add wmt16 into dataset.

python/paddle/v2/dataset/__init__.py

@@ -24,11 +24,23 @@ import conll05
 import uci_housing
 import sentiment
 import wmt14
+import wmt16
 import mq2007
 import flowers
 import voc2012
 
 __all__ = [
-    'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'sentiment'
-    'uci_housing', 'wmt14', 'mq2007', 'flowers', 'voc2012'
+    'mnist',
+    'imikolov',
+    'imdb',
+    'cifar',
+    'movielens',
+    'conll05',
+    'sentiment'
+    'uci_housing',
+    'wmt14',
+    'wmt16',
+    'mq2007',
+    'flowers',
+    'voc2012',
 ]

python/paddle/v2/dataset/common.py

@@ -25,8 +25,12 @@ import glob
 import cPickle as pickle
 
 __all__ = [
-    'DATA_HOME', 'download', 'md5file', 'split', 'cluster_files_reader',
-    'convert'
+    'DATA_HOME',
+    'download',
+    'md5file',
+    'split',
+    'cluster_files_reader',
+    'convert',
 ]
 
 DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
@@ -58,12 +62,15 @@ def md5file(fname):
     return hash_md5.hexdigest()
 
 
-def download(url, module_name, md5sum):
+def download(url, module_name, md5sum, save_name=None):
     dirname = os.path.join(DATA_HOME, module_name)
     if not os.path.exists(dirname):
         os.makedirs(dirname)
 
-    filename = os.path.join(dirname, url.split('/')[-1])
+    filename = os.path.join(dirname,
+                            url.split('/')[-1]
+                            if save_name is None else save_name)
+
     retry = 0
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
@@ -196,9 +203,11 @@ def convert(output_path, reader, line_count, name_prefix):
     Convert data from reader to recordio format files.
 
     :param output_path: directory in which output files will be saved.
-    :param reader: a data reader, from which the convert program will read data instances.
+    :param reader: a data reader, from which the convert program will read
+                   data instances.
     :param name_prefix: the name prefix of generated files.
-    :param max_lines_to_shuffle: the max lines numbers to shuffle before writing.
+    :param max_lines_to_shuffle: the max lines numbers to shuffle before
+                                 writing.
     """
 
     assert line_count >= 1

python/paddle/v2/dataset/tests/wmt16_test.py

+# Copyright (c) 2016 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.
+
+import paddle.v2.dataset.wmt16
+import unittest
+
+
+class TestWMT16(unittest.TestCase):
+    def checkout_one_sample(self, sample):
+        # train data has 3 field: source language word indices,
+        # target language word indices, and target next word indices.
+        self.assertEqual(len(sample), 3)
+
+        # test start mark and end mark in source word indices.
+        self.assertEqual(sample[0][0], 0)
+        self.assertEqual(sample[0][-1], 1)
+
+        # test start mask in target word indices
+        self.assertEqual(sample[1][0], 0)
+
+        # test en mask in target next word indices
+        self.assertEqual(sample[2][-1], 1)
+
+    def test_train(self):
+        for idx, sample in enumerate(
+                paddle.v2.dataset.wmt16.train(
+                    src_dict_size=100000, trg_dict_size=100000)()):
+            if idx >= 10: break
+            self.checkout_one_sample(sample)
+
+    def test_test(self):
+        for idx, sample in enumerate(
+                paddle.v2.dataset.wmt16.test(
+                    src_dict_size=1000, trg_dict_size=1000)()):
+            if idx >= 10: break
+            self.checkout_one_sample(sample)
+
+    def test_val(self):
+        for idx, sample in enumerate(
+                paddle.v2.dataset.wmt16.validation(
+                    src_dict_size=1000, trg_dict_size=1000)()):
+            if idx >= 10: break
+            self.checkout_one_sample(sample)
+
+    def test_get_dict(self):
+        dict_size = 1000
+        word_dict = paddle.v2.dataset.wmt16.get_dict("en", dict_size, True)
+        self.assertEqual(len(word_dict), dict_size)
+        self.assertEqual(word_dict[0], "<s>")
+        self.assertEqual(word_dict[1], "<e>")
+        self.assertEqual(word_dict[2], "<unk>")
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/v2/dataset/wmt14.py

