import paddle.compat as cpt from paddle.dataset.common import _check_exists_and_download __all__ = [] DATA_URL = ("http://paddlemodels.bj.bcebos.com/wmt/wmt16.tar.gz") DATA_MD5 = "0c38be43600334966403524a40dcd81e" TOTAL_EN_WORDS = 11250 TOTAL_DE_WORDS = 19220 START_MARK = "" END_MARK = "" UNK_MARK = "" class WMT16(Dataset): """ Implementation of `WMT16 `_ test dataset. ACL2016 Multimodal Machine Translation. Please see this website for more details: http://www.statmt.org/wmt16/multimodal-task.html#task1 If you use the dataset created for your task, please cite the following paper: Multi30K: Multilingual English-German Image Descriptions. .. code-block:: text @article{elliott-EtAl:2016:VL16, author = {{Elliott}, D. and {Frank}, S. and {Sima"an}, K. and {Specia}, L.}, title = {Multi30K: Multilingual English-German Image Descriptions}, booktitle = {Proceedings of the 6th Workshop on Vision and Language}, year = {2016}, pages = {70--74}, year = 2016 } Args: data_file(str): path to data tar file, can be set None if :attr:`download` is True. Default None mode(str): 'train', 'test' or 'val'. Default 'train' src_dict_size(int): word dictionary size for source language word. Default -1. trg_dict_size(int): word dictionary size for target language word. Default -1. lang(str): source language, 'en' or 'de'. Default 'en'. download(bool): whether to download dataset automatically if :attr:`data_file` is not set. Default True Returns: Dataset: Instance of WMT16 dataset. The instance of dataset has 3 fields: - src_ids (np.array) - The sequence of token ids of source language. - trg_ids (np.array) - The sequence of token ids of target language. - trg_ids_next (np.array) - The next sequence of token ids of target language. Examples: .. code-block:: python import paddle from paddle.text.datasets import WMT16 class SimpleNet(paddle.nn.Layer): def __init__(self): super(SimpleNet, self).__init__() def forward(self, src_ids, trg_ids, trg_ids_next): return paddle.sum(src_ids), paddle.sum(trg_ids), paddle.sum(trg_ids_next) paddle.disable_static() wmt16 = WMT16(mode='train', src_dict_size=50, trg_dict_size=50) for i in range(10): src_ids, trg_ids, trg_ids_next = wmt16[i] src_ids = paddle.to_tensor(src_ids) trg_ids = paddle.to_tensor(trg_ids) trg_ids_next = paddle.to_tensor(trg_ids_next) model = SimpleNet() src_ids, trg_ids, trg_ids_next = model(src_ids, trg_ids, trg_ids_next) print(src_ids.numpy(), trg_ids.numpy(), trg_ids_next.numpy()) """ def __init__(self, data_file=None, mode='train', src_dict_size=-1, trg_dict_size=-1, lang='en', download=True): assert mode.lower() in ['train', 'test', 'val'], \ "mode should be 'train', 'test' or 'val', but got {}".format(mode) self.mode = mode.lower() self.data_file = data_file if self.data_file is None: assert download, "data_file is not set and downloading automatically is disabled" self.data_file = _check_exists_and_download(data_file, DATA_URL, DATA_MD5, 'wmt16', download) self.lang = lang assert src_dict_size > 0, "dict_size should be set as positive number" assert trg_dict_size > 0, "dict_size should be set as positive number" self.src_dict_size = min( src_dict_size, (TOTAL_EN_WORDS if lang == "en" else TOTAL_DE_WORDS)) self.trg_dict_size = min( trg_dict_size, (TOTAL_DE_WORDS if lang == "en" else TOTAL_EN_WORDS)) # load source and target word dict self.src_dict = self._load_dict(lang, src_dict_size) self.trg_dict = self._load_dict("de" if lang == "en" else "en", trg_dict_size) # load data self.data = self._load_data() def _load_dict(self, lang, dict_size, reverse=False): dict_path = os.path.join(paddle.dataset.common.DATA_HOME, "wmt16/%s_%d.dict" % (lang, dict_size)) dict_found = False if os.path.exists(dict_path): with open(dict_path, "rb") as d: dict_found = len(d.readlines()) == dict_size if not dict_found: self._build_dict(dict_path, dict_size, lang) word_dict = {} with open(dict_path, "rb") as fdict: for idx, line in enumerate(fdict): if reverse: word_dict[idx] = cpt.to_text(line.strip()) else: word_dict[cpt.to_text(line.strip())] = idx return word_dict def _build_dict(self, dict_path, dict_size, lang): word_dict = defaultdict(int) with tarfile.open(self.data_file, mode="r") as f: for line in f.extractfile("wmt16/train"): line = cpt.to_text(line) line_split = line.strip().split("\t") if len(line_split) != 2: continue sen = line_split[0] if self.lang == "en" else line_split[1] for w in sen.split(): word_dict[w] += 1 with open(dict_path, "wb") as fout: fout.write( cpt.to_bytes("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))) for idx, word in enumerate( sorted(six.iteritems(word_dict), key=lambda x: x[1], reverse=True)): if idx + 3 == dict_size: break fout.write(cpt.to_bytes(word[0])) fout.write(cpt.to_bytes('\n')) def _load_data(self): # the index for start mark, end mark, and unk are the same in source # language and target language. Here uses the source language # dictionary to determine their indices. start_id = self.src_dict[START_MARK] end_id = self.src_dict[END_MARK] unk_id = self.src_dict[UNK_MARK] src_col = 0 if self.lang == "en" else 1 trg_col = 1 - src_col self.src_ids = [] self.trg_ids = [] self.trg_ids_next = [] with tarfile.open(self.data_file, mode="r") as f: for line in f.extractfile("wmt16/{}".format(self.mode)): line = cpt.to_text(line) line_split = line.strip().split("\t") if len(line_split) != 2: continue src_words = line_split[src_col].split() src_ids = [start_id] + [ self.src_dict.get(w, unk_id) for w in src_words ] + [end_id] trg_words = line_split[trg_col].split() trg_ids = [self.trg_dict.get(w, unk_id) for w in trg_words] trg_ids_next = trg_ids + [end_id] trg_ids = [start_id] + trg_ids self.src_ids.append(src_ids) self.trg_ids.append(trg_ids) self.trg_ids_next.append(trg_ids_next) def __getitem__(self, idx): return (np.array(self.src_ids[idx]), np.array(self.trg_ids[idx]), np.array(self.trg_ids_next[idx])) def __len__(self): return len(self.src_ids) def get_dict(self, lang, reverse=False): """ return the word dictionary for the specified language. Args: lang(string): A string indicating which language is the source language. Available options are: "en" for English and "de" for Germany. reverse(bool): If reverse is set to False, the returned python dictionary will use word as key and use index as value. If reverse is set to True, the returned python dictionary will use index as key and word as value. Returns: dict: The word dictionary for the specific language. Examples: .. code-block:: python from paddle.text.datasets import WMT16 wmt16 = WMT16(mode='train', src_dict_size=50, trg_dict_size=50) en_dict = wmt16.get_dict('en') """ dict_size = self.src_dict_size if lang == self.lang else self.trg_dict_size dict_path = os.path.join(paddle.dataset.common.DATA_HOME, "wmt16/%s_%d.dict" % (lang, dict_size)) assert os.path.exists(dict_path), "Word dictionary does not exist. " "Please invoke paddle.dataset.wmt16.train/test/validation first " "to build the dictionary." return self._load_dict(lang, dict_size)