Merge pull request #7661 from lcy-seso/wmt16_en_ger

Add WMT16 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'
+    'mnist',
-    'uci_housing', 'wmt14', 'mq2007', 'flowers', 'voc2012'
+    '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',
+    'DATA_HOME',
-    'convert'
+    '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

@@ -0,0 +1,66 @@
+# 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']
+__all__ = [
-
+    'train',
-URL_DEV_TEST = 'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
+    '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.
+# this is a small set of data for test. The original data is too large and
-URL_TRAIN = 'http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz'
+# 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'
@@ -42,8 +50,8 @@ UNK = "<unk>"
 UNK_IDX = 2
 
 
-def __read_to_dict__(tar_file, dict_size):
+def __read_to_dict(tar_file, dict_size):
-    def __to_dict__(fd, size):
+    def __to_dict(fd, size):
         out_dict = dict()
         for line_count, line in enumerate(fd):
             if line_count < size:
@@ -58,19 +66,19 @@ def __read_to_dict__(tar_file, dict_size):
             if each_item.name.endswith("src.dict")
         ]
         assert len(names) == 1
-        src_dict = __to_dict__(f.extractfile(names[0]), dict_size)
+        src_dict = __to_dict(f.extractfile(names[0]), dict_size)
         names = [
             each_item.name for each_item in f
             if each_item.name.endswith("trg.dict")
         ]
         assert len(names) == 1
-        trg_dict = __to_dict__(f.extractfile(names[0]), dict_size)
+        trg_dict = __to_dict(f.extractfile(names[0]), dict_size)
         return src_dict, trg_dict
 
 
 def reader_creator(tar_file, file_name, dict_size):
     def reader():
-        src_dict, trg_dict = __read_to_dict__(tar_file, dict_size)
+        src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
         with tarfile.open(tar_file, mode='r') as f:
             names = [
                 each_item.name for each_item in f
@@ -152,7 +160,7 @@ def get_dict(dict_size, reverse=True):
     # if reverse = False, return dict = {'a':'001', 'b':'002', ...}
     # else reverse = true, return dict = {'001':'a', '002':'b', ...}
     tar_file = paddle.v2.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
-    src_dict, trg_dict = __read_to_dict__(tar_file, dict_size)
+    src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
     if reverse:
         src_dict = {v: k for k, v in src_dict.items()}
         trg_dict = {v: k for k, v in trg_dict.items()}

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

@@ -20,13 +20,32 @@ import contextlib
 from ..registry import autodoc
 
 __all__ = [
-    'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard',
+    'split_lod_tensor',
-    'BlockGuardWithCompletion', 'StaticRNNMemoryLink', 'WhileGuard', 'While',
+    'merge_lod_tensor',
-    'lod_rank_table', 'max_sequence_len', 'topk', 'lod_tensor_to_array',
+    'BlockGuard',
-    'array_to_lod_tensor', 'increment', 'array_write', 'create_array',
+    'BlockGuardWithCompletion',
-    'less_than', 'array_read', 'shrink_memory', 'array_length', 'IfElse',
+    'StaticRNNMemoryLink',
-    'DynamicRNN', 'ConditionalBlock', 'StaticRNN', 'reorder_lod_tensor_by_rank',
+    'WhileGuard',
-    'ParallelDo', 'Print'
+    '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',
 ]