@@ -25,12 +25,20 @@ import gzip
 import paddle.v2.dataset.common
 from paddle.v2.parameters import Parameters
 
-__all__ = ['train', 'test', 'build_dict', 'convert']
-
-URL_DEV_TEST = 'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
+__all__ = [
+    'train',
+    'test',
+    'get_dict',
+    'convert',
+]
+
+URL_DEV_TEST = ('http://www-lium.univ-lemans.fr/~schwenk/'
+                'cslm_joint_paper/data/dev+test.tgz')
 MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
-# this is a small set of data for test. The original data is too large and will be add later.
-URL_TRAIN = 'http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz'
+# this is a small set of data for test. The original data is too large and
+# will be add later.
+URL_TRAIN = ('http://paddlepaddle.cdn.bcebos.com/demo/'
+             'wmt_shrinked_data/wmt14.tgz')
 MD5_TRAIN = '0791583d57d5beb693b9414c5b36798c'
 # BLEU of this trained model is 26.92
 URL_MODEL = 'http://paddlepaddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.gz'

python/paddle/v2/dataset/wmt16.py

+# Copyright (c) 2016 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.
+"""
+ACL2016 Multimodal Machine Translation. Please see this websit 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.
+
+@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
+}
+"""
+
+import os
+import tarfile
+import gzip
+from collections import defaultdict
+
+import paddle.v2.dataset.common
+
+__all__ = [
+    "train",
+    "test",
+    "validation",
+    "convert",
+    "fetch",
+    "get_dict",
+]
+
+DATA_URL = ("http://cloud.dlnel.org/filepub/"
+            "?uuid=46a0808e-ddd8-427c-bacd-0dbc6d045fed")
+DATA_MD5 = "0c38be43600334966403524a40dcd81e"
+
+TOTAL_EN_WORDS = 11250
+TOTAL_DE_WORDS = 19220
+
+START_MARK = "<s>"
+END_MARK = "<e>"
+UNK_MARK = "<unk>"
+
+
+def __build_dict__(tar_file, dict_size, save_path, lang):
+    word_dict = defaultdict(int)
+    with tarfile.open(tar_file, mode="r") as f:
+        for line in f.extractfile("wmt16/train"):
+            line_split = line.strip().split("\t")
+            if len(line_split) != 2: continue
+            sen = line_split[0] if lang == "en" else line_split[1]
+            for w in sen.split():
+                word_dict[w] += 1
+
+    with open(save_path, "w") as fout:
+        fout.write("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))
+        for idx, word in enumerate(
+                sorted(
+                    word_dict.iteritems(), key=lambda x: x[1], reverse=True)):
+            if idx + 3 == dict_size: break
+            fout.write("%s\n" % (word[0]))
+
+
+def __load_dict__(tar_file, dict_size, lang, reverse=False):
+    dict_path = os.path.join(paddle.v2.dataset.common.DATA_HOME,
+                             "wmt16/%s_%d.dict" % (lang, dict_size))
+    if not os.path.exists(dict_path) or (
+            len(open(dict_path, "r").readlines()) != dict_size):
+        __build_dict__(tar_file, dict_size, dict_path, lang)
+
+    word_dict = {}
+    with open(dict_path, "r") as fdict:
+        for idx, line in enumerate(fdict):
+            if reverse:
+                word_dict[idx] = line.strip()
+            else:
+                word_dict[line.strip()] = idx
+    return word_dict
+
+
+def __get_dict_size__(src_dict_size, trg_dict_size, src_lang):
+    src_dict_size = min(src_dict_size, (TOTAL_EN_WORDS if src_lang == "en" else
+                                        TOTAL_DE_WORDS))
+    trg_dict_size = min(trg_dict_size, (TOTAL_DE_WORDS if src_lang == "en" else
+                                        TOTAL_ENG_WORDS))
+    return src_dict_size, trg_dict_size
+
+
+def reader_creator(tar_file, file_name, src_dict_size, trg_dict_size, src_lang):
+    def reader():
+        src_dict = __load_dict__(tar_file, src_dict_size, src_lang)
+        trg_dict = __load_dict__(tar_file, trg_dict_size,
+                                 ("de" if src_lang == "en" else "en"))
+
+        # the indice 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 = src_dict[START_MARK]
+        end_id = src_dict[END_MARK]
+        unk_id = src_dict[UNK_MARK]
+
+        src_col = 0 if src_lang == "en" else 1
+        trg_col = 1 - src_col
+
+        with tarfile.open(tar_file, mode="r") as f:
+            for line in f.extractfile(file_name):
+                line_split = line.strip().split("\t")
+                if len(line_split) != 2:
+                    continue
+                src_words = line_split[src_col].split()
+                src_ids = [start_id] + [
+                    src_dict.get(w, unk_id) for w in src_words
+                ] + [end_id]
+
+                trg_words = line_split[trg_col].split()
+                trg_ids = [trg_dict.get(w, unk_id) for w in trg_words]
+
+                trg_ids_next = trg_ids + [end_id]
+                trg_ids = [start_id] + trg_ids
+
+                yield src_ids, trg_ids, trg_ids_next
+
+    return reader
+
+
+def train(src_dict_size, trg_dict_size, src_lang="en"):
+    """
+    WMT16 train set reader.
+
+    This function returns the reader for train data. Each sample the reader
+    returns is made up of three fields: the source language word index sequence,
+    target language word index sequence and next word index sequence.
+
+
+    NOTE:
+    The original like for training data is:
+    http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz
+
+    paddle.dataset.wmt16 provides a tokenized version of the original dataset by
+    using moses's tokenization script:
+    https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl
+
+    Args:
+        src_dict_size(int): Size of the source language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        trg_dict_size(int): Size of the target language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        src_lang(string): A string indicating which language is the source
+                          language. Available options are: "en" for English
+                          and "de" for Germany.
+
+    Returns:
+        callable: The train reader.
+    """
+
+    assert (src_lang in ["en", "de"], ("An error language type.  Only support: "
+                                       "en (for English); de(for Germany)"))
+    src_dict_size, trg_dict_size = __get_dict_size__(src_dict_size,
+                                                     trg_dict_size, src_lang)
+
+    return reader_creator(
+        tar_file=paddle.v2.dataset.common.download(DATA_URL, "wmt16", DATA_MD5,
+                                                   "wmt16.tar.gz"),
+        file_name="wmt16/train",
+        src_dict_size=src_dict_size,
+        trg_dict_size=trg_dict_size,
+        src_lang=src_lang)
+
+
+def test(src_dict_size, trg_dict_size, src_lang="en"):
+    """
+    WMT16 test set reader.
+
+    This function returns the reader for test data. Each sample the reader
+    returns is made up of three fields: the source language word index sequence,
+    target language word index sequence and next word index sequence.
+
+    NOTE:
+    The original like for test data is:
+    http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/mmt16_task1_test.tar.gz
+
+    paddle.dataset.wmt16 provides a tokenized version of the original dataset by
+    using moses's tokenization script:
+    https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl
+
+    Args:
+        src_dict_size(int): Size of the source language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        trg_dict_size(int): Size of the target language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        src_lang(string): A string indicating which language is the source
+                          language. Available options are: "en" for English
+                          and "de" for Germany.
+
+    Returns:
+        callable: The test reader.
+    """
+
+    assert (src_lang in ["en", "de"],
+            ("An error language type.  "
+             "Only support: en (for English); de(for Germany)"))
+
+    src_dict_size, trg_dict_size = __get_dict_size__(src_dict_size,
+                                                     trg_dict_size, src_lang)
+
+    return reader_creator(
+        tar_file=paddle.v2.dataset.common.download(DATA_URL, "wmt16", DATA_MD5,
+                                                   "wmt16.tar.gz"),
+        file_name="wmt16/test",
+        src_dict_size=src_dict_size,
+        trg_dict_size=trg_dict_size,
+        src_lang=src_lang)
+
+
+def validation(src_dict_size, trg_dict_size, src_lang="en"):
+    """
+    WMT16 validation set reader.
+
+    This function returns the reader for validation data. Each sample the reader
+    returns is made up of three fields: the source language word index sequence,
+    target language word index sequence and next word index sequence.
+
+    NOTE:
+    The original like for validation data is:
+    http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz
+
+    paddle.dataset.wmt16 provides a tokenized version of the original dataset by
+    using moses's tokenization script:
+    https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl
+
+    Args:
+        src_dict_size(int): Size of the source language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        trg_dict_size(int): Size of the target language dictionary. Three
+                            special tokens will be added into the dictionary:
+                            <s> for start mark, <e> for end mark, and <unk> for
+                            unknown word.
+        src_lang(string): A string indicating which language is the source
+                          language. Available options are: "en" for English
+                          and "de" for Germany.
+
+    Returns:
+        callable: The validation reader.
+    """
+    assert (src_lang in ["en", "de"],
+            ("An error language type.  "
+             "Only support: en (for English); de(for Germany)"))
+    src_dict_size, trg_dict_size = __get_dict_size__(src_dict_size,
+                                                     trg_dict_size, src_lang)
+
+    return reader_creator(
+        tar_file=paddle.v2.dataset.common.download(DATA_URL, "wmt16", DATA_MD5,
+                                                   "wmt16.tar.gz"),
+        file_name="wmt16/val",
+        src_dict_size=src_dict_size,
+        trg_dict_size=trg_dict_size,
+        src_lang=src_lang)
+
+
+def get_dict(lang, dict_size, 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.
+        dict_size(int): Size of the specified language dictionary.
+        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.
+    """
+
+    if lang == "en": dict_size = min(dict_size, TOTAL_EN_WORDS)
+    else: dict_size = min(dict_size, TOTAL_DE_WORDS)
+
+    dict_path = os.path.join(paddle.v2.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.")
+    tar_file = os.path.join(paddle.v2.dataset.common.DATA_HOME, "wmt16.tar.gz")
+    return __load_dict__(tar_file, dict_size, lang, reverse)
+
+
+def fetch():
+    """download the entire dataset.
+    """
+    paddle.v4.dataset.common.download(DATA_URL, "wmt16", DATA_MD5,
+                                      "wmt16.tar.gz")
+
+
+def convert(path, src_dict_size, trg_dict_size, src_lang):
+    """Converts dataset to recordio format.
+    """
+
+    paddle.v2.dataset.common.convert(
+        path,
+        train(
+            src_dict_size=src_dict_size,
+            trg_dict_size=trg_dict_size,
+            src_lang=src_lang),
+        1000,
+        "wmt16_train")
+    paddle.v2.dataset.common.convert(
+        path,
+        test(
+            src_dict_size=src_dict_size,
+            trg_dict_size=trg_dict_size,
+            src_lang=src_lang),
+        1000,
+        "wmt16_test")
+    paddle.v2.dataset.common.convert(
+        path,
+        validation(
+            src_dict_size=src_dict_size,
+            trg_dict_size=trg_dict_size,
+            src_lang=src_lang),
+        1000,
+        "wmt16_validation")

python/paddle/v2/fluid/layers/control_flow.py

@@ -19,13 +19,32 @@ import contextlib
 from ..registry import autodoc
 
 __all__ = [
-    'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard',
-    'BlockGuardWithCompletion', 'StaticRNNMemoryLink', 'WhileGuard', 'While',
-    'lod_rank_table', 'max_sequence_len', 'topk', 'lod_tensor_to_array',
-    'array_to_lod_tensor', 'increment', 'array_write', 'create_array',
-    'less_than', 'array_read', 'shrink_memory', 'array_length', 'IfElse',
-    'DynamicRNN', 'ConditionalBlock', 'StaticRNN', 'reorder_lod_tensor_by_rank',
-    'ParallelDo', 'Print'
+    'split_lod_tensor',
+    'merge_lod_tensor',
+    'BlockGuard',
+    'BlockGuardWithCompletion',
+    'StaticRNNMemoryLink',
+    'WhileGuard',
+    'While',
+    'lod_rank_table',
+    'max_sequence_len',
+    'topk',
+    'lod_tensor_to_array',
+    'array_to_lod_tensor',
+    'increment',
+    'array_write',
+    'create_array',
+    'less_than',
+    'array_read',
+    'shrink_memory',
+    'array_length',
+    'IfElse',
+    'DynamicRNN',
+    'ConditionalBlock',
+    'StaticRNN',
+    'reorder_lod_tensor_by_rank',
+    'ParallelDo',
+    'Print',
 ]